Vision and Mission Of AI &DL PLATOON

Vision

Our vision is to stimulate the students and faculty to focus on research and applications of AI and Deep Learning to significantly improve people’s lives.

Mission

Our mission is to

Adopt both theoretical and application aspects of AI and Deep Learning.

Address the societal needs for the betterment of human life by conducting industry oriented research on cutting edge technologies of AI and Deep Learning.

Apply effective and efficient intelligent human to machine interaction in various fields like agriculture, business, health care ,education, NLP and cyber security etc.

Hello All,

We received a lot of queries regarding the format of the Review Report. Many people are sending it in different styles and formats. To make it easier for all of you, we have made a Format to keep track of the ongoing performance. Same Document can be updated for every review report till March 2020. This will also act as a Micro Planner/Calendar. However, please feel free to make any changes/ additions/deletions as per your requirements.

We will request you to please send your internal review Report prepared by the Research Group along with the comments of the Internal Review Committee. The Detailed problem statement(s) and the overall (High-Level) design of the Product(s) should also be part of this report, so that a meaningful, useful and engaging review and discussion can be done.

The Review will be done on Skype. The Minimum quorum for the interaction is that at least three Members should be present from your institution for the interaction. Also as a prerequisite, we should have all the three documents as given above before the date of your review.

Our skype id is deepak.garg39 and/or Anurag.goswami@bennett.edu.in

You are requested to send a connect request from your Skype Id which will be used for the interaction. The System should be pre-tested, so as not to have any Audio-Video problems during the interaction. For testing the connection, kindly write to anurag.goswami@bennett.edu.in

Please feel free to reach us at any time for any clarifications.1

Research as per the Nos given in the PPT

# No of Publications you will produce by the end of two yearsScopus Indexed - 05SCI Indexed - 02Conference Papers (to be published by IEEE, ACM, Springer, Elsevier or other graded and highly ranked conference only)

If already Published/Accepted for publication (only those under this initiative)Scopus/SCI/Conference Author Names and Title in Full Journal/Conference Full Name Volume, Page No, Month and Year

Publication 1 Scopus indexed Springer Conference

SSSN.Usha Devi N, R.Mohan“Long Short Term Memory with CA to predict Stock Market”

International Conference on Data Engineering Communication Technology

Accepted (in Feb) and Conference date is on 15th -16th March

Publication 2And so on

For those to be papers to be published in coming months/next year upto 2022Scopus/SCI/Conference Who is responsible/champion Target Date Current Status

Publication 1 SCI -1, Conf-1 SSSN.Usha Devi N Nov-2020 Framework DevelopedPublication 2 Scopus -2, Conf-1 S.Chandra Sekhar March 2020 Under Literature SurveyPublication 3 SCI-1, Conf-1 E.Suneetha Dec-2019 Under Literature SurveyPublication 4 Scopus-1, Conf-1 A.Karuna Feb 2020 Under Literature SurveyPublication 5 Scopus-1, Conf-1 Dr.L.Sumalatha Mar-2020 Under Literature Survey

# No of Funding Grants you will write to different funding agencies -03

Funding Agency Investigator Names and Title of Proposal

Amount Date Submitted/Awarded

Funding Proposal 1Funding Proposal 2And so on

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For those to be submitted in coming months/next yearFunding Agency Who is responsible/champion Target Date Current Status

Funding Proposal 1

SERB Dr.L.Sumalatha, E.Suneetha May 2019 Designing

Funding Proposal 2

SERB S.ChandraSekhar April 2020 At Inception

Funding Proposal 3

DST SSSN.Usha Devi N April 2020 At Inception

And so on

# No of sabbaticals, you propose to avail for your Research Group Members (With consent of the faculty involved)Name Starting Date Ending Date Google Form Should have been Filled

Faculty 1Faculty 2And so on

# No of MTech. Students, PhD students who will do Thesis related to AI M.Tech -15, Ph.D-03(The below mentioned M.Tech. Project have undergone for assessment of literature survey, problem statement in Review –I and Presently they in design and implementation phase .The Review-II is scheduled in the month of June 2019. )

Name of the students Starting Date Ending Date Other details(MTech/PhD)Name of Supervisor (Who is responsible)

