The 2nd International Conference on Artificial Intelligence and Human-Computer Interaction (ArtInHCI 2024)

Professor Yalan Ye
University of Electronic Science and Technology of China

Areas of Expertise: Computer Vision, Image Processing, Deep Learning, Transfer Learning, Biomedical Signal Processing, Human-Machine Hybrid Intelligence, Multimodal Human-Computer Interaction

Professor Huiyu Zhou
School of Computing and Mathematical Sciences, University of Leicester, United Kingdom

Areas of Expertise: Machine Learning, Computer Vision, Intelligent Systems, Signal Processing

Speech Title: When Parkinson’s disease meets artificial intelligence

Speech Abstract: Parkinson’s disease (PD) is a severe condition that affects the brain. PD causes huge problems in humans such as shaking and stiffness that become worse over time. Early diagnosis and prognosis of PD results in effective and personalised treatment, reduced care costs and better quality of life. In this talk, first of all, Zhou introduces fundamental knowledge about PD and the technologies used for PD identification. This talk is divided into two streams, animal mice- and human-based PD identification. Afterwards, Zhou reports how his research group deal with immersive challenges such as single and ...more...

Brief Introduction: Dr. Huiyu Zhou received a Bachelor of Engineering degree in Radio Technology from Huazhong University of Science and Technology of China, and a Master of Science degree in Biomedical Engineering from University of Dundee of United Kingdom, respectively. He was awarded a Doctor of Philosophy degree in Computer Vision from Heriot-Watt University, Edinburgh, United Kingdom. Dr. Zhou currently is a full Professor at School of Computing and Mathematical Sciences, University of Leicester, United Kingdom. He has published over 500 peer-reviewed papers in the field. His research work has been or is being supported by UK EPSRC, ESRC, AHRC, MRC, EU, Innovate UK, Royal Society, British Heart Foundation, Leverhulme Trust, Puffin Trust, Alzheimer’s Research UK, Invest NI and industry. Homepage: https://le.ac.uk/people/huiyu-zhou.

Associate Professor Teh Sin Yin
CMILT Chartered Member, MLogM
Operations & Business Analytics, School of Management
Universiti Sains Malaysia

Areas of Expertise: Data Mining, Machine Learning, Artificial Intelligence, Business Analytics, Statistical Process/Quality Control, Robust Statistics, TRIZ, etc.

Speech Title: The Future of AI in Industry: Innovations, Directions, and Strategies

Speech Abstract: As Artificial Intelligence (AI) continues to reshape industries worldwide, understanding its current and future impact is crucial for organizations seeking to stay competitive. According to Gartner, by 2025, 75% of organizations will be using AI in some form. This statistic highlights AI's growing influence across sectors such as healthcare and transportation. The transformative role of AI will be explored, emphasizing innovations that drive significant changes. Future directions will examine emerging trends and applications of AI for addressing global challenges. Additionally, actionable strategies will be outlined to help organizations and individuals prepare for an AI-driven future. This preparation will focus on continuous upskilling, fostering a culture of innovation, and investing in AI research. Valuable insights will be provided to navigate the rapidly evolving AI landscape, ensuring organizations are well-positioned to leverage AI's full potential in their respective fields.

Brief Introduction: TEH Sin Yin is an Associate Professor of Operations and Business Analytics in the School of Management, Universiti Sains Malaysia (USM). She has strong academic background in Statistics (USM), Executive Program (Applied Business Analytics) and Executive Program (AI: Implications for Business Strategy) awarded by Massachusetts Institute of Technology (MIT). She was a fellow and member of the United Nations System Staff College (UNSSC) and Data-Pop Alliance, New York. She was also research fellow at the City University of Hong Kong, University Tunku Abdul Rahman (UTAR) and AK Shipping. She is the founder of Business Analytics programme in USM. ...more...

