Esam Othman
Research areas: Computer Science · Artificial Intelligence · Advanced Neural Network Applications · AI in cancer detection · Explainable Artificial Intelligence (XAI)
0000-0001-6432-8716
About
Esam Othman received the B.Sc. (with First Class Honors) degree in Computer Engineering from Umm Al-Qura University, Mecca, Saudi Arabia, in 2007, and the M.Sc. and Ph.D. degrees in Computer Engineering from King Saud University, Riyadh, Saudi Arabia, in 2012 and 2016, respectively.
From 2009 to 2018, he worked as a Researcher in Computer Engineering at King Saud University, Riyadh, Saudi Arabia. In 2018, he was appointed as an Assistant Professor in Applied Computer Sciences at King Saud University, where he served until 2024. In August 2024, he joined Prince Sultan University as an Assistant Professor in the College of Computer and Information Sciences.
His research interests include machine learning, pattern recognition, remote sensing, and deep learning.
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Publications (21)
- Whisper in Focus: Parameter-Efficient Stuttering Disfluency Classification Save
- Recalibrating Human–Machine Relations through Bias-Aware Machine Learning: Technical Pathways to Fairness and Trust Save
- Breast Cancer Multi-Class Classification Using ViT Model Save
- MuSeRAN: Multimodal Sentiment Analysis with Recurrent Attention Networks Save
- Using Pretrained Vision Transformer for Breast Cancer Binary Classification Save
- Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models Save
- Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights Save
- Hybrid evolutionary algorithm based relevance feedback approach for image retrieval Save
- Optimized recommendations by user profiling using apriori algorithm Save
- Particle Swarm Optimization Algorithm for Detecting Distributed Predicates Save
- Moving average multi directional local features for speaker recognition Save
- A Dense Phase Descriptor for Human Ear Recognition Save
- Convolutional Neural Networks for Electrocardiogram Classification Save
- Tile-based semisupervised classification of large-scale VHR remote sensing images Save
- Domain Adaptation Network for Cross-Scene Classification Save
- Encoding Motion Cues for Pedestrian Path Prediction in Dense Crowd Scenarios Save
- Three-Layer Convex Network for Domain Adaptation in Multitemporal VHR Images Save
- Using convolutional features and a sparse autoencoder for land-use scene classification Save
- A deep learning approach for unsupervised domain adaptation in multitemporal remote sensing images Save
- A novel method for minimizing data overheads in store and forward networks Save