Proceedings of Fourth International Conference on Computing and Communication Networks
Description
This book includes selected peer-reviewed papers presented at fourth International Conference on Computing and Communication Networks (ICCCN 2024), held at Manchester Metropolitan University, UK, during 17-18 October 2024. The book covers topics of network and computing technologies, artificial intelligence and machine learning, security and privacy, communication systems, cyber physical systems, data analytics, cyber security for industry 4.0, and smart and sustainable environmental systems.
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Persons
Dr. Akshi Kumar is Senior Lecturer (Associate Professor) and Director-post graduate Research (PGR) in the Department of Computing at Goldsmiths, University of London, UK. Her academic path spans from Doctorate in the domain of web mining at the University of Delhi, India, to Post-Doctoral on explainable AI for NLP from Brazil. She held pivotal positions at Manchester Metropolitan University, UK, as well as at NSUT and DTU in New Delhi, India. Recognized as an "Exceptional Promise in AI and Data Science" by the Royal Academy of Engineering, UK, in 2022, she has received international accolades, including being named in Stanford's "Top 2% Scientist" list in 2021 and 2022, respectively. She has successfully guided 6 doctorates and 33 master theses. Serving as Associate and Guest Editor for impactful journals, her research encompasses affective computing, social media and network analytics, natural language processing (NLP), and AI-driven health care.
Prof. (Dr.) Abhishek Swaroop completed his B.Tech. (CSE) from GBP University of Agriculture & Technology, M.Tech. from Punjabi University Patiala, and Ph.D. from NIT Kurukshetra. He has 28 years of teaching and industrial experience. He has served in reputed educational institutions such as Jaypee Institute of Information Technology, Noida, Sharda University Greater Noida, and Galgotias University Greater Noida. He is actively engaged in research. One of his Ph.D. scholars has completed his Ph.D. from NIT Kurukshetra, and he is currently supervising 4 Ph.D. students. He has guided 12 M.Tech. Dissertations also. He has authored 3 books and 5 book chapters. His 10 papers are indexed in DBLP and 16 papers are SCI. He had been part of the organizing committee of three IEEE conferences (ICCCA-2015, ICCCA-2016, and ICCCA-2017) and one Springer conference (ICICC-2018) as Technical Program Chair. He is Member of various professional societies like CSI and ACM and editorial board of various reputed journals.
Dr. Pancham Shukla is Principal Teaching Fellow in the Faculty of Engineering at Imperial College London. Dr. Shukla holds more than 25 years of teaching and research experience at various Universities in the UK and India. He holds Ph.D. in electrical and electronic engineering from Imperial College London. Dr. Pancham Shukla has supervised a broad range of projects and published papers in signal/image processing, electronic and communications systems. He has organized conferences and has served as Technical Session Chair along with reviewing many international conference and journal papers. Dr. Shukla received Vice Chancellor's award for outstanding contribution to learning and teaching as well as Student Union's award as Outstanding Member of academic staff while working at London Metropolitan University.
Content
Human-Artificial intelligence, Social Intelligence on Metacognitive and Technological Skill Enhancement in Active Learning environments.- A robust technique for survival prediction on pre-operative MRI images using SE-CNN and Cox regression.- Face Teachable Machine model combining Machine Learning and Deep Learning for Disease Diagnosis.- Autism Spectrum Disorder Prediction using Particle Swarm Optimization and Convolution Neural Network.- Chaotic Harris Hawks Optimization based Feature Selection with Attention Deep Learning Driven Intrusion Detection in CPS Environment.- Anti Occlusion Multi object Tracking Based on YOLOv5.- Analysis of Crop Disease Detection using image data with deep leaning techniques.