MD Rabiul Islam – Biomedical Signal Processing Award – Best Researcher

Mr. MD Rabiul Islam - Biomedical Signal Processing Award - Best Researcher

Texas A&M University - Bangladesh

Author Profile

Early Academic Pursuits

Mr. Md. Rabiul Islam embarked on his academic journey with a solid foundation in Electrical and Electronic Engineering (EEE) at Khulna University of Engineering & Technology (KUET), Bangladesh. He demonstrated exceptional academic prowess, achieving a remarkable CGPA of 4.00 out of 4.00 in his M.Sc. Eng. (EEE) and 3.63 out of 4.00 in his B.Sc. Eng. (EEE). His dedication and passion for learning were evident from his early years, as evidenced by his consistent academic excellence throughout his educational endeavors.

Professional Endeavors

Mr. Rabiul Islam's professional journey has been characterized by a blend of teaching, research, and leadership roles. He started as a Lecturer at the Department of EEE, North Western University, Khulna, Bangladesh, where he imparted knowledge in electrical circuits, digital signal processing, and measurement and instrumentation. His commitment to academia led him to progressively higher positions, culminating in his current role as an Assistant Professor (on Leave) at the Department of EEE, Bangladesh Army University of Engineering & Technology, Natore, Bangladesh.

Contributions and Research Focus

Driven by a passion for Artificial Intelligence (AI), Rabiul Islam has made significant contributions to the field of medical imaging, AI, machine learning, signal processing, computer vision, and pattern recognition. His research interests span a wide spectrum, including emotion recognition from EEG signals, fall detection systems, text detection, skin lesion classification, and more. His current focus lies in medical image analysis, particularly in cardiac imaging, exploring the application of AI in heart disease diagnosis and assessment.

Accolades and Recognition

Mr. Rabiul Islam's academic and research contributions have earned him prestigious accolades and recognition. He has authored numerous journal papers and conference papers, demonstrating his expertise in various domains. Notably, his paper titled "Multiple Regression Analysis Based Non-Invasive Blood Glucose Level Estimation Using Photoplethysmography" received the Best Paper Award at the 2021 International Conference on Automation, Control, and Mechatronics for Industry 4.0.

The Biomedical Data Analysis and Signal Processing Award recognizes outstanding contributions in the realm of biomedical signal processing and data analysis. This prestigious accolade celebrates advancements in techniques and methodologies utilized to extract meaningful insights from various biomedical signals and data sources. From intricate physiological signals to complex medical imaging data, recipients of this award demonstrate innovation and excellence in applying signal processing techniques, such as digital signal processing, time-frequency analysis, and pattern recognition, to analyze and interpret biomedical information.

Impact and Influence

Through his research endeavors, Rabiul Islam has contributed significantly to advancing knowledge and innovation in the fields of AI, machine learning, and biomedical signal processing. His work on emotion recognition, fall detection systems, and medical image analysis has the potential to revolutionize healthcare diagnostics and enhance patient care. His publications have been widely cited, indicating the impact and influence of his research within the scientific community.

Legacy and Future Contributions On Biomedical Signal Processing Award

As a dedicated scholar and educator, Rabiul Islam's legacy lies in his commitment to academic excellence, research innovation, and mentorship. He continues to inspire future generations of researchers through his teaching, supervision, and scholarly activities. Moving forward, he aims to further explore the intersection of AI, healthcare, and technology to address pressing societal challenges and improve the quality of life globally. His future contributions are poised to leave a lasting imprint on the fields of AI and medical imaging, shaping the landscape of healthcare and technology.

Citations

  • Citations   861
  • h-index       12
  • i10-index    14

Notable Publication

Snehashis Majhi – Computer Vision – Best Researcher Award

Mr. Snehashis Majhi - Computer Vision - Best Researcher Award 

Institut National de Recherche en Informatique et en Automatique - France

Author Profile

Early Academic Pursuits

Mr. Snehashis Majhi embarked on his academic journey with a Bachelor of Technology in Computer Science and Engineering from the National Institute of Science and Technology, Berhampur, India. During this time, he delved into the realm of computer vision with his thesis titled "Design and Development of CBIR system using Classification Confidence," under the guidance of Dr. Jatindra Kumar Dash. This early exposure laid the foundation for his future endeavors in research and development.

Professional Endeavors

Mr. Majhi's professional journey commenced with an internship at INRIA, Sophia Antipolis Cedex BP 93, France, where he contributed to the development of a deep learning pipeline for human activity detection in smart home scenarios. This experience provided him with practical insights into real-world applications of deep learning technologies.

Following his internship, Majhi ventured into the field of glaucoma detection during his tenure as a Research Fellow at MNIT Jaipur, India. Here, he developed a deep convolutional neural network for detecting glaucoma diseases in fundus images, showcasing his versatility in applying deep learning techniques to healthcare domains.

Currently serving as a Research Scholar at INRIA Sophia Antipolis, France, Majhi's primary focus lies in developing human-scene centric video abnormality detection methods for smart-city surveillance systems. His research involves collaboration with Toyota Motor Europe and Woven Planet, demonstrating his ability to work on interdisciplinary projects with industry partners.

Contributions and Research Focus

Mr. Majhi's research contributions span various aspects of computer vision and deep learning. Noteworthy among his works is the development of the Human-Scene Network (HSN) and the Outlier-Embedded Cross Temporal Scale Transformer (OE-CTST) for video anomaly detection. These innovations address critical challenges in surveillance systems, showcasing Majhi's expertise in designing novel architectures and algorithms for complex tasks.

His research endeavors also encompass weakly-supervised learning techniques and optimization methods tailored for anomaly detection, underscoring his commitment to advancing the state-of-the-art in video analysis.

Accolades and Recognition

Mr. Majhi's contributions have been recognized through publications in reputable conferences and journals, including IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) and International Conference on Neural Information Processing (ICONIP). His papers have received commendable rankings, reflecting the significance and impact of his research in the scientific community.

Impact and Influence

Through his research and academic pursuits, Mr. Majhi has made significant contributions to the fields of computer vision and machine learning. His innovative approaches to video anomaly detection have the potential to enhance surveillance systems' effectiveness, thereby contributing to the advancement of public safety and security measures.

Legacy and Future Contributions

Mr. Majhi's legacy lies in his pioneering work in video anomaly detection and deep learning applications. His research serves as a stepping stone for future advancements in smart-city surveillance, healthcare diagnostics, and beyond. As he continues his academic and professional journey, Majhi is poised to make further contributions to the field, driving innovation and addressing societal challenges through cutting-edge technologies.

Citations

  • Citations   97
  • h-index       5
  • i10-index   4

Notable Publication