TECHNICAL REPORT ON

DST-ICPS DIVISION SPONSORED

3-DAY NATIONAL LEVEL WORKSHOP ON

“INTELLIGENT AUTOMATION THROUGH MACHINE LEARNING

(Artificial Intelligence (AI), Machine Learning (ML) & Deep Learning (DL)”

26TH

DEC 2019 – 28TH

DEC 2019

SUBMITTED By

Convener / Principal Investigator

Dr. S. MADHU Professor, Department Of CSE

DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING

GURU NANAK INSTITUTIONS TECHNICAL CAMPUS (AUTONOMOUS)

HYDERABAD-501506.

Organizing Committee Chief Patrons

Sardar Tavinder Singh Kohli, Chairman-GNI

Sardar Gagandeep Singh Kohli, Vice-Chairman-GNI

Patron

Dr. H. S. Saini, Managing Director-GNI

Co-Patrons

Dr. M. Ramalinga Reddy, Director, Guru Nanak Institutions Technical Campus.

Dr. Rishi Sayal, Associate Director, Guru Nanak Institutions Technical Campus.

Dr. S. V. Ranganayakulu, Dean R&D, Guru Nanak Institutions Technical Campus.

Convener(PI)

Dr. S. Madhu, Professor, Dept. of CSE, Guru Nanak Institutions Technical Campus.

Co-Conveners

Dr. J. Rajeshwar, HOD & Prof., Dept. of CSE, Guru Nanak Institutions Technical Campus.

Prof. V. Devasekhar, HOD & Prof. Dept. of CSE, Guru Nanak Institutions Technical Campus.

Coordinators

Dr. E. Madhusudhana Reddy, Prof., Dept. of CSE, Guru Nanak Institutions Technical Campus.

Dr. M. V. Narayana, Prof. Dept. of CSE, Guru Nanak Institutions Technical Campus.

Dr. Ch. Subba Lakshmi, Prof., Dept. of CSE, Guru Nanak Institutions Technical Campus.

Organizing Members

Mr. Lalu. B., Associate Prof., Dept of CSE, Guru Nanak Institutions Technical Campus.

Mr. D. Saidulu, Associate Prof., Dept of CSE, Guru Nanak Institutions Technical Campus.

Mr. A. Ugendhar, Associate Prof., Dept of CSE, Guru Nanak Institutions Technical Campus.

Mr. A. Ravi, Associate Prof., Dept of CSE, Guru Nanak Institutions Technical Campus.

Mr. S. Siva Shankar Rao, Associate Prof., Dept of CSE, Guru Nanak Institutions Technical

Campus.

Mrs. V. Swathi, Assistant Prof. Dept of CSE, Guru Nanak Institutions Technical Campus.

Mrs. Ch. Sushma, Assistant Prof. Dept of CSE, Guru Nanak Institutions Technical Campus.

Mr. M. Yadagiri, Assistant Prof. Dept of CSE, Guru Nanak Institutions Technical Campus.

Mr. K. Raveendra Kumar, Assistant Prof. Dept of CSE, Guru Nanak Institutions Technical

Campus.

Mrs. Padma Rajini, Assistant Prof. Dept of CSE, Guru Nanak Institutions Technical Campus.

Mr. Hari Shankar, Assistant Prof. Dept of CSE, Guru Nanak Institutions Technical Campus.

Mr. Shaik Kashim, Assistant Prof. Dept of CSE, Guru Nanak Institutions Technical Campus.

List of Resource Persons

1. Dr. Kanchi Gopinath, Professor, Dept. of Computer Science and Automation, Indian

Institute of Science, Bangalore.

2. Dr. M. Venkatesan, Assistant Professor, Dept. of Computer Science and Engineering,

National Institute Technology, Surathkal.

3. Dr. A. Govardhan, Professor, Dept. of Computer Science and Engineering, JNTUH,

Hyderabad.

4. Dr. Manjaiah. D. H, Professor, Dept. of Computer Science and Engineering Mangalore

University.

