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International Journal of Soft Computing and EngineeringInternational Journal of Soft Computing and EngineeringInternational Journal of Soft Computing and Engineering
n E d n g i na e g e n i r t i n u g p m o C t f o S I n f t eo l r n a a n r t i u o o n J a l
IJSCEIJSCE
Exploring Innovation
www.ijsce.org
EXPLORING INNOVA
TION
ISSN : 2231 - 2307Website: www.ijsce.org
Volume-7 Issue-3, July 2017Volume-7 Issue-3, July 2017
Published by: Blue Eyes Intelligence Engineering and Sciences Publication Pvt. Ltd.
Published by: Blue Eyes Intelligence Engineering and Sciences Publication Pvt. Ltd.
Editor In Chief
Dr. Shiv K Sahu
Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT)
Director, Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., Bhopal (M.P.), India
Dr. Shachi Sahu
Ph.D. (Chemistry), M.Sc. (Organic Chemistry)
Additional Director, Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., Bhopal (M.P.), India
Vice Editor In Chief
Dr. Vahid Nourani
Professor, Faculty of Civil Engineering, University of Tabriz, Iran
Prof. (Dr.) Anuranjan Misra
Professor & Head, Computer Science & Engineering and Information Technology & Engineering, Noida International University,
Noida (U.P.), India
Advisory Chair
Dr. Deepak Garg
Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India, Senior Member of IEEE,
Secretary of IEEE Computer Society (Delhi Section), Life Member of Computer Society of India (CSI), Indian Society of Technical
Education (ISTE), Indian Science Congress Association Kolkata.
Dr. Vijay Anant Athavale
Director of SVS Group of Institutions, Mawana, Meerut (U.P.) India/ U.P. Technical University, India
Dr. T.C. Manjunath
Principal & Professor, HKBK College of Engg, Nagawara, Arabic College Road, Bengaluru-560045, Karnataka, India
Dr. Kosta Yogeshwar Prasad
Director, Technical Campus, Marwadi Education Foundation’s Group of Institutions, Rajkot-Morbi Highway, Gauridad, Rajkot,
Gujarat, India
Dr. Dinesh Varshney
Director of College Development Counceling, Devi Ahilya University, Indore (M.P.), Professor, School of Physics, Devi Ahilya
University, Indore (M.P.), and Regional Director, Madhya Pradesh Bhoj (Open) University, Indore (M.P.), India
Technical Chair
Dr. Haw Su Cheng
Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia, 63100 Cyberjaya
Dr. Hossein Rajabalipour Cheshmehgaz
Industrial Modeling and Computing Department, Faculty of Computer Science and Information Systems, Universiti Teknologi
Malaysia (UTM) 81310, Skudai, Malaysia
Dr. Sudhinder Singh Chowhan
Associate Professor, Institute of Management and Computer Science, NIMS University, Jaipur (Rajasthan), India
Dr. Neeta Sharma
Professor & Head, Department of Communication Skils, Technocrat Institute of Technology, Bhopal(M.P.), India
Dr. Ashish Rastogi
Associate Professor, Department of CSIT, Guru Ghansi Das University, Bilaspur (C.G.), India
Dr. Santosh Kumar Nanda
Professor, Department of Computer Science and Engineering, Eastern Academy of Science and Technology (EAST), Khurda (Orisa),
India
Dr. Hai Shanker Hota
Associate Professor, Department of CSIT, Guru Ghansi Das University, Bilaspur (C.G.), India
Dr. Sunil Kumar Singla
Professor, Department of Electrical and Instrumentation Engineering, Thapar University, Patiala (Punjab), India
Dr. A. K. Verma
Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India
Dr. Durgesh Mishra
Chairman, IEEE Computer Society Chapter Bombay Section, Chairman IEEE MP Subsection, Professor & Dean (R&D), Acropolis
Institute of Technology, Indore (M.P.), India
Managing Chair
Mr. Jitendra Kumar Sen
International Journal of Soft Computing and Engineering (IJSCE)
Reviewer Chair
Dr. R. Devi Priya
Associate Professor, Department of Information Technology, Kongu Engineering College, Erode, Tamil Nadu-638052, India.
Dr. P. Rathnakumar
Professor & Head, Department of Mechanical Engineering, Navodaya Institute of Technology, Raichur, Karnataka 584103, India.
Dr. Abhinav Vidwans
Associate Professor, Department of Computer Science and Egineering, Vikrant Group of Institutions Campus, Morar, Gwalior
474001, India.
Dr. A. K. Priya
Associate Professor, Department of Civil Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore, Tamil
Nadu 641407, India.
Dr. K Ashok Reddy
Associate Professor, Department of Mechanical Engineering, MLR Institute of Technology, Hyderabad, Telangana, India.
Dr. T. V. Surya Narayana
Assistant Professor, Department of Information Technology, Manipal University, SMUDDE, Gangtok, Sikkim, India.
Dr. Srinivasa Raju Rallabandi
Assistant Professor, Department of Mathematics, Gandhi Institute of Technology and Management, Hyderabad (Telangana). India.
Dr. Deepika Garg
Assistant Professor, Department of Applied Science, GD Goenka University, Gurgaon, Haryana-122103. India.
Dr. Girish Madhukar Tere
Assistant Professor, Department of Computer Science, Thakur College of Science and Commerce, Affiliated to University of Mumbai,
Mumbai, Maharashtra-400098, India.
Dr. Sameh G.Salem
Associate Professor, Department of Electrical Engineering, Military Technical College, Cairo Governorate, Egypt.
Dr. Abhishek Singh
Associate Professor, Department of Mathematics, African Institute for Agrarian Studies, Amity University, Noida- 201304. (U.P).
India.
Dr. Kompella Venkata Ramana
Associate Professor, Department of Computer Science and Systems Engineering, Engineering College, Andhra University,
Visakhapatnam (A.P.)-530003. India.
Dr. Bala Siddulu Malga
Assistant Professor, Department of Mathematics, Gandhi Institute of Technology and Management, Visakhapatnam (Andhra
Pradesh)-530045. India.
Dr. Meeravali Shaik
Professor, Department of Master of Business Administration, Rise Krishna Sai Prakasam Group of Institutions, Valluru, Ongole,
(A.P.)-523272. India.
Dr. Mohammad Valipour
Assistant Professor, Department of Water Sciences and Engineering, Payame Noor University, Tehran, Iran.
Dr. Arvind Kumar Drave
Associate Professor, Department of Mechanical Engineering, Indian Institute of Technology, Kanpur (Uttar Pradesh)-208016. India.
Dr. Krishna Banana
Assistant Professor, Department of Commerce and Business Administration, Acharya Nagajuna University Ongole Campus, Ongole.
Prakasam (Andra Pradesh). India.
Dr. Christo Ananth
Associate Professor, Department of Electrical & Communication Engineering, Francis Xavier Engineering College, Tirunelveli (Tamil
Nadu)-627003. India.
Dr. Dhananjaya Reddy
Assistant Professor, Department of Mathematics, Govt. Degree College, Puttur (Andhra Pradesh)-517583. India.
Dr. Gamal Abd El-Nasser Ahmed Mohamed Said
Department of Computer and Information Technology, Arab Academy for Science and Technology and Maritime Transport
(AASTMT) Alexandria, Egypt.
