An AICTE-sponsored ATAL Faculty Development Program on Exploring Technological Trends in Machine Learning and Artificial Intelligence – Need of the Hour was organized by the Department of Electronics and Communication Engineering from October 9th, 2023, to October 14th, 2023. The program was presided over by Principal Dr. V. Suresh Babu, and the resource persons for the program were:
- Dr. Sivaram Panigrahi, Associate Professor, NIT Rourkela
- Dr. Chittaranjan Nayak, Associate Professor, VIT Vellore
- Mr. Sathyanarayanan, Senior Application Engineer, VI Solutions, Bangalore
- Dr. Rathna G N, Principal Research Scientist, Indian Institute of Science, Bangalore
- Dr. Saroj Meher, Associate Professor, Systems Science and Informatics Unit, Indian Statistical Institute, Bengaluru
- Dr. Asis Kumar Tripathy, Professor, Department of Information Technology, VIT Vellore
- Dr. M Pallikonda Rajasekaran, Professor and R&D Director, Kalasalingam University, Tamil Nadu
- Dr. Tapan Kumar Das, Professor, Department of Smart Computing, VIT Vellore
- Dr. Deepak K T, Assistant Professor, Dept. of ECE, IIIT Dharwad
- Dr. Sudipta Mohapatra, Professor, IIT Kharagpur
The welcome address and inaugural speech were given by Principal Dr. V. Suresh Babu. The venue for the FDP was Seminar Hall 5, from 9:30 AM to 5:00 PM.
Day 1: Foundation of Artificial Intelligence, Machine Learning and Deep Learning – Dr. Sibarama Panigrahi (09.10.23 – FN)
The first-day session was conducted by Dr. Sibarama Panigrahi in the forenoon. He began the session with the foundation of Artificial Intelligence, Machine Learning, and Deep Learning. The other topics discussed included the Mathematical Model for Neural Networks, Differentiation and its Application to Train Neural Networks, Deep Neural Networks, Recent Advances in Deep Learning, Activation Functions, Weight Initialization (Xavier & Glorot, He), Dropout and Regularization, Batch Normalization, Optimizers (SGD, NAG, AdaGrad, AdaDelta, RMSPROP, ADAM), Building and Training Deep Neural Networks using Python, and Hyper-Parameter Optimization of Deep Neural Networks on Day 1 morning. All faculties were able to relate these concepts to their research and were actively engaged in seeking solutions to their research challenges.
Dr. Sibarama Panigrahi along with the dignitaries in the inaugural session
The afternoon session was conducted by Dr. Chittaranjan Nayak, where he primarily focused on Machine Learning Techniques for Computer Vision. Machine learning (ML) algorithms identify common patterns in images or videos and apply that knowledge to accurately identify unknown images. Computer vision systems use artificial intelligence (AI) technology to mimic the capabilities of the human brain responsible for object recognition and classification.
End of day 1 session with the resource persons Dr. Chittaranjan Nayak and Dr. Sibarama Panigrahi.
Day 2: Speech analysis and its applications using Machine learning – Mr. Sathyanarayanan (10.10.23 – FN)
On the second day, the session was conducted by Mr. Sathyanarayanan, who explained key concepts in speech recognition applications and available devices. Advanced solutions in this field utilize AI and machine learning, integrating grammar, syntax, structure, and composition of audio and voice signals to understand and process human speech. Mr. Sathyanarayanan discussed numerous applications in human interaction, including call-centers, e-learning, autonomous driver emotion detection, and analysis of physiological diseases.
Mr. Sathyanarayanan from VI Solutions is welcomed by our Dean/Academics.
Day 2: Object and Image Memorability Analysis using Deep Learning Approach – Dr. Rathna G N (10.10.23 – AN)
Dr. Rathna G N initiated the session on image classification, recognition, and related problems, employing a deep learning method that simulates how the human brain responds to images and analyzes image data thoroughly. This approach has demonstrated excellent performance in large-scale image processing and has found extensive applications across various fields. Additionally, she conducted a demo on Machine Learning-based Image Processing Using MATLAB. The demo showcased tasks like removing image noise and performing image-to-image translation using deep neural networks.
Demo on Machine Learning based Image Processing Using MATLAB
Dr. Rathna G N delivering her interesting lecture during the afternoon session.
Day 3: Medical Image Processing using Deep Learning – Dr. Saroj Meher -11.10.23 (FN)
On the third day of the FDP, Dr. Saroj Meher commenced the session on Medical Image Processing using Deep Learning. He delivered an engaging lecture on how deep learning has revolutionized medical image analysis, producing outstanding results in tasks such as registration, segmentation, feature extraction, and classification. The availability of computational resources and the resurgence of deep convolutional neural networks have driven these advancements. Dr. Meher discussed a specific example of detecting diseases or abnormalities from X-ray images and classifying them into various disease types or severities in radiology
Dr. Saroj K Meher sharing his valuable knowledge on deep learning to the participants.