5. Dr. P. Chenna Reddy, Professor, Dept. of Computer Science and Engineering, JNTUA.

6. Dr. C. Shoba Bindu, Professor, Dept. of Computer Science and Engineering JNTUA.

7. Dr. K. P. Supreethi, Professor, Dept. of Computer Science and Engineering JNTUH.

8. Dr. G. Narasimha, Professor, Dept. of Computer Science and Engineering JNTUH.

9. Dr. E. Grace Mary Kanaga, Associate Professor, Dept. of Computer Science and

Engineering Karunya University.

10. Ms. Nimrita Kaul, Assistant Professor, Dept. of Computer Science and Engineering Reva

University.

11. Mr. L. Rama Raju, Data Analyst, Data Jango, Hyderabad

Workshop Report on “Intelligent Automation through Machine Learning”.

The Department of Computer Science & Engineering organized a 3 day

National Workshop on “Intelligent Automation through Machine Learning”

sponsored by DST-ICPS division for Faculties/Doctorial students/PG students/UG

students from 26th to 28

th December, 2019.The Workshop is specifically meant for

the Schedule Tribe (ST) category, In view to make the participants gain in-depth

knowledge in the field of Machine Learning.

Instantaneously good response came from the students and teachers from

different academic institutions once the workshop brochure was released. 60

people including teachers and students from the following colleges attended the

workshop.

JNU from Delhi

Madras university from Chennai

Osmania university from Hyderabad

Andhra University from Vizag

SV College of Engineering from Tirupati

TKR Engineering College from Hyderabad

Sri Indu Engineering College from Hyderabad

Institute of Aeronautical Engineering college from Hyderabad

Sreyas Engineering college from Hyderabad

SR Engineering College

Guru Nanak Institutions Technical Campus(Host Institute)

Initially the workshop was planned for 50 participants, but due to huge

response we selected 60 participants from 150 Registrations. We have given scope

mainly from Schedule Tribe (ST) i. e. 30 participants (50% of participants belong

to Schedule Tribe (ST)) and remaining are from other categories (out of 60

participants 33 are faculties, 13 are Ph. D Students, and 14 are PG/UG students).

Objectives of the workshop

The course should enable all participants including Faculties in Academics,

students of UG, PG and Ph. D working in the area of ML to:

Learn the fundamentals of machine learning and its applications in various

industries

To design and analyze various machine learning algorithms and techniques

with a modern outlook focusing on recent advances.

Explore supervised and unsupervised learning paradigms of machine

learning.

Learn the machine learning algorithms for development of Intelligent

automation

To explore Deep learning technique and various feature extraction strategies.

Outcome of the workshop

After completion of Three days workshop, Participants would be able to:

Extract features that can be used for a particular machine learning approach

in various applications.

To compare and contrast pros and cons of various machine learning

techniques and to get an insight of when to apply a particular machine

learning approach.

To mathematically analyze various machine learning approaches and

paradigms.

Practice the experiments of machine learning using python programming,

using R programming, rapid miner tool.

Workshop Outcome for Scheduled Tribes

30 members were trained in all concepts of Machine Learning from

Schedule Tribe (ST) (i.e. 50% participants).

Two of our Faculties who had attended the workshop have submitted

research papers to conference taking the help of one resource person.

One of our faculty who has attended the workshop have submitted research

papers to SCOPUS Indexed Journal taking the help of one resource person.

Doctorial students who have attended the workshop have narrowed their

research problem and fine tuned their proposed ML algorithms taking the

help of resource person.

A team of UG students have submitted projects to Smart India Hackathon

2020, using ML.

DAY-1: 26.12.2019(Thursday) Inaugural Session

For the enhancement of Participants and leading them to an enlightened

route where they can be a part of Intelligent Automation through Machine

Learning a 3-Day National Level Workshop has been started at Guru Nanak

Institutions Technical Campus (Autonomous)-Hyderabad which was Sponsored

by Department of Science & Technology under ICPS Division, Govt. of India. And

was organized by Department of CSE. The auspicious event was scheduled for

2020. The main objective of this Workshop was to provide knowledge on the

leading technology in our current generation which is the field of Artificial

Intelligent, Machine Learning and Deep Learning.