Professor Ljiljana Trajkovic
Ph.D., P.Eng., FIEEE
2021-2023 EiC, IEEE Transactions on Human-Machine Systems
2019-2020 IEEE Division X Delegate/Director
Past President, IEEE Systems, Man, and Cybernetics Society
Past President, IEEE Circuits and Systems Society
A professor in the School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada

Areas of Expertise: communication networks and dynamical systems

Speech Title: Data Mining and Machine Learning for Analysis of Network Traffic

Speech Abstract: Collection and analysis of data from deployed networks is essential for understanding modern communication networks. Data mining and statistical analysis of network data are often employed to determine traffic loads, analyze patterns of users' behavior, and predict future network traffic while various machine learning techniques proved valuable for predicting anomalous traffic behavior. In described case studies, traffic traces collected from various deployed networks and the Internet are used to characterize and model network traffic, analyze Internet topologies, and classify network anomalies.

Brief Introduction: Ljiljana Trajkovic received the Dipl. Ing. degree from University of Pristina, Yugoslavia, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, and the Ph.D. degree in electrical engineering from University of California at Los Angeles. She is currently a professor in the School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada. Her research interests include communication networks and dynamical systems. Dr. Trajkovic served as IEEE Division X Delegate/Director, President of the IEEE Systems, Man, and Cybernetics Society, and President of the IEEE Circuits and Systems Society. She serves as Editor-in-Chief of the IEEE Transactions on Human-Machine Systems. She was a Distinguished Lecturer of the IEEE Circuits and System Society and a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society. She is a Fellow of the IEEE.

Associate Professor Ts. Dr. Aslina Baharum
Programme Leader of Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Malaysia

Areas of Expertise: User Experience (UX)/ User Interface (UI), Human-Computer Interaction (HCI)/ Interaction Design, Product & Service Design, Software Engineering & Mobile Development, Information Visualization & Analytics, Multimedia, Information and Communication Technology, Information System, Software Engineering and Entre/Technopreneurship

Speech Title: From Innovation to Interaction: Intelligent Control in Next-Gen UI/UX

Speech Abstract: As we move further into the digital age, the convergence of Artificial Intelligence (AI) and Human-Computer Interaction (HCI) is revolutionizing user experience (UX) design. This study will explore how intelligent control mechanisms are being integrated into next-generation UI/UX, transforming the way users interact with technology. We will examine the current challenges and opportunities in combining AI with UI/UX, highlighting key innovations that are reshaping the industry. One pressing issue is the need for balance AI automation with user control, ensuring interfaces remain intuitive without sacrificing ...more...

Brief Introduction: Dr. Aslina holds the esteemed position of Associate Professor and Programme Leader of Department of Computing and Information Systems (DCIS) Postgraduate at the School of Engineering and Technology, Sunway University. Previously, she has served as a Senior Lecture at the Faculty of Computer and Mathematical Sciences in Universiti Teknologi MARA (UiTM), and as a Senior Lecturer at the Faculty of Computing and Informatics in Universiti Malaysia Sabah (UMS), where she led the User Experience (UX) research group. Completing her academic journey, she also brings valuable industry experiences as a former Information Technology (IT) Officer at the Forest Research ...more...

Luís Miguel Domingues Ferreira Silva
Postdoctoral Researcher, NOVA School of Science and Technology

Areas of Expertise: The exploration of biosignals, nonlinear analysis, advanced statistical methodologies, and deployment of cutting-edge artificial intelligence techniques, namely on Generative AI

Speech Title: AISym4Med: A Generative Artificial Intelligence Platform to Improve Healthcare Data System

Speech Abstract: Envision a world where the progress of life-saving medical technologies is hampered not by a scarcity of innovation, but by a paucity of accessible, high-quality data that respects the stringent privacy regulations and the inherently sensitive nature of patient information. This constraint hinders the development and validation of advanced medical algorithms, as well as the education and training of healthcare professionals. ...more...

