Assist. Prof. Dr. Mohanned M. H. AL-Khafaji | Artificial Intelligence | Best Researcher Award

Published on by Computer Scientists Awards

Assist. Prof. Dr. Mohanned M. H. AL-Khafaji | Artificial Intelligence | Best Researcher Award

Engineering | University of Technology | Iraq

Dr. Mohanned Mohammed Hussein Al-Khafaji is an accomplished researcher and academic leader in production engineering, specializing in intelligent manufacturing systems, laser material processing, neural network modeling, and fuzzy logic control applications. As Dean of the College of Production Engineering and Metallurgy at the University of Technology, Baghdad, his research integrates computational modeling, automation, and artificial intelligence to enhance production efficiency and precision engineering. He has made significant contributions to the development of computer-controlled manufacturing systems, laser-based material processing, and predictive modeling using advanced algorithms. His work on CO₂ laser processing, neural network-based machining analysis, and hybrid intelligent systems has advanced industrial automation and smart manufacturing processes. Dr. Al-Khafaji’s research also explores mechatronics, robotic systems, and additive manufacturing, emphasizing simulation tools like Abaqus, COMSOL Multiphysics, and MATLAB. His scientific output reflects substantial academic influence, with 15 Scopus-indexed documents, 41 citations from 37 documents, and an h-index of 3. On Google Scholar, he has accumulated 125 citations, an h-index of 6, and an i10-index of 4, underscoring his growing impact in engineering research.

Profile

Scopus | ORCID | Google Scholar

Featured Publications

Al-Khafaji, M. M. H., & Hubeatir, K. A. (2021). CO2 laser micro-engraving of PMMA complemented by Taguchi and ANOVA methods. Journal of Physics: Conference Series, 1795(1), 012062.

Al-Khafaji, M. M. H. (2018). Neural network modeling of cutting force and chip thickness ratio for turning aluminum alloy 7075-T6. Al-Khwarizmi Engineering Journal, 14(1), 67–76.

Khayoon, M. A., Hubeatir, K. A., & Al-Khafaji, M. M. (2021). Laser transmission welding is a promising joining technology technique – A recent review. Journal of Physics: Conference Series, 1973(1), 012023.

Momena, T. F. A., Mohammed, M. M. H., & Al-Khafaji, M. M. H. (2023). Smart robot vision for a pick and place robotic system. Engineering and Technology Journal, 40(6), 1–15.

Shaker, F., Al-Khafaji, M., & Hubeatir, K. (2020). Effect of different laser welding parameters on welding strength in polymer transmission welding using semiconductor. Engineering and Technology Journal, 38(5), 761–768.*

Prof. Dr. Mohamed Maher Ben Ismail | Artificial Intelligence | Best Researcher Award

Published on by Computer Scientists Awards

Prof. Dr. Mohamed Maher Ben Ismail | Artificial Intelligence | Best Researcher Award

Prof. Dr. Mohamed Maher Ben Ismail, King Saud University, Saudi Arabia

Dr. Mohamed Maher Ben Ismail is a distinguished full professor in the Computer Science Department at the College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia . With a prolific academic and research background spanning over two decades, Dr. Ben Ismail is recognized for his contributions in artificial intelligence, image processing, and data mining. His work bridges theory and practical applications in machine learning and statistical modeling, making him a leading voice in his field 🌐📚.

Professional Profile

Google Scholar

Scopus

🎓 Education Background

Dr. Ben Ismail holds a Ph.D. in Computer Engineering and Computer Science from the University of Louisville, USA (2011) 🇺🇸, where his dissertation focused on image annotation and retrieval using multi-modal feature clustering. He also earned a Master’s in Automatic and Signal Processing and a Bachelor’s in Electrical Engineering from the National School of Engineering of Tunis, Tunisia 🇹🇳. His early academic journey was distinguished by excellence in mathematics, physics, and competitive engineering entrance exams 🧠📘.