Dr. Srijit Biswas
Professor, Department of Civil Engineering, Manav Rachna International University, Faridabad (Haryana)-121004, India.
Dr. K. Suresh Babu
Professor & HOD, Department of Computer Science & Engineering, RISE Krishna Sai Prakasam Group of Institutions, Ongole
(Andhra Pradesh)-523272, India.
Dr. K. Krisnaveni
Associate Professor, Department of Computer Science, Sri S. Ramaswamy Naidu Memorial College, Sattur, Virudhunagar Dist,
(Tamil Nadu) India.
Dr. R. Venkat Reddy
Professor, Department of Mechanical Engineering, Anurag Group of Institutions (CVSR), Venkatapur (Telangana)-501301, India.
Dr. Hamid Ali Abed AL-Asadi
Professor, Department of Computer Science, Faculty of Education for Pure Science, Basra University, Basra, Iraq.
S.
No
Volume-7 Issue-3, July 2017, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.
Page
No.
1.
Authors: Vipul Dalal, Latesh Malik
Paper Title: Data Clustering Approach for Automatic Text Summarization of Hindi Documents using Particle
Swarm Optimization and Semantic Graph
Abstract: Automatic text summarization is a process of describing important information from given document
using intelligent algorithms. A lot of methods have been proposed by researchers for summarization of English text.
Automatic summarization of Indian text has received a very little attention so far. In this paper, we have proposed a
data clustering approach for summarizing Hindi text using semantic graph of the document and Particle Swarm
Optimization (PSO) algorithm. PSO is one of the most powerful bio-inspired algorithms used to obtain optimal
solution. The subject-object-verb (SOV) triples are extracted from the document. These triples are used to construct
semantic graph of the document and finally clustered into summary and non-summary groups. A classifier is
trained using PSO algorithm which is then used to obtain document summary.
Keywords: bio-inspired algorithms, text mining, text summarization, semantic graph, PSO, data clustering
References: 1. LUHN, H.P., 1958. “THE AUTOMATIC CREATION OF LITERATURE ABSTRACTS”. IBM J. RES. DEVELOP., 2: 159-165. 2. P. B. Baxendale, "Machine-made index for technical literature: an experiment," IBM J. Res. Dev., vol. 2, pp. 354-361, 1958.
3. Edmundson, H. P. (1969). New methods in automatic extracting. Journal of the ACM, 16(2):264-285.
4. Lin, C.Y. 1999. “Training a selection function for extraction”. Proceedings of the 18th Annual International ACM Conference on Information and Knowledge Management, pp:55-62.
5. Massih R. Amini, Nicolas Usunier, and Patrick Gallinari, "Automatic Text Summarization Based on Word-Clusters and Ranking
Algorithms", ECIR 2005, LNCS 3408, pp. 142–156, (2005). 6. Rafeeq Al-Hashemi, "Text Summarization Extraction System (TSES) Using Extracted Keywords", International Arab Journal of e-
Technology, Vol. 1, No. 4, June, pp. 164-168, (2010).
7. Jade Goldstein, Jaime Carbonell. “SUMMARIZATION: (1) USING MMR FOR DIVERSITY- BASED RERANKING AND (2) EVALUATING SUMMARIES”. Carnegie Group Inc.'s Tipster III Summarization Project
8. Aysun Güran, Eren Bekar, Selim Akyokuş “A Comparison of Feature and Semantic-Based Summarization Algorithms or Turkish”. INISTA
2010, International Symposium on Innovations in Intelligent Systems and Applicaitons, 21-24June 2010, Kayseri & Cappadocia,TURKEY. 9. Ono, K., Sumita, K., and Miike, S. (1994). “Abstract generation based on rhetorical structure extraction.” In Proceedings of Coling '94,
pages 344{348, Morristown,NJ, USA.
10. Marcu, D. (1998a). “Improving summarization through rhetorical parsing tuning”. In Proceedings of The Sixth Workshop on Very Large Corpora, pages 206-215, pages 206,215, Montreal, Canada.
11. Giuseppe Carenini and Jackie Chi Kit Cheung, “Extractive vs. NLG-based Abstractive Summarization of Evaluative Text: The Effect of
Corpus Controversiality”. 12. Pierre-Etienne Genest, Guy Lapalme, “Framework for Abstractive Summarization using Text-to-Text Generation”, Workshop on
Monolingual Text-To-Text Generation, pages 64–73,Proceedings of the 49th Annual Meeting of the Association for Computational
Linguistics, pages 64–73,Portland, Oregon, 24 June 2011. c 2011 Association for Computational Linguistics. 13. Vipul Dalal, Dr. Latesh Malik.: “A Survey of Extractive & Abstractive Text Summarization”, 6th International Conference on Emerging
Trends in Engineering & Tecnology (ICETET), 2013
14. M. S. Binwahlan, Salim, N., & Suanmali, L.: “Swarm based features selection for text summarization”, International Journal of Computer Science and Network Security IJCSNS, vol. 9, pp. 175-179, 2009b.
15. M. S. Binwahlan, Salim, N., & Suanmali, L.: “Swarm Based Text Summarization”, Computer Science and Information Technology –
Spring Conference, 2009. IACSITSC '09. International Association of, 2009, pp. 145-150. 16. Albaraa Abuobieda M. Ali, Naomie Salim, Rihab Eltayeb Ahmed, Mohammed Salem Binwahlan, Ladda Sunamali, Ahmed Hamza.:
“Pseudo Genetic And Probabilistic-Based Feature Selection Method For Extractive Single Document Summarization”, Journal of
Theoretical and Applied Information Technology, 15th October 2011. Vol. 32 No.1, ISSN: 1992-8645, E-ISSN: 1817-3195. 17. Alkesh Patel, Tanveer Siddiqui, U. S. Tiwary.: “A language independent approach to multilingual text summarization”, Conference
RIAO2007, Pittsburgh PA, U.S.A. May 30-June 1, 2007 - Copyright C.I.D. Paris, France
18. Naresh Kumar Nagwani, Shrish Verma.: “A Frequent Term and Semantic Similarity based Single Document Text Summarization Algorithm”, International Journal of Computer Applications (0975 – 8887) Volume 17– No.2, March 2011.
19. Kamal Sarkar.: “Bengali Text Summarization By Sentence Extraction”
20. Upendra Mishra, Chandra Prakash.: MAULIK: “An Effective Stemmer for Hindi Language”, International Journal on Computer Science and Engineering (IJCSE), ISSN : 0975-3397, Vol. 4 No. 05 May 2012
21. Vishal Gupta, Gurpreet Singh Lehal.: “Preprocessing Phase of Punjabi language Text Summarization”
22. Jurij Leskovec, Natasa Milic-Frayling, Marko Grobelnik.: “Extracting Summary Sentences Based on the Document Semantic Graph, Microsoft Research, Microsoft Corporation
23. Regina Barzilay, Michael Elhadad.: “Using Lexical Chains for Text Summarization”, In Proceedings of the Intelligent Scalable Text
Summarization Workshop (ISTS'97). Madrid: ACL, 1997. 10-17. 24. Kavita Ganesan, ChengXiang Zhai, Jiawei Han.: “Opinosis: A Graph-Based Approach to Abstractive Summarization of Highly Redundant
Opinions”.