Brief Introduction: Luís Miguel Domingues Ferreira Silva is a researcher at NOVA School of Science and Technology. He holds a post-doctoral fellowship in Biomedical Engineering. His academic journey previously included a post-doctoral role in Biomechanics in the University of Nebraska, following the attainment of his PhD in Human Movement from the University of Lisbon. His expertise is characterized by the exploration of biosignals, nonlinear analysis, advanced statistical methodologies, and deployment of cutting-edge artificial intelligence techniques, namely on Generative AI. The main goal is discerning motor and physiological patterns pertinent to occupational risk factors. Has taught subjects such as Anatomy and Physiology, Biomechanics, Statistics and Machine Learning, and Physics I.

Professor. Ji Zhang
Full Professor in Computer Science at the University of Southern Queensland (UniSQ), Australia
IET Fellow, RSA Fellow, BCS Fellow, IAAST Fellow
IEEE Senior Member
Australian Endeavour Fellow, Queensland International Fellow, Izaak Walton Killam Scholar
Program Director of Master of Information Technology, UniSQ

Areas of Expertise: Big data analytics, data mining, machine learning and computational intelligence

Speech Title: Navigating the Convergence of IoT and Big Data Analytics

Speech Abstract: The Internet of Things (IoT), like Big Data, has transitioned from marketing term to valuable technology, and has started to play an increasingly important role in many important applications nowadays in our life. In this talk, I will cover the history, development and key enabling techniques of IoT. I will also present some of the recent research projects and results of our team in big data analytics in IOT including outlier detection in large streaming data and IoT-enabled telehealth informatics.

Brief Introduction: Prof. Ji Zhang is currently a Full Professor in Computer Science at the University of Southern Queensland (UniSQ), Australia. Prof. Zhang is an IET Fellow, RSA Fellow, BCS Fellow, IAAST Fellow, IEEE Senior Member, Australian Endeavour Fellow, Queensland International Fellow (Australia) and Izaak Walton Killam Scholar (Canada), co-leader of the global large data mining algorithm open-source platform SPMF, former vice president of the IEEE Queensland Computer Society, academic expert of the Australian Academy of Science, former Principal Advisor for Research in Division of ICT Services at the Information Technology Center of the University of Southern Queensland, and visiting professor at Michigan State University in the USA, Nanyang Technological University in Singapore, and University of Tsukuba in Japan.

Professor Liang Liao
Zhongyuan University of Technology

Areas of Expertise: Machine learning, high-dimensional data analysis, image analysis, computer vision, and their mathematical models and interpretability

Speech Title: Generalized Matrix Theory over Finite-Dimensional Algebra with Applications to Image Analysis: A Unified Representation Perspective

Speech Abstract: In this talk, we explore an emerging matrix model that utilizes fixed-sized numerical arrays as matrix entries. These arrays, which can be added, multiplied via higher-order circular convolution, and scaled by scalars, constitute a finite-dimensional algebra. This framework of hyper-matrices extends traditional matrix concepts, providing a powerful tool for modeling complicate higher-order image analysis problems. By leveraging representation theory, we offer a unified perspective that transforms these hyper-matrices and their array entries into classical matrices. This representation-centric perspective elucidates the underlying mechanisms of the hyper-matrix model and demystifies its application, thus enhancing the capacity for hyper-linear modeling in advanced image analysis.

Brief Introduction: Liang Liao earned his B.Eng. degree in Electronics and Information Technology from Northwestern Polytechnical University in 1994. He subsequently obtained an M.Sc. from Southwest Jiaotong University in 2002 and a Ph.D. from South China University of Technology in 2008.
Following his Ph.D., he pursued a postdoctoral research fellowship at Northwestern Polytechnical University. From 2013 to 2023, he served as the Associate Dean of the Department of Electronics and Information Engineering at Zhongyuan University of Technology.
Currently, he is a full professor in the School of Mathematics and Information Science at Zhongyuan University of Technology, which is soon to be renamed Henan University of Electronic Science and Technology. He also holds the position of Director at both the Henan Provincial Center for Distinguished Foreign Scientists in Machine Learning and Image Analysis and the Zhengzhou International Science and Technology Cooperation Base for Machine Intelligence and Image Analysis. In addition to his focus on electronics and information technology, he is a regular reviewer for ZBMATH, headquartered in Germany.
His research interests include machine learning, high-dimensional data analysis, image analysis, computer vision, and their mathematical models and interpretability.

