A proposed deep learning model for multichannel ECG noise reduction Jay Prakash Maurya, Manish Manoria, Sunil Joshi Discover Artificial Intelligence, 2025 Heart disease is a critical concern of healthcare for everyone in today’s era. An effective and noninvasive indication of heart disease is an electrocardiogram (ECG). Understanding regular ECG signal patterns and comparisons with irregular, patterns may help to identify the serious nature of heart diseases such as arrhythmia. Comparison of ECG signal patterns is very difficult manually, and machine-based interpretation is a demand of society. Errors in ECG interpretation might result from noise contamination of ECG signals. ECG pretreatment from noise is therefore needed for precise analysis. This article proposed a novel deep learning-based solution for multichannel ECG noise reduction, through utilizing the capabilities of fully convolutional neural network along with the Jacobin regularization to ensure confining and preserving local information. Cascaded layered approach was framed in encoder and decoder sections of model where denoising and reconstruction process worked and compared on standard performance parameters with recent denoising deep autoencoders. This proposed work FCN-DAE with Jacobin regularization uses the noise stress test database (NSTDB) for the noise signal of ECG data sourced from the PhysioNet repository. The proposed model achieves 4.763 * 10 –2 mv 2 for signal space diversity (SSD), 0.288 mv for the median absolute deviation and 1.859 for the root mean square error (RMSE) in the conducted experiment. The experimental findings demonstrate that complex noise from the ECG signal may be removed up to 97.02%.
Experiment and simulation of proposed V2I system: An approach using quantum key distribution for e-mobility system Jay Prakash Maurya, Vijay Kumar Trivedi, Vinesh Kumar, Dheresh Soni Networking Transport and Quality of Service in Vehicular Networks, 2025 Research and development in Next generation networks is a growing field, including subfields like Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to everything (V2X) that helps application development and participating towards advanced smart city projects in India. Smart City projects in India demands fast communication channel, secure channel as well as data exchange policies to control infrastructure components. This article proposed a real-time simulation of a next generation network improving existing infrastructure of cities through the involvement of Quantum Computing. Quantum key based protocol BB84 has been simulated and tested in this article work for comparing Traditional and modern V2I based software defined network. Comparative results of proposed simulation in PoliQI and NS3 found effective in terms of security, performance, and scalability for different numbers of nodes. This article delivers a computational technique that may be used creates secure channels for key exchange between cloud service providers and customers, ensuring encryption keys
Engaging Students beyond Copy-Paste: Quizzler as a Solution for Active, AI-Powered Learning Vinesh Kumar, Abhishek Shrivastava, Jay Prakash Maurya 2025 7th International Conference on Information Systems and Computer Networks Iscon 2025, 2025 Content copying and pasting from AI tools like Chatgpt or Gemini has become a problem in the digital classroom environment in recent years, endangering students' ability to learn and think critically. This paper introduces Quizzler, which is an AI-powered platform for student to support adaptive learning and assessment. Quizzler has context-aware questions, question difficulty adjusting, adaptive learning, and gamification feature in comparison of traditional learning and evaluation methods. Quizzler has advantages in terms of learning rate, engagement time, and performance improvement are highlighted by a comparative analysis with other quiz tools. Students can move from passively reproducing knowledge to actively participating and exercising critical thought. As a scalable and pedagogically based solution for contemporary online education platform, Quizzler promote engagement, academic integrity, and deeper learning outcomes.
Leveraging the Effect of CNN to Identify Severity of Lumpy Skin Disease in Cattle Vinesh Kumar, Jay Prakash Maurya, Abhishek Shrivastava 2024 International Conference on Artificial Intelligence and Quantum Computation Based Sensor Applications Icaiqsa 2024 Proceedings, 2025 There have been research articles in the past that identified the presence of Lumpy Skin Disease (LSD) in Cattle through machine-learning methods. This paper is focused not only on confirming the presence of the virus but also on predicting the stage of the LSD virus in a Cattle skin image sample. In this paper, several machine-learning classification algorithms including Support Vector Machines, Naive Bayes, Random Forest, AdaBoost, K-nearest neighbors, and finally, Convolutional Neural Networks, were analyzed to classify the image samples of Cattle skin into 3 categories: Normal (LSD-negative), Mild LSD, and Severe LSD classes. A custom-made dataset consisting of 3454 image samples from the three classes was built to train the models. There is no cure for LSD, and the infected cattle's immune response is the only way out; however, as a supporting aid, primary anti-viral treatment can be given for a faster recovery. This should be administered to the cattle with a higher probability of getting cured and staying alive. Hence, the detection of the stage of the disease is significant in terms of the investment of time and treatment to needy cattle as healthcare facilities and government resources are limited. After analyzing the results from the models trained on the algorithms mentioned earlier, the best accuracy was obtained through Convolutional Neural Networks with pre-trained base model VGG-16 with 92.93% on the training dataset and 88.93% on the validation dataset. This model eliminates possibilities of human bias to empower the government in granting fair compensation in case of death due to LSD and to maximize resource utilization in the best possible way if integrated into a user-friendly application.
