Biography

Dr. Mas Ira Syafila Mohd Hilmi Tan received the B.A.Sc. in Electronic Physics and Instrumentation from University Malaysia Terengganu, the M.Sc. in Solid State Physics from Universiti Sains Malaysia and Ph.D. in Electrical and Electronic Engineering from University Malaysia Pahang. After completed her Ph.D., she joined Centre of Image and Vision Computing at Multimedia University, Cyberjaya as a Postdoctoral Research Fellow. Currently, she is working as a lecturer at Department of Artificial Intelligence (AIT), School of Artificial Intelligence and Robotics, Xiamen University Malaysia. Her research interest is in IoT and AI for detection systems, tackling complex challenges such as plant disease assessment trough spectral processing for sustainable agriculture practices. 

Research Interest

Plant assessment, NIR/VIS spectral analysis, chemometrics, non-destructive detection, real-time detection and classification, smart sensing, embedded AI system (AIoT)

Educational Background

• PhD (Electrical and Electronics Engineering), Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Malaysia (2023)

• MSc (Solid State Physics), Universiti Sains Malaysia, Malaysia (2018)

• BASc (Electronic Physics and Instrumentation), Universiti Malaysia Terengganu, Malaysia (2017)
   

Professional Bodies 

Graduate Technologist (Electrical & Electronics Technology), Malaysia Board of Technologists (MBOT), Reg. No.: GT25100173 (2025)

Working Experience

• Lecturer, Department of Artificial Intelligence (AIT), School of Artifical Intelligence and Robotics, Xiamen University Malaysia, Malaysia (2025 – Present)

• Postdoctoral Research Fellow, Centre for Image and Visual Computing, Multimedia University, Malaysia (2024 – 2025)

• Research Assistant, Universiti Malaysia Pahang, Malaysia (2019 – 2023)

• Substitute Teacher, Sekolah Kebangsaan Felda Mata Air & Sekolah Kebangsaan Titi Tinggi, Malaysia (2017)

• Equipment and Process Engineer Intern, First Solar Malaysia Sdn. Bhd., Malaysia (2015)
   

Research Experience

• Postdoctoral Research (2024 – 2025): 
  Enhancing Early Plant Disease Detection: 1D to 2D Spectral Transformations 

• PhD Research (2019 – 2023): 
  Terahertz Sensing Analysis for Early Detection of Ganoderma Boninense Disease Using Near Infrared (NIR) Spectrometer
  Research Funding: Supported by CREST (Collaborative Research in Engineering, Science and Technology) through the i-GRASP Grant Programme, under project CREST T14C2-16/006 (UIC190810).
  Collaboration: UniKL, E-PROLINK, MARDI, UMP, USM
  Patent filled: System and Method for Early Detecting and Analyzing the Health of Oil Palm Trees (PI2022002790, filed 30-05-2022)

• MSc Research (2018): 
  Laser Ablation Synthesis of Au, Ag, Cu, Al and Ni Nanoparticles in Distilled Water

• BASc Research (2017): 
  Analysis of DC-to-DC Converter Controller using State Space Method
   

Publications

Journal Articles:

• Tan, M.I.S.M.H., Jamlos, M.F., Omar, A.F., Kamarudin, K., and Jamlos, M.A. (2023). Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques. Chemometrics and Intelligent Laboratory Systems, 232, 104718. [WoS, SCIE, Q1, IF: 4.175

• Tan, M.I.S.M.H., Omar, A.F., Rashid, M., and Hashim, U. (2019). VIS–NIR spectral and particles distribution of Au, Ag, Cu, Al and Ni nanoparticles synthesized in distilled water using laser ablation. Results in Physics, 14, 102497. [WoS, SCIE, Q1, IF: 4.565]

• Mohd Hilmi Tan, M.I.S., Jamlos, M.F., Omar, A.F., Dzaharudin, F., Chalermwisutkul, S., and Akkaraekthalin, P. (2021). Ganoderma boninense disease detection by near-infrared spectroscopy classification: A review. Sensors, 21(9), 3052. [WoS, SCIE, Q1, IF: 3.576]

• Ali, E., Jamlos, M.F., Raypah, M.E., Tan, M.I.S.M.H., Bakhit, A.A., Nordin, M.A.H., and Nugroho, A. (2024). A comprehensive review of sensor-based and spectroscopy-based systems for monitoring water quality in freshwater aquaculture systems. Journal of Advanced Research in Applied Sciences and Engineering Technology, 56(3), 37–54. [Scopus, Q2, SJR: 0.23]

Conference Proceedings:

• Tan, M.I.S.M.H., Wong, L.K., Loh, Y.P., and Pee, C.Y. (2024, December). Enhancing early plant disease detection: 1D to 2D spectral transformations. In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 1–6). IEEE. [Scopus]

• Rauf, S.S.A., Tan, M.I.S.M.H., and Loh, Y.P. (2024, December). Multi-band satellite image analysis for multi-label classification. In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 1–6). IEEE. [Scopus]

• Chai, M.X., Fam, Y.D., Octaviano, Q.N., Pee, C.Y., Wong, L.K., Tan, M.I.S.M.H., and See, J. (2024, December). Improved cassava plant disease classification with leaf detection. In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 1–6). IEEE. [Scopus]
   

Book Chapter:

• Tan, M.I.S.M.H., Jamlos, M.F., Omar, A.F., Dzaharudin, F., Azmi, M.A.M., Ahmad, M.N., Rahman, N.A.A., and Khairi, K.A. (2021). Near-infrared spectroscopy for Ganoderma boninense detection: An outlook. In Recent Trends in Mechatronics Towards Industry 4.0: Selected Articles from iM3F 2020, Malaysia (pp. 117–126). Singapore: Springer. [Scopus]

Honors / Awards

• Gold Award (Category: Computing and Informatics), InventX Invention Exhibition 2024, Cassava-Spectra2D: Enhancing Early Cassava Disease Classification via 1D to 2D Spectral Data (Lead)

• Best of the Best (Poster category), InventX Invention Exhibition 2024, Cassava-Spectra2D: Enhancing Early Cassava Disease Classification via 1D to 2D Spectral Data (Lead)

• Gold Award (Category: Computing and Informatics), InventX Invention Exhibition 2024, Improved Cassava Plant Disease Classification with Leaf Detection (Collaboration)

• Best of the Best (Poster category), InventX Invention Exhibition 2024, Improved Cassava Plant Disease Classification with Leaf Detection (Collaboration)