25. Eduard Hovy and Chin-Yew Lin.: “Automated Text Summarization in SUMMARIST”, In I. Mani and M. Maybury (eds), Advances in Automated Text Summarization. MIT Press.
26. Udo Hahn, Inderjeet Mani. : “The Challenges of Automatic Text Summarization”, IEEE Computer Society Press Los Alamitos, CA, USA, Volume 33 Issue 11, November 2000, Page 29-36 ISSN:0018-9162.
27. Chetana Thaokar, Latesh Malik, “Test Model for Summarizing Hindi Text using Extraction Method”, Proceedings of 2013 IEEE
Conference on Information and Communication Technologies (ICT 2013). 28. Reddy Siva. Natural Language Processing Tools. December. 2012 URL: http://sivareddy.in/downloads
1-3
2.
Authors: Haeeder Munther Noman
Paper Title: PCF and DCF Performances Evaluation for a Non Transition 802.11 Wireless Network using OPNET
Modular
Abstract: Wireless Local Area Networks (WLANs) take increased a percentage of acceptance as they can offer an
access to independent site network among computing systems. IEEE 802.11 WLAN is the best organized wireless
knowledge with probably show a key role in the wireless tele-communication networks for the next generation.
Many access techniques have been utilized in Wireless Networks, mainly DCF in addition to PCF can be the
4-8
essential access methods. Main features to the 802.11 WLAN technologies deal with simplicities, flexibilities, and
effectiveness of cost. 802.11 standards specify Many _mechanisms of essential access: Distributed Coordination
Function (DCF) and Point Coordination Function (PCF) present in the MAC layer of the OSI Protocol stack. This
paper mainly deals with a performance presented at these mechanisms from where the end to end delay, throughput
and average delays.
Keywords: Wireless LAN, IEEE 802.11, DCF, PCF, Opnet Simulator.
References: 1. Bhaskar, B. Mallick, “Performance Evaluation of MAC Protocol for IEEE 802.11, 802.11Ext. WLAN and IEEE 802.15.4 WPAN using NS-
2”, International Journal of Computer Applications, Volume 119 – No.16, June 2015. 2. Boskovic, B. and Markovic, M. (2000). On Spread Spectrum Modulation Techniques Applied in IEEE 802.11 Wireless LAN Standard. 4,
238-241.
3. Kaur, M. Bala, H. Bajaj, “Performance Evaluation of Wlan by Varying Pcf, Dcf and Enhanced Dcf Slots to Improve Quality of Service”, IOSR Journal of Computer Engineering (IOSRJCE), Vol. 2, Issue 5 (July-Aug. 2012), PP 29-33.
4. Sarah Shaaban, Dr. Hesham M. El Badawy, Prof. Dr. Attallah Hashad, "Performance Evaluation of the IEEE 802.11 Wireless LAN
Standards," Proceedings of World Congress on Engineering, vol. I, 2-4, 2008. 5. J. Alonso-Zárate, C. Crespo, Ch.Skianis, L. Alonso, Ch. Verikoukis, “Distributed Point Coordination Function for IEEE 802.11 Wireless Ad
hoc Networks”, Elsevier Ad Hoc Networks Journal, October 2011, doi:10.1016/j.adhoc.2011.09.004.
6. Moustafa A. Youssef, Arunchandar Vasan, Raymond E. Miller, "Specification and analysis of the DCF and PCF protocols in the 802.11
standard using systems of communicating machines", 2002, ISSN:1092- 1648,pp:132 – 141.Symposium, 4, 11-14.Telecommunications
Review-4, 5, 287-291.
7. Mohammad Hussain Ali, Manal Kadhim Odah, “Simulation Study 0f 802.11b DCF Using OPNET Simulator”, Eng. & Tech. Journal, Vol. 27, No6, 2009.
8. N. Singha, K. Aroraa, S. Goyal, “Performance of Wireless LAN in DCF and EDCF using OPNET”, IJESM Vol.2, No.3 (2012).
9. I.Kaur, M. Bala, H. Bajaj, “Performance Evaluation of Wlan by Varying Pcf, Dcf and Enhanced Dcf Slots To Improve Quality of Service”, IOSR Journal of Computer Engineering (IOSRJCE), Volume 2, Issue 5 (July-Aug. 2012), PP 29-33.
10. OPNET LABS, “Creating Wireless Network,” 200.
3.
Authors: Neety Bansal, Parvinder Kaur
Paper Title: A Survey on Soft Computing Based Approaches for Fuzzy Model Identification
Abstract: The identification of an optimized fuzzy model is one of the key issues in the field of fuzzy system
modeling. This can be formulated as a search and optimisation problem and many hard computing as well as soft
computing approaches are available in the literature to solve this problem. In this paper we have made an attempt to
present a survey on fuzzy model identification using some soft computing techniques like ACO, BBO, BB-BC,
ABC, etc.
Keywords: Fuzzy system, Fuzzy model identification, Soft computing, Nature inspired approaches.
References: 1. L.A.Zadeh, “Fuzzy Sets,” Information and Control, Vol.8, pp. 338-353, 1965.
2. John Yen and Reza Langari, “Fuzzy Logic Intelligence, Control and Information,” Prentice Hall, New Jersey, 1999.
3. Plamen A. et al., “Identification of Evolving Fuzzy Rule-Based Models,” IEEE Transactions on Fuzzy Systems, Vol. 10, No.5, pp.667-677, 2002.
4. P. Bhalla et al., “Soft Computing Approaches to Fuzzy System Identification: A Survey,” 3rd International Conference on Intelligent
Systems and Networks (IISN-2009), February 14-16,2009. 5. L.A. Zadeh, “Fuzzy logic, neural networks, and soft computing,” Commun. ACM, vol. 37, pp. 77-84, 1994.
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systems,” IEEE Trans. Neural Networks, vol. 9, pp. 756-767, 1998. 7. C.L. Karr and E.J. Gentry, “Fuzzy Control of pH using genetic algorithms,” IEEE Transactions on Fuzzy Systems, Vol. 1, No. 1, pp.46-53,
1993.
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Transactions on Evolutionary Computation, vol. 7, no. 4, Aug. 2003. 10. Andreas Bastian, “Identifying fuzzy models utilizing genetic programming”, Fuzzy sets and systems– Elsevier, 2000.
11. M. Setnes and H. Roubos, “GA-fuzzy modeling and classification: complexity and performance”, IEEE transactions on Fuzzy Systems,
vol. 8, 2000. 12. Haralambos Sarimveis, George Bafas, “Fuzzy model predictive control of non-linear processes using genetic algorithms”, Fuzzy sets and
systems– Elsevier, Oct. 2002.
13. Eghbal G. Mansoori, M.J. Zolghadri and S.D. Katebi, “SGERD: A steady-state genetic algorithm for extracting fuzzy classification rules from data,” IEEE Transactions on Fuzzy Systems, Vol.16, No.4, pp. 1061-1071, Aug. 2008.
14. Z. Ning, Y S. Ong, K.W. Wong and K.T. Seow, “Parameter identification using Memetic algorithms for fuzzy systems,” Proc. of the fourth
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219, 1998. 17. L. Fu, “Rule generation from neural networks,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 24, No. 8, pp. 1114-1124, Aug.
