Dr. Hameed Mutlag Farhan, Electrical and Computer Engineering, Turkey
Dr. Farhan, a researcher with a Ph.D. in Electrical and Computer Engineering from Altinbas University, specializes in AI and IoT with a thesis on “Optimal Feature Tuning Model by Variants of Convolutional Neural Network with LSTM for Driver Distraction Detection.” His academic achievements include a high GPA of 3.86/4 and a Master’s in the same field with a focus on “Digital Image Watermarking for Copyright Protection.” Dr. Farhan has pursued additional certifications in Object-Oriented Programming, Database Programming, and Cisco Networking. His research, with publications on deep neural networks for speaker identification and intelligent face mask detection, demonstrates his expertise in deep learning and IoT. His innovative work addresses real-world challenges using advanced technology.
Suitability for the Award
Dr. Hameed Mutlag Farhan is highly suitable for the Research for Best Researcher Award due to his comprehensive academic background, innovative research in deep learning and IoT, and his impactful publications. His work has addressed critical issues such as driver distraction, digital image watermarking for copyright protection, and face mask detection in IoT contexts—each of which has significant societal and technological implications.
Academic Background:
Dr. Farhan holds a Ph.D. in Electrical and Computer Engineering from Altinbas University, with a thesis on “Optimal Feature Tuning Model by Variants of Convolutional Neural Network with LSTM for Driver Distract Detection in IoT Platform,” showcasing advanced research in AI and IoT. His academic excellence is further evidenced by a high GPA of 3.86/4.
He completed his Master of Science in Electrical and Computer Engineering from Altinbas University, focusing on “Digital Image Watermarking for Copyright Protection,” with a GPA of 3.64/4, highlighting his foundational expertise in digital image processing.
Certifications:
Dr. Farhan has pursued additional certifications in relevant areas, including Object-Oriented Programming, Database Programming, and Cisco Networking Academy courses. His recent internship in IoT research at the University of València further underscores his commitment to staying at the forefront of emerging technologies.
Research Contributions:
Dr. Farhan has made notable contributions to the field through his publications. His work includes a study on “Speaker Identification Model Based on Deep Neural Networks,” which has garnered attention in the Iraqi Journal for Computer Science and Mathematics, and an “Optimized Face Detector-Based Intelligent Face Mask Detection Model in IoT Using Deep Learning Approach,” published in Applied Soft Computing. These publications highlight his expertise in deep learning, image processing, and IoT applications.
Research Focus and Innovation:
Dr. Farhan’s research interests, particularly in optimizing models using deep learning and IoT technologies, align well with contemporary advancements in the field. His work on driver distraction detection and intelligent face mask detection showcases his ability to address real-world problems using cutting-edge technology.
Publication Top Notes:
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Title: Speaker Identification Model Based on Deep Neural Networks
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Title: Optimized Face Detector-Based Intelligent Face Mask Detection Model in IoT Using Deep Learning Approach
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Title: Data Mining and Analysis for Predicting Electrical Energy Consumption
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Title: Data Mining Techniques for Extraction and Analysis of COVID-19 Data
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Title: Optic Disc Segmentation in Human Retina Images with Meta Heuristic Optimization