Student 1 SSSN.Usha Devi N Feb,2016 Nov,2020 Ph.D, Dr.R.Mohan, Prof of CSE, NITTStudent 2 E.Suneetha Sept,2014 Sept,2020 Ph.D, Dr.L.Sumalatha, Prof of CSE, JNTUKStudent 3 A.Jyothi Oct,2015 Nov,2020 Ph.D, Dr.L.Sumalatha, Prof of CSE, JNTUKStudent 4 S.Divyasri Aug,2018 Aug,2019 M.Tech, Dr.L.Sumalatha, Prof of CSE, JNTUKStudent 5 G.Mounika Aug,2018 Aug,2019 M.Tech, Dr.L.Sumalatha, Prof of CSE, JNTUKStudent 6 K.Varun Aug,2018 Aug,2019 M.Tech,S.ChandraSekhar,AsstProf,CSE,JNTUKStudent 7 R.Nagalakshmi Aug,2018 Aug,2019 M.Tech,SSSNUshaDeviN,AsstProf,CSE,JNTUKStudent 8 K.Sireesha Aug,2018 Aug,2019 M.Tech,SSSNUshaDeviN,AsstProf,CSE,JNTUKStudent 9 K.Sandhya Rechal Aug,2018 Aug,2019 M.Tech,SSSNUshaDeviN,AsstProf,CSE,JNTUKStudent 10 Y.Sandhya Aug,2018 Aug,2019 M.Tech,A.Karuna,AsstProf,CSE,JNTUK

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Student 11 P.mounika Aug,2018 Aug,2019 M.Tech,E.Suneetha,AsstProf,CSE,JNTUKStudent 12 G.Anjaneyulu Aug,2018 Aug2019 M.Tech,E.Suneetha,AsstProf,CSE,JNTUKStudent 13 Avinash Aug,2018 Aug,2019 M.Tech,E.Suneetha,AsstProf,CSE,JNTUK

Development as per the Nos given in the PPT# No of new datasets you will create for deep learning applications -02

Name of the Dataset Total estimated records or training samples it will have

Target Date Other details(Source/Who is responsible)

Dataset 1 Telugu Senti Word Net 25000 Jan 2020 E.SuneethaDataset 2 Financial Term

Dictionary10000 June 2020 SSSN.Usha Devi N

And so on

# No of Products/Applications you will develop (with support from students, Research Scholars)

Name of the Product Target Date Champion/Who is responsible

Area/Domain/ Specifications

Product 1 Attendance System using Face Recognition

July 2019 S.Chandra Sekhar Education

Product 2 Student Feedback analysis using Deep learning

Sep 2019 E.Suneetha Education

Product 3 Student Performance Analysis

Aug 2019 SSSN.Usha Devi N Education

Product 4 JNTUK Communication System

Jan 2019 Dr.L.Sumalatha Education

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Problem Statement and other details about the Project

Write at least 500-1000 words.

1) Student Performance Analysis Using Multi Layer Perceptron

Problem Statement

Assessing Academic performance of students is a vital task in education system. A personalized student performance assessment

system is required to reduce the risk factor in achieving his/her goals. Performance of student can be accessed through variety of parameters like

student regularity to the classes, scores in cycle tests, assignment marks etc. Every parameter will have set of values that are bounded by upper and

lower limits. Each student performance will vary with parameter value which intern reveals student interest. Analyzing the linear relation between

student performance parameters and his/her attainment will reveal student interest further it will be helpful in training the student ,course

improvement etc in order to make his/her objective succeed. The complexity of the system lies in analyzing high dimension data set. A multilayer

perceptron predictor is suitable in handling such a high dimension data.

Methodology

In this system the input is student data belongs to particular class. The input dimension table is as follows

Table 1.Atributes of student

Sno Attribute Name1 Student Roll Number2 Student Name3 Student regularity4 Cycle Test1 Score5 Cycle Test2 Score6 Assignmen1 Marks

5

+1

x1

x3

x2

y

+1

h1

h2

Input Layer Hidden Layer

Output

7 Assignment2 Marks8 Extra Curricular Activities9 Co curricular activities10 Project s grades11 Participation in training programs/workshops12 Awards/Rewards

This feature set is preprocessed then normalized as next step. The normalized data is submitted to MLP as shown in fig 1, as input for training .Once it is trained then we can submit new input to get the performance as output. Keras API is a high level programming useful in implementing Multi Layer Perceptron in an effective way.

