Embedded Artificial Intelligence

Arpita Nath Boruah is an Assistant Professor of Computer Science and Engineering in Assam down town University, Assam, India. She earned her doctorate degree from National Institute of Technology, Silchar, Assam in the department of Computer Science and Engineering. She holds an M.Tech. degree in Computer Science & Engineering from NERIST, Arunachal Pradesh and a Bachelor’s Degree in Computer Science & Engineering from Jorhat Engineering College, Assam. She is currently involved in research areas that include Data Mining, Machine Learning, Data Science and transparent decision-making system. She has published several international journal articles in SCI/SCIE indexed journals of various reputed publishers including and has attended many international conferences. Mrinal Goswami received his B.Tech. Degree in Computer Science and Engineering from North Eastern Regional Institute of Science & Technology, India in 2012. He received his MTech. and Ph.D. in Computer Science and Engineering from the National Institute of Technology, Durgapur, West Bengal, India, in 2014 and 2019, respectively. He has over eight years of research and teaching experience as Assistant Professor. Currently, he is associated with the Faculty of Engineering and Technology, Programme of Computer Science and Engineering, Assam down town University, Guwahati, Assam, India, and working as an Associate Professor. Before this, he worked as an Assistant Professor in the Department of Computer Science & Engineering at the University of Petroleum and Energy Studies (NIRF Rank 54 & NACC A accredited) and North Eastern Regional Institute Science & Technology (Deemed-to-be-University under the Ministry of Education, Govt of India). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and Cellular Automata India Chapter. He has published several international research articles on design, testing, security, and fault-tolerance issues in digital systems, Quantum dot Cellular Automata, Quantum Computing, and AI. Manoj Kumar completed his Ph.D. from The Northcap University and M.Sc. (Information Security and Digital Forensics) from Technological University Dublin, Ireland in 2013. He received fully-funded scholarship for the M.Tech and M.Sc. program from Irish Government and MHRD respectively. Dr. Kumar has more than 13+ years of research, teaching, and corporate experience. He is currently working on the post of Associate Professor-Cyber Security in the University of Wollongong in Dubai, UAE and Research Head for Network and Cyber Security Cluster @UOWD. He has published over 170 articles in various journals and conferences. He published over 10 patents and delivered three international research funded projects. He published three authored books and eight edited books. His specializations are Digital Forensics, Machine Learning, Information Security, Image Processing, IOT and Computer Networking. Dr. Kumar is member of numerous renowned professional bodies including IEEE, ACM, IAENG, ISTS and UACEE are a few of them. Dr. Kumar received the Best Researcher Award in 2020, an outstanding Scientist Award in 2021, and a Young Researcher Award in 2021 from recognized international professional societies. Octavio Loyola-González received his PhD degree in Computer Science from the National Institute for Astrophysics, Optics, and Electronics, Mexico. He has won several awards from different institutions due to his research work on applied projects; consequently, he is a Member of the National System of Researchers in Mexico (Rank1). He worked as a distinguished professor and researcher at Tecnologico de Monterrey, Campus Puebla, for undergraduate and graduate programs of Computer Sciences. He was Managing Director for Altair Management Consultants, having branches in USA, UK, Spain, and Mexico; where he was leading several teams of data scientists for AI & ML, Business Intelligence reporting, and traditional consulting. Currently, Dr. Octavio Loyola-González serves as an AI Executive Consulting Manager at NTT DATA. His responsibilities include bringing in clients from the automotive, manufacturing, real estate, infrastructure, and services sectors for deals related to Advanced Analytics (AI & GenAI). Additionally, he oversees the operations and activities of a team of data scientists, guiding them in both technical and business strategy aspects. Furthermore, he is actively involved in publishing scientific papers and books in reputable journals to promote advancements in AI research.

Section A: Overview of Embedded Systems and Artificial Intelligence 1. Unleashing Intelligence at the Edge: Exploring Machine Learning in Embedded Systems 2. Fusion of Edge Computing in AI-Enabled Embedded Technologies 3. Developing Edge AI for Embedded Systems 4. AI at the Edge: Merging Intelligence and Distributed Computing SECTION B: Case Studies and Practical Applications of AI-enabled Embedded Systems 5. Transformative Impact of AI-Enabled Embedded Systems in Financial Services: Case Studies and Practical Applications 6. Embedded AI Approaches for Multi-organ Critical Care Diagnostics Support and Decision making – Current trends and Emerging Scenarios 7. Embedded AI-Based Approaches for Skin Cancer Detection: Machine Learning Techniques and Applications 8. Artificial Intelligence and Automated Deep Learning for Medical Imaging 9. A comprehensive review on Embedded systems security using
Machine Learning 10. Embedding Business Analysis for Successful AI-Powered Digital Transformation 11. AI in Implementation of EDEEC protocol for 4-Level Scalable
Heterogeneous Wireless Sensor Networks 12. Side Channel Attack-Resistant Embedded Systems 13. Smart Irrigation System using IoT-based devices 14. Revolutionizing Healthcare from Inside - Leveraging Expanded Reality with Ingestible Sensors 15. Object Detection using Opencv 16. Analytical Study of Dominating features of Intelligent Controller over Conventional Controller SECTION C: Ethical Considerations in Embedded AI 17. Data Security and Ethical Considerations in Embedded AI Systems 18. Security of Social Media Content for AI-Embedded Systems: Comparative Analysis