The book presents selected papers from NIELIT's International Conference on Communication, Electronics and Digital Technology (NICE-DT 2023) held during February 10-11, 2023, in New Delhi, India. The book covers state-of-the-art research insights on artificial intelligence, machine learning, big data, data analytics, cyber security and forensic, network and mobile security, advance computing, cloud computing, quantum computing, VLSI and semiconductors, electronics system, Internet of Things, robotics and automations, blockchain and software technology, digital technologies for future,…mehr
The book presents selected papers from NIELIT's International Conference on Communication, Electronics and Digital Technology (NICE-DT 2023) held during February 10-11, 2023, in New Delhi, India. The book covers state-of-the-art research insights on artificial intelligence, machine learning, big data, data analytics, cyber security and forensic, network and mobile security, advance computing, cloud computing, quantum computing, VLSI and semiconductors, electronics system, Internet of Things, robotics and automations, blockchain and software technology, digital technologies for future, assistive technology for divyangjan (people with disabilities) and Strategy for Digital Skilling for building a global Future Ready workforce.
Prof. Sri Niwas Singh is the director, Atal Bihari Bajpayee-Indian Institute of Information Technology and Management (ABV-IIITM), Gwalior (MP), India (on leave from Professor (HAG), Department of Electrical Engineering, Indian Institute of Technology Kanpur, India). He was the vice-chancellor of MMM University of Technology Gorakhpur from April 2017 to July 2020. Dr. Singh received several awards including Young Engineer Award 2000 of Indian National Academy of Engineering (INAE) and Young Engineer Award of CBIP New Delhi (India), in 1996. He is also a recipient of INAE Outstanding Teacher Award 2016 and IEEE R10 region (Asia-Pacific) Outstanding Volunteer Award 2016. Dr. Sri Niwas Singh is appointed as IEEE Distinguish Lecturer of Power & Energy Society from 2019 and Industry Application Society for 2019-2021. He is also a recipient of NPSC 2020 Academic Excellence Award and 2021 IEEE Industry Application Society (IAS) Outstanding Educator/Mentor Award. His research interests include power system restructuring, FACTS, power system optimization and control, security analysis, wind power, etc. Prof. Singh has published more than 550 papers (h-index = 59, Citation = 14k+) in international/national journals/conferences and supervised 41 Ph.Ds. (7 Ph.Ds. under progress). He has also written 30 chapters, 8 edited books, and 2 textbooks. Dr. Singh is a fellows of IEEE (USA), IET (UK), INAE, IE(I), and IETE. Dr. Saurov Mahanta, Ph.D., is a resident of Guwahati, Assam, India, and working for the National Institute of Electronics and Information Technology, Guwahati, under the Ministry of Electronics and Information Technology, Govt. of India. Dr. Mahanta did his Master's in Molecular Biology and Biotechnology from Tezpur University, Tezpur, Assam, and he got the Ph.D. degree from Gauhati University, Assam, for his work on Computational Drug Discovery for Tuberculosis. Dr. Mahanta is actively involved in bioinformatics-based drug discovery works using combinatorial chemistry, molecular docking, molecular modeling, molecular dynamics, and related analysis. His research area includes structure-based drug discovery studies for COVID-19, breast and stomach cancer, Alzheimer's disease, and tuberculosis. He is also involved in nano-informatics-based research using proteins of different pathogens. Dr. Mahanta has 15 years of research experience, and he is so far credited with 25 research publications in SCI journals, 1 edited book, and 2 chapters. Prof. Yumnam Jayanta Singh is the executive director, National Institute of Electronics and Information Technology, Govt. of India, Guwahati. He has worked with several universities, such as Don Bosco University, India; Swinburne University of Technology, Malaysia; Misurata University and Skyline University (UAE). He had worked with MNCs such as NTT Data and TechMahindra. Prof. Yumnam Jayanta Singh is a member of the Data Science Society, IASTED, EUROSIS, IETE, IEEE, etc. His research interests include cloud computing, data warehouse and mining, blockchain, distributed database management systems, digital image processing, and software engineering and testing. Prof. Yumnam Jayanta Singh has published several research papers in international, national journals, and conferences. He has supervised Ph.D. and Master students. He is one of the founder editors of ADBU-AJET. He has executed several projects of national importance and was in the team developing the Strategy Plan of Govt. of India and State Govt., such as whitepaper on blockchain and IT park policy.