Fig1.Multi Layer Perceptron

References

[1]Rianne Conijn, Chris Snijders, Ad Kleingeld, and Uwe Matzat ,“Predicting Student Performance using from LMS Data: A Comparison of

17Blended Courses Using Moodle LMS”,IEEE Transactions on Learning Technologies,March 2017.

[2]FebriantiWidyahastuti and Vinay UtamiTjhin,“Predicting Student Performance in Final Examination Using Linear Regression and Multi Layer

Perceptron ”,IEEE 10th Conference on Human System Interactions,2017 July.

[3]Augusto Sandoval ,Carlo Gonzalez ,Rosa Alacron ,Karim Pichara ,Maximiliano Montenegro,6

“Centralized Student Performance Prediction in Large Courses Based on Low-Cost Variables in an institutional Context”, Elsevier -The Internet and

Higher Education, April 2018.

2) Feature Based Opinion Mining on Student Feedback

Problem Statement

In academia,feedback is an essential part of effective learning.Understanding the voice of the student (VOS) requires collecting student’s opinionson quality that includes their needs, expectations, perception, and satisfaction. It can improve a student's confidence, self-awareness and enthusiasm for learning. It consists of student’s opinions as either structured or unstructured data. Analyzing feedback is essential in order to enhance the performance of academic institutions and further to adopt improvement measures for high quality teaching-learning process. Opinion Mining/Sentiment Analysis analyzes the students feedback timely based on various academic features like teaching, learning, tutorials, remedial sessions, soft skills, employment training and placement, assessment, results analysis, etc. Further it classifies the extracted feedback by applying different machine learning algorithms.

Methodology

Opinion mining aims for distinguishing the emotions expressed within the reviews, classifying them into positive or negative and summarizing into the form that is quickly understood by users. Feature based opinion mining performs fine-grain analysis by recognizing individual features of an object upon which user has expressed opinion. 

Machine Learning Approach: The machine learning method consists following steps

Step1-Data collection and Pre Processing: Student Opinions are acquired from social media; mobile phones and reviews posted on discussion forum and then pre processes the opinions to recover the accuracy of opinion mining process

Step2- N-gram Feature Extraction:Aspect or feature extraction involves the identification of feature aspect from the user’s opinion using N-gram approach. The extracted features were used to represent document in vector form on which the training and classification steps were based

Step3-Feedback Classification: A classifier is trained to differentiate among opinion labels by analyzing the relevant features. The performance of this approach depends on feature selection method and the choice of machine learning algorithm and also on the quality of training data.

Step4- Score Calculation: The trained classifier is used to identify sentiments for new reviews and classify them as positive, negative or neutral opinions. We can adopt different visualization methods to view the summarized scores of opinions.

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Fig;1: Generalized Architecture for Feature based Opinion Mining on Student feedback.

References

[1] A. Angelprrethi,S.Britto Ramesh Kumar “An Enhanced Architecture for Feature Based Opinion Mining from Product Reviews” in  IEEE World Congress on Computing and Communication Technologies (WCCCT) 2017.

[2] Solanki Yogesh Ganeshbhai, Bhumika K Shah “Feature based opinion mining: A survey” IEEE International Advance Computing Conference (IACC) 2015

3) Face Recognition Based Attendance System

Problem Statement

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Student Opinions

SentiWordNet

Polarity of opinions

Score Calculation

Classification-Machine Learning

Featured data

Feature Extraction

Opinion Pre Processing

Face recognition is one of the most researched areas in biometrics since many years. Several approaches were proposed on the same. All the research works proposed are focuses on identifying face from its corresponding image and verifying the same with existing database.

Turk and Pentland proposed a method which projected face images onto a feature space which was named face space. The face space was defined by the “eigenfaces,” which were the eigenvectors of the set faces. This framework provided the ability to learn to recognize new faces in an unsupervised manner. Hence, most researchers’ work was focused on facial feature extraction.

