Mr. Abdullah Al Mamun | Deep Learning | Young Scientist Award

Mr. Abdullah Al Mamun | Deep Learning | Young Scientist Award

Lecturer, Dhaka University of Engineering & Technology (DUET), Gazipur, Bangladesh

Abdullah Al Mamun is a passionate researcher and academic professional specializing in Internet of Things (IoT), Machine Learning ๐Ÿค–, and Explainable Artificial Intelligence (XAI). Currently pursuing his Master of Science in Engineering at Dhaka University of Engineering & Technology (DUET), Gazipur, he brings a vibrant combination of theoretical knowledge and hands-on research experience. His dynamic involvement in projects across sustainability, computer vision ๐Ÿง , and intelligent systems has positioned him as a promising contributor to the technology and research domain.

Publication Profile

ORCID

๐ŸŽ“ Education Background

Abdullah Al Mamun is presently pursuing his M.Sc. in Computer Science and Engineering at DUET, Gazipur (since October 2024), where he has already completed his Bachelor of Science in Computer Science and Engineering with distinction in 2024 ๐ŸŽ“. His consistent academic journey showcases his dedication to computing, innovation, and advanced research.

๐Ÿ’ผ Professional Experience

Mamun is currently working as a Lecturer at the Department of CSE, Model Institute of Science and Technology, Gazipur, while also serving as a part-time Research Assistant in the Multimedia Signal & Image Processing research group at Woosong University, South Korea ๐ŸŒ. With three years of tutoring experience at ACME DUET Admission Coaching Center, and two internships in web development and CMS technologies, he has gained broad teaching, mentoring, and development experience across various platforms ๐Ÿ–ฅ๏ธ. His administrative roles in DUET Career & Research Club and DUET Computer Society also underscore his leadership and community contributions.

๐Ÿ† Awards and Honors

Abdullah has been recognized for his academic and problem-solving excellence. He earned the “Second Runner-Up” at BEYOND THE METRICS-2023 hosted by IUT (OIC), and “Runner-Up” at the Intra DUET Programming Contest (IDPC) 2022 ๐Ÿ…. He has actively participated in events like NASA Space App Challenge 2024 and DUET TECH FEST-2023, reflecting his engagement in competitive and innovation-driven activities ๐Ÿš€.

๐Ÿ”ฌ Research Focus

Abdullahโ€™s core research interests lie in IoT and sustainability, Machine Learning, Computer Vision, Explainable AI, and Reinforcement Learning ๐Ÿง ๐Ÿ“ก. He has been instrumental in implementing real-world projects such as IoT-based energy monitoring systems and child safety monitoring, defect detection via XAI, and skin cancer classification using optimized deep learning models. His collaborative projects with global research teams exhibit his strong contribution to the evolving field of intelligent systems and digital transformation.

โœ… Conclusion

With an impressive blend of academic rigor, technical skills, and collaborative research experience, Abdullah Al Mamun is making impactful strides in the field of computer science ๐Ÿงฉ. His work exemplifies innovation, sustainability, and intelligence in engineering systems. He continues to grow as a researcher dedicated to contributing to global scientific advancements ๐ŸŒ.

๐Ÿ“š Top Publicationsย 

  1. Developed an IoT-based Smart Solar Energy Monitoring System for Environmental Sustainability โ€“ 3rd International Conference on Advancement in Electrical and Electronic Engineering, 2024.
    Cited by: 7 articles ๐Ÿ“‘

  2. Developing an IoT-based Child Safety and Monitoring System: An Efficient Approach โ€“ IEEE 26th International Conference on Computer and Information Technology (ICCIT), 2023.
    Cited by: 13 articles ๐Ÿ”

  3. Software Defects Identification: Results Using Machine Learning and Explainable Artificial Intelligence Techniques โ€“ IEEE Journal, 2024.
    Cited by: 15 articles โš™๏ธ

  4. IoT-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8 โ€“ MDPI Sensors Journal, 2023.
    Cited by: 10 articles ๐Ÿš—

  5. Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN โ€“ 2nd NCIM Conference, Bangladesh, 2024.
    Cited by: 5 articles ๐Ÿงฌ

  6. Enhancing DBSCAN Dynamically: A Novel Approach to Parameter Initialization and Outlier Reduction โ€“ Bachelor Thesis, DUET, 2024.
    Cited by: 3 articles ๐Ÿ”

 

Mr. Muhammad Tauqeer Iqbal | Machine Learning | Best Researcher Award

Mr. Muhammad Tauqeer Iqbal | Machine Learning | Best Researcher Award

Mr. Muhammad Tauqeer Iqbal , Yangzhou University, China

Iqbal Muhammad Tauqeer is a passionate researcher and master’s student at Yangzhou University, China , specializing in the domain of Machine Learning ๐Ÿค–. With a solid foundation in both industry and academia, he has combined practical management experience with cutting-edge AI research. His dedication to data science applications and computer vision has led to a notable publication recognized as a best paper, showcasing his potential in the rapidly evolving tech landscape ๐ŸŒŸ.