Cutting-Edge Image Recognition Leveraging Deep Learning and Machine Learning for Enhanced Accuracy Abhishek Shrivastava, Vinesh Kumar, Jay Prakash Maurya 2024 International Conference on Artificial Intelligence and Quantum Computation Based Sensor Applications Icaiqsa 2024 Proceedings, 2025 This paper investigates advanced techniques in image recognition and classification by integrating deep learning and machine learning approaches to achieve higher accuracy. Through the implementation of sophisticated training algorithms, the study demonstrates enhanced performance in recognizing and categorizing images across various data models. A major turning point in the development of image identification technology came in 2012 when deep neural networks were introduced. These networks surpassed earlier cutting-edge algorithms and completely changed the computer vision industry. This progress has brought us closer to achieving human-level accuracy in tasks such as identity verification. The role of large datasets like ImageNet is crucial, as they provide the foundation for the success of deep learning. With continuous research pushing the limits of picture identification and producing major advances in human knowledge, deep learning has a huge influence on business, society, and technology. Additional research in this area might lead to creative uses that revolutionize our relationship with our surroundings. Key topics discussed include data pre-processing, post-processing, model optimization, and accuracy enhancement. The findings highlight the potential of cutting-edge technologies to advance image classification and recognition in various sectors, such as medical imaging and visual analysis. The approach emphasizes scalability and adaptability, ensuring that models can be effectively applied to real-world scenarios. Future research will focus on refining these models to handle even more complex image datasets, further enhancing their practical utility and reliability.
Open Market: A Proposal To Improve Engagement and Motivation Among Students Towards Basic Programming Subjects Vinesh Kumar, Vartika Pandey, Jay Prakash Maurya, Abhishek Shrivastava 2024 1st International Conference on Advanced Computing and Emerging Technologies Acet 2024, 2025 Gamification can be applied to grab the attention of people in wide range of environments. This work sheds light upon the prospects of its application in classroom to deliver the education related to computer programming is called “Open Market”. To study the impact upon students after applying this strategy, the level of motivation, engagement and learning were explored. This paper proposed a novel engagement model to improve learning level in student and comp are the impact of gamification method from traditional methods in terms of assessment methods for “Open Market”. This paper also encourages student’s agenda in student learning process by analyzing standard deviation in learning in number of parameters like age, subject, motivation type, and intrinsic method for engagement in programming subjects like C, C++, Python, Java etc. The outcome of the study contends in the support of open market strategy to be indulged in the teaching and learning mechanism.
An Optimized Machine Learning Model for R and T Peak Detection in ECG Signal Jay Prakash Maurya, Vinesh Kumar, Vijay Kumar Trivedi, Abhishek Shrivastava 2024 International Conference on Artificial Intelligence and Quantum Computation Based Sensor Applications Icaiqsa 2024 Proceedings, 2025 Detection of R and T peaks in electrocardiogram (ECG) signals is essential for monitoring and diagnosing heart-related diseases. In generic cases, these peaks suffer through different types of noise and variability, which are of important clinical significance in identifying diseases like myocardial infarction, ischemia, and electrolyte imbalance. Machine learning techniques are integrated computer-aided diagnosis systems (CIDS) [1] to reduce manual effort in peak analysis by medical professionals. Machine learning models are used for learning from ECG nonlinear patterns and tested to mark actual R and T peaks. Once the machine learning model is trained and tested. Minimizing error or loss in the implemented machine learning model is possible through tuning parameters and finding the best fitness value. This article proposed an optimized metaheuristic approach (finding best fitness) for convolutional neural network (CNN), to detect R and T peaks in captured ECG, aimed, at improving the accuracy and robustness of real-time signal data. Before training and testing optimization, different approaches of metaheuristics optimization were evaluated based on best fitness values over iteration and found optimization technique having lowest convergence. Applying Particle Swarm Optimization (PSO) with CNN achieved 99.57% accuracy, 99.13% precision, 99.28% recall, and 99.25% F1-Score on the publicly available ECG database. The paper results that the proposed approach not only detects peaks but also improves the capabilities of the model for real-time cardiac monitoring systems development.