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18. R. Setiono and W. K. Leow, “FERNN: An algorithm for fast extraction of rules from neural networks,” Appl. Intell., 2000, to be published. 19. R. Setiono and H. Liu, “Neuro Linear: From neural networks to oblique decision rules,” Neurocomputation, vol. 17, pp. 1–24, 1997.
20. Wen Yu and Xiaoou Li, “Fuzzy Identification Using Fuzzy Neural Networks With Stable Learning Algorithms”, IEEE Transactions On
Fuzzy Systems, Vol. 12, No. 3, June 2004. 21. G Leng, TM McGinnity, G Prasad, “An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network”, Fuzzy
sets and systems- Elsevier, 2005. 22. Rahib Hidayat Abiyev, Okyay Kaynak, “Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel
Structure and a Comparative Study”, IEEE Transactions on Industrial Electronics, Vol. 55, Issue: 8, Aug. 2008.
23. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. New York: Oxford University Press.
24. He Zhenya, Wei Chengjian, Yang Luxi, Gao Xiqi, Yao Susu, R.C. Eberhart, Yuhui Shi, “Extracting rules from fuzzy neural network by
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particle swarm optimisation”, Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, 1998. 25. R. Marinke ; E. Araujo ; Ld.S. Coelho ; I. Matiko, “Particle swarm optimization (PSO) applied to fuzzy modeling in a thermal-vacuum
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27. Arun Khosla, Shakti Kumar, K.K.Aggarwal, Jagatpreet Singh, “Particle Swarm Optimizer for building fuzzy models,” Proceeding of one
week workshop on applied soft computing SOCO-2005, Haryana Engg.College, Jagadhri, India, July 25-30, pp 43-71, 2005. 28. Ernesto Araujo, Leandro dos S. Coelho, “Particle swarm approaches using Lozi map chaotic sequences to fuzzy modeling of an
experimental thermal-vacuum system”, Applied Soft Computing- Elsevier, October 2007.
29. Marco Dorigo and Thomas Stutzle, Ant Colony Optimization, Eastern Economy Edition, PHI, 2005. 30. J. Casillas, O. Cordon and F. Herrera, “Learning fuzzy rules using ant colony optimization algorithms,” Proc. 2nd Int. Workshop Ant
Algorithms, 2000, pp. 13-21.
31. Shakti Kumar, “Rulebase generation using ant colony optimization,” Proc. of the one-week workshop on applied soft computing (SOCO-2006), Haryana Engg. College, Jgadhri, July 2006.
32. Shakti K., P. Bhalla and S.Sharma, “Automatic Fuzzy Rule-base Generation for Intersystem Handover using Ant Colony Optimization
Algorithm,” International Conference on Intelligent Systems and Networks (IISN-2007), Feb 23-25, 2007, MAIMT, Jagadhri, Haryana, India, pp. 764-773.
33. Shakti K., P. Bhalla, “Fuzzy Rulebase Generation from Numerical Data using Ant Colony Optimization,” MAIMT- Journal of IT &
Management. Vol.1, No.1 May - Oct. 2007, pp. 33-47. 34. Chia-Feng Juang ; Po-Han Chang, “Designing Fuzzy-Rule-Based Systems Using Continuous Ant-Colony Optimization”, IEEE
Transactions on Fuzzy Systems (Vol.: 18, Issue: 1, Feb. 2010).
35. SM Vieira, JMC Sousa, TA Runkler, “Two cooperative ant colonies for feature selection using fuzzy models”, Expert Systems with
Applications, 2010 – Elsevier.
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713. 37. S. Kumar, P. Bhalla, A. Singh, “Fuzzy Rule base Generation from Numerical Data using Biogeography-based Optimization”, Journal of
Institution of Engineers IE (I), vol. 90, pp. 8-13, July 2009.
38. Yu-Jun Zheng ; Hai-Feng Ling ; Sheng-Yong Chen ; Jin-Yun Xue, “A Hybrid Neuro-Fuzzy Network Based on Differential Biogeography-Based Optimization for Online Population Classification in Earthquakes”, IEEE Transactions on Fuzzy Systems ( Volume: 23, Issue: 4,
Aug. 2015 ).
39. Erol, O. K. and Eksin, I. 2006. A new optimization method: Big Bang-Big Crunch, Advances in Engineering Software. 37(2): 106-111. 40. S. Kumar, P. Bhalla, A. Singh, “Fuzzy Rule base Generation from Numerical Data using Big Bang-Big Crunch Optimization”, Journal of
Institution of Engineers IE (I), vol. 91, pp. 18-25, January 2011.
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42. S. Kumar, S.S Walia, A. Singh, “Parallel Big Bang-Big Crunch Algorithm”, International Journal of Advanced Computing, ISSN:2051-
0845,Vol.46, Issue.3, Sept. 2013. 43. Engin Yesil, “Interval type-2 fuzzy PID load frequency controller using Big Bang–Big Crunch optimization”, Applied Soft Computing,
Volume 15, February 2014, Pages 100–112.
44. Karaboga, D. 2005. An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University,
Engineering Faculty, Computer Engineering Department.
45. S. Kumar, P. Bhalla, A. Singh, “Fuzzy Rule base Generation: An Artificial Bee Colony Approach”, 5th International Multi Conference on Intelligent Systems, Sustainable, New and Renewable Energy Technology and Nanotechnology (IISN-2011), February 18 -20, 2011.
46. Dervis Karaboga and Celal Ozturk, “Fuzzy clustering with artificial bee colony algorithm”, Scientific Research and Essays Vol. 5(14), pp.
1899-1902, 18 July, 2010. 47. Fayssal Beloufa, M.A. Chikh, “Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm”, Computer
methods and programs in biomedicine, 2013 – Elsevier.
48. Hacene Habbi, Yassine Boudouaoui, Dervis Karaboga, Celal Ozturk, “Self-generated fuzzy systems design using artificial bee colony optimization”, Information Sciences, 2015 – Elsevier.
49. S. Kumar, S.S. Walia, P. Kaur, “Fuzzy System Identification: A Firefly Optimisation Approach”, International Journal of Electronics and
Communication Engineering (IJECE) ISSN(P): 2278-9901; ISSN(E): 2278-991X Vol. 3, Issue 6, Nov 2014. 50. N. Susila, S. Chandramathi, Rohit Kishore, “A Fuzzy-based Firefly Algorithm for Dynamic Load Balancing in Cloud Computing
Environment”, Journal Of Emerging Technologies In Web Intelligence, Vol. 6, No. 4, November 2014.
51. Nguyen Cong Long and Phayung Meesad, “An optimal design for type–2 fuzzy logic system using hybrid of chaos firefly algorithm and genetic algorithm and its application to sea level prediction”, Journal of Intelligent & Fuzzy Systems 27 (2014) 1335–1346.
52. K. Mohana Sundaram, R. Senthil Kumar, C. Krishnakumar and K. R. Sugavanam, “Fuzzy Logic and Firefly Algorithm based Hybrid
System for Energy Efficient Operation of Three Phase Induction Motor Drives”, Indian Journal of Science and Technology, Vol 9(1), January 2016.
53. P. Kaur, S. Kumar, A.P. Singh, “Nature Inspired Approaches for Identification of Optimized Fuzzy Model: A Comparative Study”, J. of
Mult.-Valued Logic & Soft Computing, Vol. 25, pp. 555-587, March 2014. 54. Sana Bouzaida, Anis Sakly and Faouzi M’Sahli, “Extracting TSK-type Neuro-Fuzzy model using the Hunting search algorithm”,
International Journal of General Systems, Vol. 43, 2014.