The auspicious workshop has initiated with the Inaugural Session in which

the honorable Dr. Rishi Sayal, Associate Director& Co-Patron, Dr. S. Madhu,

Professor, Dept. of CSE & Convener , Dr. J. Rajeshwar, HOD & Prof., Dept. of

CSE & Co-Convener, Prof. V. Devasekhar, HOD & Prof. Dept. of CSE & Co-

Conveners of this workshop had warmly welcomed our Chief guest and Resource

person of Day1 Session1 Dr. Kanchi Gopinath, IISc, Bangalore with a bouquet.

We are grateful to have a warm introduction and for unwavering enthusiasm

and support from the convener Dr. S. Madhu. He gave a brief about workshop

topics and also about the importance of getting advanced on the current technology

and also how to use them to lead in the advanced society. Later on, the sessions

have been started.

Photo 1: Dr. K. Gopinath released Proceedings of 3 Days Workshop

The Chief Guest Dr. K. Gopinath, Professor from IISc- Banglaore released

workshop proceedings and Dr. Rishi Sayal , Co-Patron and Associate director of

Guru Nanak Institutions Technical Campus, he has releaded E-Proceedings of the

workshop. Dr. Rishi Sayal addressed about gathering related to ML useses and

application and importance of workshop to faculties and students. Dr. K. Gopinath

is spoke about the workshop and advances of Machine Learning and how is it

related to human life, and also addressed the job opportunities in the field of

AI,ML & DL, IOT related things of ML, how it is related to smart devices, how

ML is useful for decision making for solving real world problems.

Technical Session: Day 1: 26.12.2019

Session 1: Introduction to Machine Learning and Classification Machine Learning

The technical session was started immediately at 09:15 AM and Dr. K.

Gopinath, Professor, Dept. of CSA, IISc-Bangalore delivered his energetic talk on

Machine Learning and wide variety of examples on where and in which field the

machine learning can be implemented and it has given a brief idea to the listeners

and also created an interest to every individual over the topic. He gave a detailed

explanation about machine learning model.

Photo 2: Dr. K. Gopinath presentation on Introduction to Machine learning and Classification of ML

The concepts which are named to be classification of Machine Learning (a)

Supervised learning, (b)Unsupervised learning, and (c) Reinforcement learning,

and How to use Hidden Markov Models for classification test patterns for hidden

information and also summarized his presentation for every 15 minutes. Inspired

every participant to give their extent to which they can improve their ability to gain

knowledge from the several websites which are available in the internet and

motivated them to learn from that website and improve self-study. This session

came to an end at 11.00 AM.

Session 2: Regression Analysis

The Second Session was chaired by Mr. Venkata Rama Raju, Data Analyst,

Data Jango, Hyderabad. He delivered continued on the introduction part and we

were privileged to have him with us who had sixteen years of experience in

software services and product development, out of which he worked in the USA

for 6 years.

Photo 3: Mr.V. Rama Raju talk on Regression Analysis

His way of delivery of content to the participant made them listen carefully about

the Regression analysis. Gave a brief on linear Regression i.e. how does it work,

how do companies use it, correlation is not causation.Linear Regression: The term

“linearity” in algebra refers to a linear relationship between two or more variables.

If we draw this relationship in a two-dimensional space (between two variables),

we get a straight line.Simple Linear Regression: In statistics, simple linear

regression is a linear regression model with a single explanatory variable.

Session 3: Multiple Linear Regression Analysis

The post lunch session started by Mr. V. Rama Raju with continuation of session

two and Multiple Linear Regression: Multiple linear regression (MLR), also

known simply as multiple regression, is a statistical technique that uses several

explanatory variables to predict the outcome of a response variable. Finally he

demonstrated how to build a Linear Regression model with SGDRegressor in

python

Session 4: Unsupervised Learning Method

The Last session of the day 1, we are privileged to have her as our resource

person. She boosted up the session by making small talk on her introduction (about

her). She was concerned about the topic coverage and delivery to the enthused

participants and started the session with a brief on the topics she was going to

cover.