Associate Professor Yuping Song
Shanghai Normal University

Areas of Expertise: Financial big data analysis and Artificial Intelligence in Finance

Speech Title: Realized volatility forecasting for stocks and futures indices with rolling CEEMDAN and machine learning models

Speech Abstract: As an essential index for measuring market risk, realized volatility (RV) possesses mixed features and volatility aggregation, which makes it difficult for machine learning (ML) models to identify its features and trends directly for accurate prediction. Hence, this study first uses the rolling CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) method to decompose the original RV sequence of the major stock market indices as well as the bean and the metal futures indices. Furthermore, the study employs eight ML methods to predict the decomposed sub-sequences. Finally, the prediction outcomes for the sub-sequences are reconstructed. Through 1-step, 5-step, and 22-step predictions, various evaluation criteria, and the Diebold-Mariano (DM) test, the study find that the hybrid CEEMDAN-RF model possesses a better RV prediction ability than the non-hybrid models. Taking the Shanghai Stock Exchange (SSE) as an example, the mean square error (MSE) evaluation criterion of the RF model is reduced by 52.07% after introducing the CEEMDAN decomposition method. Compared with the other hybrid models, the improvement percentages of the MSE for the hybrid CEEMDAN-RF model are 2.56%, 0.72%, 5.03%, 36.89%, 44.14%, 44.15%, and 58.19%, respectively. The empirical findings indicate that the hybrid CEEMDAN method and ML model can provide accurate, robust results for RV prediction of stocks indices and futures indices.

Brief Introduction: Published over thirty papers as the first author (corresponding author) in SCI and SSCI journals such as Expert System with Application, Journal of Forecasting, and Computational Economics; Also served as an anonymous reviewer for many SCI and SSCI journals.

Assoc. Prof. Le Nguyen Quoc Khanh
Taipei Medical University (TMU), Taiwan

Areas of Expertise: Artificial Intelligence (Machine Learning & Deep Learning); Bioinformatics & Computational Biology; Genomics, Proteomics, Radiomics; Medical informatics

Speech Title: AI in Medical Imaging: Current Status and Prospects

Speech Abstract: Artificial Intelligence (AI) has emerged as a transformative force in medical imaging, offering new avenues for diagnosis, prognosis, and treatment planning. This talk will explore the current landscape of AI applications in medical imaging, focusing on the integration of deep learning and machine learning models with medical imaging modalities such as MRI, CT, and histopathology. We will review key advancements, including automated image analysis, tumor detection, segmentation, and the role of AI in enhancing radiomics. The challenges surrounding data quality, model interpretability, and regulatory considerations will also be addressed. Additionally, the talk will provide an outlook on the future prospects of AI in this domain, emphasizing emerging trends such as multimodal data integration, explainable AI (XAI), and personalized medicine.

Brief Introduction: Dr. Le Nguyen Quoc Khanh is an Associate Professor in the In-Service Master Program in Artificial Intelligence in Medicine at the College of Medicine, Taipei Medical University (TMU), Taiwan. Prior to his tenure at TMU, he served as a Research Fellow at Nanyang Technological University, Singapore. Dr. Khanh is an active Editorial Board Member for several prestigious SCI journals, including Heliyon, BMC Genomics, iMeta, PLOS ONE, and BMC Bioinformatics. Since 2017, he has been a dedicated Program Committee Member for numerous international conferences such as IJCAI 2024, GIW ISCB ASIA 2023, IEEE BIBM 2023, IEEE BIBM 2022, IEEE BIBM 2021, GIW/ISCB-Asia 2020, IEEE BIBM 2020, ISMCO 2020, IEEE CBMS 2020, IEEE BIBM 2019, InCoB 2019, IEEE CBMS 2019, and IEEE BIBE 2019. His research interests center on the application of AI (including machine learning, deep learning, and natural language processing) in multidisciplinary studies, pushing the boundaries of what is possible in modern medicine and beyond.