Belhumeur et al. presented an approach based on linear discriminant analysis (LDA). Zhao et al. proposed an approach which was based on PCA and LDA; they projected the face image to a face subspace via PCA and then LDA is used to obtain a linear classifier. There are also some representative approaches such as locality preserving projections (LPP) which were proposed by He et al., as well as marginal fisher analysis (MFA) presented by Yan et al.

Recent methods include face recognition methods includes several methods like multiple samples for each subject, Augmented Lagrangian Methods (ALM), partial faces recognition. Methods based on illumination variation, image misalignment, and partial occlusion. These algorithms can be summarized into two categories based on extracting the facial features: holistic template matching based systems and geometrical local feature based systems. All the approaches use only one sort of features in their approaches. We wish to work on a variety of biological characteristics to identify the face using Deep Learning.

Deep Learning is subset of Machine Learning with different capabilities. Deep Learning model analyzes data with logic similar to human. Deep Learning structures algorithms in layers to create an artificial Neural Network (ANN) that can learn and make intelligent decisions on its own.

Methodologies:

Method 1:

The proposed method performs the following steps for recognizing a face

1) Features are extracted from the faces stored in database

2) The extracted features are fed into the neural network with different hidden layers

3) The features go through different layers to identify the Faces.

The architecture is shown in Figure 1.

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Face Database

Feature Extraction

Features

…

…

…

…

Neural Network

Identified Face

Fig 1: Face Recognition System Architecture

Method 2:

Face Recognition is a challenging task especially in unconstrained environment due to various poses and external factors such as illusions. Use of

deep learning techniques for Face Recognition can yield best results in terms of performance. Face recognition technique involves two important

steps like Face Detection and Face alignment. The inherent correlation between detection and alignment should be exploited for which MultiTask

Cascaded Convolutional Neural Networks (MTCNN) can be used. MTCNN uses three stage architecture to predict the face and landmark location.

MTCNN consists of three networks, namely P-Net (Proposal Network), R-Net (Refine Network) and O-Net (Output Network) [Rong Fu et al, 2017].

P-Net is used to obtain the candidate window and bounding box. R-Net structure is used to remove false positive regions by bounding box regression

and non-maximum suppression. O-Net is similar to R-Net but has more supervision over face area and also outputs five face landmarks. In method 2,

an improved MTCNN is proposed which is trained with optimized features set. Methods like PCA & LDA are used to reduce the feature set.

Reducing the features will improve the computational speed of MTCNN. 10

References

1. Rong Fu, Dan Wang, et al. University Classroom Attendance Based on Deep Learning. 10th International Conference on Intelligent Computation Technology and Automation, 2017, 128-131

2. M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '91), pp. 586–591, June 1991.

3. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces: recognition using class specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711–720, 1997.

4. W. Zhao, R. Chellappa, and A. Krishnaswamy, “Discriminant analysis of principal components for face recognition,” in Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition (FG '98), pp. 336–341, Springer, Nara, Japan, April 1998.

5. X. He, S. Yan, Y. Hu, P. Niyogi, and H.-J. Zhang, “Face recognition using laplacianfaces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328–340, 2005.

6. S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang, and S. Lin, “Graph embedding and extensions: a general framework for dimensionality reduction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 40–51, 2007.

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# No of blogs -01

Title of Blog Who will Write/Responsible Where it will be uploaded

Target date

Blog 1 AI Platoon Research Group Members UCEK Website Blog will be launched in March 2019Blog 2And so on

# No of Articles

Title of Article Who will Write/ Responsible Where it will be uploaded

Target date

Article 1 Student Feedback Analysis

E.Suneetha AI Platoons Blog June 2019

Article 2 A Study on Regression Analysis

SSSN.Usha Devi N AI Platoons Blog March 2019

Article 3 Face Recognition Methods

S.Chandra Sekhar AI Platoons Blog June 2019

# No of Books to be written as part of this initiative

Title of Book Who will Write/Responsible Target Date Publication/AgencyBook 1Book 2And so on

# No of Book Chapter to be written as part of this initiative -03Title of Book Chapter Who will Write/ Responsible Target Date Publication/Agency