4.
Authors: Amrapali Bansal, A. K. Upadhyay
Paper Title: Microsoft Power BI
Abstract: With the changeable business circumstances, the significance of Business Intelligence has gained lots of
deliberation. Business Intelligence tools can provide the standarization with a fast and persuasive decision making
process based on the multiple data sources, which might be able to affect the survival of the organization on the
market. And because of the changes in the extrinsic business environment and the profession needs, a new access of
BI solution, Self-service BI solution, is introduced and during the last few years, the number of the market players
using the approach has increased expeditiously. The objective of this paper was to build a BI solution according to
one of Self-service BI solutions: Power BI presents by Microsoft, one of the leading professionals in the area of
MSBI. This research contains two parts. The first part is the theory package which covers the BI and Self-service BI
approaches in order to provide the readers with an overall under-standing of these concepts. It also sets up the
understructure for the empirical part of this research paper project. The research paper started with analyzing BI and
Self-service BI and the relationship between them. After this, the Microsoft BI solution was introduced before
moving to the back-ground facts about Power BI. The second part of this research represents how to use Power BI to
build a best BI solution based on the business scenario. During this testing process, the compulsory steps for
building a BI solution were popularized also covering the main range of capabilities in the tool package. The
consequence of this research paper was a BI solution built using Power BI and it met the requirements set for it. The
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observational case presented in this study can be used as a supportive user guide for all those who are concerned
about Microsoft Power BI.
Keywords: Auto-Scheduling; analytics; exploring; Integration; impactful; Intelligence; Visualization
References: 1. https://support.office.com/en-us/article/Power-BI-Getting-Started-Guides-bd30711a-7ccf-49e8-aafa-2e8f481e675d?ui=en-US&rs=en-
US&ad=US
2. https://powerbi.microsoft.com/en-us/documentation/powerbi-desktop-getting-started/
3. Microsoft Community : http://community.powerbi.com/
5.
Authors: Fernandes R. J, Javali F. M, Patil S. B
Paper Title: Analysis and Design of Reinforced Concrete Beams and Columns using open STAAD
Abstract: Structural designers especially in India use STAAD software to execute the structural analysis, but for
the design purpose still manual calculations and excel spread sheets are being used. It leads to cumbersome and time
consuming process to obtain analysis results from STAAD Pro to design calculations, hence to automate this process
an MS Excel spread sheet has been developed. A vba program has been developed to access the analysis results from
STAAD Pro to MS Excel such that the design process is fully automated which reduces manual interference.
Keywords: MS Excel, Open STAAD, VBA, IS 456:2000, Analysis, Design, Beam, Column.
References: 1. Jonathan Meyer, “SCR Pile Cap Foundation Design Using STAAD v8i & Excel,” Structures Congress 2011, pp. 2485-2495, April 2012
2. Ishwaragouda S. Patil and Dr. Satish A. Annigeri, “Introduction to PSA as a Free Structural Analysis Software,” Bonfring International Journal of Man Machine Interface, Vol. 4, Special Issue, July 2016
3. P.Mujumdar and J. U. Maheswari, “Integrated Framework for Automating the Structural Design Iteration,” Proceedings of the International
Symposium on Automation and Robotics in Construction, 2015 4. Purva Mujumdar and Vasant Matsagar, “Design Optimization of Steel Members Using Openstaad and Genetic Algorithm,” Advances in
Structural Engineering, V. Matsagar (ed.), Springer India 2015, pp.233-244
5. Bentley, “OpenSTAAD V8i (SELECT series 4) Reference manual,” 2012. Available: http://www.bentley.com 6. Bentley, STAAD Pro V8i. Available: http://www.bentley.com
7. Microsoft Excel. Visual Basic Applications for Excel. http://www.office.microsoft.com
8. Tim Burnett (2009, November), “VBA for office 2010,” Kingfisher Computer Consulting [online]. https://msdn.microsoft.com/en-us/library/office/ee814735(v=office.14).aspx#VBA Programming 101
9. IS 456:2000, “Indian standard code of practice for plain and reinforced concrete – code of practice,” Bureau of Indian Standards, New
Delhi, 2000. 10. SP 16-1980, “Design aids for reinforced concrete to IS 456-1978,” Bureau of Indian Standards, New Delhi, 1980.
11. SP 24-1983, “Explanatory Handbook on Indian standard code of practice for plain and reinforced concrete (IS 456-1978),” Bureau of Indian
Standards, New Delhi, 1983. 12. IS 875(Part 1)-1987, “Design Loads (Other than Earthquake) For Buildings and Structures,” Bureau of Indian Standards, New Delhi, 1987.
13. IS 875(Part 2)-1987, “Design Loads (Other than Earthquake) For Buildings and Structures,” Bureau of Indian Standards, New Delhi, 1987.
14. Guy Hart-Davis, “Mastering VBA,” 2nd edition, WILEY dreamtech, 2006. 15. Website:http://www.civilnstructural.com/soft-tools/(OpenSTAAD learning videos).
16. Website: https://www.excelcampus.com/ (MS Excel VBA coding_ language).
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6.
Authors: Chander Shekhar Devra
Paper Title: Issues and Challenges with Product Life Cycle Management (PLM) System Implementation Guidelines
Abstract: Deployment of PLM System is today’s need for all commercial organizations. For successful
implementation of PLM system solution, Commercial organisation adopt various available implementation
guidelines. Sometime available implementation guidelines may results into un-successful implementation/ re-
implementation. Each Unsuccessful / re-implementation leads to waste of time, money & efforts. There is a need for
analysis of current available implementation guidelines with bit detailing in real PLM implementation project in
Indian context specifically process manufacturing industry. Paper will provide reliable guideline for successful PLM
implementation specific to Indian Process manufacturing Industries. It will reduce the failure rate of PLM
implementation. It will provide faster PLM implementation. It will save cost & efforts for implementation.
Keywords: PLM System, successful, specifically process manufacturing industry.
References: 1. Stark, J. (2004) Product Lifecycle Management: 21st century Paradigm for Product Realisation, Springer-Verlag, New York. 2. Mattias B. (2012) „Evaluating PLM Implementations Using a Guidelines-based Approach‟ thesis for the degree of licentiate of engineering,
Department of Product and Production Development, Chalmers University of Technology, Gothenburg, Sweden.
3. Pikosz, P., Malmström, J. and Malmqvist, J. (1997) „Strategies for introducing PDM systems in engineering companies‟, Advances in Concurrent Engineering – CE97, 20–22 August, Rochester Hills, MI, USA, pp.425–434
4. Rangan, R., Rohde, S., Peak, R., Chadha, B. and Bliznakov, P. (2005) „Streamlining product lifecycle processes: a survey of product
lifecycle management implementations, directions, and challenges‟, Journal of Computing and Information Science in Engineering, Vol. 5, No. 3, pp.227–237
5. Jennings, M. and Rangan, R. (2004) „Managing complex vehicle system simulation models for manufacturing system development‟,
Journal of Computing and Information Science in Engineering, Vol. 4, No. 4, pp.372–378. 6. Illback, J. and Sholberg, J. (2000) „Application integration in the Boeing enterprise‟, Paper presented at the Fourth International Enterprise
Distributed Object Computing Conference (EDOC 2000), 25–28 September, Makuhari, Japan
7. Chadha, B. and Welsh, J. (2000) „Architecture concepts for simulation-based acquisition of complex systems‟, Paper presented at the 2000 Summer Computer Simulation Conference, 16–20 July, Vancouver, Canada.
