Prof. Ir. Ts. Dr. Mohd Rizal
Arshad, FASc
- Dean – School of Robotics, Xi’an Jiaotong-Liverpool University
- Deputy Vice Chancellor (Academic & International) – UniMAP
- Professor & Dean – School of Electrical and Electronic, USM
- Founder & CEO – IROSS Sdn. Bhd.
Advancing Robotics for Real-World Industrial Impact
About Me
From Research Pioneer to Robotics Innovator
Prof. Ir. Ts. Dr. Mohd Rizal Arshad, FASc., is a leading expert in control and robotics systems, with a strong focus on marine intelligent robotics and underwater technologies. With more than 26 years of research and development experience, he is widely recognised as one of the pioneers of underwater robotics in Malaysia.
He received his Bachelor of Engineering in Medical Electronics and Instrumentation and PhD in Electronic Engineering from the University of Liverpool, and a Master’s degree in Electronic Control Engineering from the University of Salford. His academic journey laid the foundation for a career dedicated to advancing robotics technologies for real-world applications.
Prof. Rizal spent over two decades at Universiti Sains Malaysia (USM), where he served as a professor and held various academic and administrative roles. He later contributed to higher education leadership as Deputy Vice-Chancellor of Universiti Malaysia Perlis (UniMAP), before taking on his current role as Dean of the School of Robotics at Xi’an Jiaotong-Liverpool University (XJTLU), China.
Beyond academia, his work bridges research and industry through the development of intelligent robotic systems for inspection, monitoring, and industrial applications. He is also the founder of IROSS Sdn. Bhd., a robotics company focused on delivering practical solutions for infrastructure inspection.
Prof. Rizal has supervised numerous postgraduate students and published extensively in the field of robotics and control systems. His professional recognitions include being a Professional Engineer (BEM), Professional Technologist (MBOT), Chartered Engineer (UK), and Chartered Marine Engineer with IMarEST. He is also a Senior Member of IEEE, founding chair of the IEEE Oceanic Engineering Society (OES) Malaysia, recipient of the IEEE OES Presidential Award (2019), and currently serves as an IEEE OES Distinguished Lecturer (2024–2026).
He actively contributes to the global robotics and control community through his roles in the Asian Control Association, the International Federation of Automatic Control (IFAC), and The Institution of Engineers Malaysia (IEM).
Expertise
Underwater
Robotics
Autonomous and remotely operated systems for marine and confined environments
Control & Intelligent Systems
Advanced control strategies for complex robotic applications
Inspection
Robotics
Robotics solutions for pipelines, culverts, and infrastructure inspection
Instrumentation & Sensors
Precision measurement and sensing systems for engineering applications
Awards & Recognition
- Academic Award – Excellence Service Award 2002 for the School of Electrical and Electronic Engineering, Universiti Sains Malaysia.
- Fellow of IMarEST, UK (2011)
- IEEE OES Presidential Award (2019)
- Fellow, Academy of Science Malaysia (2024)
- IEEE OES Distinguished Lecturer (2024–2026)
- Chartered Marine Engineer (UK)
- Fellow, International Academy of Artificial Intelligence Sciences (AAIS) (2026)
Research Publications & Perspectives
Selected research contributions, engineering innovations, and professional perspectives by Prof. Dr. Mohd Rizal Arshad.
Malaysia's Marine Technology Blueprint: Engineering the Ocean Future
IEM Jurutera Magazine | October 2026 | Pages 24–26
In this featured article, Prof. Dr. Mohd Rizal Arshad explores Malaysia’s future in marine engineering and the strategic importance of developing indigenous marine technologies. He highlights the roles of robotics, artificial intelligence, autonomous systems, and digital innovation in strengthening maritime capabilities and supporting the Blue Economy.
The article proposes five strategic national priorities and a three-phase marine technology development roadmap towards 2050.
Selected Research Publications
A Comparative Study of Machine Learning Models for Real-Time IoT-Based Soil Moisture Anomaly Detection
This study compares seven machine-learning models using 100,000 agricultural records to evaluate their effectiveness in classifying soil moisture anomalies. The findings highlight the potential of machine learning for intelligent agricultural monitoring, with LightGBM achieving the highest F1-score among the evaluated models.
Tiny Neural Networks for Leakage-Aware Agricultural Off-Band Classification: A Soil-Moisture Benchmark with a Secondary Rainfall-Derived Workflow Assessment
This research investigates ultra-compact neural networks for agricultural data classification. An 85-parameter TinyNN achieved performance comparable to a larger neural network while reducing trainable parameters by approximately 97%, demonstrating promising potential for future resource-constrained edge AI applications.
YOLO-C2ECASA: A Lightweight Attention-Enhanced Model for Three-Class Brain Tumor Localization and Classification in MRI
This study introduces YOLO-C2ECASA, a lightweight AI model designed to localize and classify glioma, meningioma, and pituitary tumors in tumor-positive MRI images. The proposed approach delivers competitive detection performance with reduced computational requirements, supporting further research into efficient medical image analysis.
List of Publications
Prof. Dr. Mohd Rizal Arshad has published extensively in underwater robotics, control systems, intelligent automation, marine engineering, and related interdisciplinary technologies.
Featured Journal Articles
- Zou, J., & Arshad, M. R. (2024). Detection of whole-body bone fractures based on improved YOLOv7. Biomedical Signal Processing and Control, 91, 105995.
- Wang, N., Xu, K., & Arshad, M. R. (2022). Maximum Power Point Tracking of Photovoltaic Array on a USV: A Fuzzy Neural-Directed Adaptive Particle Swarm Optimization Approach. International Journal of Fuzzy Systems, 24, 3403–3415.
- Tan, C. S., Mohd-Mokhtar, R., & Arshad, M. R. (2021). A Comprehensive Review of Coverage Path Planning in Robotics using Classical and Heuristic Algorithms. IEEE Access.
- Hussain, N. A. A., et al. (2020). Underactuated Coupled Nonlinear Adaptive Control Synthesis Using U-Model for Multivariable Unmanned Marine Robotics. IEEE Access, 8, 1851–1865.
Conference Proceedings
- Zou, J., Arshad, M. R., & Wang, Z. (2024). Comparative Analysis of YOLO Architectures for Automated Detection of Liver Disease in Histopathological Images. ICBSP 2024, 33–38.
- Zou, J., Arshad, M. R., & Wang, Z. (2024). Enhancing Pharmacokinetic Modeling with Fractional-Order Kinetics and Deep Learning. IEEE ICCSCE 2024, 46–51.
- Zou, J., & Arshad, M. R. (2024). Comparative Analysis of Deep Learning-Based Abdominal Multivisceral Segmentation. ROVISP 2023 Proceedings.
- Lai, L., Arshad, M. R., & Zou, J. (2025). Development and performance evaluation of a novel OTDR-based distributed optical fiber sensor using superabsorbent polymers for precision soil moisture monitoring. SPIE Proceedings, Vol. 13509.
Books & Book Chapters
- Majid, M. H. A., Arshad, M. R., & Mokhtar, R. M. (2022). Swarm Robotics Behaviors and Tasks: A Technical Review. In Control Engineering in Robotics and Industrial Automation. Springer.
- Mariappan, M., Arshad, M. R., Akmeliawati, R., & Chong, C. S. (2021). Control Engineering in Robotics and Industrial Automation: Malaysian Society for Automatic Control Engineers (MACE) Technical Series 2018. Springer Nature.
Leadership &
Roles
Universiti Sains Malaysia
1999-2019
2021-2025
Projects & Industry Work
Contact
Open for collaboration in robotics research, industrial innovation, and technology development.