Assistant Professor Dr. Teoh Wei Lin
Associate Director of Research and Enterprise in School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, Malaysia
Associate Editor of the Journal of Statistical Computation and Simulation published by Taylor & Francis
Visiting researcher of the International Chair in Data Science & Explainable Artificial Intelligence, International Research Institute for Artificial Intelligence and Data Science, Dong A University, Danang, Vietnam

Areas of Expertise: Deep Learning, Data Mining, Machine Learning, Artificial Intelligence, Genetic Algorithms, Statistical Process Control, etc.

Speech Title: Smart Integrated Optimal Control Chart: An Omnibus Sequential Probability Ratio Test for Concurrent Mean and Variance Monitoring

Speech Abstract: Statistical Process Control (SPC) is a data-driven approach used to monitor, control, and enhance manufacturing processes through statistical techniques. By detecting variations that may lead to defects, SPC ensures that processes remain stable and operate efficiently. With the integration of Artificial Intelligence, SPC has evolved to enable real-time monitoring, allowing for the immediate detection of process deviations. This continuous inspection improves response times, reduces waste, and enhances product quality. Traditional control schemes based on the Sequential Probability Ratio Test (SPRT) typically focus on monitoring the process mean. However, in many manufacturing environments, external factors can affect both the mean and variance simultaneously. To address this limitation, this research proposes a joint monitoring approach using the Omnibus SPRT (OSPRT) chart, which combines mean and variance information within a single framework. The research further develops an optimal design strategy aimed at minimising average extra quadratic loss, making the OSPRT chart more effective in managing process variations. Comprehensive analysis shows that the OSPRT chart outperforms traditional methods such as the classical Xbar-S chart and various cumulative sum charts, requiring fewer samples on average to make accurate decisions. The effectiveness of this approach is demonstrated through its application to a wire bonding industrial dataset, where the OSPRT chart significantly enhances process monitoring and decision-making. This highlights its potential to transform manufacturing by integrating smart technology for more efficient, data-driven process control.

Brief Introduction: Dr. Teoh Wei Lin is an Assistant Professor in the School of Mathematical and Computer Sciences at Heriot-Watt University Malaysia and serves as the Associate Director of Research in the same school. She is an Associate Editor for the Journal of Statistical Computation and Simulation, a Taylor & Francis publication indexed in both the Web of Science (WoS) and SCOPUS databases. Additionally, she holds the position of visiting researcher of the International Chair in Data Science & Explainable Artificial Intelligence at the International Research Institute for Artificial Intelligence and Data Science, located at Dong A University in Danang, Vietnam.
In recognition of her contributions to academia and national innovation, Dr. Teoh received the MIMOS Prestigious Award in 2013, which is the highest honour awarded by MIMOS. She has authored or co-authored over 90 papers published in reputable peer-reviewed international journals and conference proceedings, including many first quartile (Q1) journals indexed in the WoS database. Dr. Teoh’s active research network includes collaborations with researchers from Malaysia, France, the United Kingdom, Italy, China, Vietnam, India, Bangladesh, and Pakistan.

Assistant Professor Chong Zhi Lin
Universiti Tunku Abdul Rahman, Malaysia

Areas of Expertise: Artificial Neural Network, Genetic Algorithms, Statistical Process Control

Speech Title: AI-Driven Industrial Transformation: From Autonomous Systems to Predictive Maintenance

Speech Abstract: This keynote will delve into the transformative power of Artificial Intelligence (AI) across multiple industries, highlighting the significant role AI plays in reshaping traditional operations. Attendees will be introduced to key AI concepts, including machine learning, deep learning, and computer vision. The speech will explore the practical applications of AI in sectors like healthcare, transportation, retail, finance, and manufacturing, demonstrating how AI-driven technologies, such as autonomous systems, predictive maintenance, and intelligent decision-making, are enhancing efficiency, productivity, and sustainability. The session will also address the future potential of AI, emphasizing its critical impact on the industrial landscape.

