Editor-In-Chief Chair Dr. Shiv Kumar

Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT), Senior Member of IEEE, Member of the Elsevier Advisory Panel

CEO, Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India

Additional Director, Technocrats Institute of Technology and Science, Bhopal (MP), India

Associated Editor-In-Chief Members Dr. Hitesh Kumar

Ph.D.(ME), M.E.(ME), B.E. (ME)

Professor and Head, Department of Mechanical Engineering, Technocrats Institute of Technology, Bhopal (MP), India

Dr. Gamal Abd El-Nasser Ahmed Mohamed Said

Ph.D(CSE), MS(CSE), BSc(EE)

Department of Computer and Information Technology , Port Training Institute, Arab Academy for Science, Technology and Maritime

Transport, Egypt

Associated Editor-In-Chief Members Dr. Mayank Singh

PDF (Purs), Ph.D(CSE), ME(Software Engineering), BE(CSE), SMACM, MIEEE, LMCSI, SMIACSIT

Department of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu-

Natal, Durban, South Africa.

Scientific Editors Prof. (Dr.) Hamid Saremi

Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran

Dr. Moinuddin Sarker

Vice President of Research & Development, Head of Science Team, Natural State Research, Inc., 37 Brown House Road (2nd Floor)

Stamford, USA.

Dr. Fadiya Samson Oluwaseun

Assistant Professor, Girne American University, as a Lecturer & International Admission Officer (African Region) Girne, Northern

Cyprus, Turkey.

Dr. Robert Brian Smith

International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie Centre, North Ryde, New South Wales, Australia

Dr. Durgesh Mishra

Professor (CSE) and Director, Microsoft Innovation Centre, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh India

Executive Editor Dr. Deepak Garg

Professor, Department Of Computer Science And Engineering, Bennett University, Times Group, Greater Noida (UP), India

Executive Editor Members Dr. Vahid Nourani

Professor, Faculty of Civil Engineering, University of Tabriz, Iran.

Dr. Saber Mohamed Abd-Allah

Associate Professor, Department of Biochemistry, Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China.

Dr. Xiaoguang Yue

Associate Professor, Department of Computer and Information, Southwest Forestry University, Kunming (Yunnan), China.

Dr. Labib Francis Gergis Rofaiel

Associate Professor, Department of Digital Communications and Electronics, Misr Academy for Engineering and Technology,

Mansoura, Egypt.

Dr. Hugo A.F.A. Santos

ICES, Institute for Computational Engineering and Sciences, The University of Texas, Austin, USA.

Dr. Sunandan Bhunia

Associate Professor & Head, Department of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia

(Bengal), India.

Technical Program Committee Dr. Mohd. Nazri Ismail

Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia.

Technical Program Committee Members Dr. Haw Su Cheng

Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia (Cyberjaya), Malaysia.

Dr. Hasan. A. M Al Dabbas

Chairperson, Vice Dean Faculty of Engineering, Department of Mechanical Engineering, Philadelphia University, Amman, Jordan.

Dr. Gabil Adilov

Professor, Department of Mathematics, Akdeniz University, Konyaaltı/Antalya, Turkey.

Manager Chair Mr. Jitendra Kumar Sen

Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India

Editorial Chair Dr. Arun Murlidhar Ingle

Director, Padmashree Dr. Vithalrao Vikhe Patil Foundation’s Institute of Business Management and Rural Development, Ahmednagar

(Maharashtra) India.

Editorial Members Dr. J. Gladson Maria Britto

Professor, Department of Computer Science & Engineering, Malla Reddy College of Engineering, Secunderabad (Telangana), India.

Dr. Wameedh Riyadh Abdul-Adheem

Academic Lecturer, Almamoon University College/Engineering of Electrical Power Techniques, Baghdad, Iraq

Dr. S. Brilly Sangeetha

Associate Professor & Principal, Department of Computer Science and Engineering, IES College of Engineering, Thrissur (Kerala),

India

Dr. Issa Atoum

Assistant Professor, Chairman of Software Engineering, Faculty of Information Technology, The World Islamic Sciences & Education University, Amman- Jordan

Dr. Umar Lawal Aliyu

Lecturer, Department of Management, Texila American University Guyana USA.

Dr. K. Kannan

Professor & Head, Department of IT, Adhiparasakthi College of Engineering, Kalavai, Vellore, (Tamilnadu), India

Dr. Mohammad Mahdi Mansouri

Associate Professor, Department of High Voltage Substation Design & Development, Yazd Regional Electric Co., Yazd Province,

Iran.

Dr. Kaushik Pal

Youngest Scientist Faculty Fellow (Independent Researcher), (Physicist & Nano Technologist), Suite.108 Wuhan University, Hubei,

Republic of China.

Dr. Wan Aezwani Wan Abu Bakar

Lecturer, Faculty of Informatics & Computing, Universiti Sultan Zainal Abidin (Uni SZA), Terengganu, Malaysia.

Dr. P. Sumitra

Professor, Vivekanandha College of Arts and Sciences for Women (Autonomous), Elayampalayam, Namakkal (DT), Tiruchengode

(Tamil Nadu), India.

Dr. S. Devikala Rameshbabu

Principal & Professor, Department of Electronics and Electrical Engineering, Bharath College of Engineering and Technology for

Women Kadapa, (Andra Pradesh), India.

Dr. V. Lakshman Narayana

Associate Professor, Department of Computer Science and Engineering, Vignan’s Nirula Institute of Technology & Science for

women, Guntur, (Andra Pradesh), India.

S. No

Volume-8 Issue-5S, May 2019, ISSN: 2278-3075 (Online) Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page No.

1.

Authors: M Sandeep, Lavanya Shivgonda, Rajeswari M, Kaushik S, Nikhil Tengli

Paper Title: Robotic ARM using Computer Vision

Abstract: A Bot which pursues Human hand developments. Its unlimited authority lies with the client and

doesn't have any knowledge of its own. Programmed robots having man-made brainpower are a danger to

society and may cause hurt in certain situations. Subsequently, having full oversight over the robot is a protected

method to work with such robots. In this paper, we have proposed a comparable arrangement of a robot.

catching pictures from the PC web cam progressively condition and procedure them as we are required. By

utilizing open source Computer vision library (OpenCV for short), a picture can be caught on the basis of its Hue

saturation value (HSV) extend. The fundamental library capacities for picture dealing with and handling are

utilized. Fundamental library capacities are utilized for stacking a picture, making windows to hold picture at run

time, sparing pictures, and to separate pictures dependent on their shading values. I have additionally connected

capacity to edge the yield picture so as to diminish the twisting in it. While handling, the pictures are changed

over from their essential plain Red, Green, and Blue (RGB) to an increasingly reasonable one that is HSV.

Keyword: Its unlimited authority lies with the client and doesn't have any knowledge of its own. References:

1. Ruchi Manish Gurav and Premanand K. Kadbe, "Continuous Finger Tracking and Contour Detection for Gesture Recognition

utilizing OpenCV " 2015 International Conference on Industrial Instrumentation and Control (ICIC).

2. Ivan Culjak, David Abram, Tomislav Pribanic, Hrvoje Dzapo and Mario Cifrek, ―A brief introduction to OpenCV‖ University of Zagreb, MIPRO 2012.

3. Viraktamath SV, Mukund Katti, Aditya Khatawkar, Pavan Kulkarni, ―Face Detection and Tracking using OpenCV,‖ The SIJ

Transaction on Computer Networks & Communication Engineering (CNCE), 2013. 4. Nidhi ―Image Processing and Object Detection‖ Department of omputer Applications, NIT Kurukshetra, Haryana, India 2015

5. Mamata s.kalas ―Real time face detection and tracking using opencv‖ Department of IT, KIT‘S College of Engg., Kolhapur 2014

6. Shamsheer Verma ―Hand Gestures Remote Controlled Robotic Arm‖ Advance in Electronic and Electric Engineering 2013. 7. Kari Pulli, Anatoly Baksheev, Kirill Kornyakov and Victor Eruhimov ―Real-Time Computer Vision with OpenCV‖ 2012

8. Neetu Saini, Sukhwinder Kaur, Hari Singh A Review:Face Detection Methods And Algorithms, International Journal

of Engineering Research & Technology June – 2013

1-5

2.

Authors: Akash Srivastava, Annu Malik, Alisha Bhatt, Aprajita Kumari, Prof. Manju More E

Paper Title: Placemento – An Android based Project for the Automation of Placement and Training Department

Abstract: Training and Placement department is one of the important area to any educational Institute, even in

this era, we are doing most of the work by using human interventions. The main aim of this paper is to automate

the Training and Placement cell of Reva University. The main feature of this project is the generation,

verification, authentication and easy analysis with maintenance of relevant data. This is achieved by means of

modern Technology like Android and database servers. This will provide the facility to maintaining student data

along with placement records of the college. This will serve as a medium of free communication and feedback

between the students and the placement department. The Planner in the application will help all the users to

select what they want to study and, plan their day accordingly. All the syllabus and faculties will be one touch

away from the users. Users can post their queries and can be in direct touch with the Training and placement

cell. The project aims to provide maximum optimization and security along with minimal manual work. This

will be helpful in efficient and better management of all placement and Training activities on campus. With the

development of this project, the University can maintain computerized records without redundant entries.

Keyword: the University can maintain computerized records without redundant entries. References: 1. Ms.Shilpa Hadkar, Snehal Baing, Trupti Harer, Sonam Wankhade, K.T.V. Reddy, ―College Collaboration Portal with Training and

residency‖, IOSR Journal of Computer Engineering (IOSR-JCE), Volume 10, Issue 2 (Mar. - Apr. 2013), PP 79-81.

2. Nilesh Bhad, Pooja Kamble, Sunita Saini, Prof. Yogesh Thorat, ―An Interactive Online Training and Placement System‖, International

Journal of Advanced Research Development, Vol. 3, Issue 11,2016. 3. Prof. Seema Shah Assistant Professor, Mr Nilesh Rathod, ―Design Paper on OnlineTraining and Placement System (OTaP)‖,

6-13

International Conference on education and Educational Technologies, 2013

4. Zirra E., March F., Building University - ―Enterprise Cooperation for the Benefit of Students, Enterprises and Companies‖, EUI-Net workshop, 2006.

5. Mr Hitesh K. Kasture, ―Training and Placement web Portal‖, International Journal on Recent and modernization. Trends in Computing

and Communication, Volume: 2 Issue: 3, 586 – 591. 6. Android app ―Placement Aptitude and Interview‖.

7. IJARIIE ―A Survey on Android App for Training and Placement Cell‖- Sanket. R. Brahmankar, Rahul. S. Ghule, Shubham. K. Chavan,

Landge. D. Ashish, Pavan. D. Borse, volume 1. 8. MJRET ―Android Based Training And Placement Automation‖- Tejashri Gosavi, Shraddha Gaikwad, Rohit Nazirkar, Amol Salke

Department of Computer Engineering, JSPM‘s ICOER, Wagholi, Pune, India. MC4-3-2015 MJRET-ISSN: 2348-6953.

9. K.G. Patel, C. K. Patil,‖ Study of Implementation of Placement System―, International Conference on emerging trends in engineering and management research,23rd march 2016

10. Talaba, D., Moja, A, Zirra, E., Guidelines towards a European standard for quality assurance of student placement, available in login

space on www.q-planet.org. 11. Tynjälä, P., Perspective into learning at the workplace, Educational Research Review, 3, 2008, pp.130-154.

12. Swati Choudhary, Monica Landge, Shital Salunke, Swarupata Sutar, Kirti M hamunkar,―Advanced training and Placement Web

Portal‖, International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 4, Issue 2 (March-April, 2016), PP. 75-77.

13. Mr R J LAIRD,― Interactive Web-based Placement Management–Principles and Practice using OPUS‖, School of Engineering,

University of Ulster, Shore Road, NEWTOWNABBEY, Co. Antrim, UK, BT37 0QB, 2008. 14. Ashwajit Ramteke, Mrunal Deogade, Prafull Deogade (2015),‖Student Automation System for placement Cell‖, IORD journal of

Science & Technology, Volume 2,jan-feb 2015, pp 104-109.

15. Prof. P. V. Phadke, Miss. T. D. Marodkar & Miss. N. A. Chunade,‖Training and Placement Office Automation System‖, Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-6, 2016.

16. Laird, R. J., & Turner, C. R. (2008a, September). ‗Placement assessment online using OPUS – the work based assessors‘ views. Paper

presented at ASET Annual Conference, Plymouth, UK. 17. A. Arjuna Rao, K. Sujatha, V. Bhagyashree, B. Dileep Kumar,‖ Automation of Training and Placement Cell ‖,International Journal of

Engineering Research and General Science Volume 4, Issue 3, May-June, 2016 ISSN 2091-2730.

18. Neha Choudhary, Sahil Nagrare, Shrikant Kale, Akshay Sontakke, Prof. Piyoosh Awthare,‖Training and Placement Guru‖, International Journal of Research in Advent Technology, Vol.5, No.2 February 2017 E-ISSN: 2321-9637

19. Praveen Rani, Dr. Rajan Vohra,‖ Generating Placement Intelligence in Higher Education Using Data Mining‖, International Journal of

Computer Science and Information Technologies, Vol. 6 (3), 2015, 2298-2302. 20. Ms. Taheniyath Shua, Ms. Shubhangi Chikte, Ms. Priya Sakharkar, Ms. Seema Bundele, Ms. Nikita Wankar, Prof. S. R. Sontakke,‖An

Automated Solution to Training and Placement Cell Activities‖, International Journal on Recent and Innovation Trends in Computing

and Communication ISSN: 2321-8169 Volume: 5 Issue: 4 . 21. Vikrant A Agaskar, Surjit H Singh, Srujan S Chaudhari, Keyur P Rajyaguru, ―To Automate Entire Placement and Training Cell for

The College using Android Application with Cloud Computing‖, International Journal of Advanced Research in Computer and

Communication Engineering Vol. 5, Issue 3, March 2016. 22. S. R. Bharama goudar, Geeta R.B., S.G. Totad , ―Web Based Student information Management System‖, International Journal of

Advanced Research in Computer and Communication Engineering Vol. 2, Issue 6, June 2013.

23. T. Jeevalatha, N. Ananthi, D. Saravana Kumar, ―Performance Analysis of undergraduate student‘s placement selection using decision tree algorithms‖. International Journal of Computer Applications, Vol.108-No 15, December 2014.

24. Dr. S.B. Vanjale, Rahul Kumar Modi, Supreet Raj, Akshit Jain,‖ Smart Training & Placement System‖, IJCST Vol. 8, Issue 2, April -

June 2017.

3.

Authors: Rajeev Kumar, Prabhudev Jagadeesh M.P

Paper Title: QoS Routing Based on Available Bandwidth for Mobile Ad hoc Network

Abstract: Mobile Ad-hoc network is a self configuring wireless network. It does not have any fixed

infrastructure. Mobile node can leave and join the network. Therefore network topology changes any time. In

Manet hosts can work as a router and forwards data from initiator to receiver. Since wireless channel is shared

and topology is dynamic, providing quality of service (QoS) is a challenging task. QoS routing can find optimal

routes that supports QoS requirement based on the received information during route discovery process. If QoS

requirement cannot be supported, the admission control mechanism reject incoming request. Bandwidth

estimation is a technique to determine available data rate on a route in the network. The term bandwidth means

data rate not the physical bandwidth in hertz. QoS routing is required because most of the real time applications

depend on the network‘s condition. QoS in terms of bandwidth ensures transmission of real time data. In this

paper a new bandwidth estimation method EAB (enhanced available bandwidth) is proposed. The performance

of EAB-AODV is compared with AODV. The performance of EAB-AODV is better than AODV in terms of

bandwidth.

Keyword: QoS Routing, Active Technique, Passive Technique, Bandwidth Estimation, Admission control,

Manet.

14-18

References: 1. Rajeev Kumar, ―A comprehensive analysis of MAC protocols for Manet‖, IEEE International Conference on Electrical,

Electronics, Communication, Computer and Optimization Techniques (ICEE CCOT), 56-58, December 2017.

2. Rajeev Kumar and Ranjeet Kumar, ―Reactive Unicast and Multicast Routing Protocols for Manet and Issues A

Comparative Analysis‖ International Conference on internet of things, International Journal of Engineering and

Technology (IJERT) , Volume 4, Issue 29, pp. 135-137, August 2016.

3. H Kaaniche, F Louati, M Farikha and F Kamoun ―A QoS Routing Based on Available Bandwidth Estimation for Wireless

Ad hoc Networks‖ International Journal of Computer Networks & Communication(IJCNC) Vol.3,No.1, January 2011.

4. Xu Zhen, Y Wenzhong, ―Bandwidth-Aware Routing for TDMA-Based Mobile Ad hoc Networks‖ IEEE The International

Conference on Information Networking (ICOIN), pp.637-642, May, 2013.

5. Hyung J Park, and Byeong Roh, ―Accurate Passive Bandwidth Estimation (APBE) in IEEE 802.11 Wireless LANS‖,

IEEE Proceeding of the 5th International Conference on Ubiquitous Information Technologies and Application,

December, 2010.

6. Isabelle G Lassous, Victor M and Cheikh Sarr. ―Retransmission-Based Available Bandwidth Estimation in IEEE802.11-

based Multihop Wireless Networks‖ Proceeding of 14th ACM International Conference Modeling, Analysis and

Simulation of Wireless and Mobile Systems. PP.377-384, November, 2011.

7. L. Chen and W. Heinzelman, ―QoS-aware Routing Based on Bandwidth Estimation in Mobile Ad Hoc Networks,‖ IEEE

Journal on Selected Areas in Communications, Special Issue on Wireless Ad Hoc Networks, vol. 23, no. 3, 2005.

8. Haitao Zhao, Garcia-Palacios, An Song , Shan Wang and Jibo Wei ―A Soft Admission Control Methodology for Wireless

Ad-Hoc Networks: Evaluating the Impact on Existing Flows before Admission‖, IEEE 7th International Symposium on

Communication Systems, Networks & Digital Signal Processing (CSNDSP), PP.51-55, September, 2010.

9. S.V. Sabojil, and C.B. Akki, ―Agent based Bandwidth Estimation in Heterogeneous Wireless Networks‖, 3rd

International Conference on Advances in Recent Technologies in Communication and Computing, 2011, pp. 256-258.

10. S. Ekelin, M. Nilsson, E. Hartikainen, A. Johnsson, J.-E. Mngs, B. Melander, and M. Bjrkman, ―Real-time Measurement

of End-to-End Available Bandwidth using Kalman Filtering‖, Proceedings of the 10th IEEE/IFIP Network Operations and

Management Symposium, 2006.

11. Obara, H., Koseki, S., & Selin, P., ―Packet train pair: A fast and efficient technique for measuring available bandwidth in

the internet‖, in SICE annual conference, 2012, pp. 1833–1836.

12. Nyambo Benny, Janseens Gerrit, Lamotte Wim, ―Bandwidth Estimation in Wireless Ad hoc Networks‖, in Journal of

Ubiquitous & Pervasive Networks ,2015, vol. 6 ,pp.19-26.

13. Zhenhui Yuan, ―MBE: Model-Based Available Bandwidth Estimation for IEEE 802.11 Data Communications‖, IEEE

Transactions on Vehicular Technology, 2012, vol. 61 , pp. 2158-2171.

14. F. D. Rango, F Guerriero, ―Link Stability and Energy Aware Routing Protocol in Distributed Wireless Networks‖, IEEE

Transactions on Parallel and Distributed systems, Vol 23, No. 4, April, 2012.

15. Arindrajit Pal, J Prakash Singh, and P Dutta, ―The Effect of Speed Variation On Different Traffic Pattern in Mobile Ad

hoc Network‖ Elsevier Procedia Technology C3IT, 2012.

4.

Authors: Pruthvi S, Pushyap Suraj Nihal, Ravin R Menon, S Samith Kumar, Shalini Tiwari

Paper Title: Smart Blind Stick using Artificial Intelligence

Abstract: Smart Blind Stick is a device designed to help guide the visually impaired by detecting objects and

portray the information to them in the form of speech. This reduces the human effort and gives better

understanding of the surrounding. Furthermore it also provides an opportunity for visually impaired people to

move from one place to another without being assisted by others. The device can also be used in old age homes

where old age people have difficulty in their day to day activities due to decreased vision. With this paper, the

aim to aid people in need to ―see‖ the surroundings. Since the field of artificial intelligence is doing great

progress now and features like object detection is getting easier and computationally feasible, these features are

implemented in the paper. The paper focuses on object detection and classification on pictures which are

captured by the device mounted on a stick whose information can then be relayed to the user in means of sound

or speech.

Keyword: Object detection, YOLO, Tensorflow, eSpeak, Raspberry pi, Blind, Visually impaired. References: 1. WHO,‖ Universal eye health: a global action plan 2014-2019‖, ISBNNo: 978 92 4 150656 4.

2. Dominic Basulto, ―Artificial intelligence is the next big tech trend.‖, Washington Post, March 25, 2014

3. Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, ―You Only Look Once: Unified, Real-Time Object Detection‖, Conference on Computer Vision and Pattern Recognition 2016, arXiv:1506.02640

4. Dionisi, A & Sardini, Emilio & Serpelloni, Mauro. (2012). ―Wearable object detection system for the blind‖. 2012 IEEE I2MTC -

19-22

International Instrumentation and Measurement Technology Conference, Proceedings. 1255-1258. 10.1109/I2MTC.2012.6229180.

5. Nada, Ayat & Mashali, Samia & Fakhr, Mahmoud & Seddik, Ahmed. (2015). ―Effective Fast Response Smart Stick for Blind People‖. 10.15224/978-1-63248-043-9-29.

6. M.Everingham, S.M.A.Eslami, L.Van Gool, C.K.I.Williams, J.Winn, and A.Zisserman. The pascal visual ob-ject classes challenge: A

retrospective. International Journal of Computer Vision, 111(1):98–136, Jan. 2015. 7. L. Chen, J. Su, M. Chen, W. Chang, C. Yang and C. Sie, "An Implementation of an Intelligent Assistance System for Visually

Impaired/Blind People," 2019 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, pp. 1-2, 2019.

8. N. Dey, A. Paul, P. Ghosh, C. Mukherjee, R. De and S. Dey, "Ultrasonic Sensor Based Smart Blind Stick," 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), Coimbatore, pp. 1-4, 2018.

9. Shaha, S. Rewari and S. Gunasekharan, "SWSVIP-Smart Walking Stick for the Visually Impaired People using Low Latency

Communication," 2018 International Conference on Smart City and Emerging Technology (ICSCET), Mumbai, pp. 1-5, 2018.

5.

Authors: Sheelavathy, Hamsavani.R, Disha J, Bhavana C, Bhoomika Rathod

Paper Title: Image Steganography Technique based on Canny Edge Detection and Hamming Code for Medical

Data

Abstract: Image steganography has major role in enhancing the confidentiality of sensitive information related

to business information, research data, and health record data and so on. Here the sensitive data considered is

Medical data. When the medical image is transmitted through in secure public network, there are chances for

medical images to be tampered. To avoid intruders in viewing the sensitive data i.e. Medical information the

need of hiding it becomes the foremost criteria. This project mainly aims at enhancing medical integrity. To

achieve medical integrity, it is required to hide the medical information within a cover image which is the

medical image here. The proposed system aims at providing high security of data integrity by using

cryptography along with steganography. The method of digital steganography is involved in the transfer of high

imperceptible method that enhances the hiding of Electronic patients record (EPR) into medical images without

major modification in the data transfer. It is predominantly required to protect and enhance the security methods

ensures that the eavesdroppers will not have any suspicion that medical image or sensitive medical data is

hidden in that image

Keyword: Digital steganography, Electronic Patients Record (EPR), edge-detection, XOR, Medical Data References: 1. Shuliang Sun, ―Image Steganography based on Hamming Code and Edge Detection‖, 2018, Research gate publications

2. Kumar Gaurav, Umesh Ghanekar, ―Image Steganography algorithm based on Edge-region detection and Hybrid Coding‖, 2018 3. Hayat Al-Dmour, Ahemd Al-Ani, ―Quality Optimized Medical Image Steganography based on Edge-Detection and Hamming

Code‖ International Conference on IEEE, 2015

4. Abbas Cheddad, Joan Condell, Kevin Curran, and Paul Mc Kevitt, ―Digital image steganography: Survey and analysis of current methods,‖ Signal Processing, vol. 90, no. 3, pp. 727–752, 2010.

5. Tayana Morkel, Jan HP Eloff, and Martin S Olivier, ―An overview of image steganography.‖ in ISSA, 2005. 6. Bremnavas, B Poorna, and GR Kanagachidambaresan, ―Medical image security using lsb and chaotic logistic map,‖ 2011.

23-25

6.

Authors: Abhishek Pratap Singh, SunilKumar S Manvi, Pratik Nimbal, Gopal Krishna Shyam

Paper Title: Face Recognition System Based on LBPH Algorithm

Abstract: In this modern time, identifying a person using a face is a standard biometric approach to

distinguishing an individual from others. So techniques are required to identify a face must be quick and

sufficiently enough to work in real time. But there are many difficulties within the execution of face

identification in low lighting condition. In this paper, we have proposed a system that is using Local Binary

Patterns Histogram algorithm for identifying a face. It can recognize both front and side faces and upgrade the

value of poor enlightened picture and also expands the recognition rate in real time.

Keyword: Face recognition, LBPH, Histograms, Identification Process References: 1. J. Hai qiang LONG,Tai zhe TAN. Computer Simulation, 2017, 34(1): 322-325.

2. J. HU Liqiao, QIU Runhe. Face recognition based on adaptive weighted HOG. Computer Engineering and Applications, 2017, 53(3): 164-168.

3. J. Olshausan B A, Field D J.Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 1996, 381(6583):607-609.

26-30

4. J. WU Qi, WANG Tang-hong, LI Zhan-li. Improved face recognition algorithm based on Gabor feature and collaborative representation. Computer Engineering and Design, 2016, 37(10): 2769-2774.

5. J. CHAO W L, DING J J, LIU J Z. Facial expression recognition based on improved local binary pattern and class-regularized locality preserving projection. Signal Processing, 2015, 117:1-10.

6. J. Yu yan JIANG, Ping LI, Qing WANG. Labeled LDA model based on shared background topic. Acta Electronica Sinica, 2015, 2013, (9): 1794-1799.

7. J.P. Bellhumeur, J.Hespanha, D. Knegman. Eigenfaces vs fisherfaces: Recognition using class specific linear projection.IEEE Transactions on Pattern Analysis and MachineIntelligence. Special Issue on FaceRecognition, 1997, 17 (1) :711-720.

8. J. Turk M, Pentland A. Eigenfaces for recognition. Journal of cognitive neuroscience, 1991, 3(1): 71-86

9. M. Joe Minichino, Joseph Howse. Learning OpenCV 3 Computer Vision with Python. 2016 16-82

10. T. Ojala, M. Pietikainen and D . Harwood, "A Comparative Study of Texture Measures with Classification based on Featured Distributions," Pattern Recognition, vol. 29, no. 1, pp. 51-59, January 1996

11. T. Ahonen, A. Hadid, and M. Pietika inen. Face recognition with local binary patterns. In European Conference on Computer Vision (ECCV), pages 469–481, 200..

12. Ahonen, T., Hadid, A, and Pietikäinen, M. (2006), Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 28(12):2037-2041

13. Viola, Paul, and Michael J. Jones. "Robust real-time face detection." International journal of computer vision 57.2 (2004): 137-154.

14. Varun Garg, Kritika Garg, Face Recognition Using Haar Cascade Classifier, Journal of Emerging Technologies and Innovative Research (JETI),, December 2016

15. Zheng Xiang, Hengliang Tan, Wienling Ye. The excellent properties of dense gird-based HOG features on face recognition compare toGaborr and LBP, 2018

16. Md. Abdur Rahim, Md. Najmul Hossain, T. Wahid, Md. Shafiul Azam, "Face recognition using Local Binary Patterns (LBP)", Global Journal of Computer Science and Technology Graphics &Vision,, Global Journals Inc. (USA), Volume 13 Issue 4 Version 1.0 Year 2013.

17. T. Chen, Y. Wotao, S. Z. Xiang, D. Comaniciu, and T. S. Huang, ―Total variation models for variable lighting face recognition‖ IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(9):2006

18. Zhao and R. Chellappa "Robust face recognition using symmetricshape-from-shadingg" Technical Report, Center for Automation Research ,University of Maryland,, 1999

7.

