Rebwar Khalid | Artificial Intelligence | Editorial Board Member

Published on by Academic Awards

Dr. Rebwar Khalid | Artificial Intelligence | Editorial Board Member

Erbil Polytechnic University | Iraq

Dr. Rebwar Khalid Hamad is an emerging researcher in artificial intelligence with a strong focus on nature-inspired algorithms, metaheuristics, and data-driven optimization systems. His work advances cutting-edge computational models such as the Krill Herd, FOX, Gravitational Search, and GOOSE algorithms, contributing significantly to optimization theory and its real-world engineering and healthcare applications. He has developed impactful frameworks for intelligent problem-solving, integrated AI-based search techniques, and enhanced algorithmic performance through systematic reviews and novel implementations. His publications in high-impact journals highlight his ability to bridge theoretical AI mechanisms with advanced data management and practical optimization challenges. Beyond research, he contributes to academic development through teaching, student supervision, and the design of data management systems. His scholarly portfolio demonstrates strong analytical capabilities, innovation in metaheuristic modeling, and a commitment to advancing the fields of artificial intelligence, data science, and computational optimization.

Profile:  Google Scholar

Featured Publications

Hamad, R. K., & Rashid, T. A. (2023). GOOSE algorithm: A powerful optimization tool for real-world engineering challenges and beyond. Evolving Systems.

Hamad, R. K., & Rashid, T. A. (2023). Current studies and applications of Krill Herd and Gravitational Search Algorithms in healthcare. Artificial Intelligence Review, 56(Suppl 1), 1243–1277.

Hamad, R. K., & Rashid, T. A. (2023). A systematic study of Krill Herd and FOX algorithms. In Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB) (pp. 168–186).

Hamad, R. K., & Rashid, T. A. (2025). A systematic study of GOOSE algorithms. In Multi-objective Optimization Techniques: Variants, Hybrids, Improvements, and Applications.

Hamad, R. K. (2024). GOOSE algorithm: A powerful optimization tool for real-world engineering challenges and beyond [Computer software]. GitHub.

Shunzhi Yang | Artificial Intelligence | Best Researcher Award

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Mr. Shunzhi Yang | Artificial Intelligence | Best Researcher Award

Mr. ShunzhiYang at Shenzhen Polytechnic University, China

Shunzhi Yang , born on November 7, 1994, is a Doctor of Engineering specializing in Artificial Intelligence and Computer Vision. He is currently a researcher and faculty member at the School of Artificial Intelligence, Shenzhen Polytechnic University. With a strong academic background and extensive research experience, he has contributed significantly to knowledge distillation, deep learning, and object recognition. His work has been published in top-tier journals, including IEEE Transactions on Pattern Analysis and Machine Intelligence. Yang’s dedication to AI innovation and education has established him as a prominent figure in the field.

Publication Profile

Google Scholar

Academic Background

Shunzhi Yang began his academic journey at Shenzhen Polytechnic University, earning a Junior College degree in Medical Electronics Engineering (2012–2015). He then pursued a Bachelor’s degree in Computer Science and Technology at Hanshan Normal University (2015–2017). His research career deepened at South China Normal University, where he completed a Master’s in Computer Science and Technology (2017–2020) under Professor Zheng Gong. Continuing at the same institution, he obtained his PhD in Software Engineering (2020–2023), working under Professors Zhenhua Huang and Mengchu Zhou. His diverse educational background laid a strong foundation for his AI research.

Professional Background

During his PhD, Shunzhi Yang was jointly trained at the Institute of Applied Artificial Intelligence in the Guangdong-Hong Kong-Macao Greater Bay Area, working under Professor Jinfeng Yang (2022–2023). He then joined the School of Artificial Intelligence at Shenzhen Polytechnic University in September 2023, where he currently contributes to AI research and education. His professional experience bridges both academia and applied AI, focusing on student-centered knowledge distillation and deep learning advancements. His role at Shenzhen Polytechnic University allows him to mentor students while actively engaging in groundbreaking AI research.

Awards and Honors

Shunzhi Yang has been recognized for his significant contributions to AI and Computer Vision. His research papers have been published in prestigious journals like IEEE Transactions on Pattern Analysis and Machine Intelligence. His work on feature map distillation and skill-transferring knowledge distillation has received acclaim from the research community. He has collaborated with leading AI researchers, contributing to major advancements in deep learning and neural networks. While specific awards and honors are not listed, his extensive publication record and impact in AI research position him as a highly respected academic in the field.

