A Special Section on Deep & Advanced Machine Learning Approaches for Human Behavior Analysis

Yizhang Jiang , Kim-Kwang Raymond Choo** and Hoon Ko***


Abstract: -

Increasingly, there have been attempts to utilize physiological information collected from different non-intrusive devices and sensors (e.g., electroencephalogram, electrocardiograph, electrodermal activity, and skin conductance) for different activities and studies, such as using the data to train machine-/deep-learning models in order to facilitate medical diagnosis and other decision-making. Given the constant advances in machine and deep learning methods, such as deep learning, transfer learning, reinforcement learning, and federated learning, we can also utilize such techniques in cognitive computing to facilitate human behavior analysis. For example, transfer learning uses data or knowledge acquired on solved problems to help solve unsolved but very relevant problems. Transfer learning is often used in cognitive computing to use differences between individuals or tasks to improve learning efficiency and effectiveness. Transfer learning can also be integrated with deep learning to take advantage of the progress of deep learning and transfer learning.

However, there are still many major challenges when using deep and machine learning algorithms to analyze human behavior. For example, deep and machine learning is used to learn the feature representation of human behavior from multimodal data; deep and machine learning is used to realize pattern mapping from one modal to another; deep and machine learning is used to realize the fusion of multiple modal data to achieve more high prediction accuracy; deep and machine learning is used to restore missing data in the modal, etc. Not surprisingly, several multimodal machine learning models have been developed in recent years, which have shown promising results when applied on applications such as multimedia descriptions and retrieval. Therefore, we posit the potential of leveraging such advances to address fundamental challenges in human behavior analysis.

In this thematic issue, we seek to provide a forum for researchers from cognitive computing and machine learning to present recent progress in deep and advanced machine learning research with applications to multimodal human behavior data. According to peer reviewers’ review comments, this special issue contains a total of seven papers, and the authors have revised the papers based on the review comments [1-7]. This special issue has been possible by the strong support of Prof. James Jong Hyuk Park, the Editor-in-Chief of Journal of Information Processing Systems (JIPS). We would like to express our gratitude to the authors for their contributions.


Yizhang Jiang

He received the B.S. degree from the Nanjing University of Science and Technology, China, in 2010 and the M.S. and Ph.D. degrees from the Jiangnan University, Wuxi, China, in 2012 and 2015, respectively. He is currently an Associate Professor with the School of Digital Media, Jiangnan University since Janunary 2016. He has been a research assistant in the Department of Computing, Hong Kong Polytechnic University, for almost 2 years. His research interests include pattern recognition, intelligent computation and their applications in medicine. He is the author/co-author of more than 60 research papers in international/national journals, including IEEE Transactions on Fuzzy Systems (TFS), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Cybernetics (TCYB), IEEE Transactions on Systems, Man, and Cybernetics: Systems (TSMC-S), IEEE Transactions on Neural Systems & Rehabilitation Engineering (TNSRE), IEEE Transactions on Medical Imaging (TMI), IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), IEEE Transactions on Intelligent Transportation Systems (TITS), IEEE Transactions on Industrial Informatics (TII), ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), ACM Transactions on Intelligent Systems and Technology (TIST), ACM Transactions on Internet Technology (TOIT), and Information Fusion. He is an Associate Editor of IEEE Access (SCIE, 2019-), an Associate Editor of Frontiers in Medical Technology (2020-), an Editorial Board Member of Technology and Health Care (SCIE, 2020-), an Editorial Board Member of Journal of Organizational and End User Computing (SSCI, SCIE, EI 2020-), and an Editorial Review Board Member of International Journal of Healthcare Information Systems and Informatics (ESCI & EI,2019-). He has served as a Publication Chair of the international conferences of EAI ICMTEL (2020) and CCISP (2020). He has also served as a leader guest editor or guest editor of several international journals, such as IEEE/ACM Transactions on Computational Biology and Bioinformatics, IEEE Transactions on Computational Social Systems, Journal of Ambient Intelligence and Humanized Computing, and Computational and Mathematical Methods in Medicine. He is also a Senior Member of IEEE.