Day 3: Supervised and Unsupervised Learning Technique for Image Processing – Dr. Asis Kumr Tripathy – 11.10.23 (AN)
In this session, Dr. Asis Kumar Tripathy delved into Image Processing Techniques. He provided practical demonstrations of both supervised and unsupervised learning techniques, highlighting the primary distinction between them: supervised learning relies on labeled input and output training data, while unsupervised learning processes unlabeled or raw data. The session included hands-on application development of supervised techniques for Image Processing using Python. Faculty members gained fundamental knowledge in coding and developed basic codes during the session.
Our Chief Guest Dr. Asis Kumar Tripathy is honored by our senior professor Dr. Remashan Kariyadan.
Dr. Asis Kumar Tripathy with the faculties after the hands on session
Day 4: Texture Classification Using Machine Learning – Dr. M. Pallikonda Rajasekaran – 12.10.23 (FN)
In this session, Dr. M. Pallikonda Rajasekaran delved into Texture Classification, a fundamental issue in computer vision and image processing that plays a significant role in various applications, including medical image analysis, remote sensing, object recognition, document analysis, environment modeling, and content-based image retrieval, among others. He also discussed Convolutional Neural Network (CNN) features used for feature extraction, with the Support Vector Machine serving as the classifier for texture classification
Dr. M. Pallikonda Rajasekaran greeted by our Dr. A. C. Niranjanappa / Dean (Research)
Resource Person along with the participants of the FDP
Day 4: Secure and Private Machine Learning – Dr. Tapan Kumar Das 12.10.23 (AN)
This session was conducted by Dr. Tapan Kumar Das, who began by discussing the application of machine learning in security. He explained how machine learning continuously learns by analyzing data to identify patterns, enabling us to better detect malware in encrypted traffic, identify insider threats, predict the locations of online “bad neighborhoods” to enhance browsing safety, and protect cloud-stored data by uncovering suspicious user behavior.
Dr. Tapan Kumar Das handling his session on Machine Learning in security.
Day 5: Conventional Programming v/s Artificial Intelligence branches of AI -Dr. K .T .Deepak – 13.10.23 (FN)
On the fifth day of the FDP, Dr. K. T. Deepak conducted the session on Conventional Programming versus Artificial Intelligence branches of AI. He primarily discussed how conventional computing can address only one problem at a time within each domain. In the realm of intelligent computing or AI research, the focus lies in attempting to mimic human intelligence through symbol manipulation and symbolically structured knowledge bases. Dr. Deepak also explored the major branches of Artificial Intelligence, highlighting practical applications in day-to-day fields.
Our Chief guest of the day Dr. K. T. Deepak is welcomed by our Head of the Department-ECE- Dr. Sajithra Varun
Day 5: Industrial Visit – ELECTRONO Solutions – 13.10.23 (AN)
According to the FDP schedule, the afternoon session on October 13th, 2023, involved a visit to ELECTRONO Solutions for the participating faculties. This company provides machine learning-based data-driven solutions aimed at enhancing operational efficiency.
Our participants of the FDP in the industrial Visit.
Day 6: Neural network and their involvement in Deep Learning -Dr. Sudipta Mohapatra – 14.10.23 (FN)
In this session, Dr. Sudipta Mohapatra delivered a valuable lecture on the involvement of neural networks in deep learning. He explained that an Artificial Neural Network is a network of interconnected (artificial) neurons or nodes, where each node represents a single information processing unit, similar to a neuron cell in the human brain. The term ‘deep’ in deep learning refers to creating numerous (>100) artificial neural networks with several hidden layers that are interlinked to pass information to each other.
Dr. Sudipta Mohapatra delivering his talk.
Overall, 49 faculty members from various departments actively participated in this ATAL FDP. The program provided a valuable learning experience for all participants. They had the opportunity to learn and engage in discussions with the resource persons regarding different articles they had published. Additionally, the practical hands-on sessions proved immensely helpful for novice researchers. The article discussions and practical sessions offered valuable insights, enhancing the participants’ understanding of various dimensions in their research endeavors.
All participants of the FDP actively taking the exam and filling the feedback form on the last day.
In conclusion, this AICTE-sponsored ATAL FDP provided a platform for novice researchers to explore innovative ideas for growth and allowed doctoral researchers to venture into new areas of their work. The enthusiastic involvement and active participation of the faculty members were evident throughout the FDP, highlighting the program’s success in fostering a spirit of learning and collaboration among the participants.
OUTCOME:
Out of the 49 faculty members who attended, everyone gained valuable insights for their new research projects and a deeper understanding of the technological advancements shaping the world. All participants actively engaged in the final exam and expressed their appreciation for the timely and relevant topics covered during the program.