technical report...mrs. padma rajini, assistant prof. dept of cse, guru nanak institutions technical...
TRANSCRIPT
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