Assoc. Prof. Dr. Mahmoud Abd-Ellah | Computer Science | Best Researcher Award

Egyptian Russian University | Egypt

Dr. Mahmoud Khaled Abd-Ellah is an accomplished Assistant Professor at the Faculty of Artificial Intelligence, Egyptian Russian University, Badr, Egypt, widely recognized for his pioneering research at the intersection of artificial intelligence, medical imaging, and deep learning. Holding a Ph.D. in Electrical Engineering from Minia University, his doctoral research focused on brain tumor diagnosis through MRI using advanced machine learning techniques. His impressive publication portfolio includes 22 Scopus-indexed papers, collectively cited 830 times by 774 documents, with an h-index of 12 demonstrating substantial scientific impact and research excellence. His scholarly work has been featured in leading journals such as Scientific Reports, Neural Computing and Applications, and Ecological Informatics, advancing AI-driven approaches for medical image analysis, automated brain tumor detection, COVID-19 classification, and environmental data modeling. Beyond research, Dr. Abd-Ellah actively contributes to academic governance and quality enhancement as a member of the Egyptian International Ranking Committee, head of the Quality Management Unit, and ranking official at the Egyptian Russian University. He is also an active member of multiple IEEE councils, engaging in the development and application of AI across engineering and biomedical domains. His ORCID profile (0000-0002-6840-2503) and Scopus ID (57191265348) further reflect his consistent record of impactful scholarship and international collaboration. With his interdisciplinary expertise, editorial service, and mentorship of Ph.D. and master’s students, Dr. Mahmoud Khaled Abd-Ellah exemplifies academic leadership, innovation, and a transformative approach to research that advances both science and society.

Profile: Scopus | Google Scholar | ORCID | ResearchGate

Featured Publications

Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2019). A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned. Magnetic Resonance Imaging, 61, 300–318.
Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2018). Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP Journal on Image and Video Processing, 2018(1), 1–10.
El-Rawy, M., Abd-Ellah, M. K., Fathi, H., & Ahmed, A. K. A. (2021). Forecasting effluent and performance of wastewater treatment plant using different machine learning techniques. Journal of Water Process Engineering, 44, 102380.
Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2016). Design and implementation of a computer-aided diagnosis system for brain tumor classification. In 2016 28th International Conference on Microelectronics (ICM) (pp. 73–76).
MostafaShokry, A. A. M. K., Awad, A. I., & Abd-Ellah, M. K. (2022). Systematic survey of advanced metering infrastructure security: Vulnerabilities, attacks, countermeasures, and future vision. Future Generation Computer Systems, 1–21.

Assist. Prof. Dr. Gokalp Çınarer | Artificial İntelligence | Best Researcher Award

Yozgat Bozok University Computer Engineering Department | Turkey

Dr. Gökalp Çınarer is an Assistant Professor in the Department of Computer Engineering at Yozgat Bozok University. His expertise lies in artificial intelligence, machine learning, image processing, and deep neural networks. Over the years, he has contributed significantly to the academic community with applications of AI in diverse fields, including medicine, agriculture, food technologies, and environmental analysis.

Professional profile👤

Google Scholar

ORCID

Scopus

Strengths for the Awards✨

Diverse Research Portfolio: Dr. Çınarer has made significant contributions across multiple domains — medicine, agriculture, food technologies, and environmental analysis — leveraging artificial intelligence and machine learning, showcasing versatility and cross-disciplinary impact.
High Research Output: With over 50 publications indexed in SCI-Expanded, SSCI, and Scopus, his research output is impressive, indicating a consistent contribution to advancing knowledge in computer engineering and AI applications.
Innovative Work in Medical Imaging: His PhD thesis on brain tumor detection through image processing and classification algorithms reflects a critical application of AI in medical diagnostics, directly contributing to healthcare advancements.
Academic Leadership: As Head of the Department of Information Processing and Computer Engineering Software at Yozgat Bozok University, Dr. Çınarer plays a pivotal role in shaping academic programs, guiding research initiatives, and mentoring students.
Teaching Excellence: He teaches a broad range of courses, including Machine Learning, Deep Learning, and Artificial Intelligence Applications, fostering the next generation of AI researchers and practitioners.

🎓 Education

Dr. Çınarer earned his PhD in Computer Engineering from Kırıkkale University between 2017 and 2021. His doctoral research focused on “Detection of Brain Tumors with Image Processing Techniques and Analysis with Classification Algorithms.”

💼 Experience

Since 2021, Dr. Çınarer has been serving as the Head of the Department of Information Processing and the Head of the Department of Computer Engineering Software at Yozgat Bozok University. He teaches undergraduate and graduate courses, including Machine Learning, Deep Learning, Python Programming, Algorithm Analysis, and Artificial Intelligence Applications.

🔬 Research Interests On Artificial Intelligence

Dr. Çınarer’s research interests encompass artificial intelligence, machine learning, image processing, and deep neural networks. His work delves into AI applications across various fields such as medicine, agriculture, food technologies, and environmental analysis.

🏆 Awards

Dr. Çınarer has been recognized for his pioneering work in artificial intelligence and its applications across multiple disciplines, earning accolades for his contributions to AI-driven medical analysis and agricultural technologies.

📚 Publications

Classification of brain tumors by machine learning algorithms
Authors: G Çınarer, BG Emiroğlu
Year: 2019
Citations: 74
Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features
Authors: G Çınarer, BG Emiroğlu, AH Yurttakal
Year: 2020
Citations: 57
Öğretmenlerin Teknolojik Araçlarla Eğitime Yönelik Tutumlarının Çeşitli Değişkenlere Göre İncelenmesi Yozgat İli Örneği
Author: G Çınarer
Year: 2016
Citations: 27
Classification of hazelnuts according to their quality using deep learning algorithms
Authors: N Erbaş, G Çınarer, K Kiliç
Year: 2022
Citations: 21
A comparative study on segmentation and classification in breast mri imaging
Authors: AH Yurttakal, H Erbay, T İkizceli, S Karacavus, G Çinarer
Year: 2018
Citations: 21
Brain Tumor Classification Using Deep Neural Network
Authors: G Çınarer, BG Emiroğlu, RS Arslan, AH Yurttakal
Year: 2020
Citations: 16
Application of various machine learning algorithms in view of predicting the CO2 emissions in the transportation sector
Authors: G Çınarer, MK Yeşilyurt, Ü Ağbulut, Z Yılbaşı, K Kılıç
Year: 2024
Citations: 11
Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation: Proceedings of the INFUS 2021 Conference
Authors: G Çınarer, C Kahraman, S Cebi, SC Onar, B Oztaysi, AC Tolga, IU Sari
Year: 2021
Citations: 9
Classification of Diabetic Rat Histopathology Images Using Convolutional Neural Networks
Authors: AH Yurttakal, H Erbay, G Çınarer, H Baş
Year: 2021
Citations: 8
A Deep Transfer Learning Framework For The Staging Of Diabetic Retinopathy
Authors: G Çınarer, K Kılıç, T Parlar
Year: 2022
Citations: 6

📄 Conclusion

Dr. Gökalp Çınarer stands at the forefront of AI research, leveraging machine learning and image processing to solve complex problems in medicine, agriculture, and environmental science. His work continues to inspire advancements in interdisciplinary fields and pave the way for future innovations.