Dr Owais Ahmed Malik


Assistant Professor, Faculty of Science



Dr Owais Ahmed Malik is an Assistant Professor in Mathematical and Computing Sciences department at Faculty of Science, Universiti Brunei Darussalam (UBD). He completed his PhD in Computer Science from Universiti Brunei Darussalam (2015), MS in Computer Science from KFUPM, Saudi Arabia (2002) and BE is Computer Systems Engineering from NED, Pakistan (1998). Dr Owais has more than ten years of progressive experience in academia and research in the field of computer science and engineering. He has been teaching various undergraduate courses including machine learning, data mining, machine perception, programming fundamentals, design and analysis of algorithms, software engineering and operating system in different national/international universities. His research interest includes designing and exploring different intelligent/pattern recognition algorithms for the analysis and classification of healthcare data, applied biomechanics, bio-signal processing and big data analytics. He has published a number of articles in internationally reputable journals and conferences.


PhD (Computer Science, UBD, Brunei Darussalam), MSc (Computer Science, KFUPM, Saudi Arabia), BE (Computer Engineering, NED-UET, Pakistan)


Machine Learning, Data Mining, Computer Vision/Image Processing, Deep Learning, Fuzzy Logic, Knowledge Based Systems, Intelligent Decision Support Systems for Rehabilitation Monitoring, Big Data Analytic, Wireless Body Area Network, Biosignal Processing and Analysis, Applied Biomechanics


Computer Vision Based Plant Species Classification

This project aims to develop an automated system for plants species classification (identification/recognition) using computer vision techniques. This is a multi-phase project so more than one candidate can apply. The prospective candidates require knowledge in the filed of image processing, machine learning and mobile application development. This project aims to achieve the following objectives: • Automate the identification process of plants species using computer vision techniques (i.e. using pictures taken by camera/mobile) • Build an initial knowledge-base of plants’ images (leaves) and other relevant information • Set-up a cloud-based framework to provide the online classification services • Develop a smart mobile application for real-time plants species recognition

Applications Invited
For PhD/MS Students

Exploratory data analysis using clustering techniques for health-survey repository

This study explores different data mining techniques for clustering/classification of health survey data. Socio-demographic and socio-economic factors are key determinants of health and wellbeing in a population. These factors are known to be important but their contribution to health and illness is not necessarily obvious to full extent. This study is a next step, from descriptive statistics towards the data analytics and predictive modelling, based on the National Health and Nutritional Status Survey (NHANSS) report published by MOH, Brunei Darussalam in 2015. This project will help in identifying the groups with prevalence of certain non-communicable disease (NCD) and then appropriate measures/steps can be taken to counter them/prevent the population by the concerned departments. For example, the study will try to identify (a student may choose the following tasks): • Common socio-economic/socio-demographic patterns for smoking population • Common socio-economic/socio-demographic/food intake patterns for obese population • Common socio-economic/socio-demographic/food intake patterns for population with high blood pressure • Common socio-economic/socio-demographic/food intake patterns for population with diabetes • Examine the inter-relationships among the socio-economic-demographic attributes • Identify interesting subsets/groups within NHANSS data demonstrating similar characteristics and health status • Improving the cluster quality using feature selection algorithms and Categorical Principal Component Analysis for de-identified National Health and Nutritional Status Survey (NHANSS) data

Applications Invited
For PhD/MS Students

Computer Vision/Image Processing and Deep Learning Applications

Various applications of computer vision/image processing techniques using sensors and machine learning/deep learning techniques; CanaKit Raspberry Pi 3 Kit, Raspberry Pi 3 IBM IoT Kit, Raspberry Pi Touchscreen Display, Raspberry Pi 3 Camera Kit, XBOX ONE KINECT SENSOR.

Applications Invited
For PhD/MS Students

Scopus Publications


Google Scholar Citations


Google Scholar h-index


Google Scholar i10-index

Scopus Publications


Instrumented measurement analysis system for soldiers’ load carriage movement using 3-D kinematics and spatio-temporal features
DNFP Damit, SMNA Senanayake, OA Malik, PN bin Pg Tuah, Measurement 95, 230-238, 2017.

Impact of intelligent biofeedback during rehabilitation of professional athletes: a model for next generation smart healthcare system
OA Malik, SMNA Senanayake, Scientia Bruneiana 15. 2016

Batch adsorption studies of the removal of methyl violet 2B by soya bean waste: isotherm, kinetics and artificial neural network modelling
MRR Kooh, MK Dahri, LBL Lim, LH Lim, OA Malik, Environmental Earth Sciences 75 (9), 1-14, 2016

Pattern Recognition of Brunei Soldier’s Based on 3-Dimensional Kinematics and Spatio-Temporal Parameters
DNFP Damit, SMNA Senanayake, OA Malik, PHNJP Tuah, Asian Conference on Intelligent Information and Database Systems, 713-722, 2016

A Real-Time Intelligent Biofeedback Gait Patterns Analysis System for Knee Injured Subjects,
P Wulandari, SMNA Senanayake, OA Malik, Asian Conference on Intelligent Information and Database Systems, 703-712, 2016


An Intelligent Recovery Progress Evaluation System for ACL Reconstructed Subjects Using Integrated 3-D Kinematics and EMG Features
OA Malik, SMN Senanayake, D Zaheer, IEEE Journal of Biomedical and Health Informatics, 1-1, 2015

A Multisensor Integration-Based Complementary Tool for Monitoring Recovery Progress of Anterior Cruciate Ligament-Reconstructed Subjects
OA Malik, SMN Arosha Senanayake, D Zaheer, IEEE/ASME Transactions on Mechatronics, 2014

A knowledge-based intelligent framework for anterior cruciate ligament rehabilitation monitoring
SMN Arosha Senanayake, OA Malik, PM Iskandar, D Zaheer, Applied Soft Computing 20, 127-141, 2013

Software level green computing for large scale systems
F Fakhar, B Javed, R ur Rasool, O Malik, K Zulfiqar, Journal of Cloud Computing: Advances, Systems and Applications 1 (1), 4, 2012

A hybrid intelligent system to improve predictive accuracy for cache prefetching
S Sarwar, Z Ul-Qayyum, OA Malik, Expert Systems with Applications 39 (2), 1626-1636, 2012


Automated Realtime Plant Species Recognition Using State of The Art Machine Learning Techniques (UBD/RSCH/1.4/FICBF((b)/2018/011


S.M.N. Arosha Senanayake and Owais Ahmed Malik, 2014, "Real Time Biofeedback Mechanism and Data Presentation for Knee Injury Rehabilitation Monitoring and a Soft Real Time Intelligent System Thereof", US Patent - 10004455 B2, June 26 2018, Brunei Patent BN/N/2014/0036 – Approved by World Intellectual Property Organization (WIPO) for patenting.

Industry, Institute, or Organisation Collaboration

Ministry of Health, Brunei Darussalam