Proceedings of International Conference on Computational Complexity and Intelligent Algorithms

Dr. Vishal Goar is an assistant professor at Engineering College Bikaner and former Dean (Research), Bikaner Technical University, with 17 years of academic experience. He has published 51 research papers (SCI/Scopus/WoS) and 53 conference papers. He has authored/edited 13 books (Springer, ACM USA, Scholars-Press) and holds two patents and three patent grants. He has supervised six Ph.D. scholars and served as organizing chair, convener, and publicity chair for Springer/IEEE/ACM conferences. His research interests include security, cloud computing, IoT, sensors, and deep learning. A life member of professional bodies such as IEANG, ACM, ISTE, IACSIT, UACEE, CSTA, SDIWC, and CSI, he also serves on the Board of Studies and Research Board of various universities.Dr. Jyoti Gajrani is an assistant professor in CSE at Govt. Engineering College Ajmer and recipient of the Visvesvaraya Best Teachers Award 2021 (AICTE). She holds an M.Tech. (IIT Bombay) and Ph.D. (MNIT Jaipur) and has 20 years of teaching and research experience. Her research focuses on security, privacy, and mobile malware analysis. She is Co-PI of two SERB-DST projects worth ₹35 lakhs and has published 45 research papers, one book, and seven book chapters. She has delivered expert talks at IITs/NITs, edited Springer and IEEE conference proceedings, and developed a MOOC course on Cyber Security. She serves on the Board of Studies at Bikaner Technical University and as Program Chair for six international conferences.Dr. Mohammad Abu Yousuf received the B.Sc.(Engineering) degree in Computer Science and Engineering from Shahjalal University of Science and Technology, Bangladesh, Master of Engineering degree in Biomedical Engineering from Kyung Hee University, South Korea, and a Ph.D. degree in Science and Engineering from Saitama University, Japan. He has more than 22 years of experience. He is working as a professor at the Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh. His research interests include biomedical image processing, human-robot interaction, computer vision, machine learning and natural language processing. He has published more than 135 research papers in different international journals and conferences.João Manuel R. S. Tavares graduated from Universidade do Porto, Portugal. He earned his M.Sc. degree and Ph.D. degree in Electrical and Computer Engineering from the Universidade do Porto. He is a senior researcher at the Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial (INEGI) and a full professor at the Department of Mechanical Engineering (DEMec) of the Faculdade de Engenharia da Universidade do Porto (FEUP). He is a co-editor of more than 80 books, co-author of more than 50 book chapters, 650 articles in international and national journals and conferences, and 3 international and 3 national patents. His main research areas include computational vision, medical imaging, biomechanics, biomedical engineering, and new product development.

• AI Models for Real-Time Blood Pressure Monitoring and Anomaly Detection.- Engineering Secure AI Solutions for Protecting Medical Data.- Developing and IoT-Driven BCI Framework for Real-Time Neural Signal Decoding to Speech Conversion.- Deep Learning Approaches for Skin Cancer Detection Using Dermoscopy Images .- Autonomous Defense System (WatchDog).- "IOT- Based Patient Health Monitoring System For Remote Location using ML Algorithms".- Revolutionizing Talent Acquisition Comparative Insights into AI-Powered Job Platforms.- Navigating the Crossroads: Gender and Career Conflict in Board Students.- A Comparative Analysis of AI-Enhanced MOOCs: User Engagement and Platform Popularity.- A Novel Sentiment-Driven Approach to Improving Cyberbullying Detection Using Advanced NLP Models.- To Compare Performance of various Machine Learning Algorithm through EEG Analysis for Schizophrenia.- Decadal Perspective of IoT in Forest Fire Detection.- A method of Image Categorization using Machine Learning.- A Comprehensive Framework for Weight Management: Leveraging  Personalized Health Data Integrating, Macro, Micronutrient, and  Behavioural Insights.