Authors: Ketaki Vinod Patil, Chakka Sai Abhishek

Paper Title: Prevention of Theft of Sandalwood trees using IOT and Arduino

Abstract: With the advancement in the technology and increasing dependency of humans on smart devices,

with the rising concern for security systems available to the society in day to day life, it has become very

important to have a technology which can monitor and protect the green cover in our society using IOT. Our

paper ‗Prevention of Theft of Sandalwood trees using IOT and Arduino deals with embedded technologies

which incorporates the inbuilt structure and script code for Arduino in this paper we present an efficient solution

to safeguard sandalwood trees which are the pride of our society. The sensors used here are connected with

Arduino. The safety statistics of sandalwood trees is continuously synced with cloud storage using the wireless

module which can be monitored easily by the concerned forest official who can also enable and disable the

sensors. The accelerometer depends on the vibrations to control the signals. Our proposed system will link the

leading technology to bring the features of security to completely safeguard our precious Sandalwood trees

present in our environment.

Keyword: Arduino, vibrational sensors (embedded accelerometer), microwave transmission, Internet of Things

(IOT), Global positioning System (GPS). References: 1. M. Banzi, ―Getting Started with arduino", O'Reilly Media, Inc., 2009 .

2. Nawrath, Martin ‖Arduino Frequency Counter Library‖ Laboratory for Experimental Computer Science at the Academy of Media Arts

Cologne Germany.

3. Yanbo Zhao, Zhaohui Ye, "A low cost GSM/GPRS based wireless home security system", IEEE Transactions on Consumer

Electronics, vol. 54, no. 2, 2008.

4. Global Positioning System: Signals, Measurements and Performance By Pratap Misra and Per Enge and GPS Positioning Guide

Geodetic Survey Division, Natural Resources Canada, 1993.

5. Andrews,A.,P.,Weill, L.R.,and Grewal,S.G.(2007). Global Positioning System Inertial Navigation, and Integration. John Wiley&Sons.

6. Parkinson,B.W., and Spilker.j.j.eds.(1996). Global Positioning System: Theory and Practice. Volume I and II. Washington, DC: American Institute of Aeronautics and Astronautics, Inc

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7. D.L.S. Andres, ―Application of Vibrational Sensor‖, pp.1-18, 2012.

8. J. Carlson, B.Creighton, D.Meyer, J.Montgomery, and A.Reiter, ―White Paper on Internet Of Things: Security Research Study‖, 2014.

9. CISCO, ―802. 11ac: The Fifth Generation Of Wi-Fi‖, Cisco Public Inf., no.march, pp. 1-25, 2014.

10. S. Deepika and M.N. Angeline, ―Security Management For Controlling Theft Using Arduino Uno‖ , vol.3, pp. 101-106, 2017.

11. M.Lakshmi neelima and M.Padma, ―A Study On Cloud Storage‖, Int. J. Comput. Sci. Mob. Comput,. Vol.35, no. 5 pp.966-971, 2014.

12. G. S. Navstar, ― Standard Positionng Service‖, p. 46, 1995.

13. L. Ren and D.Ph. ― IoT Security:Problems,Challenges and Solutions‖, 2015.

14. E.Team, ―ESP8266 Technical Reference‖, Interface,pp.1-117, 2017.

15. W.S. Technologies,‖Installation Of Vibration Sensors‖, vol. 1, no. 301, pp. 1-7.

16. R. A. P Rajan, ― Evolution of Cloud Storage as Cloud Comuting Infrastructure Service‖, IOSR J. Comput. Eng., vol. 1 , n0.1, pp.38-45, 2012.

17. P. Dhruvajyoti and J. Debashis, ―GSM Based Fire Sensor Alarm Using Arduino ―, Int. J. Sci. Eng. Res., vol. 7, no. 4,pp.259-262, 2016.

18. K. A. Salim and J.M. Idrees, ―Design And Implementation Of Web Based GPS-GPRS Vehicle Tracking System‖, IJCSET Dec, vol.3,

no. 3, pp.5343-5345, 2013.

19. K. Rose , S.Eldridge, and L. Chapin, ― The Internet Of Things: An Overview-Understanding the Issues and Challenges Of A More

Connected World‖, Internet Soc.m no. October, p. 80, 2015.

8.

Authors: Tejas Rao C, Mohammed Zainuddin, Shrishail M Patil, Shashank G, Nimrita Koul

Paper Title: Real Time Person Detection and Classification using YOLO

Abstract: A Convolutional Neural Network (CNN) is a class of deep neural network most commonly used in

analyzing visual images. Various systems and applications have been built to detect and classify the objects in a

faster way taking CNN as its foundation. In this paper, we introduce a model to identify and classify people

wearing ID card.Our model uses an object detection system called YOLO (You Only Look Once) for detecting

and classifying objects in real-time videos. In the YOLO algorithm, a single convolutional network predicts the

bounding boxes and the class probabilities for these boxes. We aim to use our model for authentication,

surveillance and security purposes at organizations, corporations and educational institutions to detect an

unauthorized person at the premises or somebody without a valid identification document. Using the object

detection and classification, we aim to build a model which would alert the respective authorities on the matter.

Keyword: Convolutional Neural Network,Object Detection and Classification, You Only Look Once(YOLO). References: 1. S. Divvala, R. Girshick, A. Farhadi, J. Redmon, "You Only Look Once: Unified, Real-Time Object Detection," in The IEEE

Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

2. A. Farhadi, J. Redmon, "YOLO9000: Better, Faster, Stronger," in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

3. A. Farhadi, J. Redmon, "YOLOv3: An Incremental Improvement," 2018.

4. S. Ioffe, C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," arXiv:1502.03167, 2015.

5. R. Benenson, M. Omran, J. Hosang, B. Schiele, S. Zhang, "How Far Are We From Solving Pedestrian Detection?," The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1259-1267, 2016.

6. J. Dang, Y. Wang, S. Wang, W. Lan, "Pedestrian Detection Based on YOLO Network Model," in The IEEE Conference on

Mechatronics and Automation (ICMA), 2018. 7. L. Zhang, P. Wang, Z. Hong, "Pedestrian Detection Based on YOLO-D Network," in The IEEE 9th International Conference on

Software Engineering and Service Science, 2018.

8. L. V. Gool, C. K. Williams, J. Winn, A. Zisserman, M. Everingham, "The Pascal Visual Object Classes (VOC) Challenge," in IJCV,

2010.

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9.

Authors: Arun kumar E, Gourish M Malage, Sunilkumar S Manvi, Kiran Kumari Patil

Paper Title: Development of Image Annotation Tool by using Region Grow Algorithm

Abstract: : Image annotation conjointly called as automatic picture tagging or linguistic compartmentalization.

It is the method through which computing systems mechanically provide the information within the style of

keywords to an image. Several techniques are planned for picture annotation from the previous decades that

provide enforcement on common place datasets. However, most of those works fail to match their ways with

easy baseline techniques to justify the necessity for advanced models and subsequent coaching. In this paper, we

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propose a region grow algorithmic program for development of image annotation tool. This method uses low-

level model options and a straight forward collection of the distances to find out closest homogenized pixels of a

given picture and mix one another to make a region of image.

Keyword: Image annotation, Region grow Algorithm. References: 1. Asullah Khalid Alham, Maozhen Li1, Suhel Hammoud and Hao Qi, ―Evaluating Machine Learning Techniques for Automatic Image

annotation,vol. 11, no. 1, january/february (2009),p.21-235. 2. O. Marques, N. Barman, "Semi-Automatic Semantic Annotation of Images Using Machine Learning Techniques" Proc. of

ISWC(2003), p. 550-565.

3. J. Liu, B. Wang, M. Li, Z. Li, W. Y. Ma, H. Lu and S. Ma, ―Dual Cross-Media Relevance Model for Image Annotation,‖ in Proceedings of the 15th International Conference on Multimedia(2007), p. 605 – 614.

4. C. F. Tsai and C. Hung, ―Automatically Annotating Images with Keywords: A Review of Image Annotation Systems,‖ Recent Patents

on Computer Science (2008), vol 1, pp 55-68. 5. R. Datta, D. Joshi, J. Li and J. Z. Wang, ―Image Retrieval: Ideas, Influences, and Trends of the New Age‖ ACM Computing Surveys

(CSUR)(2008), vol. 40, ), p. 605 – 614.

6. L. Cao, J. Luo, H. Kautz and T. S. Huang. ―Image Annotation within the Context of Personal Photo Collections Using Hierarchical Event and Scene Models‖, In (2009) IEEE Multimedia 11(2), p. 208- 219.

7. W. Viana, J. B. Filho, J. Gensel, M. Villanova-Oliver and H. Martin, "PhotoMap: From location and time to context-aware photo

Annotations", In (2008) Journal of Location Based Services 2(3), p. 211-235. 8. M. Ames and M. Naaman, ―Why We Tag: Motivations for Annotation‖. In proc. CHI, ACM Press (2007), p. 971-980.

9. U. WESTERMANN and R. JAIN, "Toward a Common Event Model for Multimedia Applications", In (2007) IEEE Multimedia 14(1),

p. 19-29. 10. M. Davis, N. V. House, J. Towle, S. King, S. Ahern, C. Burgener, Perkel, M. Finn, V.Viswanathan and M. Rothenberg, ―MMM2:

Mobile Media Metadata for Media Sharing‖, Ext. Abstracts CHI (2005), ACM Press, p. 1335-1338.

11. Tianxia Gong, Shimiao Li and Chew Lim Tan, ‖A Semantic Similarity Language Model to Improve Automatic image annotation‖, In (2010) 22nd International Conference on Tools with Artificial Intelligence.

12. Lei Ye, Philip Ogunbona and Jianqiang Wang, "Image Content Annotation Based on Visual Features‖ Proceedings of the Eighth IEEE International Symposium on Multimedia (ISM'06).

13. Yunhee Shin, Youngrae Kim and Eun Yi Kim, "Automatic textile image annotation by predicting emotional conceptsfrom visual

features‖. In (2010) Image and Vision Computing, p. 28. 14. Ran Li, YaFei Zhang, Zining Lu, Jianjiang Lu and Yulong Tian, ―Technique of Image Retrieval based on Multi-label Image

Annotation‖, In (2010) Second International Conference on MultiMedia and Information Technology.

15. T. Jiayu, ―Automatic Image Annotation and Object Detection‖ (2008) PhD thesis, University of Southampton, United Kingdom.

10.

Authors: Savita Choudhary, Vipul Gaurav, Abhijeet Singh, Susmit Agarwal

Paper Title: Autonomous Crop Irrigation System using Artificial Intelligence

Abstract: Abstract: Agriculture plays a significant role in the economy and its contribution is based on

measurable crop yield which is highly dependent upon irrigation. In a country like India, where agriculture is

largely based on the unorganized sector, irrigation techniques and patterns followed are inefficient and often lead

to unnecessary wastage of water. This calls for the need of a system which can provide an efficient and

deployable solution. In this paper, we provide an Automatic Irrigation System based on Artificial Intelligence

and Internet of Things, which can autonomously irrigate fields using soil moisture data. The system is based on

prediction algorithms which make use of historic weather data to identify and predict rainfall patterns and

climate changes; thereby creating an intelligent system which irrigates the crop fields selectively only when

required as per the weather and real-time soil moisture conditions. The system has been tested in a controlled

environment with an 80 percent accuracy, thus providing an efficient solution to the problem.

Keyword: artificial intelligence, irrigation, internet of things, prediction algorithms, machine learning, and

water conservation References: 1. India - History of Irrigation, FAO - United Nations, 2014

2. Anjal Prakash, ―Water in India: Situations and Prospects‖, Water Policy, 2014, pp. 425-441

3. Monteith, J.L., Steps in crop climatology. In: Challenges in Dryland Agriculture: A Global Perspective. P. Unger (ed.). pp. 273-282.

Proc. of the Int. Conf. on Dryland Farming (14-18 Sept., 1998). Amarillo/Bushland Texas, 1990

4. Bhagyashree K Chate ―Smart Irrigation System using Raspberry Pi‖, International Research Journal of Engineering and Technology

(IRJET), Vol 3, Issue 5, May, 2016, p-ISSN: 2395-0072

5. S. Karthick, ―Weather Prediction Analysis using Random Forest Algorithm‖, International Journal of Pure and Applied Mathematics,

Vol 118, No. 20, 2018, pp 255-262

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6. Antonio P. Leone, ―Prediction of Soil Properties with PLSR and vis-NIR Spectroscopy: Application to Mediterranean Soils from

Southern Italy‖, Current Analytical Chemistry, 2012, pp 283-299

7. Schneider, A.D., and T.A. Howell. 1995. ―Reducing Sprinkler Water Losses.‖ In: Proc. Central Plains Irrigation Short Course. Garden

City, KS. Feb. 7-8, 1995, ISSN:0918-5623

8. K. Jyotsana Vanaja, ―IOT Based Agriculture System using Node MCU‖, International Research Journal of Engineering and Technology

(IRJET) , Vol:05, Issue:03, March, 2018, p-ISSN: 2395-0072

9. Dinya Abdulahad Aziz, ―Web Server Based Smart Monitoring System Using ESP8266 NodeMCU Module‖, International Journal of Scientific and Engineering Research, Vol 9, Issue 6, June, 2018, ISSN:2229-5518

10. M. Sidiq, ―Forecasting Rainfall with Time Series Models‖, IOP Conference Series Materials Science and Engineering ,September,

2018

11. R.G. Allen, ―An Update for Calculation of Evapotranspiration Reference ‖, ICID Bulletin, Vol.43, Issue.2, 1994

12. Fan TongKe ―Smart Agriculture Based on Cloud Computing and IOT”, Journal of Convergence Information Technology, January

2013

13. Pradeep Singh, ―3.3 V Step Down Power Supply for ESP8266‖, Article by IOT Bytes

14. Mohammad Ali Jazayeri, ―Implementation and Evaluation of Four Interoperable Open Standards for the Internet of Things‖, Sensors

(Basel), September, 2015

15. Shabib Aftab, ―Rainfall Prediction using Data Mining Techniques: A Systematic Literature Review‖, International Journal of

Advanced Computer Science and Applications, Vol 9, No. 5, 2018

16. E.J. Hernandez, ―Rainfall Prediction: A Deep Learning Approach‖, International Conference on Hybrid Artificial Intelligence

Systems, April, 2016

17. Fethi Ouallouche, ―Improvement of Rainfall Estimation from MSG data using Random Forests Classification and Regression ‖, Atmospheric Research, 211, May 2018

18. M.J. Langkwist, ―A Review of Unsupervised Feature Learning and Deep Learning for Time Series and Modelling‖, Pattern Recognition Letters, June, 2014

19. Nouri, Hamideh; Beecham, Simon; Kazemi, Fatemeh; Hassanli, Ali Morad. "A review of ET measurement techniques for estimating

the water requirements of urban landscape vegetation". Urban Water J. 10 (4): 247–259, 2013

20. R.G. Allen, ―Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements‖, Food and Agriculture Organization of

the United Nations, Rome, 1998

21. Wold, S., Ruhe, A., Wold, H., Dunn, I., W., The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach

to Generalized Inverses. SIAM J. Sci. Stat. Comput. 5, 735 743. doi:10.1137/0905052, 1984

11.

Authors: Arvind Malge, Hardikkumar M. Dhaduk, Mallikarjuna Shastry P.M

Paper Title: An approach to face Detection and Recognition using Viola Jones

Abstract: The face of human may be a muddled visual dimension model and is therefore extremely difficult to

create a computing model for the cognitive basic process. The paper displays a system for perceiving the human

face smitten by image-based highlights. The technique proposed is available in 2 phases. In an image using

Viola-Jones calculation, the main preparation distinguishes the human face. Using a combination of Principle

Component Analysis and Artificial Neural Network, the distinguished face within the image is perceived at the

next stage contrasting the execution of the proposed strategy with existing ways. The proposed strategy

recognizes greater accuracy in the acknowledgement.

Keyword: Face Recognition, Viola-Jones algorithm, PCA, AAN References:

1. Anil K. Jain, ―Face Recognition: Some Challenges in Forensics‖, IEEE International Conference on Automatic Face and Gesture Recognition, pp 726-733,2011.

2. Ming Zhang and John Fulcher ―Face Perspective Understanding Using Artificial Neural Network Group Based Tree‖, IEEE

International Conference on Image Processing, Vol.3, pp 475-478, 1996. 3. Tahia Fahrin Karim, Molla Shahadat Lipu, Md. Lushanur Rahman, Faria Sultana, ―Face Recognition using PCA Based Method‖,

IEEE International Conference on Advanced Management Science, vol.13, pp 158-162, 2010.

4. Nawaf Hazim Barnouti, Wael Esam Matti ―Face Detection and Recognition Using Viola-Jones with LDA and Square Euclidean Distance‖, IJACSA - International Journal of Advanced Computer Science and Applications, Vol. 7, No. 5, 2016.

5. Muhammad Murtaza Khan, Muhammad Yonus Javed and Muhammad Almas Anjum, ―Face Recognition using Sub-Holistic

PCA‖, IEEE International Conference on Information and Communication Technology, pp 152-157, 2005. 6. Rammohan Mallipeddi and Minho Lee, ―Ensemble Based Face Recognition using Discriminant PCA Features‖ IEEE

International Conference on Computational Intelligence, pp 1-7, June 2012.

7. Abhjeet Sekhon and Pankaj Agarwal, ―Face Recognition Using Back Propagation Neural Network Technique‖, IEEE International Conference on Advances in Computer Engineering and Applications, pp 226-230, 2015.

8. Mitsuharu Matsumoto, ―Cognition Based Contrast Adjustment using Neural Network Based Face Recognition‖ IEEE

International Conference on Industrial Electronics, pp 3590-3594, 2010. 9. Dhirender Sharma and JoydipDhar, ―Face Recognition using Modular Neural Networks‖, IEEE International Conference on

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Software Technology and Engineering, 2010.

10. Janarthany Nagendrajah, ―Recognition of Expression Variant Faces- A Principle Component Analysis Based Approach for Access Control‖ IEEE International Conference on Information Theory and Information Security, pp 125-129,2010.

11. Maria De Marsico, Michele Nappi, Daniel Riccio and Harry Wechsler, ―Robust Face Recognition for Uncontrolled Pose and

Illumination Changes‖ IEEE Transaction on Systems, Man and Cybernetics, vol.43, No.1, Jan 2013.

12.

Authors: Chaitanya Krishna VB, Bhaskar Reddy PV, Chethan Kumar A, Salman Ahmed, Sampath M

Paper Title: Face Recognition Based Attendance Management System using DLIB

Abstract: Real time face recognition technology has become a prominent tool for addressing solutions to many

complex problems. Such as identification and the verification of identity. Face recognition technology also

addresses the time consumption issue that arises in other biometric systems. Taking Attendances manually is

always a monotonous job and it additionally consumes heap of our time. The prevailing biometric attendances

wastes a great deal of our time and these systems can be cheated easily. In our proposed system the attendance is

recorded by using a camera that is attached in front of classroom which is continuously recording but the system

will never store any recorded files. And the features obtained from the detected images are compared with the

features stored in the database and the system mark‘s the attendance. This paper aims at automating the whole

process and implementing a system that can‘t be cheated. The entire system is built by using a machine learning

tool called DLIB.

Keyword: DLIB, Biometric, Attendance, Face Recognition References: 1. Detection and classification of lung abnormalities by of convolutional neural network (CNN) and regions CNN features (R-CNN)

Shoji Kido ; Yasusi Hirano ; Noriaki Hashimoto 2018 International Workshop on Advanced Image Technology (IWAIT) Year: 2018

Pages: 1 – 4. 2. Study of object detection based on Faster R-CNN Bin Liu ;Wencang Zhao; Qiaoqiao Sun 2017 Chinese Automation Congress (CAC)

Year: 2017 Page s: 6233 – 6236.

3. Low-complexity HOG for efficient video saliency Teahyung Lee ; Myung Hwangbo ; Tanfer Alan ; OmeshTickoo ; Ravishankar Iyer2015 IEEE International Conference on Image Processing (ICIP)Year: 2015, Page s: 3749 - 3752.

4. Multi-posture Human Detection Based on Hybrid HOG- BO Feature Jain Stoble B; Sreeraj M.2015 Fifth International Conference on

Advances in Computing and Communications (ICACC) Year: 2015; Pages:37-40. 5. Applying MSC-HOG feature to the detection of a human on a bicycle. Heewook Jung ; Yusuke Ehara ; JooKooi Tan ; Hyoungseop

Kim ; Seiji Ishikawa 2012 12th International Conference on Control, Automation and Systems. Year: 2012 ,Pages: 514-517.

6. Fast human detection using selective block-based HOG-LBPWon-Jae Park ; Dae-Hwan Kim ; Suryanto ; Chun-GiLyuh ; Tae Moon Roh ; Sung-Jea Ko2012 19th IEEE International Conference on Image Processing Year: 2012 Page s: 601 - 604.

7. Shubhobrata Bhattacharya, Gowtham Sandeep Nainala, Prosenjit Das, AurobindaRoutray. "Smart Attendance Monitoring System

(SAMS): A Face Recognition Based Attendance System for Classroom Environment", 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT), 2018,Pages:358-360.

8. STAMP: SMTP Server Topological Analysis by Message Headers Parsing Emmanuel Lochin 2010 7th IEEE Consumer

Communications and Networking Conference Year: 2010 Page s: 1 – 2. 9. Active e-mail system SMTP protocol monitoring Algorithm R. Sureswaran;Hussein Al Bazar; O.Abouabdalla; Ahmad M.Manasrah;

Homam El-Taj 2009 2nd IEEE International Conference on Broadband Network & Multimedia Technology. Year: 2009. 10. Testing using selenium web driver ParuchuriRamya ;VemuriSindhura ; P. Vidya Sagar2017 Second International Conference on

Electrical, Computer and Communication Technologies (ICECCT) Year: 2017 Page s: 1 – 7.

11. https://en.wikipedia.org/wiki/File:Typical_cnn.png This file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license.

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13.

Authors: Ajith Shenoy, Sushma Ravindra Y, Akash Sharma, Akshay Rajan, Akshay GV

Paper Title: NLP Models Behind RASA Stack

Abstract: This paper brings about the foundation of a platform for conversational AI the Rasa platform. This

Rasa stack contains a block of open source machine learning tools exclusively used in intend to create a

contextual chatbots and assistants. The services hold by this platform undergoes a major classification of

powerful APIs and embedded together with Rasa stack which includes Rasa core and Rasa NLU in the form of

an event stream discussed throughout this paper and also the algorithm involved in building upon this platform.

Its ingredients include the Bag of words algorithm helping in simplifying representation used in the NLP, CRFs

– Conditional Random Field used in statistical modelling and machine learning platforms and also advanced

technology such as LTSM neural networks. This paper discusses all the algorithms involved in building up the

platform and also the result produced in building up the student assistant chatbot using this platform. It also

encourages the use of this RASA platform for the user required custom format as per their requirements and also

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promotes to contribute in developing the platform for better efficiency of the platform to function.

Keyword: Bag of words, Chatbot, CRFs, NLP, Rasa stack References: 1. Wenpeng Yin, Katharina Kann, Mo Yu, Hinrich Schütze ,Comparative Study of CNN and RNN for Natural Language Processing

arXiv:1702.01923 , 7 Feb 2017

2. Graves and J. Schmidhuber, ―Framewise phoneme classification with bidirectional lstm and other neural network architectures,‖ Neural Networks, vol. 18, no. 5, pp. 602–610, 2005.

3. Graves, N. Jaitly, and A.-r. Mohamed, ―Hybrid speech recognition with deep bidirectional lstm,‖ in Automatic Speech Recognition

and Understanding (ASRU), 2013 IEEE Workshop on. IEEE, 2013, pp. 273–278. 4. Rong Jin,Zhi-Hua‖Understanding bag-of-words model: A statistical framework‖ page-1, 2010. Article in International Journal of

Machine Learning and Cybernetics • December 2010

5. More than Bag-of-Words:Sentence-based Document Representation for Sentiment Analysis 2013 Georgios Paltoglou Faculty of Science and TechnologyUniversity of Wolverhampton & Mike Thelwall Faculty of Science and Technology University of

Wolverhampton

6. An introduction to conditional random fields Charles Sutton, Andrew McCallum Foundations and Trends® in Machine Learning 4 (4), 267-373, 2012

7. Conditional random field based named entity recognition in geological text N Sobhana, Pabitra Mitra, SK Ghosh International Journal

of Computer Applications 1 (3), 143-147, 201 8. Automatic keyword extraction from documents using conditional random fields

Chengzhi Zhang Journal of Computational Information Systems 4 (3), 1169-1180, 2008

9. Conditional random fields meet deep neural networks for semantic segmentation: Combining probabilistic graphical models with deep learning for structured prediction

Anurag Arnab, Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Måns Larsson, Alexander Kirillov, Bogdan

Savchynskyy, Carsten Rother, Fredrik Kahl, Philip HS Torr

14.

Authors: Hrithik Yadav, Iranna G.G, J Nimalesh, Kiran Kumar B, Archana.B

Paper Title: Smart Traffic Light

Abstract: With increase in population, there has been a significant rise in the number of vehicles on our roads.

This marked increase in vehicles has resulted in most urban areas being gridlocked in traffic jams. In order to

reduce this congestion and set up a functional system of traffic management, we have proposed the Smart Traffic

Light (STL), which uses image processing and a scheduling algorithm to automatically manage the duration of

traffic signals. Another feature of the STL is its management of signals to prioritise emergency vehicles such as

ambulances or fire engines by ensuring green signals in their specified routes in order to ensure minimum delay

in traffic.

Keyword: we have proposed the Smart Traffic Light (STL), References:

1. N.Hashim, A. Jaafar et all, "Traffic light control system for emergency vehicles using radio frequency", IOSR journal of engineering, Vol. 3, Issue. 7, pp. 43-52, July 2013.

2. Smart Traffic Control System Using Image 3. Processing‖ by Vismay Pandit, Jinesh Doshi, Dhruv Mehta, Ashay Mhatre and Abhilash Janardhan in the Journal ―International

Journal of Emerging Trends & Technology in Computer Science (IJETTCS)‖ Volume 3, Issue 1, January – February 2014 ISSN

2278-6856 4. Smart Traffic Light Control System‖ by Bilal Ghazal, Khaled ElKhatib, Khaled Chahine, Mohamad Kherfan at the ―Third

International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA)‖ in April 2016

10.1109/EECEA.2016.7470780 5. A Distributed Approach for Traffic Signal Synchronization Problem‖ by Ludovica Adacher and Marco Tiriolo in 2016 Third

International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) on 27-29 Aug. 2016

6. Automatic Traffic Using Image Processing‖ by Al Hussain Akoum in the Journal ―Journal of Software Engineering and Applications‖ 10(09):765-776 · January 2017

7. SMART TRAFFIC CONTROL SYSTEM FOR AMBULANCE‖ Article · September 2016

8. Intelligent traffic signal control system for ambulance using RFID and cloud‖ 2017 2nd International Conference on Computing and Communications Technologies (ICCCT) 23-24 Feb. 2017

9. Smart traffic control with ambulance detection‖ Varsha Srinivasan1, Yazhini Priyadharshini Rajesh1, S Yuvaraj2 and M

Manigandan2 IOP Conf. Series: Materials Science and Engineering 402 (2018) 012015 doi:10.1088/1757-899X/402/1/012015 10. ―Density Based Traffic Control System Using Image Processing ― by Uthara E. Prakash, Athira Thankappan, Vishnupriya K. T,

Arun A. Balakrishnan in the conference ―International conference on Emerging Trends and Innovations in Engineering and

Technological Research (ICETIETR2k18), At ToCH, Kochi‖ in November 2018

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15. Authors: A.Aravind, Aditya Agarwal, Ayush Jaiswal, Ayush Panjiyara, Mallikarjun Shastry P M

Paper Title: Fatigue Detection System Based on Eye Blinks of Drivers

Abstract: In recent years, road accidents have increased significantly. One of the major reasons for the

accidents as reported is driver fatigue. Therefore, there is a need for a system to measure the fatigue level of the

driver and alert the driver when he/she feels drowsy to avoid accidents. So, in this paper we propose a system

which comprises of a camera installed in the car dashboard. It will continuously monitor the blink pattern of

driver and detect whether he is feeling drowsy or not. If the system finds the driver is feeling drowsy then an

alert will be generated to avoid accident. This project attempts to contribute towards the exercise of analyzing

driver behavior-based Eye Aspect Ratio (EAR) in order to reduce preventable road accidents.