🧑‍🏫 Professional Experience

Dr. Ben Ismail currently serves as a Full Professor at King Saud University (2021–present), following roles as Associate Professor (2017–2021) and Assistant Professor (2011–2017). Previously, he worked as a Design & Development Engineer at STMicroelectronics, Tunisia, and as a Graduate Research Assistant at the University of Louisville’s Multimedia Research Lab, where he pioneered work on CBIR systems and integrated machine learning approaches. His academic role includes supervising thesis work, lecturing across AI, ML, algorithm design, and image processing 💼👨‍🏫.

🏆 Awards and Honors

Throughout his career, Dr. Ben Ismail has received numerous accolades, including the Best Faculty Member Award (2017) at King Saud University, the Graduate Dean’s Citation Award (2011), and the IEEE Outstanding CECS Student Award (2011) 🥇. He is also a member of the Golden Key International Honor Society and received early recognition through his promotion at STMicroelectronics and various graduate assistantships and scholarships 🎖️.

🔬 Research Focus

Dr. Ben Ismail’s research interests lie in Artificial Intelligence, Machine Learning, Pattern Recognition, Image Processing, Temporal Data Mining, and Information Fusion 🤖🧠. His work emphasizes robust statistical modeling and intelligent systems design, often applied to domains like IoT security, brain tumor detection, real estate prediction, and hyperspectral imaging. His prolific publication record in top-tier journals and conferences highlights his continuous contributions to advanced computational techniques and interdisciplinary innovation 📊📈.

📌 Conclusion

With a solid educational foundation, impactful research contributions, and extensive teaching experience, Dr. Mohamed Maher Ben Ismail stands as a key figure in advancing AI-driven solutions in academia and industry. His dedication to excellence and innovation marks him as a thought leader and an inspirational academic voice in the global computer science community 🌟🧑‍🔬.

📚 Top Publications Notes

  1. YOLO-Act: Unified Spatiotemporal Detection of Human Actions Across Multi-Frame Sequences
    📅 Published in: Sensors, 2025
    🔍 Cited by: 12 articles (as of mid-2025)
    🧠 Highlights: Proposes a YOLO-based system for recognizing actions across video frames.

  2. MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach
    📅 Published in: Sensors, 2025
    🔍 Cited by: 9 articles
    🧠 Highlights: Enhances brain tumor classification using deep adversarial networks.

  3. RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic
    📅 Published in: Sensors, 2024
    🔍 Cited by: 18 articles
    🔐 Highlights: Focuses on adversarial ML methods to enhance IoT network security.

  4. Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks
    📅 Published in: Cancers, 2024
    🔍 Cited by: 25 articles
    🧬 Highlights: Applies CNNs to early skin cancer detection using medical images.

  5. A Deep Learning Approach for Brain Tumor Firmness Detection Based on Five YOLO Versions
    📅 Published in: Computation, 2024
    🔍 Cited by: 14 articles
    💡 Highlights: Compares YOLOv3 to YOLOv7 models for brain scan interpretation.

  6. Toward an Improved Machine Learning-based Intrusion Detection for IoT Traffic
    📅 Published in: Computers, 2023
    🔍 Cited by: 20 articles
    🔒 Highlights: Develops a secure ML framework to prevent intrusions in smart devices.

  7. Simultaneous Deep Learning-based Classification and Regression for Company Bankruptcy Prediction
    📅 Published in: Journal of Business & Economic Management, 2023
    🔍 Cited by: 8 articles
    💼 Highlights: Innovative DL model integrating financial classification with regression.

  8. Novel Dual-Constraints Based Semi-Supervised Deep Clustering Approach
    📅 Published in: Sensors, 2025
    🔍 Cited by: 6 articles
    📊 Highlights: Enhances clustering accuracy using semi-supervised constraints in DL.

  9. Better Safe than Never: A Survey on Adversarial Machine Learning Applications towards IoT Environment
    📅 Published in: Applied Sciences, 2023
    🔍 Cited by: 22 articles
    🔍 Highlights: Comprehensive survey exploring adversarial ML attacks and defense for IoT.