8. Grieves, M. (2006) Product Lifecycle Management: Driving the Next Generation of Lean Thinking, McGraw-Hill, New York.
9. Brown, C. and Vessey, I. (2003) „Managing the next wave of enterprise systems: leveraging lessons from ERP‟, MIS Quarterly Executive, Vol. 2, No. 1, pp.65–77.
10. Wognum, P. and Kerssens-van Drongelen, I. (2005) „Process and impact of product data management implementation‟, International
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Journal of Product Development, Vol. 2, Nos. 1–2, pp.5–23. 11. Hartman, N. and Miller, C. (2006) „Examining industry perspectives related to legacy data and technology toolset implementation‟,
Engineering Design Graphics Journal, Vol. 70, No. 3, pp.12–21.
12. Berle, A. (2006) „PLM development and implementation at Volvo 3P, using Catia V5 and Enovia V5‟, Paper Presented at the 1st Nordic Conference on Product Lifecycle Management, 25–26 January, Gothenburg, Sweden.
13. Zimmerman, T. (2008) „Implementing PLM across organization – for multi-disciplinary and cross-functional product development‟, PhD
thesis, Chalmers University of Technology, Gothenburg, Sweden. 14. Aras implementation methodology, best practices for implementing the Aras plm solution suite, Aras Corporation, USA , Rapid results
business – driven PLM Implementation Methodology; website http://kalypso.com/rapidresults/
7.
Authors: Mahmoud Al-Zyood
Paper Title: Forecast Car Accident in Saudi Arabia with ARIMA Models
Abstract: Traffic accidents are the main cause of deaths and injury in Saudi Arabia, this work is a challenge to
examine the best ARIMA model for forecast a car accident. Results show that an appropriate model is simply an
ARIMA (1, 0, 0, 0) due to the fact that, the ACF has an exponential decay and the PACF has a spike at lag2 which is
an indication of the said model. The forecasted car accident cases from 1998 to 2016. The selected model with least
AIC value will be selected. We entertained nine tentative ARMA models and Chose that model which has minimum
AIC (Akaike Information Criterion).The chosen model is the first one AIC (-0.274306) The selected ARIMA (1, 0)
(0, 0), model to forecast for the future values of our time series (car accident). Forecasted for the next 7 years with
(95%) prediction intervals The prediction values of traffic accidents show that there will be increasing in deaths and
injury coming years
Keywords: Forecasting, ARIMA models, car accident, Akaike Information Criterion (AIC), Bayessian Information
Criterion (BIC).
References: 1. Armstrong, J. S. (2001). Principles of forecasting: A handbook for researchers and practitioners. Boston: Kluwer Academic.
2. Box, G.E. and Jenkins, G.M. (1994) Time Series Analysis: Forecasting and Control. Prentice Hall, Englewood Cliffs.
3. Berube, M. S. (Ed.). (1985). American heritage dictionary (2nd ed.). Boston, MA: Houghton Mifflin. 4. Box, G. E., & Jenkins, G. M. (1994). Time series analysis: Forecasting and control (3rd ed.). Englewood Cliffs, NJ: Prentice Hall.
5. Box, G. E., Jenkins, G. M., & Bacon, D. W. (1967). Models for forecasting seasonal and nonseasonal time series. In B. Harris (Ed.),
Spectral analysis of time series. New York, NY: John Wiley & Sons. 6. Boylan, J. (2005). Intermittent and lumpy demand: A forecasting challenge. The International Journal of Applied Forecasting, 1, 36-42.
7. Caldwell, J. G. (n.d.) The Box-Jenkins forecasting technique. Retrieved March 3,2012, from
http://www.foundationwebsite.org/BoxJenkins.htm
8. Cryer, J.D. and Chan, K.S. (2008) Time Series Analysis with Application in R. Springer, New York. http://dx.doi.org/10.1007/978-0-387-
75959-3
9. Hamilton, J. D. (1994). Time series analysis (Vol. 2). Princeton: Princeton university press. commandeur JJ, Bijleveld FD, Bergel-Hayat R, Antoniou C, Yannis G,
10. Papadimitriou E. On statistical inference in time series analysis of the evolution of road safety. Accid Anal Prev. 2013; 60:424–
3doi:10.1016/j.aap.2012.11.006. [PubMed: 23260716]. 11. G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis Forecasting and Control, Third ed. Englewood Cliffs, NJ:
PrenticeHall, 1994.
12. Hannan, E., (1980), The Estimation of the Order of ARMA Process, Annals of Statistics, Vol. 8,pp. 1071-1081.
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8.
Authors: Manju, Rajesh Kumar
Paper Title: Complexity of A and Its Connection with Logic
Abstract: The authors investigate the state complexity of some operations on regular languages. We prove that the
upper bounds on the state complexity of these operations, which were known to be tight for larger alphabets, are
tight also for binary alphabet. Upper and lower bounds for the finite-state complexity of arbitrary strings, and for
strings of particular types, are given and incompressible strings are studied.
Keywords: Finite Automata, Formal Languages, Logic, Regular languages, State Complexity
References: 1. J.C. Birget, Intersection and union of regular languages, and state-complexity, Inform. Process. Lett. 43 (1992) 185- 190. 2. J.C. Birget, Partial orders on words, minimal elements of regular languages, and state-complexity, Theoret. Comput. Sci. 119 (1993)
267-291.
3. J. Btzozowski and E. Leiss. On equations for regular languages, finite automata, and sequential networks, Theoret. Comput. Sci. 10 (1980) 19-35.
4. A. Chandra, D. Kozen and L. Stockmeyer, Alternation, J. ACM 28 (1981) 114-133.
5. J. Cohen, D. Penin and J.-E. Pin, On the expressive power of temporal logic, J. Comput. System Sci. 46 (1993) 271-294. 6. J. Hopcroft and J. Ullman, Introduction to Automata, L anguages and Computation (Addison-Wesley, Reading, MA, 19791.
7. D. Kozen, On parallelism in Turing machines, in: Proc. Ann. Symp. on Founaiuions of Computer Science (1976) 89-97.
8. E. Leiss, Succinct representation of regular languages by boolean automata, Theoret. Comput. Sci. 13 ( 198 1) 323-330. 9. E. Leiss, Succinct representation of regular languages by boolean automata, Part II, Theoret. Comput. Sci. 38 (1985) 133-136.
10. A.R. Meyer and M.J. Fischer, Economy of description by automata, grammars, and formal systems, in: Proc. 12Th IEEE Ann. Symp. on
Switching atul Automata Theory (1971) 188-191.
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9.