Kim-Kwang Raymond Choo

He holds a Ph.D. in information technology from Queensland University of Technology, Australia. Prior to starting his Cloud Technology Endowed Professorship at UTSA, Professor Choo spent 5 years working for the University of South Australia, and 5 years working for the Australian Government Australian Institute of Criminology. He was also a visiting scholar at INTERPOL Global Complex for Innovation between October 2015 and February 2016 and a visiting Fulbright scholar at Rutgers University School of Criminal Justice and Palo Alto Research Center (formerly Xerox PARC) in 2009. He received the UTSA College of Business Col. Jean Piccione and Lt. Col. Philip Piccione Endowed Research Award for Tenured Faculty in 2018. In 2016, he was named Cybersecurity Educator of the Year – APAC (2016 Cybersecurity Excellence Awards [CEAs are produced in cooperation with the Information Security Community on LinkedIn]), and in 2015, he and his team won the Digital Forensics Research Challenge organized by Germany’s University of Erlangen-Nuremberg. Other awards include Best Research Paper Award at the 20th European Symposium on Research in Computer Security (ESORICS 2015), Australia New Zealand Policing Advisory Agency (ANZPAA); National Institute of Forensic Sciences Highly Commended Award; 2010 Australian Capital Territory Pearcey Award for "Taking a risk and making a difference in the development of the Australian ICT industry,"; 2008 Australia Day Achievement Medallion in recognition of his dedication and contribution to the Australian Government Australian Institute of Criminology, and through it to the public service of the nation; and the British Computer Society’s Wilkes Award for the best paper published in the 2007 volume of the Computer Journal (Oxford University Press). In April 2017 he was appointed an Honorary Commander, 502nd Air Base Wing, Joint Base San Antonio-Fort Sam Houston, USA. He is also a Fellow of the Australian Computer Society and a Senior Member of IEEE.


Hoon Ko

He received B.S. at Howon University in 1998, and M.S. in 2000, Ph.D. in 2004 at Soongsil University, South Korea. He had worked Daejin University as a visiting professor from 2002 to 2006 and at Korea Advanced Institute of Science and Technology (KAIST) in South Korea in 2007. He also had worked at GECAD/ISEP/IPP in Porto, Portugal as a Post-Doc from 2008 to 2013, and at the Department of Informatics, J. E. Purkinje University (UJEP), Usti nad Labem, Czech Republic as a research professor from June 2013 to July 2015. Now he is working at IT Research Institute, Chosun University, Gwangju, South Korea as a research professor since October 1, 2017. Still he is also a collaborator in GECAD/ISEP/IPP, Porto, Portugal since February 1, 2013. His research area is CPS security, cyber-security, context-aware security, multicast security (MSEC), RFID security, IoT security, bio-information Security, and home network security.


  • 1 X. Zhou, "Video expression recognition method based on spatiotemporal recurrent neural network and feature fusion," Journal of Information Processing Systems, vol. 17, no. 2, pp. 337-351, 2021.custom:[[[-]]]
  • 2 L. Sun, "POI recommendation method based on multi-source information fusion using deep learning in location-based social networks," Journal of Information Processing Systems, vol. 17, no. 2, pp. 352-368, 2021.custom:[[[-]]]
  • 3 J. Xu, Z. Hu, J. Zou, "Personalized product recommendation method for analyzing user behavior using DeepFM," Journal of Information Processing Systems, vol. 17, no. 2, pp. 369-384, 2021.custom:[[[-]]]
  • 4 H. Han, "Residual learning based CNN for gesture recognition in robot interaction," Journal of Information Processing Systems, vol. 17, no. 2, pp. 385-398, 2021.custom:[[[-]]]
  • 5 L. Zhao, "A facial expression recognition method using two-stream convolutional networks in natural scenes," Journal of Information Processing Systems, vol. 17, no. 2, pp. 399-410, 2021.custom:[[[-]]]
  • 6 Y. Zhou, "Vehicle image recognition using deep convolution neural network and compressed dictionary learning," Journal of Information Processing Systems, vol. 17, no. 2, pp. 411-425, 2021.custom:[[[-]]]
  • 7 H. Cao, "Personalized web service recommendation method based on hybrid social network and multi-objective immune optimization," Journal of Information Processing Systems, vol. 17, no. 2, pp. 426-439, 2021.custom:[[[-]]]