An empirical study of intrusion detection system using feature reduction based on evolutionary algorithms and swarm intelligence methods International Journal of Applied Engineering Research, 2017
RECENT SCHOLAR PUBLICATIONS
AI-Driven Multi-Scan Diagnostic System for Early and Accurate Detection of Multiple Sclerosis Using Deep Learning and Ensemble Models V Kumar, JP Maurya, A Shrivastava, H Shrivastava 2026 International Conference on Emerging Smart Computing and Informatics … , 2026 2026
Health and Lifestyle Survey Data JP Maurya, P Mathur, U Patil, A Thakur, aniket gawai, ... 10.17632/6622pm2t95.1 , 2025 2025
Engaging Students Beyond Copy-Paste: Quizzler as a Solution for Active, AI-Powered Learning V Kumar, A Shrivastava, JP Maurya 2025 7th International Conference on Information Systems and Computer … , 2025 2025
Long-Range Unmanned Aerial Vehicle (UAV) Data Transmission Technology Based on Fiber Optic Lasers A Shrivastava, V Kumar, JP Maurya 2025 7th International Conference on Information Systems and Computer … , 2025 2025
Enhancing E-Learning Platforms with Computer Vision for Real-Time Engagement & Monitoring JP Maurya, V Kumar, VK Trivedi, S Pandey 2025 7th International Conference on Information Systems and Computer … , 2025 2025
ECG Image dataset for Myocardial Infarction Complication VKT Jay Prakash Maurya , Vinesh Kumar DOI: 10.17632/5dhyd9t26m.2 , 2025 2025
A proposed deep learning model for multichannel ECG noise reduction JP Maurya, M Manoria, S Joshi Discover Artificial Intelligence 5 (1), 65 , 2025 2025 Citations: 1
Back-Propagation Deep Neural Network VK Trivedi, S Sahu, V Panse, JP Maurya Intelligent Computing and Communication: Proceedings of 7th ICICC 2024 … , 2025 2025
Wellness Management Using Incident Response Strategies and Recovery Tools: Practices and Proposal in Healthcare JP Maurya, MK Muchahari, M Bakhshi, V Kumar, RK Dhanaraj Cybersecurity in Healthcare Applications, 230-245 , 2025 2025
FOG Computing and Blockchain-Supported Identity Management for IoMT: An Advancement in Personalized Healthcare JP Maurya, V Kumar, S Aanjankumar, M Sathyamoothy, BR Aslina Cybersecurity in Healthcare Applications, 61-79 , 2025 2025
Experiment and Simulation of Proposed V2I System: An Approach Using Quantum Key Distribution for E-Mobility System JP Maurya, VK Trivedi, V Kumar, D Soni Networking, Transport, and Quality of Service in Vehicular Networks, 87-112 , 2025 2025
Leveraging the effect of CNN to identify severity of lumpy skin disease in cattle V Kumar, JP Maurya, A Shrivastava 2024 International Conference on Artificial Intelligence and Quantum … , 2024 2024 Citations: 7
An optimized machine learning model for R and T peak detection in ECG signal JP Maurya, V Kumar, VK Trivedi, A Shrivastava 2024 International Conference on Artificial Intelligence and Quantum … , 2024 2024 Citations: 2
Cutting-edge image recognition leveraging deep learning and machine learning for enhanced accuracy A Shrivastava, V Kumar, JP Maurya 2024 International Conference on Artificial Intelligence and Quantum … , 2024 2024 Citations: 7
Breast Cancer Tissue Detection System Based on K-Mean Clustering and Deep Neural Network VK Trivedi, P Suman, JK Sheikh, JP Maurya, N Pateria 2024 13th International Conference on System Modeling & Advancement in … , 2024 2024
Performance Exploration of Network Intrusion Detection System with Neural Network Classifier on The KDD Dataset S Devaraju, D Soni, S Jawahar, JP Maurya, V Tiwari Journal homepage: http://iieta. org/journals/ijsse 14 (5), 1431-1437 , 2024 2024 Citations: 2
An Early Detection of Breast Cancer Tissue with Gradient-Based Back-Propagation Deep Neural Network VK Trivedi, S Sahu, V Panse, JP Maurya International Conference on Intelligent Computing and Communication, 133-147 , 2024 2024
Open market: a proposal to improve engagement and motivation among students towards basic programming subjects V Kumar, V Pandey, JP Maurya, A Shrivastava 2024 1st International Conference on Advanced Computing and Emerging … , 2024 2024 Citations: 4
Implementation of weight adjusting GNN with differentiable pooling for user preference-aware fake news detection JP Maurya, V Richhariya, B Gour, V Kumar 2024 Citations: 4