Book Chapter 1 DeepLearning Architectures, Algorithms and Applications

SSSN.Usha Devi N Nov 2019 Springer Verlag(under Review)

Book Chapter 2 A Survey on NLP Dr.L.Sumalatha, E.Suneetha Dec 2019 Springer

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Techniques for Telugu Language

# No of VideosTitle of Video Who will Make/Responsible Target Date Where it will be uploaded

Video 1Video 2And so on

# No of MOOCs

Title of MOOC Who will Write/ Responsible Target Date Where it will be availableMOOC 1MOOC 2And so on

# No of Tutorials

Title of Tutorial Who will Make/Responsible Target Date Where it will be availableTutorial 1 Rule Based Systems SSSN.Usha Devi N, A.Karuna Dec 2019 AI Platoons blogTutorial 2 Feature Based

Classification of Student Feedback

E.Suneetha, S.Chandra Sekhar Dec 2019 AI Platoons blog

And so on

# How many Industries/Domain Experts you can contact related to your Product or otherwise for your department/college: Under Negotiation

Name of Industry Name of Person and Designation Email PhoneIndustry Person 1

Data Science Himanshu Singh,Senior Data Scientist Himanshuit3036@gmail.com 09502799873

Industry Person 2

H-Bots Kisshhan PSV kisshhan@h-bots.com 9703902345

And so on

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Skilling as per the Nos given in the PPT# No of students/teachers expected to complete online certifications or Course Completions (related to AI)

Following data to be given only if anyone has completed the course Data will be furnished as & when required targets are metName of the

Student/Teacher Date of Completion Name of the Course Name of the Portal like NPTEL/edX/Coursera etc

Name 1 SSSN Usha Devi N 8th Dec 2018

Natural Language Processing with

Deep Learning in Python

Udemy

Name 2 SSSN Usha Devi N (On going) will be completed in April

AI-Knowledge Representation NPTEL

Name 3 K.Sireesha (On going) will be completed in May

Machine Learning for Science and

ApplicationsCoursera

Name 4 R.Nagalakshmi (On going) will be completed in May

Machine Learning for Science and

ApplicationsCoursera

Name 5 K.Sandhya Rechal (On going) will be completed in May

Machine Learning for Science and

ApplicationsCoursera

Name 6 Duvvuri Ravi Chandra 13th May 2019 Machine Learning CourseraName 7 Shaik Ashraf Ali 30th April 2019 Machine Learning CourseraName 8 Ruppa Jagannadharao 16 May 2019 Machine Learning Coursera

Name 9 Chivatam Sri Krishna Chaitanya 18th May 2019 Machine Learning Coursera

Name 10 Shaik Sajid Ali 30th March, 2019 Machine Learning CourseraName 11 Sanapala. Sowmya 17th April, 2019 Machine Learning CourseraName 12 Gundu. Aswinikumari 22nd April, 2019 Machine Learning Coursera

Name 13 Namavarapu. Pravallika 6th May, 2019 Machine Learning Coursera

Name 14 Atyam Lakshmi Anisha 29th April, 2019 Machine Learning CourseraName 15 N. Harika 20th May, 2019 Machine Learning CourseraName 16 G. Sai Sireesha 6th May, 2019 Machine Learning Coursera

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Name 17 M.Anil Kumar 29th April, 2019 Machine Learning CourseraName 18 N. Jagadeesh 30th April, 2019 Machine Learning CourseraName 19 G.Indu Priya 7th May, 2019 Machine Learning CourseraName 20 S. Nikhileswari 7th May, 2019 Machine Learning CourseraName 21 H. Yaswanth 13th May, 2019 Machine Learning CourseraName 22 Sk. Ashraf Ali 30th April, 2019 Machine Learning CourseraName 23 K. Preethi 30th April, 2019 Machine Learning CourseraName 24 R. Jagannadha Rao 16th May, 2019 Machine Learning CourseraName 25 T. Dmarani 30th April, 2019 Machine Learning Coursera

Name 26 P.Kanaka Durga Prasad 10th May, 2019 Machine Learning Coursera

Name 27 D. Hema 30th April, 2019 Machine Learning Coursera#No of new courses (elective/Core) you will introduce (related to AI)