Research Focus

Shunzhi Yang’s research primarily focuses on Artificial Intelligence, Computer Vision, and Knowledge Distillation. His work includes student-centered learning models, adaptive temperature distillation, and lightweight deep learning architectures for low-resolution object recognition. He is particularly interested in improving AI efficiency for real-world applications, such as edge computing and neural network optimization. His research spans top-tier journals, covering essential advancements in AI-based knowledge transfer and model compression. His contributions help make AI systems more effective, efficient, and applicable to various domains, including education and autonomous systems.

Publication Top Notes

Making accurate object detection at the edge: Review and new approach

📅 2022 |  Cited by: 79 |  Artificial Intelligence Review 55 (3), 2245-2274

EdgeRNN: A compact speech recognition network with spatio-temporal features for edge computing

📅 2020 |  Cited by: 68 | IEEE Access 8, 81468-81478

Feature map distillation of thin nets for low-resolution object recognition

📅 2022 | Cited by: 66 | IEEE Transactions on Image Processing 31, 1364-1379

EdgeCRNN: An edge-computing oriented model of acoustic feature enhancement for keyword spotting

📅 2022 | Cited by: 24 | Journal of Ambient Intelligence and Humanized Computing, 1-11

Conclusion

Shunzhi Yang is a highly qualified candidate for the Best Researcher Award, given his strong academic background, extensive research contributions, and impactful publications in top-tier AI journals. Holding a PhD in Software Engineering, he has specialized in Artificial Intelligence and Computer Vision, collaborating with distinguished professors and contributing to key advancements in knowledge distillation, skill transfer learning, and deep learning optimization. His work has been recognized in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, showcasing his influence in the AI research community. Additionally, his experience in applied AI research within the Guangdong-Hong Kong-Macao Greater Bay Area and his role in AI education at Shenzhen Polytechnic University further reinforce his eligibility. If the award primarily values high-impact research, influential publications, and AI advancements, Yang stands as a strong contender; however, factors like patents, real-world implementations, and leadership roles may require further evaluation for a holistic comparison with other candidates.

 

 

Md Erfan | Machine Learning | Best Researcher Award

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Mr. Md Erfan | Machine Learning | Best Researcher Award

Mr. Md Erfan, University of Barishal, Bangladesh

Assistant Professor, Department of Computer Science and Engineering, University of Barishal, Bangladesh. His research focuses on flaky test detection, compilation error resolution, and AI applications in automation, decision-making, and problem-solving. He holds an MSSE and BSSE from the University of Dhaka. Erfan has published in Elsevier, Springer, and IEEE, exploring NLP, machine learning, and software engineering. He serves as Project Coordinator for Bangladesh’s EDGE Project and has mentored in NASA Space Apps Challenge. An athlete, he won medals in national athletic competitions. 

Publication Profile

Google Scholar

Education 🎓📚

Md Erfan holds a Master of Science in Software Engineering (MSSE) 🖥️ from the Institute of Information Technology, University of Dhaka (2016), with an impressive CGPA of 3.81/4.0 (WES Equivalent: 3.97/4.00). His thesis, supervised by Dr. Md Shariful Islam, focused on an Efficient Runtime Code Offloading Mechanism for Mobile Cloud Computing ☁️💻. He also earned a Bachelor of Science in Software Engineering (BSSE) 🏆 from the same institute in 2014, achieving a CGPA of 3.80/4.0 (WES Equivalent: 3.88/4.00). His undergraduate thesis, guided by Dr. Kazi Muhaimin-us-Sakib, explored approximating social ties based on call logs 📞📊.

Research Experience 🔬📊

In Summer 2024, Md Erfan worked as a Research Student in the UIUC+/ASSIP Summer Research Program 🎓. Collaborating with Dr. Wing Lam (George Mason University) 🏛️ and Dr. August Shi (University of Texas at Austin) 🤖, he focused on automating the end-to-end reproduction of flaky test methods 🛠️. His work involved leveraging issue data, compiling code, running tests, analyzing results, and logging dependencies. Additionally, he created Dockerized environments 🐳 to ensure reproducibility, enhancing software testing efficiency and reliability. His contributions aimed at improving software quality assurance and automation in test debugging 🔍✅.

Professional Experience 💼📚

Md Erfan is an Assistant Professor (2020–Present) at the Department of Computer Science and Engineering, University of Barishal 🏛️, where he teaches Software Engineering, Software Quality Assurance, Data Structures, Algorithms, and Mathematical Analysis 📖💻. Since January 2024, he has also served as a Project Coordinator for the EDGE Project 🌐, managing a 5 crore BDT ($384,615 USD) fund 💰 to enhance digital governance and the economy in Bangladesh. Previously, he worked as a Lecturer (2016–2020) 🎓, a Trainer (2015–2016) 🖥️, and a Software Engineer Intern (2014) 🔍, focusing on testing tools and Microsoft SharePoint development.

