Assist. Prof. Dr . Masoud Kargar | Artificial Intelligence | Best Researcher Award

Islamic Azad University, Iran

Assist. Prof. Dr. Masoud Kargar is a distinguished researcher and educator in the field of Computer Engineering with expertise spanning artificial intelligence, machine learning, reinforcement learning, and software engineering. He earned his Ph.D. in Software Engineering from Islamic Azad University, Qazvin Branch in 2020, where his doctoral research focused on multi-programming language software system modularization, following a strong academic foundation in computer engineering and programming. Currently serving as an Assistant Professor at the Islamic Azad University, Tabriz Branch, he has over two decades of teaching and supervisory experience across multiple Iranian universities, mentoring numerous master’s and doctoral students in advanced topics such as deep learning, natural language processing, data mining, and intelligent systems. His research interests center on applied AI, big data, optimization algorithms, and their real-world applications in healthcare, finance, and smart cities. Dr. Kargar is highly skilled in programming, software development, data analytics, and advanced modeling, with expertise in Python, C++, MATLAB, and AI frameworks such as TensorFlow and PyTorch. He has authored more than 40 research documents indexed in Scopus, IEEE, IET, and Springer, receiving over 500 citations with an h-index of 11, alongside publishing books and book chapters on programming, deep learning, and generative adversarial networks. His excellence has been recognized with several awards, including the prestigious Professor Kambiz Badie’ Award in Artificial Intelligence (2025) and Best Paper Award at the International Symposium on Telecommunications (2024). In addition, he serves as Associate Editor of the Iran Journal of Computer Science (Springer), an active peer reviewer for leading journals, and has held leadership roles as Director of ICT and head of AI research groups. In conclusion, Dr. Kargar’s blend of academic rigor, innovative research, mentorship, and international recognition underscores his strong contributions to advancing computer science and artificial intelligence.

Profiles: Scopus | ORCID

Featured Publications

Bayani, A., & Kargar, M. (2024). LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network. Physiological Reports.

Kargar, M. (2020). New internal metric for software clustering algorithms validity. IET Software.

Kargar, M. (2020). Improving the modularization quality of heterogeneous multi-programming software systems by unifying structural and semantic concepts. The Journal of Supercomputing.

Kargar, M., Izadkhah, H., & Isazadeh, A. (2019). Tarimliq: A new internal metric for software clustering analysis. 2019 Iranian Conference on Electrical Engineering (ICEE). IEEE.

Izadkhah, H., Kargar, M., & Isazadeh, A. (2019). Towards comprehension of the multi-programming language software systems. 2019 IEEE Conference on Knowledge Based Engineering and Innovation (KBEI). IEEE

Mr. Praveen Naik | Artificial Intelligence Award | Best Researcher Award

Researcher at Meru University of Science and Technology, Kenya

Mr. Erick Mutwiri Kirimi is a dedicated and accomplished individual with a strong background in mathematics. With a Bachelor’s degree in Education Science and ongoing PhD studies in Computational and Applied Mathematics, he has developed a deep understanding of mathematical concepts and their practical applications. Mr. Kirimi’s academic journey includes serving as a part-time lecturer at several universities, where he imparts his knowledge to students. He has also gained valuable teaching experience as a mathematics and chemistry teacher, including serving as the Head of the Mathematics Department. His academic achievements are further highlighted by scholarships, including a full scholarship for his PhD studies in Computational Mathematics and a partial scholarship for his PhD studies in Applied Mathematics. These scholarships reflect his commitment to academic excellence and his potential to make significant contributions to the field of mathematics. Mr. Kirimi’s research skills, teaching abilities, leadership qualities, computer proficiency, and strong communication and interpersonal skills make him a well-rounded individual poised for success in his academic and professional endeavors.

Professional Profiles:

Professional Experience:

Praveen Naik has been a Research Fellow at the National Institute of Technology Karnataka, Surathkal since 2020. In this role, he has conducted research on “Investigation of Arecanut Images for Grading through Non-Destructive Methods.” His contributions to the project include dataset curation, the development of a lightweight and efficient model, implementation of an Adaptive Genetic-Based Model Optimization, introduction of a non-destructive methodology, and successful resolution of Arecanut grading challenges. Prior to his current position, Praveen Naik served as a Senior Assistant Professor at Shri Madhwa Vadiraja Institute of Technology and Management from 2013 to 2020. During this time, he managed a variety of subjects, crafted compelling curricula, and conducted impactful lectures. He also provided mentorship to students, collaborated seamlessly with peers, and efficiently managed administrative responsibilities within the academic setting. From 2010 to 2011, Praveen Naik worked as a Software Programmer at SouthCan Software, where he played a pivotal role in the Milk Dairy Project. His responsibilities included supervising day-to-day dairy operations, overseeing tasks such as data entry, manipulation, and transactions. He also contributed to report customization using SQL-Server Reporting Service, thereby enhancing reporting functionalities for the organization.

Academic:

Since 2020, Praveen Naik has been pursuing a Ph.D. in Information Technology at the National Institute of Technology Karnataka, Surathkal. Prior to this, he completed his M.Tech in Computer Science and Engineering from Atria Institute of Technology, Bengaluru, from 2011 to 2013. Praveen’s academic journey began with a Bachelor’s degree in Information Science and Engineering from Nitte Mahalinga Adyanthaya Memorial Institute of Technology, Nitte, which he completed from 2006 to 2010.

Areas of Specialization:

Praveen Naik has specialized expertise in Object Detection, Model Optimization, and Deep Learning, with a focus on YOLOv5. His experience includes extensive work in Dataset Curation, ensuring high-quality data inputs for machine learning models.

Achievements:

Praveen Naik has demonstrated his academic prowess by qualifying in several prestigious examinations. He cleared the GATE (Graduate Aptitude Test in Engineering) in 2020, showcasing his proficiency in engineering concepts. Additionally, he achieved qualification in the UGC-NET (University Grants Commission – National Eligibility Test) in 2020, indicating his in-depth knowledge and understanding of his field. Furthermore, he passed the K-SET (Karnataka State Eligibility Test) in 2019, demonstrating his expertise and competence in the field of education.

Publications:

Flower Phenotype Recognition and Analysis using YoloV5 Models
- Authors: PM Naik, B Rudra
- Year: 2022
- Journal: Grenze International Journal of Engineering & Technology (GIJET)
- Volume: 8
- Issue: 2
- Citations: 3
Deep learning-based arecanut detection for X-ray radiography: improving performance and efficiency for automated classification and quality control
- Authors: PM Naik, B Rudra
- Year: 2024
- Journal: Nondestructive Testing and Evaluation
- Pages: 1-21
Classification of Arecanut X-Ray Images for Quality Assessment Using Adaptive Genetic Algorithm and Deep Learning
- Authors: PM Naik, B Rudra
- Year: 2023
- Journal: IEEE Access
- Volume: 11
- Pages: 127619-127636
Prevention of Webshell Attack using Machine Learning Techniques
- Authors: S YC, PM Naik, B Rudra
- Year: 2021
- Journal: Grenze International Journal of Engineering & Technology (GIJET)
- Volume: 7
- Issue: 1

Masoud Kargar | Artificial Intelligence | Best Researcher Award