Facial Emotion Recognition Using Conventional Machine Learning and Deep Learning Methods: Current Achievements, Analysis and Remaining Challenges by Amjad Rehman Khan

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A simple segmentation approach for unconstrained cursive handwritten words in conjuction with the neural network / by Amjad Rehman Khan, author, Zulkifli Mohammad, author

Off-line cursive handwriting word recognition, a survey of methods and performances / by Amjad Rehman Khan, author, Dzulkifli Mohammad, author

Contour Vs non - contour based word segmentation from handwritten text lines : an experimenal analysis / by Fajri Kurniawan, author, Amjad Rehman Khan, author, Dzulkifli Mohamad, author

Offline cursive handwritten character recognization using heuristics techniques [electronic resource] by 490232 Amjad Rehman Khan, Fakulti Sains Komputer dan Sistem Maklumat

Off-line cursive handwriting segmentation a heuristic approach by Amjad Rehman Khan, author, Dzulkifli Mohamad, author, Fajri Kurniawan, author

Offline cursive handwritten character recognization using heuristics techniques / by 490232 Amjad Rehman Khan, Dzulkifli Muhammad, supervisor, Fakulti Sains Komputer dan Sistem Maklumat

Mobile Application Usability Evaluation: A Study Based on Demography by Wahaj Ali, Omer Riaz, Shahzad Mumtaz, Amjad Rehman Khan, Tanzila Saba, Saeed Ali Bahaj

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IoT- Enabled Firmness Grades of Tomato in Cold Supply Chain Using Fusion of Whale Optimization Algorithm and Extreme Learning Machine by Ali Haider, Rafaqat Kazmi, Teg Alam, Rab Nawaz Bashir, Haitham Nobanee, Amjad Rehman Khan, Aqsa

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Scene Graph Generation With Structured Aspect of Segmenting the Big Distributed Clusters by Amjad Rehman Khan, Hamza Mukhtar, Tanzila Saba, Omer Riaz, Muhammad Usman Ghani Khan, Saeed Ali Bahaj

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A 7L and 11L High Step-Up SCMLI Topology With Reduced Component Voltage Stress by Jagabar Sathik Mohamed Ali, Amjad Rehman Khan, Gopinath Narayanan Pandurangan, Prem Ponnusamy, Faten S. Alamri, Saeed Ali Bahaj

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Brain Tumor Classification and Detection Based DL Models: A Systematic Review by Karrar Neamah, Farhan Mohamed, Myasar Mundher Adnan, Tanzila Saba, Saeed Ali Bahaj, Karrar Abdulameer Kadhim, Amjad Rehman Khan

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Intrusion Detection System for Wireless Sensor Networks: A Machine Learning Based Approach by Halima Sadia, Saima Farhan, Yasin Ul Haq, Rabia Sana, Tariq Mahmood, Saeed Ali Omer Bahaj, Amjad Rehman Khan

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Multi-Agent System for Students Cognitive Assessment in E-Learning Environment by Rimsha Shahzad, Muhammad Aslam, Shaha T. Al-Otaibi, Muhammad Saqib Javed, Amjad Rehman Khan, Saeed Ali Bahaj, Tanzila Saba

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An Improved Automatic Image Annotation Approach Using Convolutional Neural Network-Slantlet Transform by Myasar Mundher Adnan, Mohd Shafry Mohd Rahim, Amjad Rehman Khan, Tanzila Saba, Suliman Mohamed Fati, Saeed Ali Bahaj

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Synergistic Integration of Transfer Learning and Deep Learning for Enhanced Object Detection in Digital Images by Safa Riyadh Waheed, Norhaida Mohd Suaib, Mohd Shafry Mohd Rahim, Amjad Rehman Khan, Saeed Ali Bahaj, Tanzila Saba

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Automated Image Annotation With Novel Features Based on Deep ResNet50-SLT by Myasar Mundher Adnan, Mohd Shafry Mohd Rahim, Amjad Rehman Khan, Ahmed Alkhayyat, Faten S. Alamri, Tanzila Saba, Saeed Ali Bahaj

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A new approach for segmenting difficult cursive handwritten words from benchmark database / by Dzulkifli Mohamad, author, Amjad Rehman Khan, author, Fajri Kurniawan, author, International Conference Information & Communication Technology and Systems (4th: 2008: Surabaya, Indonesia)

Interpretable classification framework for diabetic retinopathy / by Saif Hameed Abbood Al-Waeli, 1984-, author 653341, Haza Nuzly Abdull Hamed, supervisor 308916, Mohd. Shafry Mohd. Rahim, supervisor 514272, Amjad Rehman Khan, supervisor 490232, Fakulti Komputeran 538688

Enhanced deep learning model for classification of anterior criciate ligament tears / by Mazhar Javed Awan, author 654512, Mohd. Shafry Mohd. Rahim, supervisor 514272, Naomie Salim, supervisor 612144, Ardiyansyah Syahrom, 1978-, supervisor 515233, Amjad Rehman Khan, supervisor 490232, Fakulti Komputeran 538688