Work place: Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh
E-mail: mskaiser@juniv.edu
Website:
Research Interests:
Biography
M. Shamim Kaiser is currently working as a Professor at the Institute of Information Technology of Jahangirnagar University, Savar, Dhaka-1342, and Bangladesh. He received his Bachelor's and Master's degrees in Applied Physics Electronics and Communication Engineering from the University of Dhaka, Bangladesh in 2002 and 2004 respectively, and the Ph. D. degree in Telecommunication Engineering from the Asian Institute of Technology (AIT) Pathumthani, Thailand, in 2010. His current research interests include Data Analytics, Machine Learning, Wireless Network & Signal processing, Cognitive Radio Network, Big data and Cyber Security, Renewable Energy. He has authored more than 100 papers in different peer-reviewed journals and conferences. He is an Associate Editor of the IEEE Access Journal, Guest Editor of Brain Informatics Journal, and Cognitive Computation Journal. Dr. Kaiser is a Life Member of Bangladesh Electronic Society; Bangladesh Physical Society. He is also a senior member of IEEE, USA and IEICE, Japan, and active volunteer of the IEEE Bangladesh Section. He is the founding Chapter Chair of the IEEE Bangladesh Section Computer Society Chapter.
By Biman Barua M. Shamim Kaiser
DOI: https://doi.org/10.5815/ijeme.2026.03.03, Pub. Date: 8 Jun. 2026
This paper introduces a novel approach an AI-powered Multi-Agent System (MAS) for dynamically optimizing support to enhance real-time travel reservation-side customer experience. It has an architecture with specialized agents working together under a centralized agent manager, including natural language processing, booking, optimization, and context-aware modules. The system proposes to address common constraints encountered in traditional travel platforms: delayed response to user queries, ambiguity treated poorly, and adaptation to user preferences not incorporated. Through simulated environments and realistic use cases, the MAS enables complex travel requests to be dealt with, availability to be changed dynamically, and user satisfaction to be enhanced. The modular architecture design allows easy integration into larger smart tourism infrastructures. This study thus pushes the frontier further by merging AI, multi-agent collaboration, and user-centered design in a time-sensitive application world. Future directions include adaptive learning agents, multilingual interaction capabilities, and broadening the domain applications to hotel management and intelligent itinerary planning.
[...] Read more.Subscribe to receive issue release notifications and newsletters from MECS Press journals