Keyword: Blink pattern, Camera, Car dashboard, Driver fatigue, Drowsy, Eye Aspect Ratio (EAR) References: 1. B. Hariri, S. Abtahi, S. Shirmohammadi and L. Martel, "Demo: Vision based smart in-car camera system for driver yawning

detection," 2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras, Ghent, 2011, pp. 1-2.

2. ―Predicting driver drowsiness using vehicle measures: Recent insights and future

challenges‖ is a paper presented byCharles C.Liu, Simon G.Hosking, Michael G.Lenné

3. Awais M, Badruddin N, Drieberg M ―A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve

System Performance and Wearability‖

4. V. Kazemi and J. Sullivan, "One millisecond face alignment with an ensemble of regression trees," 2014 IEEE Conference on

Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 1867-1874.

5. W. Zhihong and X. Xiaohong, "Study on Histogram Equalization," 2011 2nd International Symposium on Intelligence Information

Processing and Trusted Computing, Hubei, 2011, pp. 177-179.

6. W. Kubinger, M. Vincze and M. Ayromlou, "The role of gamma correction in colour image processing," 9th European Signal

Processing Conference (EUSIPCO 1998), Rhodes, 1998, pp. 1-4.

7. Qiong Wang, Jingyu Yang, Mingwu Ren and Yujie Zheng, "Driver Fatigue Detection: A Survey," 2006 6th World Congress on

Intelligent Control and Automation, Dalian, 2006, pp. 8587-8591.

8. B.M. KusumaKumari, P. Ramakanth Kumar, "A survey on drowsy driver detection system", Big Data Analytics and

Computational Intelligence (ICBDAC) 2017 International Conference on, pp. 272-279, 2017.

9. A. R. Beukman, G. P. Hancke, B. J. Silva, "A multi-sensor system for detection of driver fatigue", Industrial Informatics (INDIN)

2016 IEEE 14th International Conference on, pp. 870-873, 2016.

10. S. Vitabile, A. Paola and F. Sorbello, "Bright Pupil Detection in an Embedded, Real-time Drowsiness Monitoring System", in 24th

IEEE International Conference on Advanced Information Networking and Applications, 2010.

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16.

Authors: Shiva kumar.R.Naik, Kshitij Yadav, Hariom Yadav, Niha.C.Gowda, Mounika

Paper Title: Data Recovery from Encrypted Image and Recovering Image

Abstract: This paper refers to the data hiding technique in an encrypted image and restoring image as it was

before to its fullest. There are three bids of the framework to this process, which are a content owner, data hiding

and recipient. The content owner encrypts the image with ciphertext making it an encrypted image. Data hider

channelizes encrypted image into 3 different channels and adds each with additional bits in order to obtain

marked encrypted image. At the recipient end, the noise from the image could be removed consuming the

extraction key and the image obtained will be intact as original. Utilizing RDH_EI method, we not only receive

secret information but also, the image is recovered using progressive recovery.

Keyword: Data hiding, Information hiding, encrypting images, recovering encrypted image References:

1. X. Hu, W. Zhang, X. Li, and N. Yu, Optimized Histograms Modification for Reversible Data Hiding, IEEE,2014 2. X. Li, W. Zhang, B. Ou, and B. Yang. A short audit on reversible information concealing, IEEE China,2015

3. H. Wang, W. Zhang, and N. Yu, Protecting Patient Confidential Information dependent on ECG Reversible information

hiding,2015 4. Z. Fu, X. Sun, Q. Liu, et al. Accomplishing effective cloud hunt administrations, Transactions on Communications, 98(1): 190-

200,2017

5. X. Zhang, Reversible information stowing away in scrambled pictures, IEEE Signal Processing Letters, 18(4): 255–258,2015 6. W. Hong, T. Chen, and H. Wu, An improved reversible information stowing away in scrambled pictures, 19(4): 199–202,2014

7. M.Li,D.Xiao, A.Kulsoom, and Y.Zhang, Improved reversible information stowing away for scrambled pictures utilizing full

embeddingstrategy,51(9): 690-691, 2017 8. J. Zhou, W. Sun, L. Dong, et al. Secure reversible picture information stowing away over scrambled area by means of key

76-79

modulation,2016M Chaumont and O Strouss, encrypted image processing, LIRMM laboratory,2018

17.

Authors: K Anitha, Tejus Khadri, Tejas Prurvimuth, Venkatesh T.D, Yash Nesarikar

Paper Title: Wide Area Disaster Management System using Mobile Intranet

Abstract: On the occasion of a large-scale disaster, sharing of information functionality with everyone is

important. However, there were many cases where information sharing was not actually well functioned because

the disaster information network infrastructure did not consider the system failure when the disaster happened.

In our findings, we focus on the fact that the disaster management systems are operated on each local area. The

system redundancy is realized by sharing the system resources and integrating the disaster information into a

large disaster system while decentralizing the system and network loads. And, the system failure can be

recovered by introducing system failure detection function for server failure and link disconnection and

dynamically reconstructing the network system. In order to verify the effectiveness of the suggested method, we

constructed a nationwide, disaster information network prototype system over Japan Gigabit Network (JGN2),

implemented Wide-area Disaster Information sharing system (WIDIS) and evaluated its functionality and

performance.

Keyword: Japan Gigabit Network (JGN2), Wide-area Disaster Information Sharing system (WIDIS). References:

1. G"USAR Task Force Locations". FEMA. Archived from the original on July 5, 2012. Retrieved August 28, 2006.

2. "Official Sahana Wiki Page for GSoC 2013". Sahana Software Foundation.

3. Serval Project 2. Tom Simonite July 11, 2013. "A Crowdfunding Campaign to Set Smartphones Free From Cellular Networks |

MIT Technology Review". Technologyreview.com. Retrieved 2013-07-12.

4. Y. Shibata, D. Nakamura, N. Uchida, K. Takahata, "Residents Oriented Disaster In-formation Network", Proc. on Symposium on Applications and the Internet, pp. 317-322, 2003

5. https://www.researchgate.net/publication/274070620_WideArea_Collaboration_in_the_aftermath_of_the_March_11_disasters_in_Japan_Implications_for_responsible_disaster_management

6. https://web.archive.org/web/20160103042149/http://www.isoc.org/inet2000/cdproceedings/8l/8l_3.htm

7. Disaster information system and its wireless recovery protocol Noriki Uchida, Hideaki Asahi, Yoshitaka ShibataPublished in

International Symposium on Applications and the International Symposium on Applications and the Internet Workshops. 2004

DOI:10.1109/SAINTW.2004.1268592

8. Paper on Proposed System for Placing Free Call over Wi-Fi Network Using Voip and SIP Bhushan R. Jichkar

9. "I.430 : Basic user-network interface - Layer 1 specification". International Telecommunication Union. 2010-08-23. Retrieved 2015-05-07.

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18.

Authors: Manaswini Nagaraj, Vignesh Prabhakar, Sailaja Thota

Paper Title: Classification of Mammograms using Attention Learning for Localization of Malignancy

Abstract: Mammography is a specialized medical imaging that uses a low-dose x-ray system to examine the

breasts. A mammogram is a mammography exam report that helps in the detection and diagnosis of breast

diseases in women at an early stage. This project proposes to classify mammography breast scans into their

respective classes and uses attention learning to localize the specific pixels of malignancy using a heat map

overlay. The attention learning model is a standard encoder-decoder circuit wherein convolutional neural

networks perform the encoding and recurrent neural networks perform the decoding. Convolutional neural

networks enable feature extraction from the mammography scans which is thereafter fed into a recurrent neural

network that focuses on the region of malignancy based on the weights assigned to the extracted features over a

series of iterations during which the weights are continuously adjusted owing to the feedback received from the

previous iteration or epoch. Mammography images are equalized, enhanced and augmented before extracting the

features and assigning weights to them as a part of the data preprocessing procedures. This procedure would

essentially help in tumor localization in case of breast cancers.

Keyword: Attention learning, Convolutional neural networks, Encoder-Decoder, Recurrent neural networks. References:

1. Zaiane, Osmar R., Maria-Luiza Antonie, and Alexandru Coman. "Mammography classification by an association rule-based classifier."

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Proceedings of the Third International Conference on Multimedia Data Mining. Springer-Verlag, 2002.

2. Beura, Shradhananda, Banshidhar Majhi, and Ratnakar Dash. "Mammogram classification using two-dimensional discrete wavelet

transform and gray-level co-occurrence matrix for detection of breast cancer." Neurocomputing 154 (2015): 1-14.

3. Yoon, Sejong, and Saejoon Kim. "AdaBoost-based multiple SVM-RFE for classification of mammograms in DDSM." BMC medical

informatics and decision making 9.1 (2009): S1.

4. Nithya, R., and B. Santhi. "Classification of normal and abnormal patterns in digital mammograms for diagnosis of breast cancer."

International Journal of Computer Applications28.6 (2011): 21-25.

5. Balleyguier, Corinne, et al. "BIRADS™ classification in mammography." European journal of radiology 61.2 (2007): 192-194.

6. Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Ricketts, I., et al. (2015). Mammographic Image Analysis Society

(MIAS) database v1.21 [Dataset].

7. Rebecca Sawyer Lee, Francisco Gimenez, Assaf Hoogi, Daniel Rubin (2016). Curated Breast Imaging Subset of DDSM. The Cancer

Imaging Archive.http://dx.doi.org/10.7937/K9/TCIA.2016.7O02S9CY

19.

Authors: P.S.R.V Aditya, P Dinesh Sai, Purvaja Varati, R Rohan Singh, Ranjitha U.N

Paper Title: Emotion Recognition using Open CV

Abstract: The human face plays a pivotal role in identifying emotions, regardless of subject-independent

features. For human-computer interaction, facial expressions form a platform for non-verbal communication.

In this regard, a system which detects and analyses facial expressions, needs to be robust enough to account

for human faces having multiple variability such as color, orientation, posture and so on. Our paper focuses on

the technicalities which makes the system capable of addressing the variability associated with facial

expressions. This is achieved using concepts of machine learning, deep learning and artificial intelligence. The

focus extends to making human-machine interaction not only an interactive process, but also a user friendly

one. The implementation makes use of a Haar Cascade Classifier, Tensorflow and openCv.

Keyword: Facial Expressions, Haar Cascade Classifier, machine learning, non-verbal communication,

OpenCV, Tensorflow References: 1. A. Mehrabian, Communication Without Words, Psychology Today, Vol. 2, no. 4, 1968 2. Phillip Ian Wilson, Dr. John Fernandez, Facial Feature Detection Using HAAR CLASSIFIERS, Journal of Computing Sciences in

Colleges, Volume 21 Issue 4, April 2006

3. Mohamed A. Berbar, Hamdy M. Kelash, and Amany A. Kandeel, IEEE conference on Faces and Facial Features Detection in Color Images, 2014

4. Dilbag Singh, Human Emotion Recognition System, IJIGSP, vol.4, no.8, pp.50-56, 2012.

5. NishantHariprasad Pandey, Dr. Preeti Bajaj, ShubhangiGiripunje, Design and Implementation of Real Time FacialEmotion Recognition System, International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November-2013 174

6. Carlos Busso, Zhigang Deng *, SerdarYildirim, MurtazaBulut, Chul Min Lee, Abe Kazemzadeh, Sungbok Lee, Ulrich Neumann*,

Shrikanth Narayanan, Analysis of Emotion Recognition using Facial Expressions, Speech and Multimodal Information, Proceedings of

the 6th International Conference on Multimodal Interfaces, ICMI 2004, State College, PA, USA, October 13-15, 2004

7. Kadir, Kushsairy&KhairiKamaruddin, Mohd& Nasir, Haidawati& I Safie, Sairul&Bakti, Zulkifli. (2014), A comparative study between

LBP and Haar-like features for Face Detection using OpenCV, 2014 4th International Conference on Engineering Technology and Technopreneuship (ICE2T), 335-339, 10.1109/ICE2T.2014.7006273.

91-93

20.

Authors: Guna Shekar B, Darshan C, Ganesh Horamata B V, Basavaraddi Mulimani, Sarvamangala D R

Paper Title: BMTC e-pass Application

Abstract: Bengaluru Metropolitan Transport Corporation (BMTC) is a prominent public transport service provider in 94-98

Bengaluru. It makes commuting favourable and cheap compared to other modes of transport within the city. The organization

enhances its services by analyzing passenger demands and providing them the necessary services. BMTC working model is

coagulating with information technology in terms of ITS (Intelligent Transport System- Global Positioning System [GPS]

enabled buses, electronic ticketing machine [ETM]). In spite of these advancements, it charges its service fares through paper

tickets and passes which need to be purchased by paying cash. In the Digital era, technological solutions pave the way for

digitizing mechanisms for traditional methods or problems and synchronizing information in real-time. These paper pass

which proves to be beneficiary for passengers are being misused by means of transferring, reusing the day pass when there is

lack of inspection. We have designed a solution to overcome misuse and also to encourage digital transaction for a cashless

economy. The solution is mobile application electronic pass (e-pass).

Keyword: Android Mobile Application, BMTC, e-pass, QR code, digital payment. References: 1. Mrs. D.Anuradha, M.V. Durga Devi, K. Keerthana, K.Dhanasree "SMART BUS TICKET SYSTEM USING QR CODE IN

ANDROID APP" vol. 06, no. 03, pp 2395-0072, Mar 2018

2. Sanam Kazi, Murtuza Bagasrawala, Farheen Shaikh, Anamta Sayyed ―Smart E-Ticketing System for Public Transport Bus‖

3. ―Vehicle Tracking and Locking System Based on GSM and GPS‖, R. Ramani, S. Valarmathy, Dr. N. SuthanthiraVanitha, S. Selvaraju,

M. Thiruppathi, R. Thangam, MECS I.J. Intelligent Systems and Applications, 2013, 09.

4. ―Taking an Electronic Ticketing System to the Cloud: Design and Discussion‖. Filipe Araujo, Marilia Curado, Pedro Furtado, Raul

Barbosa CISUC, Dept. of Informatics Engineering, University of Coimbra, Portugal filipius@uc.pt, Marilia, pdf, rbarbosa@dei.uc.pt

2013.

5. ―Public Transport System Ticketing system using RFID and ARM processor Perspective Mumbai bus facility B.E.S.T‖, Saurabh

Chatterjee, Prof. BalramTimande, International Journal of Electronics and Computer Science Engineering., 2012.

6. ―Bus Tracking & Ticketing using USSD Real-time application of USSD Protocol in Traffic Monitoring‖, Siddhartha Sarma, Journal of

Emerging Technologies and Innovative Research (JETER) www.jetir.org, Dec 2014 (Volume 1 Issue 7).

7. Thimmaraja Yadava. G, Prem Narayankar, Beeresh H.V, ―An Approach for RFID ticketing used for personal navigator for a public

transport systems‖, International Journal of Technical Research and Applications, issue 3, vol.2, 2014, pp.109-112.

8. Macia Mut, M. magda, Payeras-C (2007)-‖ A survey of electronic ticketing applied to transport‖-https://crisesdeim.urv.cat/web/docs/publications /journals/721.pdf

9. Wang, J L and Loui, M C (2009). ―Privacy and ethical issues in location based tracking systemrey.

10. Thimmaraja Yadava. G, Prem Narayankar, Beeresh H.V, ―An Approach for RFID ticketing used for personal navigator for a public

transport systems‖, International Journal of Technical Research and Applications, issue 3, vol.2, 2014, pp.109-112.

11. Z. Wei, Y. Song, H. Liu, Y. Sheng, X. Wang, "The research and implementation of GPS intelligent transmission strategy based on on-

board Android smartphones", Computer Science and Network Technology (ICCSNT) 2013 3rd International Conference on, pp. 1230-

1233, 2013.

12. M. Yu, D. Zhang, Y. Cheng, and M. Wang, ―An RFID electronic tag based automatic vehicle identification system for traffic iot

applications‖ in Proc. Chin. Control Decision Conf. (CCDC), May 2011,pp. 4192–4197.

21.

Authors: Esha Kashyap, S.R.Kannan, Mark Last

Paper Title: Effect of Kernel Learning in Unsupervised Learning for Clustering High Dimensional Databases

Abstract: This paper reviews the effectiveness of kernel learning in unsupervised data analysis using

clustering. Cluster analysis is an explorative data analysis tool that assists in discovering hidden patterns or

natural grouping and has many effective applications in various disciplines. The unison of kernel learning with

the objective of unsupervised clustering algorithms facilitates in recognizing non linear structures in high

dimensional data containing outliers with heavy noise. The recent kernel clustering methods considered in this

paper are the kernelized versions of K-Means, Fuzzy C-Means, Possibilistic C-Means and Intuitionistic Fuzzy

C-Means. Computational complexities in kernel based clustering algorithms are quiet prominent and our

objective is to understand the performance gains while using kernels in clustering. Experimental studies of this

paper substantiate that kernel based clustering algorithms yields significant improvements over their traditional

counterparts.

Keyword: Unsupervised clustering, Data Analysis, Kernel learning, Partition clustering.

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References: 1. Atanassov, Krassimir T. "Intuitionistic fuzzy sets." In Intuitionistic fuzzy sets, pp. 1-137. Physica, Heidelberg, 1999.

2. James C Bezdek. Objective function clustering. In Pattern recognition with fuzzy objective function algorithms, pages 43-93. Springer, 1981.

3. James Christian Bezdek. Fuzzy mathematics in pattern classification. Ph. D. Dissertation, Applied Mathematics, Cornell University,

1973. 4. Battista Biggio and Fabio Roli. Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognition, 84:317-

331, 2018.

5. Jonathan H Chen and Steven M Asch. Machine learning and prediction in medicine beyond the peak of inated expectations. The New England journal of medicine, 376(26):2507, 2017.

6. Keh-Shih Chuang, Hong-Long Tzeng, Sharon Chen, Jay Wu, and Tzong-Jer Chen. Fuzzy c-means clustering with spatial information

for image segmentation. computerized medical imaging and graphics, 30(1):9-15, 2006. 7. Rajesh N Dave. Fuzzy shell-clustering and applications to circle detection in digital images. International Journal Of General System,

16(4):343-355, 1990.

8. Rajesh N Dave. Robust fuzzy clustering algorithms. In Fuzzy Systems, 1993., Second IEEE International Conference on, pages 1281-1286. IEEE, 1993.

9. Inderjit S Dhillon, Yuqiang Guan, and Brian Kulis. Kernel k-means: spectral clustering and normalized cuts. In Proceedings of the

tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 551-556. ACM, 2004. 10. Joseph C Dunn. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. 1973.

11. Hichem Frigui and Raghu Krishnapuram. Clustering by competitive agglomeration. Pattern recognition, 30(7):1109-1119, 1997.

12. Guojun Gan, Jianhong Wu, and Zijiang Yang. A fuzzy subspace algorithm for clustering high dimensional data. In International Conference on Advanced Data Mining and Applications, pages 271-278. Springer, 2006.

13. Isak Gath and Dan Hoory. Fuzzy clustering of elliptic ring-shaped clusters. Pattern recognition letters, 16(7):727-741, 1995.

14. Daniel Graves and Witold Pedrycz. Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study. Fuzzy sets and systems, 161(4): 522-543, 2010.

15. Michael Gregory, Philip Kohn, J Shane Kippenhan, Enock Teefe, Jacob Morse, Venkata Mattay, Daniel Weinberger, Joseph Callicott, and Karen Berman. S215. Aggregating genetic and brain networks associated with risk for schizophrenia via spectral clustering of

working memory activation and pgc2 loci. Biological Psychiatry, 83(9):S431, 2018.

16. Donald E Gustafson and William C Kessel. Fuzzy clustering with a fuzzy covariance matrix. In Decision and Control including the 17th Symposium on Adaptive Processes, 1978 IEEE Conference on, pages 761-766. IEEE, 1979.

17. Richard J Hathaway and James C Bezdek. Nerf c-means: Non-euclidean relational fuzzy clustering. Pattern recognition, 27(3):429-

437, 1994. 18. Richard J Hathaway and James C Bezdek. Fuzzy c-means clustering of incomplete data. IEEE Transactions on Systems, Man, and

Cybernetics, Part B (Cybernetics), 31(5):735-744, 2001.

19. Ching-Wen Huang, Kuo-Ping Lin, Ming-Chang Wu, Kuo-Chen Hung, Gia-Shie Liu, and Chih-Hung Jen. Intuitionistic fuzzy c-means clustering algorithm with neighborhood attraction in segmenting medical image. Soft Computing, 19(2):459-470, 2015.

20. Paraskevas Iatropoulos, Erica Daina, Manuela Curreri, Rossella Piras, Elisabetta Valoti, Caterina Mele, Elena Bresin, Sara Gamba,

Marta Alberti, Matteo Breno, et al. Cluster analysis identi_es distinct pathogenetic patterns in c3 glomerulopathies/ immune complex-mediated membranoproliferative gn. J Am Soc Nephrol, 29:283-294, 2018.

21. Stelios Krinidis and Vassilios Chatzis. A robust fuzzy local information c-means clustering algorithm. IEEE transactions on image

processing, 19(5):1328-1337, 2010. 22. Raghuram Krishnapuram and James M Keller. The possibilistic c-means algorithm: insights and recommendations. IEEE transactions

on Fuzzy Systems, 4 (3):385-393, 1996.

23. Kuo-Ping Lin. A novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm. IEEE Transactions on Fuzzy systems, 22(5):1074-1087, 2014.

24. Enrique H Ruspini. A new approach to clustering. Information and control, 15 (1):22-32, 1969.

25. Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet: A unified embedding for face recognition and clustering. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.

26. Xiao-hongWu. A possibilistic c-means clustering algorithm based on kernel methods. In Communications, Circuits and Systems

Proceedings, 2006 International Conference on, volume 3, pages 2062{2066. IEEE, 2006. 27. Zhiwen Yu, Peinan Luo, Jane You, Hau-San Wong, Hareton Leung, Si Wu, Jun Zhang, and Guoqiang Han. Incremental semi-

supervised clustering ensemble for high dimensional data clustering. IEEE Transactions on Knowledge and Data Engineering,

28(3):701-714, 2016.

22.

Authors: Ashok Kumar J M, Arun Kumar C, Abishek B R, Thirumagal E

Paper Title: Crime Detection in Surveillance Videos

Abstract: During the most recent couple of decades, surveillance cameras have been introduced in numerous

areas. Examination of the data caught utilizing these cameras can assume powerful jobs in web based observing

different occasion expectation and objective driven applications including inconsistencies and interruption

identification. Wrongdoing has raised in our everyday lives, observation recordings are utilized to catch an

assortment of true irregularities. Observing consequently a wide basic open zone is a test to be tended to. We can

abuse ongoing PC vision calculations so as to supplant human work. The video observation framework is two-

dimensional spatial data over a third measurement, that recognizes and predicts strange practices expecting to

accomplish a shrewd reconnaissance idea. In this paper, we audit various methodologies used to learn

inconsistencies by abusing both ordinary and atypical recordings. To abstain from clarifying the peculiar

106-111

fragments or clasps in preparing recordings, which is very tedious, the learning calculation adapts irregularity

through the different examples of positioning structures by utilizing the feebly marked preparing recordings.

Keyword: anomaly detection, surveillance systems, computer vision, feature extraction, object detection, object

tracking, C3D, CNN, deep learning. References: 1. Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks", NIPS

Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 Pages 1097-1105, 2012.

2. Yann LeCun, Leon Bottou, Yoshua Bengio, Patrick Haner, "Gradient-Based Learning Applied to Document Recognition", Proceedings

of the IEEE (Volume: 86, Issue: 11, Nov 1998).

3. Dinesh Kumar Saini, Dikshika Ahir, Amit Ganatra, "Techniques and Challenges in Building Intelligent Systems: Anomaly Detection

in Camera Surveillance", Proceedings of First International Conference on Information and Communication Technology for Intelligent

Systems, 2016.

4. Longlong Jing, Xiaodong Yang, Yingli Tian, "Video you only look once: Overall temporal convolutions for action recognition",

Journal of Visual Communication and Image Representation, Volume 52, April 2018, Pages 58-65.

5. Mikael Priks, "The Effects of Surveillance Cameras on Crime: Evidence from the Stockholm Subway". The Economic Journal,

Volume 125, Issue 588, November 2015, Pages F289–F305.

6. S. Mohammadi, A. Perina, H. Kiani, M. Vittorio, "Angry crowds: Detecting violent events in videos", ECCV, 2016.

7. Daniel Moreira, Sandra Avila, Mauricio Perez, Daniel Moraes, Vanessa Testoni, Eduardo Valle, Siome Goldenstein, Anderson Rocha,

―Temporal Robust Features for Violence Detection‖, IEEE Winter Conference on Applications of Computer Vision (WACV), 2017.

8. S. Kamijo, Y. Matsushita, K. Ikeuchi, and M. Sakauchi, "Traffic monitoring and accident detection at intersections", IEEE

Transactions on Intelligent Transportation Systems, Volume: 1, Issue: 2, Page(s):108–118, Jun 2000.

9. Xiaohui Huang, Pan He, Anand Rangarajan, Sanjay Ranka, ―Intelligent Intersection: Two-Stream Convolutional Networks for Real-

time Near Accident Detection in Traffic Video‖, ACM Transactions on Spatial Algorithms and Systems (TSAS), 2019.

10. Yu Yao, Mingze Xu, Yuchen Wang, David J. Crandall, Ella M. Atkins, ―Unsupervised Traffic Accident Detection in First-Person

Videos‖, arXiv preprint arXiv:1903.00618, 2019.

11. Alec Radford, Luke Metz, Soumith Chintala, ―Unsupervised Representation Learning with Deep Convolutional Generative Adversarial

Networks‖, arXiv preprint arXiv:1511.06434, 2016.

12. Rie Johnson and Tong Zhang, ―Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding‖,

Advances in Neural Information Processing Systems 28 (NIPS 2015).

13. Waqas Sultani, Chen Chen, Mubarak Shah, ―Real-world Anomaly Detection in Surveillance Videos‖, IEEE Conference on Computer

Vision and Pattern Recognition (CVPR), 2018.

14. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, "Deep Residual Learning for Image Recognition", IEEE Conference on

Computer Vision and Pattern Recognition (CVPR), 2015.

15. Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y N, "Reading Digits in Natural Images with

Unsupervised Feature Learning", NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011.

16. Karen Simonyan and Andrew Zisserman, ―Very Deep Convolutional Networks for Large-Scale Image Recognition‖, International

Conference on Learning Representations (ICLR), 2015.

17. Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger, ―Densely Connected Convolutional Networks‖, IEEE

Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

18. Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke,

Andrew Rabinovich, ―Going Deeper with Convolutions‖, IEEE Conference on Computer Vision and Pattern Recognition (CVPR),

2015.

19. Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah, "UCF101: A Dataset of 101 Human Action Classes from Videos in The

Wild.", CRCV-TR-12-01, November 2012.

20. Andrej Karpathy and George Toderici and Sanketh Shetty and Thomas Leung and Rahul Sukthankar and Li Fei-Fei, ―Large-scale

Video Classification with Convolutional Neural Networks‖, IEEE Conference on Computer Vision and Pattern Recognition (CVPR),

2014.

21. Du Tran, Jamie Ray, Zheng Shou, Shih-Fu Chang, Manohar Paluri, ―ConvNet Architecture Search for Spatiotemporal Feature

Learning‖, arXiv preprint arXiv:1708.05038, 2017.

22. Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, Li Fei-Fei, ―Large-Scale Video Classification

with Convolutional Neural Networks‖, IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2014.

23. Karen Simonyan and Andrew Zisserman, ―Two-Stream Convolutional Networks for Action Recognition in Videos‖, Advances in

Neural Information Processing Systems 27 (NIPS 2014).

24. Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach, Subhashini Venugopalan, Sergio Guadarrama, Kate Saenko, Trevor Darrell,

―Long-term Recurrent Convolutional Networks for Visual Recognition and Description‖, IEEE Transactions on Pattern Analysis and

Machine Intelligence, Volume: 39, Issue: 4, Page(s): 677 - 691, 2017.