Photo 4: Dr. K. P Supreethi, Professor, Dept. of CSE, JNTUH talk on Unsupervised Learning

She took a concept which is one of the main topics in pattern matching and

recognition which is known to be Clustering. It is basically a type of unsupervised

learning method. An unsupervised learning method is a method in which we draw

references from datasets consisting of input data without labeled responses.

Generally, it is used as a process to find meaningful structure, explanatory

underlying processes, generative features, and groupings inherent in a set of

examples. Clustering is the task of dividing the population or data points into a

number of groups such that data points in the same groups are more similar to

other data points in the same group and dissimilar to the data points in other

groups. It is basically a collection of objects on the basis of similarity and

dissimilarity between them. Some of the clustering methods notified by her are: 1.

Density-Based Method, 2. Hierarchical Based Methods, 3. Partitioning Methods

and 4. Grid-based Methods.

Technical Session: Day 2: 27.12.2019

Session 5: SVM & Neural Networks

Second Day of the First session , the first talk was given by Dr. E. Grace

Mary Kanaga, Karunya University on A Support Vector Machine (SVM) is a

discriminative classifier formally defined by a separating hyperplane. In other

words, given labeled training data (supervised learning), the algorithm outputs an

optimal hyper plane which categorizes new examples. In this algorithm, we plot

each data item as a point in n-dimensional space (where n is the number of features

you have) with the value of each feature being the value of a particular coordinate.

Then, we perform classification by finding the hyper-plane that differentiates the

two classes very well (look at the below snapshot).

Photo 5: Dr Grace Mary talk on SVM&NN

Neural Networks, A neural network is a network or circuit of neurons, or in a

modern sense, an artificial neural network, composed of artificial neurons or

nodes. Thus a neural network is either a biological neural network, made up of

real biological neurons or an artificial neural network, for solving artificial

intelligence (AI) problems. A neural network (NN), in the case of artificial

neurons called artificial neural network (ANN) or simulated neural network

(SNN), is an interconnected group of natural or artificial neurons that uses a

mathematical or computational model for information processing based on a

connectionist approach to computation. In most cases, an ANN is an adaptive

system that changes its structure based on external or internal information that

flows through the network and also practically demonstrated both concepts in

python.

Session 6: Introduction to Python, Jupiter Network, NumPy & Pandas,

Matplotlib

The Pre-Lunch session on the second day started by Ms. Nimrita Koul, Reva

University. Ms. Nimrita Koul has continued the lecture on one computation tools

to work with machine learning efficiently in the second session. She gave a brief

intro on python and python packages utilized for Machine Learning algorithms

effectively.

Photo 6: Ms. Nimrita Koul talk on Python

She covered the basic introduction of python and some of the topics in

python. Python is a popular programming language. Python can be used on a

server to create web applications. Python can be used to handle big data and

perform complex mathematics.

Basics of python: Variables and Types, Lists, Conditions, Loops, Functions,

Classes and Objects, Dictionaries, Modules and Packages, and Advanced Python

Topics: Numpy, Pandas Basics, Matplotlib. She continued her lecture on the high

used tool of python’s Jupiter notebook.

Session 7: Introduction to R Programming

The Post-Lunch session on the second day started by Resource person Mr

.Venkat Rama Raju on R Programming. He delivered the basics of R tool and Data

structures which are useful for implementing Machine Learning approaches

Photo 7: Mr. V. Rama Raju talk on R Programming

He demonstrated R tool environment , how to run R toll and some of the

most useful concepts in R Data types, Data structures such as Vectors, List,

Matrix, and Data frames

Session 8: Random Forest

The Last session of Second day started by Mr. V. Rama Raju. He covered

about the Random forest algorithm and importance of Random Forrest algorithm.

