Marlia Mohd Hanafiah

Dr Marlia Mohd Hanafiah is a Professor and Head, Centre for Tropical Climate Change System, Institute of Climate Change, The National University of Malaysia, Malaysia. Areas of Research Expertise: Life Cycle Impact Assessment (LCA) and Environmental Foot printing of Green Materials and Energy • Environmental Engineering • Wastewater Treatment & Water Management • Green Technology & Sustainability. She has a total academic teaching experience of 15+ years with more than 100 publications in reputed international conferences, journals and online book chapter contributions (Indexed By: SCI, SCIE, SSCI, Scopus, DBLP). She received research grant and consultation (as project leader & team member) of RM 7,284,719.00

Publications

Exploring the Advancements and Future Directions of Digital Twins in Healthcare 6.0
Exploring the Advancements and Future Directions of Digital Twins in Healthcare 6.0
Archi Dubey, C. Kishor Kumar Reddy, Srinath Doss, Marlia Mohd Hanafiah. © 2024. 320 pages.
The healthcare industry is increasingly complex, demanding personalized treatments and efficient operational processes. Traditional research methods need help to keep pace with...
A Study of Machine Learning Methods-Based Affective Disorders Detection Using Multi-Class Classification
A Study of Machine Learning Methods-Based Affective Disorders Detection Using Multi-Class Classification
Bhavya Tungana, Kishor Kumar Reddy C., Marlia Mohd Hanafiah, Srinath Doss. © 2023. 20 pages.
Psychological health issues like stress, anxiety and depression is becoming general due to various factors. Different severity levels of stress, anxiety and depression have...
An Efficient Early Diagnosis and Healthcare Monitoring System for Mental Disorders Using Machine Learning
An Efficient Early Diagnosis and Healthcare Monitoring System for Mental Disorders Using Machine Learning
Kishor Kumar Reddy C., Pullisani Satvika, Srinath Doss, Marlia M. Hanafiah. © 2023. 23 pages.
Recently, the increase in internal health problems in society has led to an increase in research on the development of mechanistic capacity models to detect or predict internal...