Implementation of Leaf Disease Detection Using One-Shot & Region Inception Image Recognition Technique SDAG Jay Prakash Maurya, Dheresh Soni PEIS [PEIS 2023. Lecture Notes in Electrical Engineering, vol 1098. Springer … , 2024 2024 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
An empirical study of intrusion detection system using feature reduction based on evolutionary algorithms and swarm intelligence methods R Dubey, D Rathore, D Kushwaha, JP Maurya International Journal of Applied Engineering Research 12 (19), 8884-8889 , 2017 2017.0 Citations: 29
Analyze the performance of bio-medical image compression technique using particle swarm optimization JP Maurya, AS Thakur, D Rathore 2018 International Conference on Advanced Computation and Telecommunication … , 2018 2018.0 Citations: 9
Cardiac Arrhythmia Classification Using Cascaded Deep Learning Approach (LSTM & RNN) JP Maurya, M Manoria, S Joshi International Conference on Machine Learning, Image Processing, Network … , 2022 2022.0 Citations: 8
An Enhanced approach for content based image retrieval PS Patheja, AW Akhilesh, MJ Prakash Research Journal of Recent Sciences 1 (ISC-2011), 415-418 , 2012 2012.0 Citations: 8
Leveraging the effect of CNN to identify severity of lumpy skin disease in cattle V Kumar, JP Maurya, A Shrivastava 2024 International Conference on Artificial Intelligence and Quantum … , 2024 2024.0 Citations: 7
Cutting-edge image recognition leveraging deep learning and machine learning for enhanced accuracy A Shrivastava, V Kumar, JP Maurya 2024 International Conference on Artificial Intelligence and Quantum … , 2024 2024.0 Citations: 7
Maurya Jay Prakash,“An Enhanced Approach for Content Based Image Retrieval”, International Science Congress Association PS Patheja, A Waoo Akhilesh Research Journal of Recent Sciences, ISSN, 2277-2502 , 2012 2012.0 Citations: 6
Fog Computing and Blockchain-Based IoMT for Personalized Healthcare JP Maurya, M Kumar, V Kumar Emerging Technologies and Security in Cloud Computing, 219-235 , 2024 2024.0 Citations: 5
Comparative Analysis Using Hive and Pig on Consumers Data PJ Prof. Jay Prakash Maurya International Journal of Computer Science and Information Technologies, 8 … , 2017 2017.0 Citations: 5
A survey on face recognition techniques JP Maurya, S Sharma Computer Engineering and Intelligent Systems 4 (6), 11-17 , 2013 2013.0 Citations: 5
Open market: a proposal to improve engagement and motivation among students towards basic programming subjects V Kumar, V Pandey, JP Maurya, A Shrivastava 2024 1st International Conference on Advanced Computing and Emerging … , 2024 2024.0 Citations: 4
Implementation of weight adjusting GNN with differentiable pooling for user preference-aware fake news detection JP Maurya, V Richhariya, B Gour, V Kumar 2024.0 Citations: 4
An Exploratory Review of Web Content Mining Techniques and Methods N Parmar, V Richhariya, JP Maurya International Journal of Advanced Research in Computer and Communication … , 2016 2016.0 Citations: 3
An optimized machine learning model for R and T peak detection in ECG signal JP Maurya, V Kumar, VK Trivedi, A Shrivastava 2024 International Conference on Artificial Intelligence and Quantum … , 2024 2024.0 Citations: 2
Performance Exploration of Network Intrusion Detection System with Neural Network Classifier on The KDD Dataset S Devaraju, D Soni, S Jawahar, JP Maurya, V Tiwari Journal homepage: http://iieta. org/journals/ijsse 14 (5), 1431-1437 , 2024 2024.0 Citations: 2
Intelligent and Smart Agriculture System Using Cooperative Approach B Gour, JP Maurya Intelligent Sensor Node-Based Systems, 211-228 , 2023 2023.0 Citations: 2
Performance of AODV against Malicious node in Mobile Ad Hoc Network V Richhriya, JP Maurya, T Saxena Int J Eng Cur Trends (IJERCT) 2 (3) , 2020 2020.0 Citations: 2
A Survey on: Methods of Web Behavior Prediction by: Utilizing Different Features J Maurya, S Singh, H Patil, P Jain International Journal 4 (3) , 2014 2014.0 Citations: 2
Maurya Jay Prakash,“An Enhanced Approch for Content Based Image Retrieval” PS Patheja, A WaooAkhilesh Research Journal of Recent Sciences ISSN, 2277-2502 , 0 Citations: 2
A proposed deep learning model for multichannel ECG noise reduction JP Maurya, M Manoria, S Joshi Discover Artificial Intelligence 5 (1), 65 , 2025 2025.0 Citations: 1