25. Sepp Hochreiter and Jürgen Schmidhuber, ―Long Short-Term Memory‖, Neural Computation, Volume 9, Issue 8, November 15, 1997,

p.1735-1780.

26. Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri, ―Learning Spatiotemporal Features with 3D

Convolutional Networks‖, IEEE International Conference on Computer Vision (ICCV) Pages 4489-4497, 2015.

27. Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville, ―Describing Videos by

Exploiting Temporal Structure‖, IEEE International Conference on Computer Vision (ICCV), 2015.

28. Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman, ―Convolutional Two-Stream Network Fusion for Video Action Recognition‖,

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

29. Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, Luc Van Gool, ―Temporal Segment Networks: Towards

Good Practices for Deep Action Recognition‖, European Conference on Computer Vision (ECCV), 2016.

30. Rohit Girdhar, Deva Ramanan, Abhinav Gupta, Josef Sivic, Bryan Russell, "ActionVLAD: Learning spatiotemporal aggregation for

action classification", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

31. Yi Zhu, Zhenzhong Lan, Shawn Newsam, Alexander G. Hauptmann, ―Hidden Two-Stream Convolutional Networks for Action

Recognition‖, Asian Conference on Computer Vision (ACCV), 2018.

32. Joao Carreira and Andrew Zisserman, ―Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset‖, arXiv preprint

arXiv:1705.07750, 2017.

33. Ali Diba, Mohsen Fayyaz, Vivek Sharma, Amir Hossein Karami, Mohammad Mahdi Arzani, Rahman Yousefzadeh, Luc Van Gool,

―Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification‖, arXiv preprint arXiv:1711.08200, 2017

34. Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, Manohar Paluri, ―A Closer Look at Spatiotemporal Convolutions

for Action Recognition‖, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

35. Dan Xu, Elisa Ricci, Yan Yan, Jingkuan Song, Nicu Sebe, ―Learning Deep Representations of Appearance and Motion for Anomalous

Event Detection‖, British Machine Vision Conference (BMVC), 2015

36. Shandong Wu, Brian E. Moore, Mubarak Shah, ―Chaotic invariants of Lagrangian particle trajectories for anomaly detection in

crowded scenes‖, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.

37. Arslan Basharat, Alexei Gritai, Mubarak Shah, ―Learning Object Motion Patterns for Anomaly Detection and Improved Object

Detection‖, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.

38. Xinyi Cui, Qingshan Liu, Mingchen Gao, Dimitris N. Metaxas, ―Abnormal detection using interaction energy potentials‖, IEEE

Conference on Computer Vision and Pattern Recognition (CVPR), 2011.

39. Borislav Anti c and Bj orn Ommer, ―Video Parsing for Abnormality Detection‖, International Conference on Computer Vision(ICCV),

2011.

40. Timothy Hospedales, Shaogang Gong, Tao Xiang, ―A Markov Clustering Topic Model for Mining Behaviour in Video‖, International

Conference on Computer Vision(ICCV), 2009.

41. Yingying Zhu, Nandita M. Nayak, Amit K. Roy-Chowdhury, ―Context-Aware Activity Recognition and Anomaly Detection in Video‖,

IEEE Journal of Selected Topics in Signal Processing, 2013.

42. Weixin Li, Weixin Li, Weixin Li, ―Anomaly Detection and Localization in Crowded Scenes‖, IEEE Transactions on Pattern Analysis

and Machine Intelligence, Volume 36 Issue 1, January 2014

43. Louis Kratz, Ko Nishino, ―Anomaly Detection in Extremely Crowded Scenes Using Spatio-Temporal Motion Pattern Models‖, IEEE

Conference on Computer Vision and Pattern Recognition (CVPR), 2009.

44. Cewu Lu, Jianping Shi, Jiaya Jia, ―Abnormal Event Detection at 150 FPS in MATLAB‖, International Conference on Computer

Vision(ICCV), 2013.

45. Bin Zhao, Li Fei-Fei, Eric P. Xing, ―Online Detection of Unusual Events in Videos via Dynamic Sparse Coding‖, IEEE Conference on

Computer Vision and Pattern Recognition (CVPR), 2011.

46. Mahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K. Roy-Chowdhury, Larry S. Davis, ―Learning Temporal Regularity in Video

Sequences‖, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

47. Yuan Gao, Hong Liu, Xiaohu Sun, Can Wang, Yi Liu, "Violence detection using Oriented VIolent Flows", Image and Vision

Computing, 2016.

48. J.F.P. Kooija, M.C. Liem, J.D. Krijnders, T.C. Andringa, D.M.Gavrila, ―Multi-modal human aggression detection‖, Computer Vision

and Image Understanding, 2016.

49. Ankur Datta, Mubarak Shah, Niels Da Vitoria Lobo, ―Person-on-Person Violence Detection in Video Data‖, International Conference

on Pattern Recognition (ICPR), 2002.

50. Sara Sabour, Nicholas Frosst, Geoffrey E Hinton, ―Dynamic Routing Between Capsules‖, Advances in Neural Information Processing

Systems 31 (NIPS 2017).

23.

Authors: Naveen Chandra Gowda, Sunil Kumar, Subham Majumbdar, Koneti Naga Abhishek, Parikshit

Sarode

Paper Title: Android Application on Plant Disease Identification using Tensorflow

Abstract: — The changes in environment and climate lead to various diseases in plants. These diseases are

sometimes difficult to identify without the right knowledge and expertise. The farmers and other plantation

growers do not possess the expertise and resources to correctly identify the diseases of plants and their remedies.

To handle this problem machine learning technology can be used, which can correctly identity the disease of the

plants and display the remedies to the end user. Any new emerging disease can be added by proper botanist and

their associations for the awareness of farmers. The machine learning system learns about the plant diseases

from large datasets and gets trained to correctly identify new test cases given as an input by the farmers through

the camera of their smart phones. Here we propose the methodology uses tensorflow incorporated with android

application which can suggest the user about the disease.

Keyword: Android Application, Machine Learning, Prediction, Plant Diseases, TensorFlow, Image Dataset,

Farmer References: 1. . Ghaiwat, Parul Arora, ―Detectio and Classification of Plant Leaf Diseases Using Image processing Techniques: A Review‖ 3, 2014. 2. Savita N. Ghaiwat, Parul Arora presents and K-nearest neighbor (KNN) method for predicting the class of test example.

3. Renuka Rajendra Kajale, ―Detection and Reorganization of Plant Leaf Disease Using Image Processing and Android O.S.‖ March-

April 2015. 4. A.S.Deokar, Akshay Pophale, Swapnil Patil, Prajakta Nazarkar, Sukanya Mungase, Harshad Shetye, Tejas Rane, Tanmay Pawar,

Prof. Anuradha Dandwate― An Analysis of Methodologies For Leaf Disease Detection Techniques ‖ February 2016.

5. Yeh, R.A., et al.: Semantic image inpainting with deep generative models. In: CVPR. pp. 5485–5493 (2017). 6. Sukhvir Kaur, Shreelekha Pandey and Shivani Goel, ―Semi-automatic leaf disease detection andclassification system for soybean

culture‖. IET Image Process., 2018, Vol. 12 Iss. 6, pp. 1038-1048 7. Neha Mundokar, Pratiksha Kale, Minal Bhalgat, Shalaka Koske, ―Fruit Disease Detection Using Color Analysis and ANN with E-

Nose‖, Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-5, 2017 ISSN: 2454-1362,pp-1919-1923.

8. V Pooja ; Rahul Das ; V Kanchana, ―Identification of plant leaf diseases using image processing techniques‖, 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), April 2018

9. Boikobo Tlhobogang ; Muhammad Wannous, ―Design of plant disease detection system: A transfer learning approach work in

progress‖, IEEE International Conference on Applied System Invention (ICASI), 2018, 10. AakankshaRastogi, Ritika Arora, and Shanu Sharma, proposed "Leaf Disease Detection and Grading using Computer Vision

Technology & Fuzzy Logic"2nd International Conference on Signal Processing and Integrated Networks2015.

11. Rutu Gandhi ; Shubham Nimbalkar ; Nandita Yelamanchili ; Surabhi Ponkshe, ―Plant disease detection using CNNs and GANs as an augmentative approach‖, IEEE International Conference on Innovative Research and Development (ICIRD), 2018

12. Bhavini J. Samajpati ; Sheshang D. Degadwala, ―Hybrid approach for apple fruit diseases detection and classification using random

forest classifier‖, International Conference on Communication and Signal Processing (ICCSP), 2019

112-115

24.

Authors: Sowmya Sundari L K, Harshitha Rayapuram, Keerthana M, Kusuma Rathna M, Shalini A

Paper Title: Machine Learning Based Leaf Disease Detection

Abstract: India is mainly known for land of agriculture. Majority of the population depends on agriculture.

Farmers are unaware to find the disease of the crops which may affect their livelihood. This is one of the major

problems where the farmers are facing. To overcome this problem, a device which detects the disease of the leaf

using Image processing and machine learning. With the help of image processing, the affected leaf pictures are

taken as reference detects the disease of the leaf. Mean Shift algorithm and SVM classifier are used for

segmentation and in classification of the disease. This application is used for farmers in identifying the disease

of the leaf.

Keyword: SVM , India is mainly known for land of agriculture References: 1. D. A. Shaikh , GhoraleAkshay G , Chaudhari Prashant , Kale Parmeshwar L Department of Electronics and Telecommunication

Engineering, Pravara Rural Engineering College, Loni, Rahata, Ahmednagar, India Intelligent Autonomous Farming Robot with Plant Disease Detection using Image Processing International Journal of Advanced Research in Computer and Communication Engineering

Vol. 5, Issue 4, April 2016.

2. S. Lagad and S. Karmore, "Design and development of agrobot for pesticide spraying using grading system," 2017 International

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conference of Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, 2017, pp. 279-283.

3. Arjun Prakash R, Bharathi G B,Manasa V and Gayathri S Department of ECE, SIT, Bangalore, India. Pesticide Spraying Agricultural Robot International Research Journal of Power and Engineering Vol.3(2),pp. 056-060, November, 2017.

4. Jayaprakash Sethupathy 1, Veni S Department of Mechanical Engineering Department of Electronics & Communication Engineering,

Amrita School of Engineering, Ettimadai, Coimbatore Amrita Vishwa Vidyapeetham, Amrita University, India – 641112 OpenCV Based Disease Identification of Mango Leaves International Journal of Engineering and Technology (IJET) Vol 8 No 5 Oct-Nov 2016

5. S. K. Pilli, B. Nallathambi, S. J. George and V. Diwanji, "eAGROBOT - A robot for early crop disease detection using image

processing," 2014 International Conference on Electronics and Communication Systems (ICECS), Coimbatore, 2014, pp. 1-6. 6. Z. Diao, C. Diao and Y. Wu, "Algorithms of Wheat Disease Identification in Spraying Robot System," 2017 9th International

Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, 2017, pp. 316319.

7. S. R. Maniyath et al., "Plant Disease Detection Using Machine Learning," 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), Bangalore, India, 2018, pp. 41-45.

8. Mathre, D.E. (1997). Compendium of barley diseases. American Phytopathological Society. pp. 120 pp.

9. Martens, J.W.; W.L. Seaman; T.G. Atkinson (1984). Diseases of field crops in Canada. Canadian Phytopathological Society. pp. 160 pp.

10. University of California (2012) Integrated Pest Management Program - Peach leaf Curl.

11. Royal Horticultural Society. Peach Leaf Curl. 12. ―Peach--Leaf Curl." An Online Guide to Plant Disease Control. 01 Jan 2008. Oregon State University. 14 Apr 2009.

13. LWG [Bavarian State Institute for Viticulture and Horticulture]. Field trials on peach and nectarine trees regarding peach leaf curl

14. Agrios, George N. Plant Pathology. 5th ed. Amsterdam: Elsevier Academic, 2005. 522+. 15. Goss Russ. "Fusarium Wilts Of Potato, Their Differentiation And The Effect Of Environment Upon Their Occurrence." American

Potato Journal 7th ser. XIII (1936).

16. Fusarium Diseases of Cucurbits. Fact Sheet Page: 733.00 Date: 1-1998. Thomas A. Zitter, Department of Plant Pathology, Cornell University.

17. Ghaiwat Savita N, Arora Parul, Detection and classification of plant leaf disease using image processing technique: a review. Int J

Recent Adv Eng Technol 2014: 2(3):2347-812. ISSN(online) 18. Dhaygunde Sanjay B, Kumbhar Nitin P. Agricultural plant leaf disease using image processing technique: a review. Int J Res Electr

Electron Instrum Eng 2013:2(1).

19. Patil Sanjay B et al. Leaf disease severity measurement using image processing. Int J Eng Technol 2011;3(5):297-301.

25.

Authors: Manjunath P C, Dhanush.S, G.Uday Teja, G.Srinidhi, N.Madhu Babu

Paper Title: Automatic Noise Detection and Reduction in Images

Abstract: Data classification in presence of noise will cause a lot of worse results than expected for pure

patterns. In the proposed work we tend to investigate the drawback within the case of deep convolutional neural

networks so as to propose solutions which will mitigate influence of noise. The main contributions presented in

this proposed work include using convolution neural network as an image classifier for detecting noise in the

images and using different opencv2 inbuilt methods to mitigate noise in the images. Though a number of

techniques are introduced for this purpose, using neural networks we can achieve a greater accuracy.

Keyword: Convolution neural networks, open cv2, Keras API, Jupyter Notebook, TensorFlow. References:

1. Data JaakkoLehtinen Jacob Munkberg Jon Hasselgren1 SamuliLaineTeroKarrasMiikaAittalaTimoAila. Noise2Noise: Learning

Image Restoration without Clean.

2. Zhou Wang, Member, Alan C. Bovik, Fellow,Hamid R. Sheikh,and Eero P. Simoncelli. Image Quality Assessment: From Error

Visibility to Structural Similarity.

3. Hasinoff, Sam, Sharlet, Dillon, Geiss, Ryan, Adams, Andrew, Barron, Jonathan T., Kainz, Florian, Chen, Jiawen, and Levoy,

Marc. Burst photography for high dynamic range and low-light imaging on mobile cameras. ACM Trans. Graph, 35(6):192:1–

192:12, 2016.

4. Isola, Phillip, Zhu, Jun-Yan, Zhou, Tinghui, and Efros, Alexei A. Image-to-image translation with conditional adversarial

5. MichałKoziarski and BogusławCyganeknetworks Image Recognition with Deep NeuralNetworks in Presence of Noise - Dealing

withand Taking Advantage ofDistortions.

6. Srivastava, Nitish, Hinton, Geoffrey, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan. Dropout: A simple way to

prevent neural networks from overfitting. Journal of Machine Learning Research, 15:1929–1958, 2014.

121-124

26.

Authors: Dipu Saha, Farooque Azam, Bipin Kumar, Faizan Abedin, Daryl Jose

Paper Title: IoT Based Smart Card Pollution and Traffic Control System

Abstract: In today's world, the urban mobility is one of the major problems, especially in metropolitan cities.

Improvisation is required in existing traffic management systems in order to manage it efficiently. One of the

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primary complications among Indian cities are that the existing infrastructure cannot be expanded more. Poor

and fragmented mobility has been considered a key contributor to Congested Traffic. Widening of Roads,

making Flyovers, increasing public buses are not the only Solution for these challenges. Hence, we propose a

smart card (RFID) based pollution and traffic control system, which has been implemented using Arduino Uno

R3 along with RFID module interfaced with standalone application built over JAVA platform.

Keyword: Traffic management system, congested traffic, RFID, Arduino Uno R3, JAVA. References: 1. W. Li, L. Dai, L. Xiao-ming, L. Zheng-xi, ―Regional traffic State Consensus Optimization based on Computational Experiments‖, In

Proc. of IEEE Int. Annual Conf., pp.1-6, 2013.

2. M. Chang-xi, Z. Jun-wei, Q. Yong-sheng ,―Study on urban loop-road traffic coordination control system based on spit-layer parallel

cusp catastrophe particle swarm optimization algorithm‖, In Proc. of 2nd Int. Asia Conf. on Info. in Control, Automation and Robotics, pp.1-4, 2010

3. B. kumar, ―Computerized filling station management system‖, In Proc. of 2nd Int. Conf. on Sc. Tech. Engg. and Management , pp.1-6,

2015. 4. M. Gawade, S. Gawde, S. Kanade, R. A. Jadhav, ―Automated Fuel Pump Security System‖, Int. J. on Recent and Innovation Trends

in Computing and Comm., vol. 3, no. 11, pp.1-3, 2015.

5. A. Jadhav, L. Patil, L. Patil, A.D. Sonawane, ―Smart Automatic Petrol Pump System‖, Int. J. of Sc. Tech. and Management, vol 6, no. 4, pp.1-4, 2017.

6. R. Berkowicz, O. Hertel, S. E. Larsen, N. N. Sørensen, M. Nielsen, ―Modelling traffic pollution in streets‖, In Proc. of Nat. Environ.

Research Inst. Report, pp.1-54, 1997. 7. B. P. Chandra, ―Odd–even traffic rule implementation during winter 2016 in Delhi did not reduce traffic emissions of VOCs, carbon

dioxide, methane and carbon mono-oxide‖, Research Comm., vol. 114, no. 6, pp.1-8, 2018.

8. Aminah Hardwan Ahmed, ―Adaptive intelligent traffic control systems for congestion management‖, Global Journal of Applied Sciences and Technology, Vol. 3, Issue. 12, pp.1-12, 2018.

27.

Authors: Kishen. V, M. S. Sathvik Murthy , Mithilesh Kumar , Nimrita Koul

Paper Title: Intelligent Traffic Management System

Abstract: The importance of traffic signals is increasing owing to the drastic increase in population. Ensuring

road safety is of high priority. In this project, we introduce an Intelligent Traffic Management System (ITMS)

capable of managing traffic of varying densities, without the need of a traffic warden to physically monitor a

particular intersection. This system is designed to retrieve the live traffic feed from a junction and process the

same using the TensorFlow Object Detection API over OpenCV to detect the severity of the traffic based on the

number of vehicles detected. Upon determining the number of vehicles, the corresponding signal, based on the

traffic intensity is given. (More vehicles detected – Green light for longer duration and vice versa. ) Thus, this

system dynamically adapts to the prevailing traffic conditions and grants the corrosponding traffic light sequence

for the required duration to maximize the flow of vehicular traffic. The system is designed to ensure smooth

traffic flow by decreasing the wait period of vehicles at intersections and automates the process of controlling

traffic signal.

Keyword: Intelligent Traffic Management System, OpenCV, Tensorflow, Object Detection. References: 1. Vikramaditya Dangi, Amol Parab, "Image Processing Based Intelligent Traffic Controller", Undergraduate Academic Research Journal

(UARJ), ISSN: 2278 – 1129, Volume-1, Issue-1, 2012.

2. Raoul de Charette and FawziNashashibi, ―Traffic light recognition using Image processing Compared to Learning Processes‖. 3. Mriganka Panjwani, Nikhil Tyagi, Ms. D. Shalini, Prof. K Venkata Lakshmi Narayana, ―Smart Traffic Control Using Image

Processing‖.

4. F. Mehboob, M. Abbas, R. Almotaeryi, R. Jiang, S. Al-Maadeed and A. Bouridane, "Traffic Flow Estimation from Road Surveillance," 2015 IEEE International Symposium on Multimedia (ISM), Miami, FL, 2015, pp. 605-608.

5. M. Yang, R. Jhang and J. Hou, "Traffic flow estimation and vehicle-type classification using vision-based spatial-temporal profile

analysis," in IET Computer Vision, vol. 7, no. 5, pp. 394-404, October 2013. 6. Ankit Chaudhary, Reinhard Klette, J. L. Raheja and Xia Jin, ‖Introduction to the special issue on computer vision in road safety and

intelligent traffic‖, EURASIP Journal on Image and Video Processing 20172017:16

7. F. Espinosa, C. Gordillo, R. Jiménez and O. Avilés, "Dynamic traffic light controller using machine vision and optimization algorithms," 2012 Workshop on Engineering Applications, Bogota, 2012, pp. 1-6.

130-132

28.

Authors: Malathi Kulkarni, Vishwanath.R.Hulipalled

Paper Title: News Rank: Ranking News Topics based on Social Media Factors

Abstract: All media sources, particularly the news media have educated the everyday news. These days,

internet based media like twitter gives us an immense amount of information that is created by client, which

potentially contains news related information. These sources to be helpful we should remove undesirable

information and concentrate just the information which is like the news media. Indeed, even though the

unwanted information can still exist, so which is vital to give need to its usage. For this prioritization,

information must be positioned utilizing three components. First Media Focus(MF) of the Topic which

principally centers around both internet based life and news media, Next User Attention(UA) which depends on

clients‘ interests and User Interaction(UI), which is on how client responds to that specific topic. This is an

unsupervised framework NewsRank--- which find the news topics which is applicable in both news media and

internet based life and after that ranking the news topics utilizing degree of three elements.

Keyword: Media focus, Prioritization,Unsupervised,,User Attention,User Interaction References: 1. .M. Blei, A. Y. Ng, and M. I. Jordan , ―Latent Dirichlet allocation‖, j. Mach. Learn. Res., vol. 3. Pp. 993-1022, jan 2003

2. T. Hofmann , ―Probablistic laten semantic Analysis‖ in proc. 15th conf. uncertainity Artif.intell., 1999,pp. 289-296.

3. T. Hofmann , ―Probablistic laten semantic Analysis‖ in proc.22nd Annu. Int. ACM SIGIR conf. res. Develop. Inf. Retrieval ,

Berkeley,CA. USA,1999,pp.50-57.

4. C. Wartena and R. Brussee, ―Topic Detection by Clustering Keywords‖, in proc. 19 th Int. Workshop Database Expert Syst.

Appl.(DEXA),Turin, Italy. 2008, pp. 54-58.

5. M. Cataldi, L. Di Caro, and C. Schifanella, ―Emerging topic detection on Twitter based on temporal and social terms evaluation,‖ in

Proc. 10th Int. Workshop Multimedia Data Min. (MDMKDD), Washington, DC, USA, 2010, Art. no. 4. [Online]. Available:

http://doi.acm.org/10.1145/1814245.1814249.

6. W.X. Zhao et al., ―Comparing Twitter and traditional media using topic models,‖ in Advances in information retrieval . Heidelberg,

Germany: Springer Berlin Heidelberg, 2011 , pp.338-349.

7. C. Wang M Zhang. L.Ru and S.Ma, ―Automatic online news topic ranking using mediafocus and user attention based on aging

theory.‖ In proc 17th conf. Inf .Knowl.Manag., Napa County, CA,USA,2008, pp.1033-1042.

8. C.C Chen, Y-T. Chen, Y.Sun, and M.C. Chen, ― Life cycle Modeling of news events using aging Theory,‖ in Machine Learning.

ECML 2003.Heidelberg, Germany: Springer Berlin Heidelberg, 2003, pp. 47-59

9. J. Sankaranarayanan , H. Samet, B. E. Teitler , M. D. Lieberman, and J. Sperling, ―Twitterstand: News in Tweets,‖ in proc. 17 th ACM SIGSPATIAL Int. Conf. Adv. Geograph. Inf. Syst., Seattle, WA, USA, 2009, pp 42-51.

10. K. Shubhankar , A.P.Singh , and V.Pudi, ― An efficient algorithm for topic ranking and modeling topic evolution ,‖ in Database Expert Syst. Appl., Toulouse,France,2011, pp, 320-330

11. S. Brin and L. Page, ―Reprint of: The anatomy of a large-scale hypertextual web search engine,‖ Comput. Netw., vol. 56, no. 18, pp.

3825–3833, 2012.

12. E. Kwan, P.-L. Hsu, J.-H. Liang, and Y.-S. Chen, ―Event identification for social streams using keyword-based evolving graph

sequences,‖ in Proc. IEEE/ACM Int. Conf. Adv. Soc. Netw. Anal. Min., Niagara Falls, ON, Canada, 2013, pp. 450–457.

13. R. Mihalcea and P. Tarau, ―TextRank: Bringing order into texts,‖ in Proc. EMNLP, vol. 4. Barcelona, Spain, 2004.

14. Y. Matsuo and M. Ishizuka, ―Keyword extraction from a single document using word co-occurrence statistical information,‖ Int. J. Artif. Intell. Tools, vol. 13, no. 1, pp. 157–169, 2004.

15. H.-H. Chen, M.-S. Lin, and Y.-C. Wei, ―Novel association measures using Web search with double checking,‖ in Proc. 21st Int. Conf.

Comput. Linguist. 44th Annu. Meeting Assoc. Comput. Linguist., 2006, pp. 1009–1016.

16. D. Bollegala, Y. Matsuo, and M. Ishizuka, ―Measuring semantic similarity between words using Web search engines,‖ in Proc. WWW,

Banff, AB, Canada, 2007, pp. 757–766.

17. Y. Matsuo, T. Sakaki, K. Uchiyama, and M. Ishizuka, ―Graph-based word clustering using a Web search engine,‖ in Proc. Conf.

Empir. Methods Nat. Lang. Process., 2006, pp. 542–550.

18. M. Girvan and M. E. J. Newman, ―Community structure in social and biological networks,‖ Proc. Nat. Acad. Sci., vol. 99, no. 12, pp.

7821–7826, 2002.

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29.

Authors: Nimrita Koul, Sunil kumar S Manvi

Paper Title: Inference of Gene Regulatory Networks for Prostate Cancer using Bayesian Networks with

Feedback and Feed Forward Loops

Abstract: The solution to any problem depends on the depth of our understanding of it. Cancer is a disease that

is being investigated at multiple levels and from multiple perspectives to understand the details of its origins and

expansions in order to be able to figure a cure for it. We can now computationally analyze the biological data

produced by genome analysis techniques like genomics, proteomics, and transcriptomics. DNA micro array

technology has made available large gene expression datasets for entire genomes. It has been clinically observed

that inside a human cell, activity of a gene often turns on or turns off one or more other genes. Such relationships

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in the co-regulation of genes is captured by gene regulatory network models which are computationally

constructed from gene expression datasets. It has been observed that healthy and diseased states of a human cell

show different regulatory interrelations between genes. In this paper, we have proposed to use a stochastic

approach called Bayesian Networks with Feedback and Feedforward loops for inference of inter dependence in

the regulation of genes in case of Prostate Cancer. It was observed that 4 of the networks revealed by the

proposed approach matched the ones observed in clinical studies.

Keyword: Bayesian Networks , Computational Genomics, Gene Expression Data, Gene Regulatory Network,

Reverse Engineering References: 1. T. R. Golub, D. K. Slonim1, P. Tamayo, C. Huard, M. Gaasenbeek J.P.Mesirov, H.Coller, M.L. Loh, and J. R. Downi, ―Molecular

classification of cancer: Class discovery and class prediction by gene expression monitoring,‖ Science, vol. 286, no. 5439, pp. 531–

538, 1999. 2. Maetschke,S.R.,Madhamshettiwar,P.B.,Davis,M.J.,andRagan,M.A.(2014). ―Supervised, semi-supervised and unsupervised inference of

gene regulatory networks‖, Brief.Bioinformatics, 15,195–211.doi:10.1093/bib/bbt034

3. Marbach,D.,Costello,J.C.,Kuffner,R.,Vega,N.M.,Prill,R.J., Camacho,D. M.,etal.(2012), ―Wisdom of crowds for robust gene network inference. Nat. Methods” 9,796–804.doi:10.1038/nmeth.2016

4. Yaghoobi, et al.: ― GRN modeling techniques‖, Journal of Medical Signals & Sensors, Vol 2 , Issue 1, Jan-Mar 2012.

5. S. D, F. P, R. K, K. D, Manola, L. C, and T. P, ―Gene expression 6. correlates of clinical prostate cancer behavior,‖ Cancer Cell, vol. 1,

7. pp. 203– 209, 2002.

8. J. C. Ang, A. Mirzal, H. Haron, and H. N. A. Hamed, ―Supervised, unsupervised, and semi-supervised feature selection: A review on gene selection,‖ IEEE/ACM Transactions on Computational Biology and bioinformatics, vol. 13, no. 5, pp. 971–989, 2016.

