Wei He | Object Detection | Best Researcher Award

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Assoc. Prof. Dr. Wei He | Object Detection | Best Researcher Award

Assoc. Prof. Dr. Wei He | Object Detection – Dean at Hunan Institute of Science and Technology, China

Dr. Wei He is an esteemed academic and researcher at the Hunan Institute of Science and Technology, where he has served in the School of Information Science and Engineering since 2009. With over a decade of experience in advanced computational imaging and intelligent systems, he has established himself as a key contributor to the fields of remote sensing, hyperspectral imaging, and object detection. Dr. He’s interdisciplinary expertise and commitment to cutting-edge innovation have positioned him as a thought leader in applied machine learning within Earth observation technologies.

Academic Profille

ORCID

Education

Dr. He earned his Ph.D. in Information and Communication Engineering from Hoseo University in South Korea in 2020. Prior to this, he completed his Master’s degree in Computer and Communication at Changsha University of Science and Technology in 2009. His academic journey began with a Bachelor’s degree in Computer Science from Hunan Institute of Science and Technology in 2006. This progressive education provided him with a strong foundation in both theoretical and applied aspects of intelligent systems, enabling his impactful research career.

Experience

Since joining the faculty at Hunan Institute of Science and Technology, Dr. He has been involved in teaching, mentoring, and pioneering research. His roles span curriculum development, guiding postgraduate research, and securing national and institutional research funding. His international education and research background allow him to engage in collaborations across academic borders, particularly in the domains of intelligent object tracking and hyperspectral image processing. His recent efforts have emphasized deploying AI-enhanced algorithms in real-time UAV tracking and high-resolution remote sensing classification systems.

Research Interest

Dr. Wei He’s research interests lie at the intersection of artificial intelligence, image analysis, and sensor data interpretation. He focuses on small-object detection in remote sensing, hyperspectral anomaly identification, and machine learning-based UAV tracking. His work blends deep learning, quaternion frequency analysis, and multilevel network architectures to extract meaningful insights from complex visual and spatial datasets. His methods emphasize not only accuracy and efficiency but also robustness in noisy and dynamic environments, which is critical for real-world deployment of intelligent vision systems.

Award

Dr. He is a strong candidate for the Best Researcher Award due to his consistent scholarly productivity, innovative methodology, and real-world impact in intelligent sensing technologies. His ability to merge theory with practical application has earned him increasing academic citations and recognition in top-tier IEEE and Scopus-indexed journals. He continues to raise the bar in remote sensing research and is regarded as a role model for young researchers in the field of applied artificial intelligence.

Publication

📘 From Weak Textures to Dense Arrangements: Leveraging Prior Knowledge for Small-Object Detection in Remote Sensing Images, IEEE Transactions on Geoscience and Remote Sensing, 2025 (Cited by 15+)
📗 GSINet: Gradual Semantic Interaction Network for Remote Sensing Object Detection, IEEE JSTARS, 2025 (Cited by 10+)
📙 Hyperspectral Anomaly Detection Using Quaternion Frequency Domain Analysis, IEEE TNNLS, 2024 (Cited by 22+)
📕 Break the Shackles of Background: UAV Tracking Based on Eccentric Loss, IEEE TIM, 2024 (Cited by 12+)
📒 Background Subtraction via Regional Multi-Feature-Frequency Model in Complex Scenes, Soft Computing, 2023 (Cited by 8+)
📘 Hyperspectral Image Classification Using Superpixel–Pixel–Subpixel Multilevel Network, IEEE TIM, 2023 (Cited by 14+)
📗 Feature Extraction Using Spectral Regression Whitening, IEEE JSTARS, 2021 (Cited by 19+)

Conclusion

Dr. Wei He exemplifies the qualities sought in the recipient of the Best Researcher Award. His contributions to remote sensing and intelligent image interpretation continue to address critical global challenges in Earth monitoring and autonomous systems. Through his prolific publications, deep theoretical insights, and application-driven research, he has advanced the state-of-the-art in machine learning for geospatial intelligence. With strong international collaborations, a growing citation record, and clear leadership potential, Dr. He is poised to make even greater contributions to academia and industry in the coming years.

Moumita Chanda | Deep Learning | Best Researcher Award

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Ms.Moumita Chanda | Deep Learning | Best Researcher Award

Lecturer IUBAT – International University of Business Agriculture and Technology  Bangladesh

Moumita Chanda is a passionate researcher and lecturer at the International University of Business Agriculture and Technology (IUBAT). She specializes in computer science and engineering, focusing on emerging technologies like machine learning, artificial intelligence, and IoT. With a robust academic background and a keen interest in interdisciplinary research, Moumita strives to contribute significantly to technological advancements and innovation.

Profile

Google Scholar

Education

🎓 Moumita Chanda earned her M.Sc. in Information and Communication Technology (ICT) from the Institute of Information Technology (IIT), Jahangirnagar University, Dhaka, with a stellar CGPA of 3.71/4.00, securing the 1st position among her peers in 2022-2023. She also holds a B.Sc. in Information Technology from the same institution, achieved in 2022, with a commendable CGPA of 3.53/4.00. Prior to her university education, she completed her Higher Secondary School at Cumilla Government Women’s College and her Secondary School Certificate at Cumilla Modern High School, both with excellent academic records.

Experience

💼 Since December 2023, Moumita has been imparting knowledge and skills as a Lecturer in the Department of Computer Science and Engineering at IUBAT. Her professional journey is marked by her commitment to teaching and research, where she integrates her extensive knowledge of modern technologies and practical experience to educate and inspire her students.

Research Interest

🔍 Moumita Chanda’s research interests are diverse and interdisciplinary, encompassing Machine Learning, Artificial Intelligence, Internet of Things (IoT), Augmented Reality (AR), Explainable Artificial Intelligence (XAI), Metaverse, Computer Vision, Image Processing, Wearable Sensor Networks, and Human-Computer Interaction (HCI). She is dedicated to exploring and advancing these fields to drive innovation and practical applications in various domains.

Awards and Achievements

🏆 Moumita’s dedication to learning and research has been recognized through various awards. She has completed several online non-credit courses from prestigious institutions, including the University of California, University of Michigan, Macquarie University, and Duke University. Additionally, she was a finalist in the Mujib 100 Idea Contest 2021, where her innovative idea “BongoDecor” aimed at reducing plastic consumption problems, was highly appreciated.

Publications

📄 Moumita Chanda has a commendable list of publications, showcasing her contributions to the field of technology and research. Some of her notable works include:

  • “A review of emerging technologies for IoT-based smart cities” in Sensors, 2022. Read more
  • “Deep learning-based human activity recognition using CNN, ConvLSTM, and LRCN” in International Journal of Cognitive Computing in Engineering, 2024. Read more
  • “Impact of Internet Connectivity on Education System in Bangladesh during Covid-19” in International Journal of Advanced Networking and Applications, 2022. Read more
  • “Smoker Recognition from Lung X-ray Images using ML” in 2023 26th International Conference on Computer and Information Technology (ICCIT), IEEE. Read more
  • “Does VGG-19 Road Segmentation Method is better than the Customized UNET Method?” Accepted in 2024 9th International Conference on Machine Learning Technologies (ICMLT 2024).