Inhaltsangabe
Quality of Service (QoS)-driven Edge Computing and Smart Hospitals: A Vision, Architectural Elements and Future Directions.- Real Time Face Mask Detector with Multi-class Classification using Deep Neural Networks.- Data Correlation Analysis to Curb Road Accidents.- Speech Recognition via Machine Learning in Recording Studio.- Clustering-Based Filter Pruning Approach for Efficient ConvNets.- SMS and E-mail Spam Classification using Natural Language Processing and Machine Learning.- Detecting Malware in Windows Environment Using Machine Learning.- Fake News Detection using LSTM Based Deep Learning Approach and Word Embedding Feature Extraction.- Safe Distance Monitoring for COVID-19 Using YOLOv3 Object Recognition Paradigm.- Machine Learning Based Depth of Anesthesia Estimation using Spectral and Statistical Features of EEG.
Quality of Service (QoS)-driven Edge Computing and Smart Hospitals: A Vision, Architectural Elements and Future Directions.- Real Time Face Mask Detector with Multi-class Classification using Deep Neural Networks.- Data Correlation Analysis to Curb Road Accidents.- Speech Recognition via Machine Learning in Recording Studio.- Clustering-Based Filter Pruning Approach for Efficient ConvNets.- SMS and E-mail Spam Classification using Natural Language Processing and Machine Learning.- Detecting Malware in Windows Environment Using Machine Learning.- Fake News Detection using LSTM Based Deep Learning Approach and Word Embedding Feature Extraction.- Safe Distance Monitoring for COVID-19 Using YOLOv3 Object Recognition Paradigm.- Machine Learning Based Depth of Anesthesia Estimation using Spectral and Statistical Features of EEG.
Quality of Service (QoS)-driven Edge Computing and Smart Hospitals: A Vision, Architectural Elements and Future Directions.- Real Time Face Mask Detector with Multi-class Classification using Deep Neural Networks.- Data Correlation Analysis to Curb Road Accidents.- Speech Recognition via Machine Learning in Recording Studio.- Clustering-Based Filter Pruning Approach for Efficient ConvNets.- SMS and E-mail Spam Classification using Natural Language Processing and Machine Learning.- Detecting Malware in Windows Environment Using Machine Learning.- Fake News Detection using LSTM Based Deep Learning Approach and Word Embedding Feature Extraction.- Safe Distance Monitoring for COVID-19 Using YOLOv3 Object Recognition Paradigm.- Machine Learning Based Depth of Anesthesia Estimation using Spectral and Statistical Features of EEG.
Quality of Service (QoS)-driven Edge Computing and Smart Hospitals: A Vision, Architectural Elements and Future Directions.- Real Time Face Mask Detector with Multi-class Classification using Deep Neural Networks.- Data Correlation Analysis to Curb Road Accidents.- Speech Recognition via Machine Learning in Recording Studio.- Clustering-Based Filter Pruning Approach for Efficient ConvNets.- SMS and E-mail Spam Classification using Natural Language Processing and Machine Learning.- Detecting Malware in Windows Environment Using Machine Learning.- Fake News Detection using LSTM Based Deep Learning Approach and Word Embedding Feature Extraction.- Safe Distance Monitoring for COVID-19 Using YOLOv3 Object Recognition Paradigm.- Machine Learning Based Depth of Anesthesia Estimation using Spectral and Statistical Features of EEG.
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