9. Nir Friedman, ―Inferring Cellular Networks Using Probabilistic Graphical Models‖, Science, Vol 303 6 Feb, 2004 803

10. Penfold,C.A.,and Wild,D.L.(2011), ―How to infer gene networks from expression profiles, revisited‖, Interface Focus 1,857–870.doi:10.1098/rsfs.2011.0053

11. Mordelet, F., and Vert,J.-P.(2008), ―SIRENE: supervised inference of regulatory networks‖, Bioinformatics 24,i76–i82.doi:10.1093/bioinformatics/btn273

12. Omranian, N.,Eloundou- Mbebi, J.M.,Mueller-Roeber, B.,and Nikoloski,Z. (2016), ―Gene regulatory network inference using fused

LASSO on multiple data sets‖, Sci.Rep. 6:20533.doi:10.1038/srep20533 13. Patel, N., and Wang,J.T .(2015), ―Semi-supervised prediction of gene regulatory networks using machine learning algorithms‖,

J.Biosci. 40,731–740.doi:10.1007/s12038-015-9558-9

14. Y. Lin, H. Yeh, S. Cheng and V. Soo, "Comparing Cancer and Normal Gene Regulatory Networks Based on Microarray Data and Transcription Factor Analysis," 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering, Boston, MA, 2007,

pp. 151-157.

doi: 10.1109/BIBE.2007.4375558

30.

Authors: Poornima S P,Priyanka C N,Reshma P, Suraj Kr Jaiswal and SurendraBabu K N

Paper Title: Stock Price Prediction using KNN and Linear Regression

Abstract: Machine learning is a method of data analysis that automates analytical model building. It is a

branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make

decisions with minimal human intervention. With the ever increasing amounts of data becoming available there

is good reason to believe that smart data analysis will become even more pervasive as a necessary ingredient for

technological progress. It is an important challenge for the people who invest their money to forecast the daily

stock prices, which helps them to put money into stock market with credence by taking risks and also variations

into considerations. In this paper, we are going to apply KNN method and linear regression for predicting the

stocks. The performance of linear Regression model on the selected data set is better when compared to KNN

algorithm technique. The stock holders can invest confidently based on the results obtained from the model.

Keyword: References: 1. R. Iacomin, ―Stock Market Prediction,‖2015 19th International Conference on System Theory, Control and Computing(ICSTCC0),

October 14-16, CheileGradistei, Romania.

2. S., Kamath, ―Stock Market Analysis‖, Master‘s Projects, pp. 326, 2012. 3. P.Domingos, ―A Few Useful Things to Know about Machine Learning,‖Communications of the ACM, Vol. 55 No. 10, October 2012.

4. Gharehchopogh, F.S., &Khaze, S.R (2012), Data Mining Application for Cyber Space Users Tendency in Blog Writing: A Case Study.

International Journal of Computer Applications, 47(18), 40-46. 5. ]Berry, M. J., &Linoff, G. S. (2004). Data Mining Techniques: for marketing, sales, and customer relationship management.

Wiley.com.

6. S AbdulsalamSulaimanOlaniyi, Adewole, Kayode S., Jimoh, R. G, ―Stock Trend Prediction Using Regression Analysis- A Data

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Mining Approach‖, ARPN Journal of Systems and Software Volume 1 No. 4, JULY 2011, Brisbane, Australia.

7. M. Ballings, D. VandenPoel, N. Hespeels, R. GrypEvaluating multiple classifiers for stock price direction prediction Expert SystAppl, 42 (20) (2015), pp. 7046-7056.

8. R. Dash, P.K. DashA hybrid stock trading framework integrating technical analysis with machine learning techniques J Finance Data

Sci, 2 (1) (2016). 9. E.A. Gerlein, M. McGinnity, A. Belatreche, S. ColemanEvaluating machine learning classification for financial trading: an empirical

approach Expert SystAppl, 54 (1) (2016), pp. 193-207

10. J. Patel, S. Shah, P. Thakkar, K. KotechaPredicting stock market index using fusion of machine learning techniques Expert SystAppl, 42 (4) (2015), pp. 2162-2172

11. K. KimFinancial time series forecasting using support vector machinesNeurocomputing, 55 (1–2) (2003), pp. 307-319

12. R.K. Nayak, D. Mishra, A.K. RathA Naïve SVM-KNN based stock market trend reversal analysis for Indian benchmark indicesAppl Soft Comput, 35 (1) (2015), pp. 670-680

13. S. Barak, M. ModarresDeveloping an approach to evaluate stocks by forecasting effective features with datamining methods Expert

SystAppl, 42 (3) (2015), pp. 1325-1339 14. C.-Y. Yeh, C.-W. Huang, S.-J. LeeA multiple-kernel support vector regression approach for stock market price forecasting Expert

SystAppl, 38 (3) (2011), pp. 2177-2186

15. Y. Xiao, J. Xiao, F. Lu, S. WangEnsembleANNs-PSO-GA approach for day-ahead stock e-exchange prices forecasting

31.

Authors: Gaurav Dhingra, Supreeth S, Neha K R, Amruthashree R V, Eshitha D

Paper Title: Traffic Management using Convolution Neural Network

Abstract: Traffic is one of the major problems in most of the metropolitan cities. Classifying the traffic

conditions are important for determining traffic control strategies and management. Traffic congestions have

negative impact on society, as a lot of time is wasted in it and controlling the congestions is necessary. By

classification we can get to know which lane has traffic, from which we can further check the reasons for traffic

and to take appropriate decisions to improve the performance. Video on traffic data is suitable source for traffic

analysis. In this paper, video surveillance data is used for classification of road traffic using Convolution Neural

Network. Convolution Neural Network requires minimal preprocessing when compared to other classification

algorithms and is known for its accuracy. The video is classified based on rating of the traffic of its content. The

Convolution Neural Network is first trained and then it is evaluated and updated using validation set. Once the

model is completely trained it is tested with the testing set. This trained model is capable of processing the live

streaming video and classifies each of the frames and gives the rating of the traffic for each lane, which can be

helpful for traffic management.

Keyword: convolution neural network, traffic management References:

1. Teresa Pamula. "Road Traffic Conditions Classification Based on Multilevel Filtering of Image Content Using Convolutional Neural Networks", IEEE Intelligent Transportation Systems Magazine, 2018

2. Manuel Lopez-Martin, Belen Carro, Antonio Sanchez-Esguevillas, Jaime Lloret. "Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things", IEEE Access, 2017

3. B S Meghana, Santoshi Kumari, T P Pushphavathi. "Comprehensive traffic management system: Real-time traffic data analysis using RFID", 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), 2017

4. Carlo Migel Bautista, Clifford Austin Dy, Miguel Inigo Manalac, Raphael ,Angelo Orve and Macario Cordel study on ―Convolutional Nueral Network for vehicle detection in low resolution traffic videos‖, 2016 IEEE region 10 symposium (TENSYMP), 2016

5. Dian-xia Hu, Zhi-peng Zheng ―Intelligent traffic management system‖ , IEEE Access 2011.

6. Hui Zhang, Kunfeng Wang,Yonglin Tian ,Chao Gou, Fei-Yue Wang ―MFR-CNN: Incorporating Multi-Scale Features and

Global Information for Traffic Object Detection‖ IEEE Access 2018. 7. ―A convolutional neural network for traffic information sensing from social media text‖ by Yuanyuan Chen ,Yisheng Lv , Xiao

Wang , Fei-Yue Wan IEEE Access 2018. 8. ―Road Traffic Conditions Classification Based on Multilevel Filtering of Image Content Using Convolutional Neural Networks‖

by Teresa Pamula IEEE Access 2018

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32.

Authors: Ranjith.R, Supreeth S, Ramya R, Ganesh prasad. M, Chaitra Lakshmi L

Paper Title: Password Processing Scheme using Enhanced Visual Cryptography and OCR in Hybrid Cloud

Environment

Abstract: to authenticate the user using the password can be achieved by simply converting the password into

the values is called hash. Even though there are many websites which can unlock the hash values by using some

cracking tools called as cyber attacks. This cyber attacks are very much common by the way of hacking the

passwords by hackers. Hackers can undeniably figure out the plain text using such software's it gives n-number

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of examples of plaintext samples which the hackers can execute and crack open or penetrate into personal

information in the Hybrid Cloud Environment. To overcome this type of trouble or mishap our system is

recommended. The proposal is that our system not only converts the plaintext into hash values but it will be

stored as an image, the user will only be having an ID and login password (at the time of user creation) . The

user when he wants to login he will receive an email in which a share-1 image will be present which is encrypted

completely even he himself can't be able to recognize what it is, the server will ask him to download the share-2

image then he has to enter his user ID for the server to recognize his User-ID. Since these images are encrypted

both share images 1&2 are encrypted by VC (Visual Cryptography) then the user has to merge these two images,

if it matches then only the user can login. This merge method undergoes OCR (0ptical cryptography

recognition). Our aim is to prevent hackers from gaining access to personal information of the users in Hybrid

Cloud Computing Environment.

Keyword: Visual cryptography, Optical Character Recognition (OCR), Hybrid Cloud Computing References:

1. Naor, M. and A. Shamir. Visual cryptography, Advances in cryptology. Eurocrypt ‘94 Proceeding LNCS, 950:1–12, 1995. 2. Everitt, Brian Cluster analysis. Chichester, West Sussex, U.K: Wiley. ISBN 9780470749913, 2011.

3. Silva, Vladimi, "Practical Eclipse Rich Client Platform Projects (1st ed.)". Apress. p. 352. ISBN 1-4302-1827-4, March 2009.

4. Riedl, C.; Zanibbi, R.; Hearst, M. A.; Zhu, S.; Menietti, M.; Crusan, J.; Metelsky, I.; Lakhani, K. (February 20, 2016). "Detecting Figures and Part Labels in Patents: Competition-Based Development of Image Processing Algorithms". International Journal on

Document Analysis andRecognition. 19 (2):155.

5. Gaw, Shirley, and Edward W. Felten, "Password management strategies for online accounts," Proceedings of the second symposiumon Usable privacy and security. ACM, 2006.

6. Nguyen, Thi Thu Trang, and Quang Uy Nguyen, "An analysis of Persuasive Text Passwords, "Information and Computer Science

(NICS), 2015 2nd National Foundation for Science and Technology Development Conference on. IEEE,2015. Tam, Leona, Myron Glassman, and Mark Vandenwauver, "The psychology of password management: a tradeoff between security

and convenience, "Behaviour & Information Technology 29.3 (2010): 233- 244. 7. Wang, Luren, Yue Li, and Kun Sun, "Amnesia: A Bilateral Generative Password Manager," 2016 IEEE 36th International

Conference on Distributed Computing Systems.

8. Gauravaram, Praveen, "Security Analysis of salt|| password Hashes,"Advanced Computer Science Applications and Technologies(ACSAT),2012 International Conference on. IEEE, 2012.

9. Christoforos Ntantogian , Stefanos Malliaros , Christos Xenakis, " Evaluation of Password Hashing Schemes in Open Source Web

Platforms", Computers & Security, Volume 84, July 2019, Pages 206-224. 10. VidyaRao, PremaK.V."Light-weight hashing method for user authentication in Internet-of-Things", Ad HocNetworks, Volume 89, 1

June 2019, Pages 97-106.

11. XunYi, ZahirTari, FengHao, LiqunChen, Joseph K.Liu, XuechaoYang, Kwok-YanLam, IbrahimKhalil, Albert Y.Zomaya" Efficient threshold password-authenticated secret sharing protocols for cloud computing", Journal of Parallel and Distributed Computing,

Volume 128, June 2019, Pages 57-70.

12. Antonio Celesti, Maria Fazio, Antonino Galletta, Lorenzo Carnevale Jiafu Wan, MassimoVillari, " An approach for the secure management of hybrid cloud–edge environments", Future Generation Computer Systems, Volume 90, January 2019, Pages 1-19

13. P. Ravi Kumar, P. Herbert Raj , P. Jelciana, "Exploring Data Security Issues and Solutions in Cloud Computing", 6th International

Conference on Smart Computing and Communications, ICSCC 2017, 7-8 December 2017, Kurukshetra, India. 14. S Supreeth, Shobha Biradar, "Scheduling virtual machines for load balancing in cloud computing platform", Int. J Sci Res, Volume

2, Issue 6, pp. 437-441,06/2013.

15. Mr. Rohith S, Mr. Vinay G, "A Novel Two Stage Binary Image Security System Using (2,2) Visual Cryptography Scheme", International Journal Of Computational Engineering Research, May-June 2012 | Vol. 2 | Issue No.3, pp.642-646.

16. Nazia Nusrath Ul Ain, Meena Kumari K S, Mujaseema Kahnum, "Password Authentication Using Image Decipherment And Ocr",

International Journal Of Innovations In Engineering Research And Technology [Ijiert], Volume 5, Issue 3, Mar.-2018 17. Iliyan Georgiev , Marcos Fajardo, Blue-noise dithered sampling, ACM SIGGRAPH 2016 Talks, July 24-28, 2016, Anaheim,

California

33.

Authors: Jayaprakash Nevara, Jyoti Mirji

Paper Title: Workspace Allocation and Management System with Realtime Feedback from IOT Sensors

Abstract: Office real estate space investments is one of the major capital expenditures done by any IT and ITES

companies. This has a major impact on the profitability of the organizations as it adds to the capital expenditure

incurred by them and also has environmental implications. As the companies grow they will be looking for more

real estate space which will lead to putting more land into commercial usage for office space. Today, most of the

companies allocate office space for employees statically on a 1:1 ratio i.e one cube per employee. But, given the

fact that the modern workforce is mobile and at any given point of time not all the employees will be working

from office for various reasons, making static allocation of office space and its usage inefficient and ineffective.

So, there is a need for managing the available space efficiently and effectively. Organizations can look for

saving real estate investments by increasing the user to cube ration by more than 1. This paper proposes a

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comprehensive system for workspace allocation and management with real time feedback from IOT sensors at

the office spaces.

Keyword: IoT, Workspace, Management, IT, ITES, Sensors References: 1. Nithin M, Suma, V, ―Workspace Management and Hot-Seating‖, In the Proceedings of the IEEE 2017 International Conference

on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2017, ISBN: 978-1-5386-2745-7

2. R. Blum, ―Arduino Programming in 24 Hours, Sams Teach Yourself 1st Edition‖, Sams Publishing, USA, pp. 40-82, 2014, ISBN

1540670163

3. P Yogesh, C Abhay, N Mandar, ―Motion Detection Using PIR Sensor‖, International Research Journal of Engineering and Technology,

Pune, pp. 4753-4756, e-ISSN: 2395-0056

4. J. Duckett, ―Web Design with HTML, CSS, JavaScript and jQuery Set‖, Wiley, Pck edition, USA, pp 60-300, ISBN 1118907442

5. J. Duckett, ―Web Design with HTML, CSS, JavaScript and jQuery Set‖, Wiley, Pck edition, USA, pp 60-300, ISBN 1118907442

6. P Sikora, ―Professional CSS3‖,Packt Publishing Limited, India, pp. 120-250, ISBN 1785880942

7. A Ravindran, ―Django: Web Development with Python‖, Packt Publishing Ltd, India, pp. 310-450, ISBN 1787121380

8. M.C. Brown, ―The complete Reference Python‖, McGraw-Hill, USA, pp. 427-471, ISBN 007212718x

34.

Authors: Nischita N. J. Mylara Reddy C.

Paper Title: Efficient Conversational AI Agent to Improve Rural and Urban Healthcare

Abstract: Conversational AI agents are software programs which works exactly like humans, they interpret the

users and accordingly react to the inputs given by them. These agents are built considering the medical

interventions required to improve the overall health of the society. The AI agent designed acts intelligently

during the process of the interaction between the humans and itself. It allows the user to use the interface by

asking interactive questions then it processes them and responds relatively. Conversational agents are not only

web based but they can also be used on other platforms like mobile phone or any other mobile devices. Despite

all these a user shall be satisfied if and only if the software is easy to use and obtains the exact results with all of

the queries being answered. The main concern with this model is to give that ease to the user to interact with the

agent thus solving the queries related to the symptoms suffered by the patients and hence predicting the disease

at an early stage by maintaining the accuracy. There are around 100000 diseases in the world according to

WHO. Most of their symptoms overlap as well hence by using this agent its possible by it to think insightfully

and predict the early symptoms of the disease. In this paper we have designed a user interface and this interacts

with the user to take the necessary inputs. This data is fed to the advanced Natural Language Understanding

(NLU) to provide the personalized prediction based on the user interaction. The predictions done by the model

uses the classification algorithms of Machine Learning. The accuracy of each of these algorithms varies.

Therefore instead of considering only one algorithm and hoping it gives the best accuracy, we can use the

Ensemble learning method to improve the overall prediction rate. This method gives better predictive indications

as it combines many models results thereby improving the overall precision. Here we train our model using

various algorithms and ensemble them to get the final results based on the technique of voting. This paper

presents a front-end interface for common man using HTML and Angular JS, NLU for text pre-processing using

Tensorflow method and ML model as a classifier, for the prediction which uses various machine learning

algorithms like SVM, Decision Tree, Random forest etc and combines them all in a majority voting ensemble

for balanced results. Therefore this model interacts with any patients be it from the rural or the urban and based

on their symptoms predicts and ranks the most probable disease accurately and reliably.

Keyword: Conversational Agent, Artifical Intelligence, SVM, Decision Tree, Random Forest, Ensemble

Learning, TensorFlow word embedding References:

1. Haolin Wang and Qingpeng Zhang, Mary Ip and Joseph Tak Fai Lau,,‖ Conversational Agents for Health Management and

Interventions ―, IEEE Computer Society, 2018

2. Liliana Laranjo, Adam G Dunn, Huong Ly Tong, Ahmet Baki Kocaballi, Jessica Chen, Rabia Bashir, Didi Surian, Blanca Gallego, Farah Magrabi, Annie YS Lau, andEnrico Coiera “Conversational agents in healthcare: a systematic review‖ Journal of the American Medical Informatics Association, 25(9), 2018, 1248–1258

3. Najmeh Fayyazifar and Najmeh Samadiani “Parkinson’s Disease Detection Using Ensemble Techniques and Genetic Algorithm‖, Artificial Intelligence and Signal Processing (AISP), IEEE. 2017

160-164

4. Tahira Mahboob, Rida Irfan, Bazelah Ghaffar “Evaluating Ensemble Prediction of Coronary Heart Disease using Receiver Operating Characteristics‖, IEEE Conference. 2017

5. Liangyuan Li， Mei Chen, Hanhu Wang, Wei Chen, Zhiyong Guo, ‖ A Cost Sensitive Ensemble Method for Medical Prediction‖ , First International Workshop on Database Technology and Applications IEEE Computer Society. 2009

6. Madhuri Gupta, Bharath Gupta, ―An Ensemble Model for Breast Cancer Prediction Using Sequential Least Squares Programming Method (SLSQP)‖, Proceedings of 2018 Eleventh International Conference on Contemporary Computing (IC3), 2-4 August, 2018,

Noida, India.

7. Qiao Pan,Yuanyuan Zhang, Min Zuo, Lan Xiang, Dehua Chen,‖ Improved Ensemble Classification Method of Thyroid Disease Based on Random Forest ―,8th International Conference on Information Technology in Medicine and Education. 2016

8. Md. Jamil-Ur Rahman, Rafi Ibn Sultan, Firoz Mahmud, Ashadullah Shawon and Afsana Khan, ―Ensemble of Multiple Models For Robust Intelligent Heart Disease Prediction System‖, 4th International Conference on Electrical Engineering and Information & Communication Technology. 2018

9. Prof. Dhomse Kanchan B. Mr. Mahale Kishor M. ,” Study of Machine Learning Algorithms for Special Disease Prediction using Principal of Component Analysis‖, International Conference on Global Trends in Signal Processing, Information Computing and Communication. 2016

10. Dinesh Kumar G, Arumugaraj K, Santhosh Kumar D, Mareeswari V , ―Prediction of Cardiovascular Disease Using Machine Learning Algorithms‖, Proceeding of 2018 IEEE International Conference on Current Trends toward Converging Technologies, Coimbatore, India.

11. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean ―Distributed representations of words and phrases and their

compositionality,‖ in Advances in Neural Information Processing Systems 26, C. J. C. Burges, L. Bottou, M. Welling, Z.

Ghahramani, and K. Q. Weinberger, Eds. Curran Associates, Inc.,. 2013

12. J. Pennington, R. Socher, and C. D. Manning, ―Glove: Global vectors for word representation,‖ in Empirical Methods in Natural

Language Processing (EMNLP), pp. 1532–1543. 2014

35.

Authors: Utkarsh Tiwari, Rohit Kumar Singh, Rohan Vijay Wargia, P Uttareshwar Vikashrao

Paper Title: Machine Learning Based Flower Recognition System

Abstract: Automatic flower plucking systems for smart agriculture are being studied for many years to support

flower harvesting. Such systems require flower recognition task to be integrated as part of the system. This paper

presents an approach for classification of flowers using a machine learning algorithm. The method categorizes

flowers into different species with the help of convolutional neural networks and deep learning techniques. The

system uses a pre-trained CNN model to improve the accuracy rate. Concepts such as Feedforward, back-

propagation and transfer learning are used to create the neural network model. Different hyper-parameter values

have been tested on the model which provides maximum accuracy of 85.0 percentage on the testing dataset. The

result is visualized in the form of bar-plots which provides the top 5 predictions of flower species for the given

input image of a flower.

Keyword: Image Recognition, Machine learning, CNN, Feedforward. References: 1. Nilsback, M. E., & Zisserman, A. (2006). A visual vocabulary for flower classification. Computer Vision and Pattern Recognition,

2006 IEEE Computer Society Conference, Vol. 2, 1447-1454. IEEE.

2. Lowe, D. G. (2004). Distinctive image features from scale invariant keypoints. International journal of computer vision, 60(2), 91-110. Springer IJCSNS International Journal of Computer Science and Network Security, VOL.17 No.4, April 2017 151.

3. Varma, M., & Zisserman, A. (2002). Classifying images of materials: Achieving viewpoint and illumination independence. Computer

Vision—ECCV 2002, 255-271. Springer Berlin Heidelberg. 4. Guru, D. S., Kumar, Y. S., & Manjunath, S. (2011). Textural features in flower classification. Mathematical and Computer Modelling,

54(3), 1030-1036. Elsevier.

5. Varma, M., & Ray, D. (2007, October). Learning the discriminative power-invariance trade-off. Computer Vision, ICCV 2007. IEEE

11th International Conference, 1-8. IEEE.

6. Chai, Y., Lempitsky, V., & Zisserman, A. (2011). BiCoS: A bi-level co-segmentation method for image classification. IEEE. Retrieved

January 16, 2016, from http://www.yuningchai.com/docs/iccv2011.pdf. 7. Nilsback, M. E., & Zisserman, A. (2008, December). Automated flower classification over a large number of classes. Computer

Vision, Graphics & Image Processing, ICVGIP 08. Sixth Indian Conference, 722-729. IEEE.

8. Nilsback, M. E., & Zisserman, A. (2009). An automatic visual flora: segmentation and classification of flower images. Doctoral dissertation, University of Oxford.

9. Nilsback, M. E., & Zisserman, A. (2010). Delving deeper into the whorl of flower segmentation. Image and Vision Computing, 28(6), 1049-1062. Elsevier.

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36.

Authors: Jayati Bhadra, M.Vinayaka Murthy, M.K. Banga

Paper Title: An Effective Authentication Scheme for Videos using Invisible Watermark

Abstract: In this paper, a novel reversible keyless invisible authentication method for video piracy protection

which uses randomized pixel for embedding real identity information is proposed. Randomization at two

different levels is not considered in any of the existing methods. Videos, with this proposed embedding of

authentication information, ensure minimum distortions and maximum resistance to the removal of

authentication information. Keyless invisible embedding process increases the security and reduces the cost.

This proposed approach enhances the randomization of the specific pixels where authentication information will

be stored in a frame and the location of such modified pixels is stored in an immediate next frame. Each pair is

identified with an embedded random number. Modified Least Significant Bit (LSB) based invisible watermark

mechanism is used to embed the bits which are cost effective due to simplicity and which can withstand

statistical attacks. During extraction, frame with pixel locations is used. The extracted information will be

compared to assure the authenticity of video. The Euclidean distance, PSNR, MSE, SSIM proved that the

proposed method can withstand visual attacks. StirMark test proved that the proposed algorithm is highly robust.

Keyword: Modified Least Significant Bit, Invisible Watermark, Video Authentication References:

1. M. Ramalingam, ―Stego machine-video steganography using modified lsb algorithm‖, World Acad. Sci. Eng. Technol. 74 , 502–505, 2011

2. Z. Li-Yi, Z. Wei-Dong, et al., ―A novel steganography algorithm based on motion vector and matrix encoding‖, in: proceedings of

the 3rd International Conference on Communication Software and Networks (ICCSN), IEEE, 2011, pp. 406–409, 2011. 3. Y. Cao, H. Zhang, X. Zhao, H. Yu, ―Video steganography based on optimized motion estimation perturbation‖, in: Proceedings

of the 3rd ACM Workshop on Information Hiding and Multimedia Security, ACM, pp. 25–31, 2015.

4. H.M. Kelash, O.F.A. Wahab, O.A. Elshakankiry, H.S. El-sayed, ―Hiding data in video sequences using steganography algorithms,‖ in: proceedings of International Conference on ICT Convergence (ICTC), IEEE, pp. 353–358, 2013.

5. Deshmukh PU, Pattewar TM, ―A novel approach for edge adaptive steganography on LSB insertion technique.‖ In: proceedings

of the International conference on information communication and embedded systems (ICICES) IEEE, Chennai, pp 27–28, 2014. 6. Feng B, Lu W, SunW ―Secure binary image steganography based on minimizing the distortion on the texture.‖ IEEE Transaction

Information Forensics Security 10(2):243–255, 2015

7. Paul G, Davidson I, Mukherjee I, Ravi SS (2012) ―Keyless steganography in spatial domain using energetic pixels.‖ In: Proceedings of the 8th international conference on information systems security (ICISS), LNCS Springer, Guwahati, vol 7671.

LNCS, pp 134–148. ISBN:978-3-642-35129-7, 2012

8. [Goutam Paul, Ian Davidson, Imon Mukherjee, S. S. Ravi, ―Keyless dynamic optimal multi-bit image steganography using energetic pixels‖ Multimedia Tools and Applications DOI 10.1007/s11042-016-3319-0, 2017

9. Cem kasapbaşi, M. & Elmasry, W. Sādhanā , ―New LSB-based colour image steganography method to enhance the efficiency in

payload capacity, security and integrity check‖, Sadhana, Springer, pp 43-68. https://doi.org/10.1007/s12046-018-0848-4, 2012. 10. Singh N., Bhardwaj J., ―Comparative Analysis for Steganographic LSB Variants.‖ In:Computing, Communication and Signal

Processing. Advances in Intelligent Systems and Computing, vol 810. Springer, Singapore, 2019.

11. Fridrich J, Lisonek P, Soukal D, ―On steganographic embedding efficiency, information hiding.‖ In: proceedings of the 8th

international workshop, Alexandria, pp 282–296, vol 4437 2008

12. Westfeld A, Pfitzmann A, ―Attacks on steganographic systems.‖ In: Proceedings the 3rd international workshop on information

hiding, LNCS 1768. Springer-Verlag, pp 61–76, 1999 13. Fridrich J, Goljan M, Dui R, ―Reliable detection of LSB steganography in color and grayscale images.‖ In: Proceedings of the

ACM workshop on multimedia and security, Ottawa, pp 27–30, 2001

14. Provos N, ―Defending against statistical steganalysis.‖ In: Proceedings of the 10th USENIX security symposium, pp 325–335, 2001

15. Paul G, Davidson I, Mukherjee I, Ravi SS, ―Keyless steganography in spatial domain using energetic pixels.‖ In:

Venkatakrishnan V et al (eds) Proceedings of the 8th international conference on information systems security (ICISS), vol 7671. LNCS, Springer, Guwahati, pp 134–148. ISBN:978-3-642-35129-7, 2008, 2012

16. Fabien A. P. Petitcolas, Ross J. Anderson, Markus G. Kuhn. ―Attacks on copyright marking systems, in David Aucsmith (Ed),

Information Hiding,‖ proceedings of the 2nd International Workshop, IH, Portland, Oregon, U.S.A., April 15-17, 1998, Proceedings, LNCS 1525, Springer-Verlag, ISBN 3-540-65386-4, pp. 219-239, 1998.