He presented Random Forest in programming Context and demonstrated random

forest algorithm in python tool and R tool.

Technical Session: Day 3: 28.12.2019

Session 9: Introduction to Rapid miner tool

The Last day of the First Session by Dr. M. Venkatesan, NIT, Surathkal.

Dr.M.Venkatesan Started his introduction to the students with an interesting topic

on a tool called Rapid Miner.

Photo 9: Dr. M. Venkatesan talk on Rapid miner tool

RapidMiner is a data science software platform developed by the company

of the same name that provides an integrated environment for data preparation,

machine learning, deep learning, text mining, and predictive analytics. RapidMiner

is developed on an open core model. RapidMiner provides 99% of an advanced

analytical solution through template-based frameworks that speed delivery and

reduce errors by nearly eliminating the need to write code.

Session 10: Hands-on session Machine Learning algorithms in Rapid miner

tool

In session 2 of day 3 Dr.M.Venkatesan has conducted a hands-on session on

the Rapid Miner tool and its applications in the real world to achieve Machine

Learning without code by simply adjusting the right algorithm on the given data.

Application: RapidMiner Studio is a visual data science workflow designer

accelerating the prototyping & validation of models.

● Easy to use visual environment for building analytics processes:

o Graphical design environment makes it simple and fast to design

better models

o Visual representation with Annotations facilitates collaboration

among all Parameter recommender indicating which parameters to

change & to which values.

Session 11: Hands-on session K nearest Neighbor Machine Learning

Algorithm in Rapid miner tool

Post Lunch session of the Last Day by Dr. M. Venkatesan had a talk on K

nearest Neighbor algorithm practical session in rapid miner tools and all

participants are practiced the KNN algorithm in rapid miner tool and observed the

performance analysis of KNN

Photo 10: Dr, M. Venkatesan clearing errors in hands- on session

Session 12: Hands-on session Naïve Bayes and Decision trees in Rapid miner

tool

Last Session of The Workshop handled by Dr. M. Venkatesan. He explained

the Concept of naïve bayes algorithm and parallely all participants are practiced

the same in rapid miner tools and compare the analysis between the KNN and

Naïve Bayes algorithm.

Photo 11: Practical Session by Dr. M. Venkatesan

The End of Session, He Presented the Decision tree concept and observed

ensured that all participants are executed all Machine Learning algorithms in rapid

miner tool.

Valedictory and Certificate Distribution

For the enthused participants Dr. Rishi Sayal, Associate Director and Dr. S.

Madhu, Professor presented the certificate for attending this technical workshop.

Photo 12: Participant receiving certificate from Dr. Rishi Sayal

Valedictory Session started with feedback from particapnts.The feedback

session has gone well every query and further support is answered well by the

session resource person. They have made the sessions in an interactive manner so

that their queries can be resolved at the time of the session explanation. Everyone

has practicals included with theory so that the participants can have a good

understanding of how the libraries and models can be used with the syntax and

how the output can be generated. They have provided good information about the

sites which can be surfed for their learning process and improvement by proving

the basic software which is to be used for the evaluation of programs written.

Written Feedback

S.

No.

Name of the

Resource Person

No. of

feedbac

k forms

No. of

Points

Obtained

Total

No. of

Points

% Grading

1 Dr. K. Gopinath-

Session-1

60 3257 3600 90.47 Excellent

2 Mr. V. Rama Raju-

Sessio-2 & 3

60 3276 3600 90 Very Good

3 Dr. K. P. Supreethi-

Session-4

60 3272 3600 90.88 Excellent

4 Dr. Grace Mary

Kanage E-Session-5

60 3289 3600 91.36 Excellent

5 Ms. Nimrita Koul-

Session-6

60 3265 3600 90.69 Excellent

6 Mr. V. Rama Raju-

Session- 7& 8

60 3284 3600 91.22 Excellent

7 Dr. M. Venkatesan-

Session- 9,10,11 & 12

60 3293 3600 91.47 Excellent

The Overall feedback of workshop is Excellent