-Applicable for Autonomous Colleges/Universities

Name of Course Person Coordinating/responsible

Which Semester or year Any other details

Course 1 Artificial Intelligence --- IIIB.Tech II SemIt is a 12 week NPTEL/SWAYAM Course is already introduced as part of curriculumR16 for 3 credits

Course 2 Deep Learning for Visual Computing --- IVB.Tech II Sem

It is a 12 week NPTEL/SWAYAM Course is already introduced as part of curriculum R16 for 3 credits

And so on

Outreach as per the nos given in the PPT# No of Collaborations you will make (national, International, academic, industrial)

Name of the Collaboration (Industry/Institution)

Date What is covered in the collaboration as part of the MoU

Contact Person with Name and Email, Phone from Industry/Institution

Collaboration 1

IN PIPELINE

Collaboration 2And so on

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Press Coverage (Send the Scanned Copy or Photo of the Press clipping) YesNames of Social Media Handles to be created for updates about this activityFacebook Link:Linkedin Link: YesTwitter Link:Instagram Link:Etc.Special Web Page to be added on your Institution Web site (May be linked to leadingindia.ai ) Yes (Details will be given shortly)Give the link hereOutreachThose events already done

Name of Event FDPs /Symposiums/ WorkshopsConferenceInvited talksExhibitionsCompetitionsIndustrial visits

Start and End date No of participantsNames of any Key Resource Persons(Any Brochures or Images of the events should be attached separately)

Event 1Events that are planned in the Future upto March 2020

Name of Event FDPs /Symposiums/ WorkshopsConferencesInvited talksExhibitionsCompetitionsIndustrial visits

Start and End date Expected No of participantsTentative Names of any Key Resource Persons

Event 1 3 day workshop on AI and Deep Learning Hands -On

Workshop 5thApril to 7th April 2019 50 ParticipantsResource persons from Leadingindia.ai

Event 2 Machine Learning Techniques for Image Processing

Workshop June 2019 50 Participants,Dr.U.S.N.Raju, NITW

Event 3 Pattern Recognition and Machine Workshop Aug 2019 50 Participants

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Learning Dr.C.R.Rao, HCU

# Name of AI Club to be started along with name and email of Student Ambassador who will lead this club

Give the detail of the whole team of the AI Club including name, email of atleast 10 students associated with this club.

Student Ambassador Name&email email: Sk.Sajid Ali sonusajid004@gmail.com

Name Email AddressG.Indupriya indupriya.gutti@gmail.comA.L.Anisha anishaatyam50@gmail.comS.Sparshika siriparapusparshika@gmail.com

S.Nikhileswari nikhileswarisandaka@gmail.comK.sailaskhmi Rajeswari saikokkiligadda567@gmail.com

K.preethi preethiluna@gmail.comG.Aswini Kumari aswini12g@gmail.com

M.Vinaysai Reddy vinaysaireddymandala@gmail.comKrishna Chaitanya subbarao.chaitanya@gmail.com

P.Abhay Datta abahaydatta123@gmail.comN.Jagadeesh nadimpalli.jagadeesh2000@gmail.com

P.kanakadurga Prasad kanakadurgaprasad.p@gmail.com

Working of the Group

Any innovative practices you have started to maintain the team spirit of your Group. Any outing or combined activity or gathering etc.

Any Other Additional Findings, Points, comments or questions.

Some of the details like project proposals, innovative ideas are unable to declare as the group are working in these areas.

Internal Review Report

Category Score Given by the Internal Review CommitteeResearch 3 Out of 5 (Five is highest)

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Development 3 Out of 5(Five is highest)Skilling 4 Out of 5(Five is highest)Outreach 4 Out of 5(Five is highest)Working of the Group

4 Out of 5(Five is highest)

Average 3.6

Name, Designation of the Internal Review Committee.

Signed Paper of the assessment done by internal review committee may be kept by you for record.

Dr P Subba Rao Dr MHM Krishna Prasad Sri G Joga Rao

Principal, University College of Engineering(A) Vice-Principal, University College of Engineering(A) Deputy Registrar, University College

JNTUK JNTUK of Engineering (A), JNTUK

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