17. Fabien A. P. Petitcolas. ―Watermarking schemes evaluation.‖ I.E.E.E. Signal Processing, vol. 17, no. 5, pp. 58–64, September

2000.

18. http://www.petitcolas.net/fabien/watermarking/stirmar\k

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37.

Authors: R. Sekhar, K. Thangavel.

Paper Title: Intrusion Detection System using Deep Neural Network and Regularization of Hyper Parameters

with Adam Optimizer

Abstract: Intrusion Detection Systems (IDSs) study is unavoidable in the field of network security due to the

present target oriented attacks for taking secret data of an organization. Classifying and detecting attacks are

highly technical and tedious. In the existing models, the accuracy of intrusion detection in network traffic is

different for different algorithms. This paper proposed a better intrusion detection system using Deep Neural

Network with regularization of the hyper parameters. Adam optimization is proposed to optimize the weights in

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the neural network. The proposed system consists of six phases namely data collection, data framing, splitting of

data for training and testing, pre-processing/encoding, regularization with Adam Optimizer, training and testing.

It produces the better accuracy in detection process than the existing Deep Neural Network model. The bench

mark data set NSL_KDD is collected and processed in the suggested system.

Keyword: Intrusion Detection Systems (IDSs), Deep Neural Network(DNN), Rectified Linear Unit (ReLU),

Adaptive moment estimation (Adam) and Stochastic Gradient Decent (SGD). References:

1. JingwenTian, MeijuanGao, Fan Zhang, ―Network Intrusion Detection Method Based on Radial Basis Function Neural Network‖, IEEE, 978-1-4244-4589-9/$25@2009.

2. Luan Qinglin, Lu Huibin, ―Research of Intrusion detection based on neural Network optimized by adaptive genetic algorithm‖,

Computer Engineering and Design, Vol. 29, no 12, pp. 3022-3025, 2008. 3. Mohammad Reza Norouzian, SobhanMerati―Classifying Attacks in a Network Intrusion Detection System Based on Artificial

Neural Networks‖, ICACT 2011, ISBN 978-89-5519-155-4, Feb 13-16, 2011.

4. Li Xiangmei,QinZhi, ―The Application of Hybrid Neural Network Algorithms in Intrusion Detection System‖ IEEE, 978-1-4244-8694-6/11/$26@2011

5. Chuanlong Yin, Yuefei Zhu, JinlongFei and Xinzheng He, ―A Deep Learning Approach for IntrusionDetection Using Recurrent

Neural Networks‖ IEEE Access, Vol.5, pp. 21954-21961, 2017. 6. Lei Xiao, Xiaohui Chen, and Xinghui Zhang, ―A Joint Optimization of Momentum Item and Levenberg-Marquardt Algorithm to

Level Up the BPNN‘s Generalization Ability‖ Research Article, , Mathematical Problems in Engineering Volume 2014, Article

ID 653072, 10 pages http://dx.doi.org/10.1155/2014/653072.

7. Kloft, M. Lakshov,P ― Security analysis of online centroid anomaly detection and Detection Using Recurrent Neural Networks‖

IEEE Access, Vol.5, pp. 21954-21961, 2017. 8. MajdLatah ,LeventToker ―Towards an Efficient Anomaly-Based Intrusion Detection for Software-Defined Networks.

Publication in IET Networks,

9. P Aggarwal and S K Sharma ― Analysis of KDD Dataset attributes- classwise for Intrusion detection‖, Procediacomput.Sci, vol,57,pp 842-851, 2015

10. PriyankaAlekar ― Cloud based intrusion detection system using BPN classifier‖ International Journal of Engineering Research in

Computer Science and Engineering (IJERCSE) Vol 5, Issue 11, November 2018, Pages 1-7. 11. RoshaniGaidhane C. Vaidya Dr. M. Raghuwanshi ―Intrusion Detection and Attack Classification using Back-propagation Neural

Network‖ International Journal of Engineering Research & Technology (IJERT),ISSN: 2278-0181 Vol. 3 Issue 3, March – 2014,

pages 1112-1115. 12. Ahmed Elsherif, ―Automatic Intrusion Detection System Using Deep Recurrent Neural Network Paradigm‖ Journal of

Information security and cybercrimes research, Vol 1, Issue 1, June 2018, Pages 28 -41.

13. Tuan A Tang, LotfiMhamdi, Des McLernon, ― Deep Learning Approach for Network Intrusion detection in Software Defined

Networking‖ IEEE 978-1-5090-3837-4/16@2016 Pages 1-6

14. SasankaPotluri, ―Accelerated Deep Neural Networks for enhanced Intrusion Detection System‖ IEEE, 978-1-5090-1314-2/16 @2016.

15. S Devaraju, S Ramakrishnan, ―Performance comparision of Intrusion detection system using neural network with kdd dataset‖,

ICTACT journal on soft computing, 2014 16. Duchi.JHazan E and Singer,Y, ―Adaptive subgradient methods for online learning and stochastic optimization‖. Journal of

Machine learning research, 12, 2121-2159, 2011.

38.

Authors: Athreya Shetty B, Akram Pasha, Amith Singh S, Shreyas N I, Adithya R Hande

Paper Title: Comparative Study of Multiple Machine Learning Algorithms for Students’ Performance Data for

Job Placement in University

Abstract: In the era of data evolution, many organizations have taken the lead in storing the data in huge data

repositories. Analysis of data comes with several challenges since the time the data is captures till the insights

are inferred from the data. Accentuating the accuracy of data analysis is of paramount importance as many

critical decisions are totally dependent on the outcomes of the analysis. Machine learning has been found as the

most effective and most preferred tool in the literature for in-memory data analytics. Universities mostly collect

the statistical data related to the students that is only either used quantitatively or sparsely analyzed to gain the

insights that could be useful for the authorities to enhance the percentage of placements in campus drives held

through early analysis of such data accurately. The work proposed in this paper formulates the problem of

predicting the likelihood of a student getting placed in a company as a binary classification problem. Then it

makes an effort to train and perform the empirical study of following multiple machine learning algorithms with

the placement data; Logistic Regression, Naïve Bayes, Support Vector Machine, K-Nearest Neighbor and

Decision Tree. The machine learning classification models are built to predict the probabilities of a student

getting placed in a company based on the student‘s academic scores, achievements, work experience

(internship), and many other relevant features. Such an analysis helps the university authorities to dynamically

create plans to enhance the unlikely students to be placed in a company participating in the campus recruitment

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held in the university. To improve these models and to avoid the models from overfitting to the training data,

strategies like K-Fold cross-validation is applied for various values of k. The machine learning models selected

are also compared for its efficiency by employing the supervised and unsupervised feature extraction techniques

such as PCA and LDA. The Decision Tree model with K as 10 for cross-validation and PCA has outperformed

all the other models producing the accuracy of 72.83% with satisfactory support and recall during

experimentation. The application focuses on the targeted group of students, to eventually improve the probability

of students getting placed during campus recruitment drives held in the university.

Keyword: The application focuses on the targeted group of students, to eventually improve the probability of

students getting placed during campus recruitment drives held in the university. References:

1. Klopfenstein K, Thomas MK. The link between advanced placement experience and early college success. Southern Economic

Journal. 2009 Jan 1:873-91 2. Berger DM. Mandatory assessment and placement: The view from an English department. New Directions for Community

Colleges. 1997 Dec;1997(100):33-41.

3. Morgan DL, Michaelides MP. Setting Cut Scores for College Placement. Research Report No. 2005-9. College Board. 2005. 4. Clement, A. and Murugavel, T., 2015. English for Employability: A Case Study of the English Language Training Need Analysis

for Engineering Students in India. English language teaching, 8(2), pp.116-125.

5. Albraikan A, Hafidh B, El Saddik A. iAware: A Real-Time Emotional Biofeedback System Based on Physiological Signals. IEEE Access. 2018;6:78780-9.

6. Miller, Ryan, Hang Ho, Vivienne Ng, Melissa Tran, Douglas Rappaport, William JA Rappaport, Stewart J. Dandorf, James

Dunleavy, Rebecca Viscusi, and Richard Amini. "Introducing a fresh cadaver model for ultrasound-guided central venous access training in undergraduate medical education." Western Journal of Emergency Medicine 17, no. 3 (2016): 362.

7. Reeves LM, Schmorrow DD, Stanney KM. Augmented cognition and cognitive state assessment technology–near-term, mid-

term, and long-term research objectives. InInternational Conference on Foundations of Augmented Cognition 2007 Jul 22 (pp. 220-228). Springer, Berlin, Heidelberg.

8. Sharma AS, Prince S, Kapoor S, Kumar K. PPS—Placement prediction system using logistic regression. In2014 IEEE

International Conference on MOOC, Innovation and Technology in Education (MITE) 2014 Dec 19 (pp. 337-341). IEEE. 9. Jeevalatha T, Ananthi N, Kumar DS. Performance analysis of undergraduate students placement selection using decision tree

algorithms. International Journal of Computer Applications. 2014 Jan 1;108(15).

10. Kabra RR, Bichkar RS. Performance prediction of engineering students using decision trees. International Journal of computer applications. 2011 Dec;36(11):8-12.

11. Kuzilek J, Hlosta M, Herrmannova D, Zdrahal Z, Wolff A. OU Analyse: analysing at-risk students at The Open University.

Learning Analytics Review. 2015 Mar 18:1-6. 12. Mishra T, Kumar D, Gupta S. Mining students' data for prediction performance. In2014 Fourth International Conference on

Advanced Computing & Communication Technologies 2014 Feb 8 (pp. 255-262). IEEE.

13. Pandey M, Sharma VK. A decision tree algorithm pertaining to the student performance analysis and prediction. International Journal of Computer Applications. 2013 Jan 1;61(13).

14. Taruna S, Pandey M. An empirical analysis of classification techniques for predicting academic performance. In2014 IEEE

International Advance Computing Conference (IACC) 2014 Feb 21 (pp. 523-528). IEEE. 15. Choudhary R, Gianey HK. Comprehensive Review On Supervised Machine Learning Algorithms. In2017 International

Conference on Machine Learning and Data Science (MLDS) 2017 Dec 14 (pp. 37-43). IEEE.

16. Kumar RS, KP JK. ANALYSIS OF STUDENT PERFORMANCE BASED ON CLASSIFICATION AND MAPREDUCE APPROACH IN BIGDATA. International Journal of Pure and Applied Mathematics. 2018;118(14):141-8.

17. Lakshmanan GT, Li Y, Strom R. Placement strategies for internet-scale data stream systems. IEEE Internet Computing. 2008

Nov;12(6):50-60. 18. Thangavel SK, Bkaratki PD, Sankar A. Student placement analyzer: A recommendation system using machine learning. In2017

4th International Conference on Advanced Computing and Communication Systems (ICACCS) 2017 Jan 6 (pp. 1-5). IEEE.

19. Giri A, Bhagavath MV, Pruthvi B, Dubey N. A Placement Prediction System using k-nearest neighbors classifier. In2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) 2016 Aug 12 (pp. 1-4). IEEE.

20. Halde RR, Deshpande A, Mahajan A. Psychology assisted prediction of academic performance using machine learning. In2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) 2016

May 20 (pp. 431-435). IEEE.

21. Thangavel SK, Bkaratki PD, Sankar A. Student placement analyzer: A recommendation system using machine learning. In2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS) 2017 Jan 6 (pp. 1-5). IEEE.

22. Shukla M, Malviya AK. Modified Classification and Prediction Model for Improving Accuracy of Student Placement Prediction.

Available at SSRN 3351006. 2019 Mar 12. 23. Pruthi K, Bhatia P. Application of Data Mining in predicting placement of students. In2015 International Conference on Green

Computing and Internet of Things (ICGCIoT) 2015 Oct 8 (pp. 528-533). IEEE.

24. Kumar M, Singh AJ, Handa D. Literature survey on educational dropout prediction. International Journal of Education and Management Engineering. 2017 Mar 1;7(2):8.

25. Python 2.7 Documentation-docs.python.org/2.7 Scikit Learn Machine Learning in Python- www.scikit-learn.org

39. Authors: Likitha Daiphule, Bhaskar Reddy, Avinash Savith, Apoorva T V, Kavyashree

Paper Title: Tracking Suicidal Tendency using Twitter Data and Machine Learning Algorithms

Abstract: Social media analytics has a major part in a person‘s life in this scenario. It is used to obtain the

thoughts and opinion, sentiments of People. In this world people are comfortable sharing their thoughts and

feelings effectively on social media rather than sharing their happiness or problems to their friends, parents or

siblings‘. Cerebral health indicators, with depression, Depression and nervousness leads to high risk of people

obligating to suicide. Digital knowledge plays a major role to find suicidal tendency of people and to help them

out. The study or research about finding the amount of people who have suicidal tendency or not was carried

over by many universities where they collected the data from twitter or any health organizations.

Twitter data is the most easily available data when compared to Facebook or any other social media site.

These observations help us to determine the percentage of people having suicidal tendency or not by many

processes which includes data preprocessing, data augmentation, testing and training, and final result

representation. We use machine learning concepts. Sentiment Analysis or opinion mining is used.

There are many reasons for suicides across the world, using this digital or social data and with the help of

machine learning we could also differentiate between the group of people who actually are depressed or people

tweeting jokes, songs etc.

Keyword: Bag Of Words, sentiment analysis, Natural Language Processing. References: 1. Girardi P, Kaplan K, Amador XF, Harkavy-Friedman J, Harrow M, Pompili M, et al. Suicide risk in Depression: Learning from the

past to change the future. Ann Gen Psychiatry 2007 Mar 16;6:10.

2. Gerhard T, Olfson M, Crystal S, Huang C, Stroup TS. Premature mortality among adults with Depression in the United States. JAMA

Psychiatry 2015 Dec;72(12):1172-1181. 3. Hor K, Taylor M. Suicide and Depression: A systematic review of rates and risk factors. J Psychopharmacol 2010 Nov;24(4

Suppl):81-90. 4. Madoff LC, Freifeld CC, Brownstein JS. Digital disease detection: Harnessing the Web for public health surveillance. N Engl J Med

2009 May 21;360(21):2153-2155, 2157.

5. Fisher S, Robinson J, Hetrick S, Bailey E, Rodrigues M, Cox G, et al. Social media and suicide prevention: A systematic review. Early Interv Psychiatry 2015 Feb 19.

6. Scourfield J, Colombo G, Burnap P, Hodorog A, Amery R. Multi-class machine classification of suicide-related communication on

Twitter. Online Soc Netw Media 2017 Aug;2:32-44.

7. Matsubayashi T, Mori K, Sawada Y Ueda M. Tweeting celebrity suicides: Users' reaction to prominent suicide deaths on Twitter and

subsequent increases in actual suicides. Soc Sci Med 2017 Dec;189:158-166.

8. Barnes MD, Jashinsky J, Hanson CL, Burton SH, West J, Giraud-Carrier C, et al. Tracking suicide risk factors through Twitter in the US. Crisis 2014;35(1):51-59.

9. Krauss MJ, Cavazos-Rehg PA, Connolly S, Sowles SJ, Bharadwaj M, Rosas C, et al. An Analysis of Depression, Self-Harm, and

Suicidal Ideation Content on Tumblr. Crisis 2016 Jul 22:1-9. 10. Zhu T, Guan L, Kwok CL, Cheng Q, Yip PS. Suicide communication on social media and its psychological mechanisms: An

examination of Chinese microblog users. Int J Environ Res Public Health 2015 Sep 11;12(9):11506-11527.

188-191

40.

Authors: Anil B, Akram Pasha, Aman, Aman Kumar Singh, Aditya Kumar Singh

Paper Title: Multiple Machine Learning Classifiers for Student’s Admission to University Prediction

Abstract: Data is the most important asset for any organization which is further processed to produce useful

information. Machine Learning and Big Data techniques are widely used for industrial sectors to generate useful

patterns helpful for earning more profits and expand businesses. From the past few years, a lot of research works

have been done by using Big Data techniques on educational data for improvement in Education System.

Machine Learning and Big Data can be useful for predicting the students‘ admission, performance of teaching,

performance of a student, identifying the group of students of similar behavior. However, the manual process of

record checking is time consuming, tedious, and error prone; due to the inherent volume and complexity of data.

In this study, the combination of linear and non-linear machine learning algorithms; Logistic Regression,

Decision Tree, k-NN, and Naïve Bayes have been chosen to perform prediction of the target class for an unseen

observation by polling. As the models built in this work are predicting the likelihood of a student taking up the

admission into any university based on the student data collected by any marketing agency, the combined

models are collectively called as the Admission Predictor. The administrative officials of any academic

institution can use this kind of an application to explore and analyze the patterns that are affecting the student

admission and come up with enhanced strategies to improve admission. Such an application not only plays a

vital role in administration, but also help the management in reformulating the marketing criteria for overall

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development of academic institution.

Keyword: Classification, Data Mining, Data Analytics, K-Fold Cross Validation, LDA, Machine Learning,

PCA. References: 1. IIE Report http://www.iie.org/Services/Project-Atlas/United-States/International-Students-In-US

2. Avrim L. Bluma, Pat Langley Selection of relevant features and examples in machine Learning. 3. Austin Waters and Risto Miikkulainen, ‖GRADE, a statistical machine learning system developed to support the work of the graduate

admissions committee at the University of Texas at Austin Department of Computer Science (UTCS).‖

4. Scikit-learn documentation https://scikit-learn.org/stable/documentation.html 5. Dineshkumar B Vaghela, Priyanka Sharma, "Students' Admission Prediction using GRBST with Distributed Data Mining" June

2015.

6. Elizabeth Murray, "Using Decision Trees to Understand Student Data". 7. Surjeet Kumar Yadav, Saurabh pal, "Data Mining Application in Enrollment Management: A Case Study", 5, March 2012.

8. Miren Tanna, "Decision Support System for Admission in Engineering Colleges based on Entrance Exam Marks", 11, August 2012 .

9. Jay Bibodi, Aasihwary Vadodaria, Anand Rawat, Jaidipkumar Patel, "Admission Prediction System Using Machine Learning". 10. Ahmad Slim, Don Hush, Tushar Ojah, Terry Babbitt,"Predicting Student Enrollment Based On Student College ".

11. Enrollment management in higher education - defining enrollment management, key offices and tasks in enrollment management,

organizational models. Education Encyclopedia - StateUniversity.com, 2013. 12. Guyon and A. Elisseeff. An introduction to variable and feature selection. J. Mach. Learn. Res., 3:1157–1182, Mar. 2003.

13. D. Hossler and Bean, John P. The strategic management of college enrolments. San Francisco, Calif. : Jossey-Bass, 1st edition, 1990.

Includes bibliographical references (p. 303-318) and index. 14. H.-A. Park. An introduction to logistic regression: From basic concepts to interpretation with particular attention to nursing domain.

Korean Society of Nursing Science, 43(2), 2013.

15. Priyanga Chandrashekar, Kai Qian, Hossain Shahriar, Prabir Bhattacharya. "Improving the accuracy of decision tree mining with data pre-processing".

16. Python 2.7 Documentation-docs.python.org/2.7

17. Tinda Yang, Kai Qain, Dan-Chia-TienLo, Lixin Tao, "Improve the prediction accuracy of naive bayes classifier using association rule mining", 2016

41.

Authors: Aditya Krishna K.V.S, Abhishek K, Allam Swaraj, Shantala Devi Patil, Gopala Krishna Shyam

Paper Title: Smart Traffic Analysis using Machine Learning

Abstract: Congestion is costly as well as annoying. India is the second largest road network in the world. Out

of the total stretch of 5.4 million km of road network, almost 97,991 km is covered by national highways.The

major cause leading to traffic congestion is the high number of vehicle which was caused by the population and

the development of economy[1].Typical urban residents spend more than ten hours a week driving of which (one

to three hours) occurs in congested situation. In smart city roads would be equipped with the sensors for

analyzing the trafficflow and also there are few traffic analysis / prediction methods use neural network and

other prediction models which are not so efficient and suitable for many real world application [1]. So, here in

this paper solution for traffic analysis using random forest algorithm is being proposed which would select only

part of data for analyze like two third of entire data and predict the traffic congestion of specific path and

notifying well in advance the vehicles intending to move to move on that specific path. Thus accurate traffic

flow information help road users for fast and safe transporting.

Keyword: Machine Learning, Traffic analysis, Styling, Random Forest References: 1. Suguna Devi.,Neetha T.,2017.,Machine Learning based traffic congestion prediction in a IoT based Smart City,Department of

computer science and engineering ,Brilliant School Educational Institutions,Hayathnagar,Hyderabad,Telangana,India,2017.

2. ―Smart City‖, Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 18 Apr. 2019. Web. 19 Apr. 2019.

3. ―Traffic Index of 2019‖, numbeo.com, Apr 2019. 4. ―Machine Learning an Introduction to mean square error and regression lines‖,Moshe Binieli,Freecodecamp.com, Mean Square Error

Image

5. ―Mean Absolute Error‖,easycalculation.com.

6. ―Root mean square error‖,easycalculation.com.

7. ―Vehicle Crashes and Machine Learning‖,Abidishakur,towardsdatascience.com, Jan 01 2019

8. NinadLanke.,SheetalKoul., Smart Traffic Management System,Sahakarnagar,Pune,Department of Information

Technology,SKNCOE,Pune, August 2013

9. Miao chong.,Ajithabraham.,and Marcin Paprzycki, Traffic Accident Analysis Using Machine Learning

Paradigms,ComputerScienceDepartment,Oklahoma State University,USA, School of Computer science and engineering,Chung-

199-202

AngUniversity,Korea, Computer Science ,SWPS,Warszawa,Poland,Decemeber 20 2004

10. ―Using Machine Learning to predict Car accident Risk‖, Daniel Wilson, May 3 2018

42.

Authors: Bharath Darshan Balar, D S Kavya, Chandana M, Anush E, Vishwanath R Hulipalled

Paper Title: Efficient Face Recognition System for Identifying Lost People

Abstract: every day which includes kids, teens, mentally challenged, old-aged people with Alzheimer's, etc.

Most of them remain untraced. This paper proposes a system that would help the police and the public by

accelerating the process of searching using face recognition. When a person goes missing, the people related to

that person or the police can upload the picture of the person which will get stored in the database. When the

public encounter a suspicious person, they can capture and upload the picture of that person into our portal. The

face recognition model in our system will try to find a match in the database with the help of face encodings. It

is performed by comparing the face encodings of the uploaded image to the face encodings of the images in the

database. If a match is found, it will be notified to the police and the people related to that person along with the

location of where the person is found. The face recognition model that we have used maintains an accuracy of

99.38% on the Labelled Faces in the Wild Benchmark which comprises of 13,000 images [1].

Keyword: Face Encodings, Face Recognition, Finding, Lost Kids, Missing People. References: 1. Huang, Gary B. And Erik G. Learned-Miller. “Labeled Faces in the Wild: Updates and New Reporting Procedures”, Department of

Computer Science, University of Massachusetts Amherst, Amherst, MA, USA, Tech Report, 2014, pp 14–003

2. S. Chandran, Pournami & Balakrishnan, Byju & Rajasekharan, Deepak & N Nishakumari, K & Devanand, P & M Sasi, P. (2018).

“Missing Child Identification System Using Deep Learning and Multiclass SVM”. 113-116. 10.1109/RAICS.2018.8635054

3. Rohit Satle , Vishnuprasad Poojary , John Abraham , Mrs. Shilpa Wakode, ―MISSING CHILD IDENTIFICATION USING FACE

RECOGNITION SYSTEM‖ Vol.3, Issue.1, July – August 2016

4. S. B. Arniker et al., "RFID based missing person identification system," International Conference on Informatics, Electronics & Vision

(ICIEV), Dhaka, 2014, pp. 1-4.

5. Birari Hetal, ―Android Based Application - Missing Person Finder‖, in Iconic Research and Engineering Journals, Vol.1, Issue 12, JUN

2018.

6. Thomas M. Omweri, ―Using a Mobile Based Web Service to Search for Missing People – A Case Study of Kenya‖, in International

Journal of Computer Applications Technology and Research, Vol. 4, Issue 7, 507 - 511, 2015.

7. Sumeet Pate, ―Robust Face Recognition System for E-Crime Alert‖, in International Journal for Research in Engineering Application

and Management, Issue 1, MAR, 2O16

8. Peace Muyambo, 2018, An Investigation on the Use of LBPH Algorithm for Face Recognition to Find Missing People in Zimbabwe,

INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 07, Issue 07 (July 2018),

203-206

43.

Authors: D.N.Punith Kumar, Akram Pasha

Paper Title: Insights of Mathematics for Big Data

Abstract: Computer Science can be considered as one of the extensions made to the pure mathematical

sciences that exhibit the design and development of many mathematical models to solve various engineering

problems. Data storage and data processing are the two major operations that are primarily focused by any

computational model while solving a problem. Mathematical modelling has been helped in producing the

various computational models across several problems that are found in the field of computer science. Among

many problems that are found in the area of computer science, data science and big data have recently geared up

to solve many business oriented problems that are purely based on data analytics to enhance the profit by taking

critical business decisions. Data Scientists and mathematicians are found to have a skeptical understanding or

too little collaboration either in knowing the mathematical concepts behind big data technologies, or too little

knowledge of applications of mathematical concepts in applications of big data, respectively. Therefore, in this

paper, an effort is made to bring out the major mathematical concepts that have contributed in fueling the

solutions for big data problems. The authors hypothesize that the work proposed in this paper would benefit any

data scientist or a mathematician to clearly understand the bridge between the math and its application in big

data analytics. The authors identify the mathematical concepts and their roles played while solving various tasks

that are encountered in the domains of big data. Further, such an endeavor is expected to open up many

opportunities for both mathematicians and big data professionals to work collaboratively, while encouraging and

207-213

contributing in enhancing interdisciplinary research across many domains of engineering.

Keyword: Big Data, Data Analytics, PCA, SVD, Laplacian Graph, Eigen values, Eigen Vectors, Linear

Algebra. References:

1. un, Zhaohao, and Paul P. Wang. "A mathematical foundation of big data." New Mathematics and Natural Computation13.02

(2017): 83-99.

2. Laval, Philippe B. "Introduction to the Mathematics of Big Data." Retrieved September 4 (2015): 2016.

3. Sun, Zhaohao, and P. Wang. "Big data, analytics and intelligence: an editorial perspective." Journal of New Mathematics and

Natural Computation 13.2 (2017): 75-81.

4. Kitchin, R., & McArdle, G. (2016). What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets. Big

Data & Society, https://doi.org/10.1177/2053951716631130.

5. Kitchin, Rob, and Gavin McArdle. "The diverse nature of big data." SSRN Electron. J (2015).

6. Kitchin, Rob. "Big data—Hype or revolution." The SAGE handbook of social media research methods (2017): 27-39.

7. Ibrahim Abaker TargioHashemaIbrarYaqoobaNor BadrulAnuaraSalimahMokhtaraAbdullahGaniaSameeUllah Khanb, The rise

of ―big data‖ on cloud computing: Review and open research issues, Information Systems,Volume 47, January 2015, Pages 98-

115.

8. Amir Gandomi ∗, Murtaza Haider, Beyond the hype: Big data concepts, methods, and analytics, International Journal of

Information Management 35 (2015) 137–144.

9. UthayasankarSivarajahMuhammad MustafaKamalZahirIraniVishanthWeerakkody, Critical analysis of Big Data challenges and

analytical methods, Journal of Business Research,Volume 70, January 2017, Pages 263-286

10. John R. Mashey (25 april 1998). "Big Data ... and the next wave of infrastress" (pdf). slides from invited talk. usenix. retrieved

28 september 2016.

11. https://www.nap.edu/read/23670/chapter/6.

12. Steve Lohr (1 February 2013). "The Origins of 'Big Data': An Etymological Detective Story". The New York Times. Retrieved

28 September 2016.

13. De Mauro, Andrea; Greco, Marco; Grimaldi, Michele (2016). "A Formal definition of Big Data based on its essential Features".

Library Review. 65 (3): 122– 135. doi:10.1108/LR-06-2015-0061.