Authors: Dragos Ionut ONESCU
Paper Title: EU and Cyber Security
Abstract: Securing network and information systems in the European Union is essential to ensure prosperity and to
keep the online economy running. The quick and constant development of information and communication
technologies, globalization, the drastic increase in data volumes and the growing number of different types of
equipment connected to data networks have an impact on daily life, the economy and the functioning of the state. On
the one hand, this level of ICT development will contribute to the improved availability and usability of services,
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enhance transparency and citizen participation in governance, and cut public as well as private sector costs. Instead,
the increasing importance of technology is accompanied by an increase in the state's growing dependence on already
entrenched e-solutions, and cements the expectation of technology operating aimlessly. Social processes are also
becoming increasingly dependent on a growing number of information technology resources, and in the future
attention must be drawn to the fact that society at large, and each individual in particular, will be able to maintain
control over the corresponding processes. The number of actors and state in cyberspace that are involved in cyber
espionage targeted at computers connected to the Internet as well as closed networks continues to grow, with their
aim being to collect information on both national security as well as economic interests. The amount and activeness
of states capable of cyber-attacks are increasing. Meaningful and effective cooperation between the public and
private sector in the development of cyber security organization as well as in preventing and resolving cyber
incidents is becoming increasingly unavoidable. National defense and internal security are dependent on the private
sector's infrastructure and resources, while at the same time the state can assist vital service providers and guarantors
of national critical information infrastructure as a coordinator and balancer of various interests, please download
TEMPLATE HELP FILE from the website.
Keywords: European Union; security; cyber security
References: 1. Cyber Security Strategy was published as the two British security strategies under the direction of the Cabinet Office, the document is
available at http://www.cabinetoffice.gov.uk/reports/cyber_security.aspex.
2. Estonia hit by „Moscow cyber war”, „The Economist”, the document is available at: http://news.bbc.co.uk/2/hi/europe/6665145.stm
3. Douglas W. Hubbard, Richard Seiersen, Patrick Cronin, How to Measure Anything in Cybersecurity Risk, Audible Studios, 2016 4. Robert K. Knake, Pete Larkin, Richard A. Clarke, Cyber War: The Next Threat to National Security and What to Do About It, Tantor
Audio, 2014
5. George Cristian Maior,2009 strategic thinking and Uncertainty in international relations in the twenty-first century, RAO, Bucharest 6. The National Security Strategy of the United Kingdom-2008, (5.6)
7. http://nato.mae.ro/node/435
8. P. W. Singer, Allan Friedman, Cybersecurity and Cyberwar: What Everyone Needs to Know®, New York Times, 2003 9. Rid Thomas, Peter McBurney, Cyber-Weapons, The RUSI Journal, 157:1
10. R. J. Vidmar. (1992, August). On the use of atmospheric plasmas as electromagnetic reflectors. IEEE Trans. Plasma Sci. [Online]. 21(3). pp.
876—880. Available: http://www.halcyon.com/pub/journals/21ps03-vidmar
10.
Authors: Rajesh Kumar, Manju
Paper Title: Complexity of Binary and Uniary Operations on Regular Grammar
Abstract: It appears that the state complexity of each operation has its own special features. Thus, it is important
and practical to calculate good estimates for some commonly used general cases. In this paper, the author consider
the state complexity of combined Boolean operations on regular language and give an exact bound for all of them in
the case when the alphabet is not fixed. Moreover, the author shows that for any fixed alphabet, this bound can be
reached in infinite cases.
Keywords: Alternating finite automaton, Automata, Combined operations, Estimation, Formal languages, Multiple
operations, State complexity
References: 1. Chandra, D. Kozen and L. Stockmeyer, Alternation, J. ACM 28 (1981) 114-133 2. E. Leiss, Succint representation of regular languages by boolean automata, Theoret. Comput. Sci. 13 (1981) 323- 330.
3. G. Liu, C. Martin-Vide, A. Salomaa, S. Yu, State complexity of basic language operations combined with reversal, Information and
Computation 206 (2008) 1178–1186. 4. G. Rozenberg, A. Salomaa, Handbook of Formal Languages, Springer-Verlag, Berlin, Heidelbergm, New York, 1997
5. J. Berstel, D. Perrin, Theory of Codes, Academic Press Inc., 1985.
6. J. Hopcroft, J. Ullman, Introduction to Automata Theory Languages and Computation, 2nd ed., Addison-Wesley, Reading, MA, 1979. 7. K. Salomaa, S. Yu, On the state complexity of combined operations and their estimation, International Journal of Foundations of Computer
Science 18 (4) (2007) 683–698.
8. M. Domaratzki, State complexity of proportional removals, Journal of Automata Languages and Combinatorics 7 (4) (2002) 455–468. 9. M. Domaratzki, K. Salomaa, State complexity of shuffle on trajectories, Journal of Automata Languages and Combinatorics 9 (2–3) (2004)
217–232.
10. S. Yu, Q. Zhuang, K. Salomaa, The state complexities of some basic operations on regular languages, Theoretical Computer Science 125 (2) (1994)315–328
11. S. Yu, Regular Languages, In [23] Ch.1 (1997) 41–110.
12. S. Yu, State complexity: Recent results and open problems, invited talk at International Colloquium on Automata, Languages and Programming 2004 Formal Language Workshop, also appears in Fundamenta Informaticae 64 1–4 (2005) 471–480.
13. S. Yu, On the state complexity of combined operations, in: invited talk at 11th International Conference on Implementation and Application
of Automata, in: Lecture Notes in Computer Science, vol. 4094, Springer, 2006, pp. 11–22.
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11.
Authors: Ngo Tung Son, Tran Binh Duong, Bui Ngoc Anh, Luong Duy Hieu
Paper Title: An Empirical Research in Autonomous Vehicles Control
Abstract: Recent years have witnessed a growing attention to automatic-driving vehicles as this is one of the key
technologies for the future industry. Even though being successful at many aspects, there has been a long interest in
designing an efficient control system for automatic driving vehicles. This paper empirically demonstrates the
efficiency of our system which only employs low cost camera for visual sensing. Our approach puts the focus on 2
main objectives in autonomous vehicle control: (1) lane detection and (2) speed and direction decisions for the sake
of fast processing. This is to help the vehicle always moves in the right lane while keeping a suitable speed. For
decision making fuzzy logic is used for effective reasoning. We test our system in mini automatic-vehicles to show
that it is not only efficient but also reliable. At a practical test, the system has won third place at the Vietnam Digital
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Race challenge 2017.
Keywords: Image Processing, Lane Detection, Support Vector Machine, Automatic-Car Control, Fuzzy Logic.
References: 1. Autonomous Cars: Self-Driving the New Auto Industry Paradigm, MORGAN STANLEY RESEARCH, November 6, 2013.
2. Overview of Autonomous Vehicle Sensors and Systems, Jaycil Z. Varghese, Professor Randy G. Boone, Proceedings of the 2015 International Conference on Operations Excellence and Service Engineering Orlando, Florida, USA, September 10-11, 2015.
3. Multi-sensor data fusion for autonomous vehicle navigation through adaptive particle filter, Tehrani Nik Nejad Hossein, Seiichi Mita, Han
Long, Intelligent Vehicles Symposium (IV), 2010 IEEE 4. An Empirical Evaluation of Deep Learning on Highway Driving, An Empirical Evaluation of Deep Learning on Highway Driving, B.
Huval, T. Wang, S. Tandon, J. Kiske, W. Song, J. Pazhayampallil, M. Andriluka, P. Rajpurkar, T. Migimatsu, R. Cheng-Yue, et al., [arXiv], (2015).
