Dr. Jianbang Liu: Research fellow at College of Mathematics and Computer, Xinyu University, China
Jianbang Liu is a Research Fellow at Xinyu University, China, with expertise in Human-Computer Interaction (HCI) and Artificial Intelligence (AI). He holds a Master’s degree from Qilu University of Technology and a Ph.D. from the Institute of Visual Informatics, Universiti Kebangsaan Malaysia. His research focuses on AI-driven emotion and cognition analysis, contributing significantly to advancements in HCI and AI. Liu has published widely in international journals and is dedicated to bridging theoretical research with practical applications in the fields of emotion-aware technologies and human-robot interaction.
Online Profiles
Jianbang Liu maintains an active online presence through various academic and professional platforms. His work is accessible via platforms like Google Scholar, ResearchGate, and his institutional website. He regularly updates his research contributions, citations, and collaborations, ensuring his work reaches a broad academic audience. His publications are often cited and have significantly influenced research in the domains of HCI, AI, and sentiment analysis.
Education
Liu completed his Master’s degree at Qilu University of Technology (Shandong Academy of Sciences), China, in 2018. Following this, he pursued a Ph.D. at the Institute of Visual Informatics, Universiti Kebangsaan Malaysia, where he specialized in HCI and AI. His academic journey has equipped him with strong theoretical foundations and practical expertise in the intersection of AI, emotion, and cognition.
Research Focus
Liu’s research focuses on the integration of Artificial Intelligence (AI) with Human-Computer Interaction (HCI), particularly in emotion and cognition analysis. His work aims to improve human-robot interaction by developing AI-driven solutions for emotional state recognition and personalized user experiences. He explores cutting-edge AI algorithms, such as backpropagation neural networks and artificial bee colony algorithms, for creating more intuitive, adaptive, and empathetic HCI systems.
Experience
As a Research Fellow at Xinyu University, Liu has extensive experience in both teaching and research. He has contributed to several high-impact projects in AI, HCI, and emotion analysis. Liu has collaborated with researchers worldwide and has worked on projects focusing on AI-powered sentiment analysis, immersive learning experiences, and human-robot interaction. His experience spans both academic publishing and practical applications in AI-driven interfaces and systems.
Research Timeline
Liu’s academic journey began with his Master’s in 2018, followed by a Ph.D. at Universiti Kebangsaan Malaysia, where he focused on HCI and AI. After completing his Ph.D. in [Year], he joined Xinyu University as a Research Fellow. Over the past few years, his research has evolved from theoretical studies to practical AI applications, contributing to multiple high-impact publications in both AI and HCI.
Awards & Honors
Liu has been recognized for his contributions to the fields of HCI and AI, though specific awards or honors are not detailed. His published work in top-tier journals, including SCI and SSCI indexed publications, has brought him recognition within the academic community. His contributions continue to shape research in AI-driven emotional intelligence and human-computer interaction.
Top-Noted Publications
Liu has authored several top-cited papers, including:
1. Local Optimal Issue in Bees Algorithm: Markov Chain Analysis and Integration with Dynamic Particle Swarm Optimisation Algorithm
- Source: Springer Series in Advanced Manufacturing, 2025
- Type: Book Chapter
- DOI: 10.1007/978-3-031-64936-3_3
- ISBN: 9783031649356 / 9783031649363
- ISSN: 1860-5168 / 2196-1735
- Contributors: Jianbang Liu, Mei Choo Ang, Kok Weng Ng, Jun Kit Chaw
This chapter addresses the local optimal issue in the Bees Algorithm and integrates it with Dynamic Particle Swarm Optimisation (DPSO), using Markov Chain analysis. It is part of the Springer series focused on advanced manufacturing techniques.
2. Assessing the Impact and Development of Immersive VR Technology in Education: Insights from Telepresence, Emotion, and Cognition
- Source: Technological Forecasting and Social Change, April 2025
- Type: Journal Article
- DOI: 10.1016/j.techfore.2025.124024
- ISSN: 0040-1625
- Contributors: Jianbang Liu, Mei Choo Ang, Jun Kit Chaw, Ah-Lian Kor, Kok Weng Ng, Meng Chun Lam
This article examines how immersive VR technology influences education, particularly in terms of telepresence, emotion, and cognitive responses, offering valuable insights into the evolving role of VR in educational settings.
3. Personalized Emotion Analysis Based on Fuzzy Multi-Modal Transformer Model
- Source: Applied Intelligence, February 2025
- Type: Journal Article
- DOI: 10.1007/s10489-024-05954-5
- ISSN: 0924-669X / 1573-7497
- Contributors: Jianbang Liu, Mei Choo Ang, Jun Kit Chaw, Kok Weng Ng, Ah-Lian Kor
This paper introduces a fuzzy multi-modal transformer model for personalized emotion analysis, which enhances the ability of systems to interpret emotional states through multiple data sources.
4. The Emotional State Transition Model Empowered by Genetic Hybridization Technology on Human–Robot Interaction
- Source: IEEE Access, 2024
- Type: Journal Article
- DOI: 10.1109/access.2024.3434689
- ISSN: 2169-3536
- Contributors: Jianbang Liu, Mei Choo Ang, Jun Kit Chaw, Kok Weng Ng, Ah-Lian Kor
This article explores the emotional state transition model used in human-robot interactions, leveraging genetic hybridization technology to create more adaptive, emotion-aware robotic systems.
5. Emotion Assessment and Application in Human-Computer Interaction Interface Based on Backpropagation Neural Network and Artificial Bee Colony Algorithm
- Source: Expert Systems with Applications, December 2023
- Type: Journal Article
- DOI: 10.1016/j.eswa.2023.120857
- ISSN: 0957-4174
- Contributors: Jianbang Liu, Mei Choo Ang, Jun Kit Chaw, Ah-Lian Kor, Kok Weng Ng
This paper discusses how emotion assessment techniques can be integrated into human-computer interaction systems using backpropagation neural networks and the artificial bee colony algorithm, contributing to more intuitive HCI interfaces.
6. Performance Evaluation of HMI Based on AHP and GRT for GUI
- Source: 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), September 2022
- Type: Conference Paper
- DOI: 10.1109/iicaiet55139.2022.9936844
- Contributors: Jianbang Liu, Mei Choo Ang, Jun Kit Chaw, Kok Weng Ng
This conference paper evaluates the performance of Human-Machine Interfaces (HMIs) based on the Analytical Hierarchy Process (AHP) and Grey Relational Theory (GRT), aiming to improve Graphical User Interface (GUI) design.