14. Hilbert, Martin. "Big Data for Development: ―A Review of Promises and Challenges. Development Policy Review".

martinhilbert.net. Retrieved 7 October2015.

15. White Paper: Big Data for Development: Opportunities & Challenges (2012) – United Nations Global Pulse". Unglobalpulse.org.

Retrieved 13 April2016.

16. Hilbert M. Big data for development: From information-to knowledge societies. Available at SSRN 2205145. 2013.

17. Elena Kvochko, Four Ways To talk About Big Data (Information Communication Technologies for Development Series)".

Worldbank.org. 2012-12-04. Retrieved 30 May 2012.

18. Daniele Medri: Big data & Business: An ongoing revolution, statistical views,21 october 2013.

19. Admire Moyo. "Data scientists predict Springbok defeat". www.itweb.co.za. Retrieved 12 December2015.

20. Zhang, Dongxia, and Robert Caiming Qiu. "Research on big data applications in Global Energy Interconnection." Global Energy

Interconnection 1.3 (2018): 352-357.

21. Kang, U., and Christos Faloutsos. "Big graph mining: algorithms and discoveries." ACM SIGKDD Explorations Newsletter 14.2

(2013): 29-36.

22. Belkin, Mikhail, and Partha Niyog,. "Laplacian eigen maps for dimensionality reduction and data representation." Neural

computation 15.6 (2003): 1373-1396.

23. Pavez, Eduardo, and Antonio Orteg,. "Generalized Laplacian precision matrix estimation for graph signal processing." 2016

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016.

24. Lei, Qi, Kai Zhong, and Inderjit S. Dhillon. "Coordinate-wise power method." Advances in Neural Information Processing

Systems. 2016.

25. Bai, Jushan, and Serena Ng. "Rank regularized estimation of approximate factor models." Journal of Econometrics (2019).

26. Valdano, Eugenio, and Alex Arenas. "Exact rank-reduction of network models." arXiv preprint arXiv: 1811.08299 (2018).

27. Muravev, Anton, et al. "Acceleration Approaches for Big Data Analysis." 2018 25th IEEE International Conference on Image

Processing (ICIP). IEEE, 2018.

28. Fan, Jianqing, et al. "Principal component analysis for big data." Wiley StatsRef: Statistics Reference Online (2018): 1-13..

29. Murakami, Daisuke, and Daniel A. Griffith. "Eigenvector spatial filtering for large data sets: fixed and random effects approach."

Geographical Analysis 51.1 (2019): 23-49.

30. Huang, Weiyu, Antonio G. Marques, and Alejandro R. Ribeiro. "Rating prediction via graph signal processing." IEEE

Transactions on Signal Processing 66.19 (2018): 5066-5081.

31. Liu, Wenhe, et al. "Learning Distilled Graph for Large-scale Social Network Data Clustering." IEEE Transactions on Knowledge

and Data Engineering (2019).

32. Kang, Zhao, et al. "Robust Graph Learning from Noisy Data," IEEE transactions on cybernetics (2019).

33. Liu, Tianchi, et al. "An adaptive graph learning method based on dual data representations for clustering." Pattern Recognition

77 (2018): 126-139.

34. Alami, Réda, "Graph Signal Processing: Towards The Diffused Spectral Clustering." (2019).

35. Wen, Lulu, et al. "Compression of smart meter big data: A survey." Renewable and Sustainable Energy Reviews 91 (2018): 59-

69.

36. Kwan, Chiman, and Yvonne Luk. "Hybrid sensor network data compression with error resiliency." Data Compression

Conference. 2018.

37. Reddy, Rajasekhar M., et al. "A new approach for the image compression to the medical images using PCA-SPIHT." Biomedical

Research (0970-938X) (2018).

38. Zhou, Jin, and Chiman Kwan. "A Hybrid Approach for Wind Tunnel Data Compression." Data Compression Conference. 2018.

39. Liu, Ting Yu, Kuan Jen Lin, and Hsi Chun Wu. "ECG data encryption then compression using singular value

40. decomposition." IEEE journal of biomedical and health informatics 22.3 (2018): 707-713.

41. Schanze, Thomas. "Compression and noise reduction of biomedical signals by singular value decomposition." IFAC-

PapersOnLine 51.2 (2018): 361-366.

42. Windmann, Stefan, et al. "Big data analysis of manufacturing processes." Journal of Physics: Conference Series. Vol. 659. No. 1.

IOP Publishing, 2015.

43. Mukhopadhyay, Sourav Kumar, M. Omair Ahmad, and M. N. S. Swamy. "SVD and ASCII Character Encoding-Based

Compression of Multiple Biosignals for Remote Healthcare Systems." IEEE transactions on biomedical circuits and systems 12.1

(2018): 137-150.

44. McLachlan, Geoffrey J., Sharon X. Lee, and Suren I. Rathnayake. "Finite mixture models." Annual review of statistics and its

application ,6 (2019): 355-378.

45. Wang, Pingxin, and Yiyu Yao. "CE3: a three-way clustering method based on mathematical morphology." Knowledge-Based

Systems 155 (2018): 54-65.

46. Lu, Tao, et al. "Understanding and modeling lossy compression schemes on HPC scientific data." 2018 IEEE International

Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2018.

47. [46] Fox, Alyson, and Thomas Manteuffel. "Algebraic multigrid for directed graph Laplacian linear systems (NS‐LAMG)."

Numerical Linear Algebra with Applications 25.3 (2018): e2152.

48. [47] Snijders, Chris, Uwe Matzat, and Ulf-Dietrich Reips. "" Big Data": big gaps of knowledge in the field of internet science."

International Journal of Internet Science 7.1 (2012): 1-5.

49. https://en.wikipedia.org/wiki/Mathematical_model.

50. Vijaylakshmi, s. p. and j. Priyadarshini. ―Big data analysis based on mathematical model: a comprehensive survey.‖ (2015).

51. Dalla Vecchia, Rodrigo. "The relationship between Big Data and Mathematical Modeling: A discussion in a mathematical

education scenario." Themes in Science and Technology, Education 8.2 (2016): 95-103.

52. Qiu, Junfei, et al. "A survey of machine learning for big data processing." EURASIP Journal on Advances in Signal Processing

2016.1 (2016): 67.

53. Sandryhaila, Aliaksei, and Jose MF Moura. "Big data analysis with signal processing on graphs." IEEE Signal Processing

Magazine 31.5 (2014): 80-90.

54. Che, Dunren, Mejdl Safran, and Zhiyong Peng. "From big data to big data mining: challenges, issues, and opportunities."

International conference on database systems for advanced applications. Springer, Berlin, Heidelberg, 2013.

55. Yu, Le, et al. "BC-PDM: data mining, social network analysis and text mining system based on cloud computing." Proceedings of

the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012.

56. Joshi, Milan, and Dhananjay Joshi. "A survey of Topological Data Analysis Methods for Big Data in Healthcare Intelligence."

International Journal of Applied Engineering Research 14.2 (2019): 584-588.

57. Nachtsheim, Abigael C., and John Stufken. "Comments on: Data science, big data and statistics." TEST (2019): 1-4.

44.

Authors: Sowmya Sundari L K, Nirmala S Guptha,Shruthi G, Thanuja K,Anitha K

Paper Title: Detection of Liver Lesion using ROBUST Machine Learning Technique

Abstract: In the present era, Computer Aided Diagnosis (CAD) is very useful for the detection of a liver

tumor. This type of study and categorization system can moderate an unnecessary biopsy. The proposed method

for the detection of liver cancer clusters in liver images using Gabor features and shape features. The mentioned

regions are categorized by the SVM classifier utilizing the most prevailing features selected from the above

features. In our project, we have proposed a systematic approach of analyzing a liver under cancer positive

environment. We have proposed a technique for tumor identification and segmentation using image smoothing

and refining methods. When we use CT images for the detection of liver tumor manual interaction is not

necessary, since it works automatically. The projected method needs to learn a few model parameters such as

tumor part, non-tumor part, and segment liver regions. The complete system is divided into the training part and

testing part respectively and this system is based mainly on SVM. The input liver image undergoes for the

preprocessing step and image segmentation. Preprocessing includes many steps like the resizing of an image,

improve the clarity of the image, conversion form colored image into grayscale. After these necessary features

are collected from the resulting image. These collected features are then fed to the SVM for training. These

collected features are compared with examination results by the SVM Classifier with the existing trained

features using RBF kernel. Contingent upon the correlation result, the classifier gives the outcome.

Keyword: Contingent upon the correlation result, the classifier gives the outcome. References:

1. X. Zhang, T. Furukawa, X. Zhou, et al., ―Detection of metastatic liver tumor in multi-phase CT images by using a spherical gray-level

differentiation searching filter,‖ Proc. SPIE: Medical Imaging 2011, Vol. 7963, pp. 79632k1-8, Feb. 2011.

2. D. Pescia, N. Paragios, S. Chemouny, "Automatic detection of liver tumors," 5th IEEE International Symposium on Biomedical

Imaging: From Nano to Macro, 672-67, .2008.

3. B. N. Li, C. K. Chui, S. Chang, S. H. Ong, ―A new unified level set method for semi-automatic liver tumor segmentation on contrast-

enhanced CT images,‖ Expert Systems with Applications, Vol. 39, pp.9661–9668, 2012.

4. Joni-Kristian Kämäräinen. Gabor features in image analysis. In Khalifa Djemal, Mohamed Deriche, editors, 3rd International

Conference on Image Processing Theory Tools and Applications, IPTA 2012, 15-18 October 2012, Istanbul, Turkey. Pages 13-14,

IEEE, 2012.

5. G.-B. Huang, D. H. Wang, and Y. Lan, "Extreme Learning Machines:A Survey," International Journal of Machine Leaning and

Cybernetics, 2(2), pp. 107-122, 2011.

6. Y. Hame and M. Pollari, ―Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric

distribution estimation,‖ Med. Image Anal. vol. 16, no. 1, pp. 140-149, June 2012.

7. T. Heimann et al., "Comparison and evaluation of methods for liver segmentation from CT datasets," IEEE trans. Medical Imaging,

vol. 28, pp.1251-1265, 2009.

8. N.S. Guptha and K.K. Patil, ―Earth mover's distance-based CBIR using adaptive regularised Kernel fuzzy C-means method of liver

cirrhosis histopathological segmentation‖, International Journal of Signal and Imaging Systems Engineering, Vol.10, No.1-2, pp.39-

46, 2017.

9. N.S. Guptha and K.K. Patil, ―Detection of macro and micro nodule using online region based-active contour model in

histopathological liver cirrhosis‖, International Journal of Intelligent Engineering and Systems- INASS Journal, Vol.11, No.2, 2018

DOI: 10.22266/ijies2018.0430.28, pp.256-265, 2018.

10. N.S. Guptha and K.K. Patil, ―Liver contour and shape analysis under pattern clustering‖, International Conference on Cognition and

Recognition-ICCR-2016, Springer Lecture Notes in Networks and Systems 2018. Print ISBN: 978-981-10-5145-6, Online ISBN978-

981-10-5146-3, DOIhttps://doi.org/10.1007/978-981-10-5146-3_31.

11. X. Zhang, T. Furukawa, X. Zhou, et al., ―Detection of metastatic liver tumor in multi-phase CT images by using a spherical gray-level

differentiation searching filter,‖ Proc. SPIE: Medical Imaging 2011, Vol. 7963, pp. 79632k1-8, Feb. 2011.

12. D. Pescia, N. Paragios, S. Chemouny, "Automatic detection of liver tumors," 5th IEEE International Symposium on Biomedical

Imaging: From Nano to Macro, 672-67, .2008.

13. B. N. Li, C. K. Chui, S. Chang, S. H. Ong, ―A new unified level set method for semi-automatic liver tumor segmentation on contrast-

214-219

enhanced CT images,‖ Expert Systems with Applications, Vol. 39, pp.9661–9668, 2012

14. Joni-Kristian Kämäräinen. Gabor features in image analysis. In Khalifa Djemal, Mohamed Deriche, editors, 3rd International

Conference on Image Processing Theory Tools and Applications, IPTA 2012, 15-18 October 2012, Istanbul, Turkey. Pages 13-14,

IEEE, 2012.

45.

Authors: Rashmi G.O, Ashwin kumar .U.M

Paper Title: Machine Learning Methods for Heart Disease Prediction

Abstract: Machine learning is utilized to empower a program to analyze information, understand correlations

and make utilization of bits of knowledge to take care of issues or potentially enhance information and for

prediction. The American Heart Association Statistics 2016 Report shows that coronary illness is the main

source of death for people, responsible for 1 in every 4 deaths. Machine learning algorithms play a very

important role in medical area. We use machine learning to understand, predict, and prevent cardiovascular

disease using numeric data. The end goal is to produce an approved machine learning application in healthcare.

In an effort to refine the search for a useful and accurate method with the dataset, the results of several

algorithms will be compared. The front-runners will be analyzed and used to develop a unique, higher-accuracy

method. Machine learning methods inclusive of Logistic Regression, Naïve Bayes, Decision tree(CART). We

use ensemble learning for better accuracy which includes algorithms like Random Forest, XGBoost, Extra trees

classifier. Also, our work adds to the present literature by giving a far reaching review of machine learning

algorithms on sickness prediction tasks. Our goal is to perform predictive analysis with these machine learning

algorithms on heart diseases using ensembles like bagging, boosting, stacking. Machine Learning algorithms

used and conclude which techniques are effective and efficient. A huge medical datasets are accessible in

different data repositories which used in the real world application.

Keyword: Machine learning, Cardiovascular disease, Decision tree(CART), Ensemble learning. References: 1. Tianqi Chen, Carlos Guestrin " XGBoost: A Scalable Tree Boosting System " KDD ’16, August 13-17, 2016, San Francisco, CA, USA,

2016 ACM.

2. H K.Gianey, R.Choudhary " Comprehensive Review On Supervised Machine Learning Algorithms " 2017 International Conference on

Machine learning and Data Science.

3. I.Yekkala, , et al. "Prediction of heart disease using Ensemble Learning and Particle Swarm Optimization" 2017 International

Conference On Smart Technology for Smart Nation.

4. A.Batra, et al. "Classification of Arrhythmia Using Conjunction of Machine Learning Algorithms and ECG Diagnostic Criteria" International Journal of Biology and Biomedicine.

5. Dinesh K.G, et al. "Prediction of Cardiovascular Disease Using Machine Learning Algorithms" Proceeding of 2018 IEEE International

Conference on Current Trends toward Converging Technologies, Coimbatore, India. 6. S.Jatav, V.Sharma "An Algorithm For Predictive Data Mining Approach In Medical Diagnosis " International Journal of Computer

Science & Information Technology (IJCSIT) Vol 10, No 1, February 2018 DOI:10.5121/

7. T.Princy. R , J. Thomas " Human Heart Disease Prediction System using Data Mining Techniques "2016 International Conference on Circuit, Power and Computing Technologies [ICCPCT]

8. UCI Machine Learning Repository :http://archive.ics.uci.edu/ml/datasets.html

9. https://blog.statsbot.co/ensemble-learning-d1dcd548e93 10. N.Bhargava "An Approach for Classification using Simple CART Algorithm in Weka" 2017 11 th International Conference on

Intelligent Systems and Control (ISCO)- 978-1-5090-2717-0/171$31 .00 ©2017 IEEE.

220-223

46.

Authors: Naveen Chandra Gowda, P. Sai Venkata Srivastav, Guru Prashanth.R, Raunak.A, Madhu Priya R

Paper Title: Steg Crypt (Encryption using steganography)

Abstract: The target of this venture is to obtain secured encryption and authentication using steganography. So

as to accomplish this, numerous organizations & universities in the world have given solutions to secured

communication, in the interim many algorithms have been created, including like AES, RSA, LSB etc. But

though these algorithms have been developed they were endured to breakdown by hackers which make them

obsolete. In this paper we try to combine many already existing algorithms like AES, LSB into one proposed

system. Firstly, the utilization of steganography along with traditional encryption is implemented in the proposed

system. Second, we try to achieve authentication of user using OTP via E-mail. Thirdly, the encrypted data is

divided and sent across many servers so it‘s impossible to get complete encrypted data in one path. By applying

the proposed model, the probability of data compromise becomes very minimal and very hard to hack.

224-229

Keyword: RSA, AES, LSB, OTP References: 1. Dr. M. Umamaheswari, Prof.S. Sivasubramanian, S.Pandiarajan. Analysis of Different Steganographic Alogorithms for secured data

hiding.

2. Achmad Kodar University of Mercu, Implementation of steganography in Image media using LSB.

3. Max Wiess, IJSCRS Vol10, Principles of steganography. 4. E. Cole and R.D. Krutz, Hiding in Plain Sight: Steganography and the Art of Covert Communication, Wiley Publishing, Inc., ISBN 0-

471-44449-9, 2003.

5. IJSPTM Vol 1, No2, Koushik Dasgupta, Hash based least significant big algorithm (HLSB). 6. Feng Pan, Li Xiang, Xiao-Yuan Yang and Yao Guo, Video Steganography using Motion Vector and Linear Block Codes, in IEEE 978-

1-4244-6055-7/10/, pp. 592-595,2010.

7. Masud K. S.M. Rahman, Hossain, M.L., A new approach for LSB based image steganography using secret key, in Proceedings of 14th International Conference on Computer and Information Technology (ICCIT-2011), pp.-286-291, Dec. 2011.

8. M. Ramalingam, ―Stego machine-video steganography using modified lsb algorithm‖, World Acad. Sci. Eng. Technol. 74 , 502–505,

2011 9. Z. Li-Yi, Z. Wei-Dong, et al., ―A novel steganography algorithm based on motion vector and matrix encoding‖, in: proceedings of the

3rd International Conference on Communication Software and Networks (ICCSN), IEEE, 2011, pp. 406–409, 2011.

10. Y. Cao, H. Zhang, X. Zhao, H. Yu, ―Video steganography based on optimized motion estimation perturbation‖, in: Proceedings of the

3rd ACM Workshop on Information Hiding and Multimedia Security, ACM, pp. 25–31, 2015.

11. H.M. Kelash, O.F.A. Wahab, O.A. Elshakankiry, H.S. El-sayed, ―Hiding data in video sequences using steganography algorithms,‖

in: proceedings of International Conference on ICT Convergence (ICTC), IEEE, pp. 353–358, 2013. 12. Deshmukh PU, Pattewar TM, ―A novel approach for edge adaptive steganography on LSB insertion technique.‖ In: proceedings of the

International conference on information communication and embedded systems (ICICES) IEEE, Chennai, pp 27–28, 2014.

13. Feng B, Lu W, SunW ―Secure binary image steganography based on minimizing the distortion on the texture.‖ IEEE Transaction Information Forensics Security 10(2):243–255, 2015

47.

Authors: Ambika Rani Subhash, Ashwin kumar UM

Paper Title: Accuracy of Classification Algorithms for Diabetes Prediction

Abstract: The illness that happens in the human body because of enormous amounts of sugar in the blood, i.e.,

when the human body has elevated amounts of glucose in the blood is Diabetes Mellitus all the more ordinarily

referred to just as diabetes. The diverse most usually happening assortments of diabetes are Prediabetes, Type2,

Type 1 and Gestational Diabetes. Type 2 diabetes, is interminable and generally happens, when the human body

does not usefully utilize the hormone, insulin, which is created by it. The Type 1 assortment happens when the

pancreatic organ doesn't deliver enough insulin as is required by the human body.Prediabetes is one that occurs

when the blood sugar levels are very high but not as much when compared to the Type 2 variety. Gestational

diabetes usually affects pregnant women and here also the blood sugar levels are very high. According to the

global report by the WHO (World Health Organization), around 422 million people suffer from the disease and a

worrying 1.6 million odd deaths are credited only to diabetes every year. However, timely diagnosis of the

disease and care of patients through simple lifestyle measures has proven to keep this deadly disease in check.

The main challenge for doctors however, is the tedious process of identifying the factors that cause the

occurrence of this disease, in an effective and timely manner. During the recent times this challenge is being

addressed through Data Mining and Machine Learning techniques. The main aim of this experimentation is for

designing a prediction model which can, with utmost accuracy, diagnose the occurrence of diabetes in patient.

These training models have been designed using the WEKA tool and four supervised machine learning

classification algorithms such as Naïve Bayes, J48, SVM and Neural Networks have been used to predict the

onset of diabetes at an early stage. The dataset used here is the Pima Indian Diabetes training Dataset

abbreviated as PIDD, which has been acquired from the UCI repository. Chi-squared tests have been applied on

this dataset to obtain only those attributes that have the highest tendency of causing diabetes in patients. The

performance of each of the classification algorithms have been compared and analyzed based on Accuracy, F-

measure, Recall, Precision and ROC curves.

Keyword: Naïve Bayes, J48, SVM, Neural Networks/Multilayer Perception, Diabetes, Chi-squared test,

WEKA, Accuracy References: 1. Abiraami TT, Sumathi A, ―Analysis of Classification Algorithms for Diabetic Heart Disease‖, International Journal of Pure and

Applied Mathematics, Vol. 118, No.20, 2018, 1925-1934.

2. Deepti Sisodia, Dilip Singh Sisodia,,‖Prediction of Diabetes using Classification Algorithms ―, ICCIDS 2018 , Procedia Computer

Science 132 (2018) 1578–1585, Science Direct. 3. Ni, Nguyen, Garibay, Ivan, Akula, Ramya. (2019), ―Supervised Machine Learning based Ensemble Model for Accurate Prediction of

230-234

Type 2 Diabetes‖, IEEE Southeastcon, April 2019

4. Sneha, N, Gangil, Tarun, ―Analysis of diabetes mellitus for early prediction using optimal features selection‖, Journal of Big Data, Vol. 13.

5. J. Anitha, A. Pethalakshmi, ―Comparison of Classification Algorithms in Diabetic Dataset‖, International Journal of Information

Technology (IJIT), Vol. 3, Issue 3, May-June 2017. 6. K. Saravananathan, T. Velmurugan, ―Analyzing Diabetic Datausing Classification Algorithms in Data Mining‖, International Journal

of Science and Technology, Vol. 9 (43), November 2016.

7. P. Chen, C. Pan, ―Diabetes Classification Model based on Boosting Algorithms‖, BMC Informatics, Vol. 19, PMCID:PMC 58722396, March 2018.

8. Quan Zou, Kaiyang Qu,Yamei Luo,Dehui Yin, Ying Ju, Hua Tang, ―Predicting Diabetes Mellitus with Machine Learning

Techniques‖, Front Genet, Vol. 9, PMCID:PMC6232260, November 2018. 9. Dilip Kumar Choubey, Sanchita Paul, Santosh Kumar, ―Classification of Pima Indian Diabetes dataset using Naïve Bayes with genetic

algorithm as an attribute selection‖, Communication and Computing Systems, ISBN 978-1-138-02952-1, 2017.

10. Aized Amin Soofi, ―Classification Techniques in Machine Learning: Application and Issues‖,Journal of Basic & Applied Sciences, Vol 13, 459-465, 2017.

11. Rahul Joshi, Minyechil Alehegn, ―Analysis and Prdiction of Diabetes disease using machine learning algorithm: Ensemble Approach‖,

International Research Journal of Engineering and Technology (IRJET), Vol 4, Issue 10, October 2017. 12. Priya. B. Patel, Paryh. P. Shah, Hmanshu D. Patel, ―Analyze Data Mining Algorithms for Prediction of Diabetes‖, International Journal

of Engineering Development and Research (IJDER), Vol. 5, Issue 3, ISSN: 2321-9939.

13. https://machinelearningmastery.com/use-machine-learning-algorithms-weka/, Accessed Marchl 03, 2019. 14. https://www.niddk.nih.gov/health-information/diabetes/overview/what-is-diabetes, Accessed April 26, 2019.

15. Centers for Disease Control and Prevention. National diabetes statistics report, 2017. Centers for Disease Control and Prevention

website. www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf (PDF, 1.3 MB) . Updated July, 18 2017. Accessed April 23, 2019.

48.

Authors: L A Lalitha, Vishwanath R Hulipalled

Paper Title: Adaptive k-Nearest Centroid Neighbor Classifier for Detecting Drifted Twitter Spam

Abstract: With the growth of Internet and its related technologies have resulted in increased usage of smart and

Internet connected devices and large amount of time is spent on Social Network. Nonetheless, because of

increase in attractiveness of Social Network, cyber offenders are spreading spam on these networks to exploit

possible targets. The spammers trap users to malware downloads or external phishing URLs, which has been an

enormous problem for online safety and user quality of exposure. However, the existing research fails to detect

spam in Twitter and has become a key issue in recent times. Recent work [14], focused on using Machine

Learning (ML) approach for detecting spam in Twitter, by making use of the statistical features of Twitter data.

However, adoption of such method affects the classification accuracy of ML algorithm. Because the Statistical

Feature characteristics of spam tweets vary with respect to time. This problem is known as ―Twitter Spam

Drift‖. To address this problem, we present a novel non-parametric Adaptive K-Nearest Centroid Neighbor

(AKNCN) Classifier. Further, for meeting real-time requirement the AKNCN is trained using one million spam

tweets and one million non-spam tweets data. The AKNCN model can discover spam more efficiently than the

state-of-the-art model. Experiment outcome shows the AKNCN attains significant performance with reference to

Accuracy (A), F-Measure (F) and Detection Rate (DR) in real-world scenarios.

Keyword: Nearest Centroid Neighbor, Machine Learning, Social Networks, Statistical Features, Spam Drift,

Twitter Spam Detection. References: 1. A. Greig. (2013). Twitter Overtakes Facebook as the Most Popular Social Network for Teens,According to Study, DailyMail, accessed

on Aug. 1, 2015. [Online]. Available: http://www.dailymail.co.uk/news/article-2475591/Twitter-overtakes-Facebook-popular-socialnetwork-teens-according-study.html.

2. F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida, ―Detecting spammer on twitter,‖ in Proc. 7th Annu. Collaboration, Electron.

Messaging, Anti-Abuse Spam Conf., p. 12, 2010. 3. C. Pash. (2014). The lure of Naked Hollywood Star Photos Sent the Internet into Meltdown in New Zealand, Bus. Insider, accessed on

Aug. 1, 2015 [Online]. Available: http://www.businessinsider.com.au/the-lure-of-naked-hollywood-starphotos-sent-the-internet-into-

meltdown-in-new-zealand-2014-9. 4. J. Oliver, P. Pajares, C. Ke, C. Chen, and Y. Xiang, ―An in-depth analysis of abuse on twitter,‖ Trend Micro, Irving, TX, USA, Tech.

Rep., 2014.

5. R. Jeyaraman. (2014). Fighting Spam With Botmaker, Twitter, accessed on Aug. 1, 2015. [Online]. Available: https://blog.twitter.com/2014/fighting-spam-with-botmaker.

6. C. Grier, K. Thomas, V. Paxson, and M. Zhang, ―@spam: The underground on 140 characters or less,‖ in Proc. 17th ACM Conf.

Comput. Commun. Security, pp. 27–37, 2010. 7. K. Thomas, C. Grier, D. Song, and V. Paxson, ―Suspended accounts in retrospect: An analysis of twitter spam,‖ in Proc. ACM

SIGCOMM Conf. Internet Meas. Cof., pp. 243–258, 2011.

8. C. Yang, R. Harkreader, J. Zhang, S. Shin, and G. Gu, ―Analyzing spammers‘ social networks for fun and profit: A case study of cyber

235-243

criminal ecosystem on twitter,‖ in Proc. 21st Int. Conf. World Wide Web, pp. 71–80, 2012.

9. C. Yang, R. Harkreader, and G. Gu, ―Empirical evaluation and new design for fighting evolving twitter spammers,‖ IEEE Trans. Inf.Forensics Security, vol. 8, no. 8, pp. 1280–1293, 2013.

10. M. Fazil and M. Abulaish, "A Hybrid Approach for Detecting Automated Spammers in Twitter," in IEEE Transactions on Information

Forensics and Security, vol. 13, no. 11, pp. 2707-2719, 2018. 11. M. Jiang, P. Cui and C. Faloutsos, "Suspicious Behavior Detection: Current Trends and Future Directions," in IEEE Intelligent

Systems, vol. 31, no. 1, pp. 31-39, 2016.

12. G. Lin, N. Sun, S. Nepal, J. Zhang, Y. Xiang and H. Hassan, "Statistical Twitter Spam Detection Demystified: Performance, Stability and Scalability," in IEEE Access, vol. 5, pp. 11142-11154, 2017.