5. P. V. C. Hough, "Method and Means for Recognizing Complex Patterns", US Patent 3,069,654, Ser. No. 17, 7156 Claims, 1962.
6. “Real-Time Lane Detection for Driving System Using Image Processing”, IRJET, Volume: 02 Issue: 05, Aug-2015. 7. Allam Shehata Hassanein , Sherien Mohammad, Mohamed Sameer, and Mohammad Ehab Ragab, "A Survey on Hough Transform, Theory,
Techniques and Applications", Informatics Department, Electronics Research Institute, El-Dokki, Giza,12622, Egypt.
8. S.ARUNADEVI, Dr. S. DANIEL MADAN RAJA, A Survey on Image Classification Algorithm Based on Per-pixel, International Journal of Engineering Research and General Science Volume 2, Issue 6, October-November, 2014 ISSN 2091-2730.
9. Vapnik (1995), The Nature of Statistical Learning Theory. Springer, Berlin.
10. Jitendra Kumar, Image Classification using SVM-RBF in the field of Image Processing, International Journal of Innovative Research in
Engineering & Multidisciplinary Physical Sciences (IJIRMPS) Volume 1, Issue 2, December 2013.
11. S. Agrawal, N. K. Verma, P. Tamrakar and P. Sircar, "Content Based Color Image Classification using SVM," 2011 Eighth International
Conference on Information Technology: New Generations, Las Vegas, NV, 2011, pp. 1090-1094. 12. Timothy J. Ross "Fuzzy Logic With Engineering Applications" Second Edition.
13. J. E. Naranjo, M. A. Sotelo, C. Gonzalez, R. Garcia and T. D. Pedro, "Using Fuzzy Logic in Automated Vehicle Control," in IEEE
Intelligent Systems, vol. 22, no. 1, pp. 36-45, Jan.-Feb. 2007. 14. Design and Implementation of Autonomous Car using Raspberry Pi, Gurjashan Singh Pannu, Mohammad Dawud Ansar, Pritha Gupta,
International Journal of Computer Applications (0975 – 8887) Volume 113 – No. 9, March 2015.
12.
Authors: Ashwani Kumar Aggarwal
Paper Title: Intelligent Electronic Surveillance Systems for Personal and Team Security in Public Places
Abstract: Public security is of prime importance for establishing law and order in any society. With the advent of
cheap and fast electronic systems, public security is prone to fall in the control of intruders. Whilst many electronic
surveillance systems available in market claim to work effectively, their operation is questionable in crowded places
where the subject under surveillance is occluded under clutter. Under such challenging task, computer vision
techniques are very helpful which work on foreground segmentation of captured images to remove clutter. Further,
images are preprocessed before applying many machine learning methods to log in the details of person behavior.
Action recognition techniques are then used to detect unusual behavior which helps in personal security in public
places.
Keywords: Artificial intelligence, computer vision, database, descriptors, feature points, image processing, machine
learning, optimization, electronic surveillance.
References: 1. M. Watney, "Intensifying State Surveillance of Electronic Communications: A Legal Solution in Addressing Extremism or Not?"
Availability, Reliability and Security (ARES), 2015 10th International Conference on, Toulouse, 2015, pp. 367-373.
2. D. C. Andrew, "Ground stations for analysis of electronic surveillance imagery," Human Interfaces in Control Rooms, Cockpits and
Command Centres, 1999. International Conference on, Bath, 1999, pp. 418-421. 3. C. Ovseník, J. Turán and A. K. Kolesárová, "Video surveillance systems with optical correlator," MIPRO, 2011 Proceedings of the 34th
International Convention, Opatija, 2011, pp. 227-230.
4. M. Yaghoobi, B. Mulgrew and M. E. Davies, "An efficient implementation of the low-complexity multi-coset sub-Nyquist wideband radar electronic surveillance," Sensor Signal Processing for Defence (SSPD), 2014, Edinburgh, 2014, pp. 1-5.
5. J. Teng, J. Zhu, Boying Zhang, D. Xuan and Y. F. Zheng, "E-V: Efficient visual surveillance with electronic footprints," INFOCOM, 2012
Proceedings IEEE, Orlando, FL, 2012, pp. 109-117. 6. G. Elkana and I. Baskara Nugraha, "Low cost embedded surveillance for public transportation," ICT for Smart Society (ICISS), 2014
International Conference on, Bandung, 2014, pp. 242-245.
7. P. Pasupathy, S. Munukutla, D. P. Neikirk and S. L. Wood, "Versatile wireless sacrificial transducers for electronic structural surveillance sensors," Sensors, 2009 IEEE, Christchurch, 2009, pp. 979-983.
8. Z. B. May, "Real-time alert system for home surveillance," Control System, Computing and Engineering (ICCSCE), 2012 IEEE
International Conference on, Penang, 2012, pp. 501-505. 9. V. M. López, A. Navarro-Crespín, C. Brañas, F. J. Azcondo, R. Schnell and R. Zane, "Frequency control and phase surveillance in resonant
electronic ballast," IECON 2011 - 37th Annual Conference on IEEE Industrial Electronics Society, Melbourne, VIC, 2011, pp. 2929-2934.
10. Gang Kang and O. P. Gandhi, "Comparison of various safety guidelines for electronic article surveillance devices with pulsed magnetic fields," in IEEE Transactions on Biomedical Engineering, vol. 50, no. 1, pp. 107-113, Jan. 2003.
11. X. Pan and Y. Wu, "Modeling and simulations of ECCM of ocean surveillance satellite electronic intelligence," Biomedical Engineering and
Informatics (BMEI), 2012 5th International Conference on, Chongqing, 2012, pp. 1476-1480. 12. M.J. Westoby, J. Brasington, N.F. Glasser, M.J. Hambrey, J.M. Reynolds, ‘Structure-from-Motion’ photogrammetry: A low-cost, effective
tool for geoscience applications, Geomorphology, Volume 179, 15 December 2012, Pages 300-314.
13. L. Zhao, S. Huang and G. Dissanayake, "Linear SLAM: A linear solution to the feature-based and pose graph SLAM based on submap joining," Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on, Tokyo, 2013, pp. 24-30.
14. Z. Kang and G. Medioni, "3D Urban Reconstruction from Wide Area Aerial Surveillance Video," Applications and Computer Vision
Workshops (WACVW), 2015 IEEE Winter, Waikoloa, HI, 2015, pp. 28-35. 15. J. Ventura and T. Höllerer, "Wide-area scene mapping for mobile visual tracking," Mixed and Augmented Reality (ISMAR), 2012 IEEE
International Symposium on, Atlanta, GA, 2012, pp. 3-12. 16. G. Bleser, H. Wuest and D. Stricker, "Online camera pose estimation in partially known and dynamic scenes," Mixed and Augmented
Reality, 2006. ISMAR 2006. IEEE/ACM International Symposium on, Santa Barbard, CA, 2006, pp. 56-65.
17. T. J. Cham, A. Ciptadi, W. C. Tan, M. T. Pham and L. T. Chia, "Estimating camera pose from a single urban ground-view omnidirectional image and a 2D building outline map," Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, San Francisco, CA,
2010, pp. 366-373.
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18. M. Chatzigiorgaki and A. N. Skodras, "Real-time keyframe extraction towards video content identification," Digital Signal Processing, 2009 16th International Conference on, Santorini-Hellas, 2009, pp. 1-6.
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