  10. Detecting Insults on Social Network Platforms Using a Deep Learning Transformer-Based Model
    📅 Published in: IGI Global Book Chapter, 2025
    🔍 Cited by: 11 articles
    🌐 Highlights: Uses transformer models to detect hate speech and insults online.

 

Dr. Ashkan Tashk | Applied AI | Excellence Award (Any Scientific field)

Published on by Computer Scientists Awards

Dr. Ashkan Tashk | Applied AI | Excellence Award (Any Scientific field)

postdoc, Technical University of Denmark.

Dr. Ashkan Tashk is a highly accomplished electrical engineer and postdoctoral researcher with deep expertise in telecommunications, machine learning, and biomedical imaging. With a strong academic and teaching background, he has worked across multiple prestigious institutions in Denmark, Germany, and Iran. His career blends theoretical knowledge with applied innovations, particularly in AI-driven healthcare technologies, contributing significantly to interdisciplinary research and development. He is known for his dedication to science communication, teaching, and AI-based applications in medicine.

Publication Profile

Google scholar

🎓 Education Background:

Ashkan Tashk received his Ph.D. in Electrical Engineering with a focus on Telecommunications in 2015, following his M.Sc. (2010) and B.Sc. (2006) in the same field. His undergraduate project involved designing and constructing a prototype sunlight tracking platform—an early indication of his strong interest in applied engineering and innovation. His academic journey provided a solid foundation in electronics, signal processing, and machine learning, which continues to influence his research today.

💼 Professional Experience:

Dr. Tashk currently serves as a Postdoctoral Researcher at Denmark’s leading universities (2019–present). Prior to that, he worked as a telecommunications expert at FREC and completed a research internship at Karlsruhe Institute of Technology (KIT), Germany. His career includes teaching roles at the University of Southern Denmark, University of Copenhagen, and various Iranian academic institutions. He has taught courses in electrical circuits, microprocessors, statistics, numerical analysis, and MATLAB programming, while also publishing Persian-language technical tutorials and conducting workshops in Europe and Iran.

🏆 Awards and Honors:

Dr. Ashkan Tashk became an IEEE Senior Member in 2022, recognizing his professional maturity and significant contributions to electrical engineering. He has served as a session chair at multiple international conferences such as ACSIT2020 in Copenhagen and ICCAIRO2019 in Athens. He has also completed prestigious programs like the “Science Communication” course by the Royal Danish Academy of Sciences and Letters and the RCR workshop at the University of Copenhagen, demonstrating his commitment to ethical and effective scientific practice.

🔬 Research Focus:

Ashkan’s research centers on the application of artificial intelligence and machine learning in biomedical engineering, particularly in image processing, ultrasound tomography, and cancer diagnostics. Notable projects include developing LSTM-RF models for metastatic prostate cancer prediction, CNN-based biomedical segmentation tools, and advanced metabolomics data imputation methods. His work also spans sonar signal processing, image-based fingerprint recognition, and microprocessor-controlled automation systems. These interdisciplinary projects reflect his strong problem-solving abilities and technological foresight.

🧩 Conclusion:

Dr. Ashkan Tashk is a dynamic academic, educator, and innovator whose work bridges electrical engineering and biomedical science using modern AI tools. His technical skill set, coupled with his teaching excellence and global collaborations, position him as a thought leader in the integration of engineering and healthcare. Fluent in Persian, English, and Danish, and proficient in tools like Python, MATLAB, and various PLC programming languages, he continues to impact both academia and industry with his visionary contributions.

📚 Top Publications & Citations:

Semantic Segmentation of Biomedical Images Using Deep Convolutional Neural Networks
Journal: Journal of Medical Imaging and Health Informatics
Cited by: 24 articles

Predicting Metastatic Prostate Cancer via Biochemical Parameters Using LSTM and RF
Journal: Computers in Biology and Medicine
Cited by: 18 articles

Machine Learning Imputation for Large-scale Metabolomics Data
 Journal: Metabolomics
Cited by: 10 articles

Eye-Tracking Data Analysis Using AI for Cognitive Study
 Journal: IEEE Transactions on Affective Computing
Cited by: 7 articles