13. S. Sedhai and A. Sun, "Semi-Supervised Spam Detection in Twitter Stream," in IEEE Transactions on Computational Social Systems,

vol. 5, no. 1, pp. 169-175, 2018. 14. C. Chen, Y. Wang, J. Zhang, Y. Xiang, W. Zhou and G. Min, "Statistical Features-Based Real-Time Detection of Drifted Twitter

Spam," in IEEE Transactions on Information Forensics and Security, vol. 12, no. 4, pp. 914-925, 2017.

15. J. Zhang, R. Zhang, Y. Zhang and G. Yan, "The Rise of Social Botnets: Attacks and Countermeasures," in IEEE Transactions on Dependable and Secure Computing. doi: 10.1109/TDSC.2016.2641441, 2016.

16. J. Song, S. Lee, and J. Kim, ―Spam filtering in twitter using senderreceiver relationship,‖ in Proc. 14th Int. Conf. Recent Adv. Intrusion

Detection, pp. 301–317, 2011. 17. T. Dasu, S. Krishnan, S. Venkatasubramanian, and K. Yi, ―An information-theoretic approach to detecting changes in

multidimensional data streams,‖ in Proc. Symp. Interface Statist., Comput. Sci., Appl., pp. 1–24, 2006.

18. J. Gama, I. Žliobait˙e, A. Bifet, M. Pechenizkiy, and A. Bouchachia, ―A survey on concept drift adaptation,‖ J. ACM Comput. Surv., vol. 46, no. 4, p. 44, 2014.

19. J. M. Romo and L. Araujo, ―Detecting malicious tweets in trending topics using a statistical analysis of language,‖ Expert Syst. Appl.,

vol. 40, no. 8, p. 2992–3000, 2013. 20. M. Bhaduri, J. Zhan, C. Chiu and F. Zhan, "A Novel Online and Non-Parametric Approach for Drift Detection in Big Data," in IEEE

Access, vol. 5, pp. 15883-15892, 2017.

21. S. Yardi, D. Romero, G. Schoenebeck, and D. Boyd, ―Detecting spam in a twitter network,‖ First Monday, vol. 15, nos. 1–4, pp. 1–13, Jan. 2010.

22. H. Kwak, C. Lee, H. Park, and S. Moon, ―What is twitter, a social network or a news media?‖ in Proc. 19th Int. Conf. World Wide

Web, pp. 591–600, 2010. 23. G. Stringhini, C. Kruegel, and G. Vigna, ―Detecting spammers on social networks,‖ in Proc. 26th Annu. Comput. Security Appl. Conf.,

pp. 1–9, 2010.

24. K. Lee, J. Caverlee, and S. Webb, ―Uncovering social spammers: Social honeypots + machine learning,‖ in Proc. 33rd Int. ACM SIGIR Conf. Res. Develop. Inf. Retr., pp. 435–442, 2010.

25. S. Lee and J. Kim, ―Warningbird: A near real-time detection system for suspicious URLs in twitter stream,‖ IEEE Trans. Depend. Sec.

Comput., vol. 10, no. 3, pp. 183–195, May 2013. 26. K. Thomas, C. Grier, J. Ma, V. Paxson, and D. Song, ―Design and evaluation of a real-time URL spam filtering service,‖ in Proc. IEEE

Symp. Security Privacy, 2011, pp. 447–462, 2011.

27. M. Egele, G. Stringhini, C. Kruegel, and G. Vigna, ―Compa: Detecting compromised accounts on social networks,‖ in Proc. Annu. Netw. Distrib. Syst. Security Symp., 2013.

28. Shirkhorshidi AS, Aghabozorgi S, Wah TY (2015) A Comparison Study on Similarity and Dissimilarity Measures in Clustering

Continuous Data. PLoS ONE 10(12): e0144059. https://doi.org/10.1371/journal.pone.0144059. 29. J. Gou, Y. Zhang, L. Du, and T. Xiong, ``A local mean-based k-nearest centroid neighbor classifier,'' Comput. J., vol. 55, no. 9, pp.

1058-1071, 2012.

30. C. Norrenbrock, ―Percolation threshold on planar Euclidean Gabriel graphs,‖ C. Eur. Phys. J. B (2016) 89: 111. https://doi.org/10.1140/epjb/e2016-60728-0, 2016.

31. Jean Cardinal, Sebastien Collette, Stefan Langerman ―EmptyRegionGraphs,‖ http://www.ulb.ac.be/di/algo/secollet/papers/ccl06b.pdf,

last accessed in July 10, 2018.

49.

Authors: Kamalalochana. S, Nirmala Guptha

Paper Title: Optimizing Random Forest to Detect Disease in Apple Leaf

Abstract: Green Revolution was introduced in agriculture to meet the food scarcity. Despite the increase of

agricultural production, farmers are challenged by infestations. Infestation reduced the crop yield. Traditional

method involved manual inspection of plants to identify diseases. With advancement in technology, the infested

plant leaves can be captured into images and subjected to processing by computing element. The computing

system are being trained to process the image using Machine Learning algorithms to classify the images.

Processing the image and detecting with improved accuracy is essential. Random Forest classifier is used to

detect the disease in Apple Leaf. The accuracy of prediction by Random Forest can be influenced by configuring

its parameters. This Paper talks about the various options that can be applied to optimize Random Forest

classifier for improving the accuracy of detecting Apple Leaf disease.

Keyword: Machine Learning Algorithm, Random Forest, Apple leaf disease detection. References: 1. ScienceDirect Article - https://doi.org/10.1016/j.aeaoa.2019.100008

244-249

2. AakankshaRastogi, Ritika Arora, and Shanu Sharma, proposed "Leaf Disease Detection and Grading using Computer Vision

Technology & Fuzzy Logic"2nd International Conference on Signal Processing and Integrated Networks2015. 3. Shima Ramesh, Mr. Ramachandra Hebbar,Niveditha M, Pooja R, Prasad Bhat N, Shashank Nand Mr. P V Vinod, ―Plant Disease

Detection Using Machine Learning‖ International Conference on Design Innovations for 3Cs Compute Communicate Control, 2018.

4. Sukhvir Kaur, Shreelekha Pandey and Shivani Goel, ―Semi-automatic leaf disease detection and classification system for soybean culture‖. IET Image Process., 2018, Vol. 12 Iss. 6, pp. 1038-1048

5. Jitesh P. Shah, Harshadkumar B. Prajapati and Vipul K. Dabhi―A Survey on Detection and Classification of Rice

6. Plant Diseases‖ 2016. 7. Sachin D. Khirade and A. B. Patil, ―Plant Disease Detection Using Image Processing‖International Conference on Computing

Communication Control and Automation , 2015.

50.

Authors: Girish G, M. Prabhakar

Paper Title: Device Contextual Content Publishing in Media & Publishing Industry using Big Data Analytics on

AWS

Abstract: Media & Publishing industry was traditionally a Paper and Print Industry. Since the revolution of

Internet, industry started moving print to the digital form. Ever since the rapid penetration of mobile phones the

media industry has rapidly scaled down paper publishing and adopted digital form successfully

Internet speeds have also increased the adoption of Digital Print‘s. With Newspapers being accessed globally

in its digital form, it is extremely important for publishers to keep their content readily accessible and rich for

various devices – Tablets, Laptops, Desktop‘s, Mobile Phones, Smart Watches, Digital reader‘s etc.

This Paper talks about an ECONOMICAL & HIGHLY SCALABLE Big Data analytics implementation using

AWS Elastic Map Reduce (EMR) to derive trends on end user usage patterns and choice of device. This will

help the publishers rapidly scale to provide device contextual content to end users with ever changing access

mechanisms

Keyword: WS-EMR, BigData, Device-Contextual, Media&Publishing References: 1. Amazon EMR documentation - https://docs.aws.amazon.com/emr/index.html 2. HiveSQL - https://hive.apache.org

3. Google Consumer Barometer

4. Reuters Institute Digital News Report

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51.

Authors: Mohan Kumar K N, S.Sampath, Mohammed Imran

Paper Title: An Overview on Disease Prediction for Preventive Care of Health Deterioration

Abstract: Machine learning in health care has recently made headlines. With the wide spread increase of

population, the need for reliable mechanism to prevent diseases has increased in manifold. In the recent days

there is an increase in health problems in majority of the population across the globe. The reason for health

problems is not specific but it has become very uncertain. If we take a sample from the population, it should not

be a surprise to see a person suffering from ailments irrespective of age and quality of life. For example

chronicle diseases are found in people at a very young age. So this situation poses a serious challenge for

clinical experts to find the root cause. It is difficult to accurately predict the future health based on the current

health status because the scenario might not be same for all the patients. Providing an affordable, high quality

health care service has become a big challenge. In this regard, preventive care of diseases is investigated for

decades. It is an area of regular extension of research works and progression day by day and there is sufficient

literature available on prediction of diseases. Our work includes a disciplined study to consolidate existing works

on prediction and classification of diseases. This paper will provide technical insight and paves way for future

developments in the health care field.

Keyword: Machine Learning, Health, Entropy, Disease Prediction, Diabetes, features, metrics,ICD, HCC. References: 1. World Health Organization. (2006). Constitution of the World Health Organization – Basic Documents, Forty-fifth edition,

Supplement, October 2006.

2. White, Tim (19 December 2014). "What is the Difference Between an 'Injury' and 'Disease' for Commonwealth Injury Claims?". Tindall Gask Bentley. Archived from the original on 27 October 2017. Retrieved 6 November 2017.

3. http://www.phac-aspc.gc.ca/ccdpc-cpcmc/topics/chronic-disease_e.htm

4. https://www.gnb.ca/0051/pub/pdf/2010/6960e-final.pdf 5. WHO (2008) Global Strategy on Diet, Physical Activity and Health

255-261

http://www.who.int/dietphysicalactivity/publications/facts/riskfactors/en/index.html.

6. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=Abstract&list_uids=16046654&query_hl=4&itool=pubmed_DocSum Pham H.H., Schrag D., Hargraves J.L., Bach P.B. (2005) Delivery of preventive services to older adults by primary

care physicians. JAMA. July 27; 294(4):473-81.

7. Institute of Medicine. (2001) Crossing the Quality Chasm: A new health system for the 21st century. Washington, DC: National Academy Press.

8. Schoen C., Osborn R., Huynh P.T., et al. taking the pulse of health care

9. Systems: Experiences of patients with health problems in six countries. 10. Health Affairs. Nov 2005;

11. Leatherman S., Berwick D., Iles D., et al. The business case for quality:

12. Case studies And ananalysis.HealthAffirs.2003;22(2):17-30 13. http://www.natpact.nhs.uk/eventmanager/uploads/dm eventrafaelbengoa1 .ppt

14. https://www.healthcatalyst.com/clinical-applications-of-machine-learning-in-Healthcare.

15. http://web.orionhealth.com/rs/981-HEV-035/images/Introduction_to_Machine_ Learning.pdf 16. Hatim Guermah, Tarik Fissaa, Bassma Guermah, ―Using Context Ontology and Linear SVM for Chronic Kidney Disease Prediction‖ ,

ACM LOPAL‘18, May 2018.

17. Nahla H. Barakat, Andrew P. Bradley, Mohamed Nabil H. Barakat, 18. ―Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus‖,

19. IEEE Transactions on Information Technology in Biomedicine, vol. 14,

20. no. 4, July 2010. 21. Duc-Hau Le, Manh-Hien Nguyen, ―Towards more realistic machine learning techniques for prediction of disease-associated genes‖,

ACM 9th International Symposium on Information and Communication Technology, Dec 15.

22. Varun Chandola, Sreenivas R. Sukumar, Jack Schryver, ―Knowledge Discovery from Massive Healthcare Claims Data‖, 19thACM conference on knowledge discovery and data mining, 2013.

23. Fred popowich, ―Using Text Processing and Natural Language Processing for health care claim processing‖, ACM transactions,

SIGKDD Exlorations, Volume 7, pp-59. 24. Duygu Çalis , Esin Dog antekin , ―An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine

Classifier‖, ELSEVIER Expert Systems with Applications 38 (2011) 8311–8315.

25. Anthony Quinn, Andrew Stranieri, John L. Yearwood, Gaudenz Hafen, ―A Classification Algorithm that Derives Weighted Sum Scores for Insight into Disease‖, ACM HIKM 2009.

26. Shen Song, Jim Warren, Patricia Riddle, ―Developing High Risk Clusters for Chronic Disease Events with Classification Association

Rule Mining‖, in the Proceedings of the Seventh Australasian Workshop on Health Informatics and Knowledge Management, vol. 153, 2014.

27. Darcy A. Davis, Nitesh V. Chawla, Nicholas Blumm, ―Predicting Individual Disease Risk Based on Medical History‖, in the

Proceedings of the 17th ACM conference on Information and knowledge management, 2008. 28. Darcy A. Davis, Nitesh V. Chawla, Nicholas Blumm, ―Predicting Individual Disease Risk Based on Medical History‖, in the

Proceedings of the 17th ACM conference on Information and knowledge management, 2008.

29. F. H. D. Araújo, A. M. Santana and P. Santos Neto, ―Evaluation of Classifiers Based on Decision Tree for Learning Medical Claim Process‖, IEEE Latin America Transactions, vol. 13, no. 1, Jan. 2015.

30. Deepa Gupta, Sangita Khare , Ashish Aggarwal, ―A Method to Predict Diagnostic Codes for Chronic Diseases using Machine Learning

Techniques‖, In the Proceedings of IEEE International Conference on Computing, Communication and Automation ,2016. 31. Huzaifa Shabbir Dhorajiwala, Er. Asadullah Shaikh, ―Survey on Decision Support System for Medical Diagnosis Using Data

Mining‖,International Journal of Engineering Research and General Science, vol. 3, issue 6, Dec 2015.

32. Jing Xie, Dongsheng Hu, Dahai Yu, Chung-Shiuan Chen, Jiang He, Dongfeng G, ―A quick self-assessment tool to identify individuals at high risk of type 2 diabetes in the Chinese general population‖, in the proceedings of J Epidemiol Community Health, 2010

33. M. Durairaj, G. Kalaiselvi, ―Prediction Of Diabetes Using Soft Computing Techniques- A Survey‖, International journal of scientific &

technology research,vol. 4, issue 3, March 2015. 34. D. Ashok Kumar and R. Govindasamy, ―Performance and Evaluation of Classification Data Mining Techniques in Diabetes‖,

International Journal of Computer Science and Information Technologies, Vol. 6 (2) , 2015.

35. Costas Sideris, Nabil Alshurafa, Mohammad Pourhomayoun, Farhad Shahmohammadi, Lauren Samy, Majid Sarrafzadeh, ―A Data-Driven Feature Extraction Framework for Predicting the Severity of Condition of Congestive Heart Failure Patients‖, IEEE

Transaction, 2015. 36. Asha Gowda Karegowda, Punya V, M.A.Jayaram, A.S .Manjunath,‖Rule based Classification for Diabetic Patients using Cascaded K-

Means and Decision Tree C4.5‖, International Journal of Computer Applications , vol. 45, no.12, May 2012.

37. Chih-Hung Jen , Chien-Chih Wangb, Bernard C. Jiang , Yan-Hua Chu , Ming-Shu Chen,―Application of classification techniques on development an early-warning system for chronic illnesses‖, Transaction of Expert Systems with Applications, ELSEVIER, 2012.

38. Djordje Gligorijevic , Jelena Stojanovic , Zoran Obradovic, ―Disease types discovery from a large database of inpatient records: A

sepsis study‖, ELSEVIER Expert Systems with Applications, vol 111, pg. 45-55, Dec 2016. 39. Xiang Wang, David Sontag, Fei Wang, ―Unsupervised Learning of Disease Progression Models‖, 20th ACM conference on

knowledge discovery and data mining, vol. 1, Aug 2014.

40. Dimitrios Zikos, Konstantinos Tsiakas, Fadiah Qudah, ―Evaluation of Classification Methods for the Prediction of Hospital Length of Stay using Medicare Claims data‖, ACM PETRA MAY-2014.

41. Chirag Nagpal, Kyle Miller, Tiffany Pellathy, Marilyn Hravnak, Gilles Clermont, Michael Pinsky, Artur Dubrawski, ―Semi-Supervised

Prediction of Comorbid Rare Conditions Using Medical Claims Data‖, in the Proceedings of IEEE International Conference on Data Mining Workshops, 2017.

42. S. M. Kamruzzaman, Ahmed Ryadh Hasan, Abu Bakar Siddiquee and Md. Ehsanul Hoque, Mazumder, ―Medical Diagnosis using

Neural Network‖, in the proceedings of 3 rd International Conference on Electrical & Computer Engineering-2004. 43. Syed Umar Amin, Kavita Agarwal, Dr. Rizwan Beg, ―Data Mining in Clinical Decision Support Systems for Diagnosis, Prediction and

Treatment of Heart Disease‖, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), vol. 2,

issue 1, Jan 2013.

44. Ravi S. Behra, Pranitha Pulumati, Ankur Agarwal, Ritesh Jain, Vinaya Rao,‖Predictive Modeling for Wellness and Chronic

Conditions‖, In the Proc. 14th International Conference on Bioinformatics and Bioengineering, 2014. 45. Ramona S. DeJesusa, Matthew M. Clark, Lila J. Finney Ruttena, Robert M. Jacobsona, Ivana T. Croghanb, Patrick M. Wilsong, Debra

J. Jacobsong, Sara M. Linkh, Chun Fang, Jennifer L. St. Sauver, ―Impact of a 12-week wellness coaching on self-care behaviors among

primary care adult patients with prediabetes‖, ELSEVIER, Preventive Medicine Reports, pp. 100–105, 2018. 46. Jelena Stojanovic, Djordje Gligorijevic, Vladan Radosavljevic, Nemanja Djuric, Mihajlo Grbovic, and Zoran Obradovic, ―Modeling

Healthcare Quality via Compact Representations of Electronic Health Records‖, ACM Transactions on computational biology and

bioinformatics, vol. 14, no. 3, 2017. 47. Min Chen, Yixue Hao, Kai hwang, Lu wang, Lin wang, ―Disease Prediction by Machine Learning Over Big Data From Healthcare

Communities‖ , IEEE. Translations and content mining, vol. 5, 2017.

48. Santiago Cede, Marian Goicoechea, Esther Torres, Úrsula Verdalles, Ana Pérez de José, Eduardo Verde, Soledad García de Vinuesa, José Lu, ―Cardiovascular risk prediction in chronic kidney disease patients‖, nefrolgia, pp. 7293–300. 2017

49. Paul Osemeke Nwani, Ambrose Ohumagho Isah, ―Chronic diseases and multimorbidity among elderly patients admitted in the medical

wards of a Nigerian tertiary hospital‖, ScienceDirect, Journal of Clinical Gerontology & Geriatrics, pp. 83-86, 2016. 50. Kumari Deepika, S. Seema, ―Predictive Analytics to Prevent and Control Chronic Diseases‖, IEEE Transaction, 2016.

51. Divya Jain , Vijendra Singh, ―Feature selection and classification systems for chronic disease prediction: A review‖, ELSEVIER,

Egyptian Informatics Journal 2018. 52. Danika Tremain, LukeWolfenden, Kate Bartlem, Emma Doherty, ―Provision of Chronic Disease Preventive Care in Community

Substance Use Services: Client and Clinician Report‖, ELSEVIER, Journal of Substance Abuse Treatment 68 (2016) 24–30.

53. Jane Murray Cramm, Anna Petra Nieboer, ―Short and long term improvements in quality of chronic care delivery predict program sustainability‖, ELSEVIER, Social Science & Medicine, 2014.

54. Konstantia Zarkogianni, Chih-Wen Cheng, Konstantina S. Nikita, Eleni Litsa, Konstantinos Mitsis, Po-Yen Wu, Chanchala D. Kaddi,

―A Review of Emerging Technologies for the Management of Diabetes Mellitus‖, IEEE transactions on Biomedical Engineering, vol. 62, no. 12, Dec 2015.

55. Gustavo Glusman, ―A Data-Rich Longitudinal Wellness Study for the Digital Age‖, IEEE pulse , 2017.

56. Kate Bartlem, Jenny Bowman, Kate Ross, Megan Freund, Paula Wye, Kathleen McElwaine, Karen Gillham, Emma Doherty, Luke Wolfenden, and John Wiggers, ―Mental health clinician attitudes to the provision of preventive care for chronic disease risk behaviours

and association with care provision‖, BMC Psychiatry ,2016.

57. Denise Y. S. Tam , Yvonne Y. C. Lo and Wendy Tsui, ―Knowledge, practices and expectations of preventive care: a qualitative study of patients attending government general outpatient clinics in Hong Kong.‖, BMC Family Practice, 2018

52.

Authors: Shivapanchakshari T G, H S Aravinda

Paper Title: Adaptive Resource Allocation using various Smart Antenna Techniques to maintain better System

Performance

Abstract: Smart antennas are capable of offering major contribution in improving system performance of

orthogonal frequency division multiplexing (OFDM) systems. The OFDM is an air-link technology required for

future wireless communication applications to address the technological challenges in fulfilling users demand.

The adaptive resource allocation techniques in OFDM systems using smart antennas is an optimistic approach

showing light towards developing various methods to improve spectral efficiency with required quality of

service (QoS). However, fully adaptive techniques increase the challenges in designing the physical layer with

minimum complexity. Now, the challenge is to investigate the possibility of achieving satisfactory system

performance without increasing complexity at MAC layer of next generation OFDM systems. In this paper,

methodology of designing a hybrid smart antenna system is proposed to achieve required QoS with minimum

system complexity.

Keyword: Adaptive resource allocation, Orthogonal Frequency Division Multiplex (OFDM), Quality of

Service (QoS), Hybrid Smart Antenna. References: 1. Rohling, Hermann, and Thomas May. "Comparison of PSK and DPSK Modulation in a Coded OFDM System." Vehicular Technology

Conference, 1997, IEEE 47th. Vol. 2. IEEE, 1997.

2. Hu, Honglin, Kai Guo, and Martin Weckerle. "Hybrid smart antennas for OFDM systems-a cross-layer approach." Personal, Indoor

and Mobile Radio Communications, 2005. PIMRC 2005. IEEE 16th International Symposium on. Vol. 4. IEEE, 2005.

3. Balanis, Constantine A., and Panayiotis I. Ioannides. "Introduction to smart antennas." Synthesis Lectures on Antennas 2.1 (2007): 1-175.

4. Alexiou, Angeliki, and Martin Haardt. "Smart antenna technologies for future wireless systems: trends and challenges." IEEE

communications Magazine 42.9 (2004): 90-97. 5. Shivapanchakshari, T. G., and H. S. Aravinda. "Review of Research Techniques to Improve System Performance of Smart

Antenna." Open Journal of Antennas and Propagation5.02 (2017): 83.

6. Rezk, Meriam, et al. "Performance comparison of a novel hybrid smart antenna system versus the fully adaptive and switched beam antenna arrays." IEEE Antennas and Wireless Propagation Letters 4.1 (2005): 285-288.

7. Teja, Kamurthi Ravi, and Shakti Raj Chopra. "Review Of Massive MIMO, Filter Bank Multi Carrier And Orthogonal Frequency

Division Multiplexing." (2017). 8. Hadzi-Velkov, Z., and Gavrilovska, L.: ‗Performance of the IEEE802.11 wireless LAN under influence of hidden terminals and Pareto

distributed packet traffic‘. Proc. IEEE ICPWC‘99, February 1999, pp. 221–225

262-265

9. Jakes, W.C.: in ‗Microwave mobile communications‘ (IEEE Press, New York, 1993, 2nd Edn.)

10. Hu, H.L., and Zhu, J.K.: ‗Performance analysis of distributed-antenna communication systems using beam-hopping under strong directional interference‘, Wirel. Pers. Commun., 2005, 32, (1), pp. 89–105

11. Razavilar, J., Rashid-Farrokhi, F., and Liu, K.J.R.: ‗Software radio architecture with smart antennas: a tutorial on algorithms and

complexity‘, IEEE J. Sel. Areas Commun., 1999, 17, (4), pp. 662–676. 12. Hu, Honglin, Kai Guo, and Martin Weckerle. "Hybrid smart antennas for OFDM systems-a cross-layer approach." Personal, Indoor

and Mobile Radio Communications, 2005. PIMRC 2005. IEEE 16th International Symposium on. Vol. 4. IEEE, 2005.

13. Shivapanchakshari T.G., Aravinda H.S. (2019) ―An Efficient Mechanism to Improve the Complexity and System Performance in OFDM Using Switched Beam Smart Antenna (SSA)‖. In: Silhavy R. (eds) Cybernetics and Automation Control Theory Methods in

Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 986. Springer, Cham

53.

Authors: Abhilash Manu, Aravind H S

Paper Title: Real Time Solution for Prosopagnosia

Abstract: The paper aims at building a prototype for solving the problem of a rare neurological disorder

‗Prosopagnosia‘. It is also called face blindness / facial agnosia. This is a behavioral disorder of face perception

that impairs the ability to detect familiar faces, which include one's own face, while other forms of visual

processing and intellectual functioning remain intact. The Extensive research has indicated 1 out of 50

people may have this neurological disorder. In order to help the significant number of affected people overcome

this difficulty, we have built a prototype which uses a camera to capture the image and an appropriate face

recognition code using the Histogram of Oriented Gradients (HOG) approach is implemented for face detection.

After detection, SVM classifier is used for classification and the name of the identified person will be displayed.

Simultaneously, the conversation is being recorded and text mining is performed to extract the keywords of the

conversation. The result is displayed on a suitable interface. The hardware module Raspberry Pi is used as a

processor for processing the incoming image and audio data.

Keyword: Prosopagnosia, Raspberry Pi, HOG, Healthcare applications, Internet of Things. References: 1. [1] IhorPaliy, VolodymyrDovgan, OgnianBoumbarov, Stanislav Panev, Anatoly Sachenko, YuriyKurylyak, Diana Zagorodnya, "Fast

and robust face detection and tracking framework", Intelligent Data Acquisition and Advanced Computing Systems (IDAACS) 2011

IEEE 6th International Conference on, vol. 1, pp. 430-434, 2011. 2. Jiajian Zeng, Siyuan Liu, Xi Li, DebbahAbderrahmane Mahdi, Fei Wu, Gang Wang, "Deep Context-Sensitive Facial Landmark

Detection with Tree-Structured Modeling", Image Processing IEEE Transactions on, vol. 27, pp. 2096-2107, 2018, ISSN 1057-7149.

3. Weiming Yang, Meirong Zhao, Yinguo Huang, Yelong Zheng, "Adaptive Online Learning Based Robust Visual Tracking", Access IEEE, vol. 6, pp. 14790-14798, 2018, ISSN 2169-3536.

4. Alexander Schmidt, Antoine Deleforge, Walter Kellermann, "Ego-noise reduction using a motor data-guided multichannel dictionary",

Intelligent Robots and Systems (IROS) 2016 IEEE/RSJ International Conference on, pp. 1281-1286, 2016, ISSN 2153-0866. 5. XinJin, Peng Yuan, Xiaodong Li, Chenggen Song, Shiming Ge, Geng Zhao, Yingya Chen, "Efficient privacy preserving Viola-Jones

type object detection via random base image representation", Multimedia and Expo (ICME) 2017 IEEE International Conference on,

pp. 673-678, 2017, ISSN 1945-788X. 6. Volker Eiselein, HajerFradi, Ivo Keller, Thomas Sikora, Jean-Luc Dugelay, "Enhancing human detection using crowd density

measures and an adaptive correction filter", Advanced Video and Signal Based Surveillance (AVSS) 2013 10th IEEE International

Conference 7. K Zhang Z Zhang Z. Li "Joint face detection and alignment using multitask cascaded convolutional networks [J]" IEEE signal

Processing Letters vol. 23 no. 10 pp. 1499-1503 2016.

8. P Voila M J. Jones "Rapid object detection using a boosted cascade of simple features [C]" Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (ICCVPR 2001) vol. 1 pp. 1-511-1-518 8-14 Dec. 2001.

9. Dalal, N. and Triggs, B., ―Histograms of Oriented Gradients for Human Detection,‖ IEEE Computer Society Conference on Computer

Vision and Pattern Recognition, 2005, San Diego, CA, USA.

266-271