Professional Profile

ORCID

๐ŸŽ“ Education Background

Iqbal is currently pursuing his Masterโ€™s degree at Yangzhou University, China ๐Ÿ“š, where his academic focus is on machine learning and its applications in computer vision. His academic pursuits have been driven by a commitment to advancing AI-driven solutions in environmental monitoring and digital recognition systems.

๐Ÿ’ผ Professional Experience

Before his transition into research, Iqbal gained valuable industry experience as an Assistant Production Manager at OPPO Mobile Company Pakistan ๐Ÿ“ฑ for over two years. This role provided him with deep insights into production workflows and industry standards, bridging the gap between theoretical learning and practical application.

๐Ÿ† Awards and Honors

Iqbal’s research has already earned accolades, with his paper titled “A Transfer Learning-Based VGG-16 Model for COD Detection in UVโ€“Vis Spectroscopy” being recognized as a Best Paper ๐Ÿฅ‡. This early recognition is a testament to the impact and novelty of his contributions to AI-powered environmental diagnostics.

๐Ÿ”ฌ Research Focus

His research interests lie primarily in Machine Learning, Deep Learning, Transfer Learning, and Computer Vision ๐Ÿง ๐Ÿ“Š. He is particularly focused on applying these techniques to UVโ€“Vis Spectroscopy and digital display recognition. He is currently working on a second research project that extends his work in pattern recognition and visual AI.

๐Ÿ”š Conclusion

With a unique blend of industrial management experience and academic rigor, Iqbal Muhammad Tauqeer is emerging as a promising contributor to the field of Artificial Intelligence. His work in machine learning models for environmental monitoring reflects not only his technical skills but also his commitment to impactful innovation ๐ŸŒ๐Ÿ”.

๐Ÿ“š Publication Top Note

  1. Title: A Transfer Learning-Based VGG-16 Model for COD Detection in UVโ€“Vis Spectroscopy
    Journal: Journal of Imaging
    Publisher: MDPI
    Published Year: 2025

 

Qiang Fan | Artificial Neural Networks | Best Researcher Award

Dr. Qiang Fan | Artificial Neural Networks | Best Researcher Award

engineer , Huazhong Institute of Electro-Optics, China

๐Ÿง‘โ€๐Ÿ”ฌ Dr. Qiang Fan is a senior engineer at the Huazhong Institute of Electro-Optics. He earned his Ph.D. from Wuhan University in 2017. Specializing in algorithm research, Dr. Fan focuses on image processing, infrared small target detection and recognition, target tracking, and deploying these algorithms on embedded platforms. His innovative work has led to significant advancements in automatic detection, recognition, and consistent tracking of small targets amidst complex backgrounds.

Profile

Scopus

 

Education

๐ŸŽ“ Dr. Qiang Fan completed his Ph.D. at Wuhan University in 2017. His academic journey has been characterized by a strong focus on algorithm research in image processing and related fields.

Experience

๐Ÿ”ฌ Dr. Qiang Fan has extensive experience as a senior engineer at the Huazhong Institute of Electro-Optics. His work primarily involves the development and deployment of image processing algorithms, particularly for infrared small target detection, recognition, and tracking. He has successfully applied for 14 invention patents, with 5 already authorized, demonstrating his innovative contributions to the field.

Research Interests

๐Ÿง  Dr. Qiang Fan’s research interests include image processing, infrared small target detection and recognition, target tracking, and the deployment of image processing algorithms on embedded platforms. His work focuses on enhancing target detection and robust tracking in complex backgrounds, addressing challenges such as occlusion and environmental interference.

Awards

๐Ÿ† Dr. Qiang Fan has applied for 14 invention patents, with 5 authorized, showcasing his contributions to technological advancements. His published research in prestigious SCI journals highlights his impact and recognition in the field of image processing and target detection.

Publications

“Automatic Detection and Recognition of Infrared Small Targets in Sea-Sky Backgrounds”

“Robust Tracking of Small Targets in Complex Backgrounds”

“Deployment of Image Processing Algorithms on Embedded Platforms”