top of page

Journals and Patents

Project 01
Human Identification Using Gait analysis and 3D Convolutional Network under supervision of Dr.P.Supraja.
 
This Project is about human recognition by their gait i.e. walking style by analyzing their gait features. It includes Object detection, Silhouette extraction, Skeletonisation, 3D CNN, recognition.
It took one day to preprocess the 18000 videos of 181 objects from 11 different angles and 8 days to train the network to recognize the object i.e. human by his/her gait features.
Research Paper: Applied.
Patent: Published in IPindia  01/2020,1, Pg- 208
Project 02
Video Captioning Using Encoder and Decoder Module under supervision of Dr.P.Supraja

In this project, we used 3D CNN with LSTM to train the neural network to generate the caption on the basis of the features present in the frames. We word embedding and vectorization for sanitization of the captions on which we trained our neural network.

After training for five continuous days we got the captions which were 92% relevant to the frames of the video.

Patent: Published in IPindia  51/2019,1, Pg- 61314

Project 03
Enhanced Human Gait Prediction under supervision of Dr.P.Supraja

In this we have used 2D CNN to recognize the human by their gait features by analyzing their walking pattern.

 

Research Paper: Accepted in the Journal of Physics and Nanotechnology.

Project 04
Model Evaluation (Chapter) in Scrivener Publishing

This is a chapter published in the book of Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools and Applications. In this chapter, I have explained how we can evaluate different kinds of model such as Computer Vision, NLP, Regression, Classification. and various other kind of model. This chapter discusses theoretical as well as mathematical steps to evaluate the trained model.

Chapter Link : Link

DOI (Book) : 10.1002/9781119821908

DOI (Chapter) : 10.1002/9781119821908.ch3

Project 05
Channel-Based Similarity Learning Using 2D Channel-Based Convolutional Neural Network

Research Paper named as Channel-Based Similarity Learning Using 2D Channel-Based Convolutional Neural Network published in Springer - Artificial Intelligence on Medical Data, Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 37)

Research Paper : Link

DOI : 10.1007/978-981-19-0151-5_28

Project 06
Conceptualizing a Channel-based overlapping CNN tower architechture for COVID-19 identification from CT-scan Images 

A Research Paper named Conceptualizing a Channel-based Overlapping CNN tower architecture for COVID-19 Identification from CT-scan Images  published in Scientific Reports.

Research Paper: Link

DOI: 10.1038/s41598-022-21700-8

Project 07
Futuristic Trends in Artificial Intelligence

Edited Book named Futuristic Trends in Artificial Intelligence  published in  Iterative International Publishers (IIP), Selfypage Developers Pvt Ltd with  ISBN: 978-93-95632-81-2

Book  : Link

Project 08
Explainable  AI (EXAI) for Sustainable Development: Trends and Applications

Edited Book named as Explainable  AI (EXAI) for Sustainable Development: Trends and Applications will be published  in  CRC Press Taylor & Francis Group.

Book: Link

ISBN: 9781032598864

Project 09
Futuristic Trends in IOT

Edited Book named as Futuristic Trends in IOT published in  Iterative International Publishers (IIP), Selfypage Developers Pvt Ltd 

Reviewer ID: IIPER1655553093

Project 10
Privacy Preservation and Secured Data Storage in Cloud Computing

Edited Book named Privacy Preservation and Secured Data Storage in Cloud Computing published in IGI Global Publisher

Book: Link

ISBN:  9798369305935

DOI: 10.4018/979-8-3693-0593-5

Project 10
3D convolution neural network-based person identification using gait cycles

Human identification plays a prominent role in terms of security. In modern times security is becoming the key term for an individual or a country, especially for countries that are facing internal or external threats. Gait analysis is interpreted as the systematic study of the locomotive in humans.The steps involve object detection, background subtraction, silhouette extraction, skeletonization, and training 3D Convolution Neural Network (3D-CNN) on these gait features.

Research Paper : Link

DOI: 10.1007/s12530-021-09397-y

Project 11
Comparative Study on Forecasting of Schedule Generation in Delhi Region for the Resilient Power Grid Using Machine Learning

In this proposed work, the focus is on Short-Term Load Forecasting (STLF) in the Delhi metropolis for the upcoming twelve months of 2020. The transformation of the conventional electrical grid into a more adaptable and interactive system due to the increasing use of Renewable Energy Resources (RES) has made accurate load prediction crucial for smart grid operation, including planning, scheduling, management, and electricity trading.

Research Paper: Link

DOI: 10.1109/TIA.2023.3316646

Project 12
Libraries for Explainable Artificial Intelligence (EXAI): Python (Chapter) in Explainable AI (XAI) for Sustainable DevelopmentTrends and Applications

This is a chapter published in the book Explainable AI (XAI) for Sustainable Development Trends and Applications. In this chapter, I have explained how we can use different libraries to explain the output of the Artificial Intelligence model to the end user to increase the trustworthiness of the AI model.  by implementing Explainable Artificial Intelligence (XAI). This chapter discusses theoretical as well as mathematical steps to evaluate the trained model.

Book Link : Link

Book ISBN : 9781032598864

Project 13
Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants

We have developed a unique ensemble model for detecting COVID-19 Omicron and Delta variants from lung CT-scan images. The ensemble model combines the Capsule Network (CapsNet) with pre-trained architectures including VGG-16, DenseNet-121, and Inception-v3. This approach aims to enhance reliability and robustness in diagnosing the variants.

Research Paper: Link

DOI: 10.3390/diagnostics13223419

Project 13
Hate Speech Detection Using LSTM (Chapter) in Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing

This is a chapter published in the book Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing. In this chapter, I have explained how we can use LSTM to detect hate speech and explain the model output to the user by implementing Explainable Artificial Intelligence (XAI). This chapter discusses theoretical as well as mathematical steps to evaluate the trained model.

Book Link : Link

bottom of page