Brief Introduction: Dr. Chong Zhi Lin is an Assistant Professor at the Faculty of Engineering & Green Technology, Universiti Tunku Abdul Rahman (UTAR). He earned his PhD in Statistics, specializing in Quality Control, from Universiti Sains Malaysia (USM) at the impressive age of 26 under the mentorship of Prof. Michael Khoo Boon Chong. Dr. Chong was a research fellow at City University of Hong Kong. To date, he has authored over 75 papers in high-impact WoS-indexed journals and conference proceedings, including Q1 and Q2 journals. His expertise has led to prestigious invitations to present at prominent events such as the International Statistical Institute (ISI) Regional Statistical Conference (RSC) in 2017 and the ISI World Statistical Congress (WSC) in 2019. He has also served as a reviewer for more than 135 WoS journals and has been awarded the Fundamental Research Grant Scheme (FRGS) twice, in 2016 and 2019. In recognition of his contributions to academia, Dr. Chong currently serves as a member of the Editorial Board for AOSIS Scholarly Books, specializing in Science, Engineering, and Technology.

Prof. Xiaohui Zou
Peking University Interdisciplinary Knowledge modeling Research Group Special Researcher Hengqin Searle Technology Co., Ltd. Director

Speech Title: Human-Computer Interaction, Collaboration, and Mutual Assistance in the Perspective of Integrated Intelligence: New Characteristics, Challenges, and Future Prospects

Speech Abstract: This paper delves into the new characteristics, challenges, and future prospects of human-computer interaction, collaboration, and mutual assistance from the perspective of integrated intelligence. As an emerging interdisciplinary field, integrated intelligence emphasizes the fusion and synergy between intelligent agents, providing a new lens for understanding the relationship between humans and computers. The article first analyzes the new characteristics of the current field of human-computer interaction, including multimodal interaction, context awareness and adaptability, as well as personalized user experiences. Subsequently, it discusses the challenges faced in human-computer interaction, collaboration, and mutual assistance within this context, such as data security and privacy protection, trust establishment between intelligent agents, and technological ethics. Finally, the paper looks forward to future trends and proposes suggestions for strengthening interdisciplinary research, promoting technological innovation and standard setting, as well as emphasizing humanistic care and ethical guidance. These suggestions aim to achieve harmonious coexistence and sustainable development of human-computer interaction, collaboration, and mutual assistance under the guidance of integrated intelligence.

Biography: According to Prof. Xiaohui Zou’s own practice and theory of Smart System Studied (Chinese means: Rongzhixue or Integrative Intelligence), he found that natural language understanding, expert knowledge expression and software pattern recognition are the super difficult problems of artificial intelligence from three aspects, namely: how to eliminate each kind of ambiguities. Focus on intelligent technical services for human- computer interaction, collaboration and complex systems, use the theory of integration to guide the construction of smart urban and rural areas, use the theory of integration to resolve financial risks, and focus on the cultivation of professional talents in integration. Main tasks: potential development, enterprise diagnosis, decision- making consultation and backbone training.

Dr. Belma Rizvanovic
NOVA UNIVERSITY LISBON

Areas of Expertise: Digital Platforms, Data Interpretation, and Product Development etc.

Biography: Dr. Belma Rizvanovic is a researcher at the department of industrial engineering and management faculty of science and technology, NOVA UNIVERSITY LISBON. As a researcher, her fields of interest focus on Digital Platforms, Data Interpretation, and Product Development. In Digital Platforms, she explores their structures and impacts. Data Interpretation involves the process of how different teams use the insights to design the decision-making process in their organizations. She is also dedicated to testing and experimenting of Product Development strategies within the digital realm. And she is a professor of entrepreneurship course faculty of science and technology, NOVA UNIVERSITY LISBON. And she is a project manager and strategic partnership MADAN PARQUE – science and technology, PARQUE.

See you in Kunming!