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Creates a collaborative learning environment.
Makes even the toughest topics accessible.
Brings enthusiasm to every interaction.
Creates dynamic and thought-provoking lessons.
Knowledgeable and truly inspiring educator.
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About Ahlam
Dr. Ahlam Khaled Mohammed Al-Dhamari is a Lecturer in the Department of Electrical and Computer Engineering at Curtin University Malaysia, Sarawak Campus, having joined in April 2024. She holds a PhD in Electrical Engineering (Computer Engineering) from Universiti Teknologi Malaysia (UTM) obtained in 2020, a Master’s degree in Computer Engineering and Networks from the University of Jordan in 2015, and a Bachelor’s degree in Computer Science and Engineering (First Class Honours) from Hodeidah University in 2008. Her academic and professional career commenced as an Assistant Lecturer at Hodeidah University from 2008 to 2012. Following her PhD, she served as a Postdoctoral Fellow at UTM under an international fellowship scheme from 2021 to 2023. She is a member of the Association for Computing Machinery (ACM), Institute of Electrical and Electronics Engineers (IEEE), and Malaysia Botball & Robotics Team (MBOT).
Dr. Al-Dhamari's research specializations include Artificial Intelligence, Computer Vision, Machine Learning, Deep Learning, Image and Video Processing, Motion Analysis, Crowd Analysis and Management, Natural Language Processing, and Computer Security. Her scholarly contributions feature numerous peer-reviewed publications. Key journal articles encompass 'Motion Segmentation Using Ward’s Hierarchical Agglomerative Clustering for Crowd Disaster Risk Mitigation' (2024, International Journal of Disaster Risk Reduction), '3D-DTaPO: Dynamic Thermal-Aware Performance Optimization for 3D Dark Silicon Many-core Systems' (2023, IEEE Access), 'Motion Pattern-Based Scene Classification Using Adaptive Synthetic Oversampling and Fully Connected Deep Neural Network' (2023, IEEE Access), 'Visual Motion Segmentation in Crowd Videos Based on Spatial-Angular Stacked Sparse Autoencoders' (2023, Computer Systems Science and Engineering), 'Multi-Scale Network with Integrated Attention Unit for Crowd Counting' (2022, Computers, Materials & Continua), 'U-ASD Net: Supervised Crowd Counting Based on Semantic Segmentation and Adaptive Scenario Discovery' (2021, IEEE Access), 'Transfer Deep Learning Along with Binary Support Vector Machine for Abnormal Behavior Detection' (2020, IEEE Access), and 'Block-Based Steganographic Algorithm Using Modulus Function and Pixel-Value Differencing' (2017, Journal of Software Engineering and Applications). She has also authored conference papers such as 'Motion Segmentation of Pedestrian Trajectories Using Angular Gaussian Mixture Model' (2023, ACM BDCI). Her work has garnered recognition through awards including the Post-Doctoral Fellowship from UTM (2021-2023), PhD Fellowship from Hodeidah University (2016-2020), and MSc Scholarship from the German Academic Exchange Service (DAAD) (2012-2015).
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