Awards and Achievements 🏆🎖️

Md Erfan has been a Regional Mentor (2021–2023) 🌍🚀 for the NASA Space Apps Challenge, guiding innovative projects. He received the Pre-graduation Merit Award (2015) 🎓 from the University of Dhaka for outstanding academic performance. Beyond academics, he has excelled in athletics, securing 3rd place 🥉 in the 5000m and 10000m races 🏃‍♂️ at the Bangladesh Inter-University Athletic Competition (2015) and 2nd place 🥈 in multiple track events (2014–2015). Since 2016, he has been the Coach and Manager ⚽🏅 of the University of Barishal Football and Athletics teams, fostering sports excellence.

 

Research Interests 🔍💻

Md Erfan’s research primarily focuses on Software Engineering, specializing in flaky test detection and mitigation as well as compilation error resolution to enhance software reliability and development efficiency. Additionally, he explores the applications of Artificial Intelligence (AI), leveraging Machine Learning (ML) 🤖, Natural Language Processing (NLP) 🗣️, and Computer Vision 👀 to tackle real-world challenges. His work aims to improve automation, decision-making, and problem-solving across various domains, ensuring smarter and more efficient technological advancements. Through his research, Erfan contributes to optimizing software development and AI-driven innovations for practical applications. 🚀

Research Focus Areas 🧑‍💻📡

Md Erfan’s research spans multiple domains in Software Engineering and Artificial Intelligence. His work focuses on Mobile Cloud Computing ☁️📱, including task allocation and code offloading for performance optimization. He explores Machine Learning 🤖 applications, such as flaky test detection, compilation error resolution, and autism spectrum disorder detection 🧠. His contributions in Natural Language Processing (NLP) 🗣️ involve cyberbullying classification and user similarity computation. Additionally, he applies Computer Vision 👁️ techniques for mosquito species identification and assistive robotics. His interdisciplinary approach integrates automation, decision-making, and problem-solving in real-world applications.

Publication Top Notes

  • Mobility aware task allocation for mobile cloud computing
    Cited by: 8
    Year: 2016 📱☁️
  • Task allocation for mobile cloud computing: State-of-the-art and open challenges
    Cited by: 4
    Year: 2016 📊
  • Identification of Vector and Non-vector Mosquito Species Using Deep Convolutional Neural Networks with Ensemble Model
    Cited by: 2
    Year: 2022 🦟🤖
  • Recurrent neural network based multiclass cyber bullying classification
    Cited by: 1
    Year: 2024 💻🗣️
  • User Similarity Computation Strategy for Collaborative Filtering Using Word Sense Disambiguation Technique
    Cited by: 1
    Year: 2023 🔍📚
  • Approximating Social Ties Based on Call Logs: Whom Should We Prioritize?
    Cited by: 1
    Year: 2015 📱📞
  • An exploration of machine learning approaches for early Autism Spectrum Disorder detection
    Year: 2025 🧠🤖
  • Experimental Study of Four Selective Code Smells Declining in Real Life Projects
    Year: 2024 🧑‍💻🔧
  • Autism Spectrum Disorder Detecting Mechanism on Social Communication Skills Using Machine Learning Approaches
    Year: 2023 🧠💡
  • Dynamic Method Level Code Offloading for Performance Improvement and Energy Saving
    Year: 2017 ⚡💻
  • A comparative study of early autism spectrum disorder detection using deep learning based models
    Year: 2017 🧠🔍
  • An Optimal Task Scheduling Mechanism for Mobile Cloud Computing
    Year: 2016 ☁️📊
  • WVGM: Water View Google Map, Introducing Water Paths on Rivers to Reach One’s Destination using Various Types of Vehicles
    Year: 2016 🌍🚗
  • A comprehensive survey of code offloading mechanisms for mobile cloud computing
    Year: 2016 ☁️🔄
  • MICROCONTROLLER BASED ROBOTICS SUPPORT FOR BLIND PEOPLE
    Year: 2016 🤖👨‍🦯

Conclusion 🌟

Mr. Md Erfan is a highly suitable candidate for the Research for Best Researcher Award due to his strong academic background, impactful research in software engineering and AI, extensive publications, leadership in digital governance projects, and active contributions to global research collaborations. His work demonstrates innovation, technical expertise, and a commitment to advancing knowledge in his field.