Richa Vij – Computer science and engineering – Best Researcher Award

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Richa Vij - Computer science and engineering - Best Researcher Award

IIT Jammu - India

👩‍🏫 ACADEMIC AND RESEARCH LEADER

Richa Vij is an Assistant Professor in the Department of Computer Science and Engineering at the Government College of Engineering and Technology, Jammu. With a focus on retinal imaging and artificial intelligence, she brings significant experience in developing innovative computational models aimed at diagnosing systemic diseases like diabetic retinopathy and Alzheimer's. Her academic contributions, both as a professor and researcher, have made her a prominent figure in the field of AI-powered medical diagnostics.

💻 PROJECT ASSOCIATE AT IIT JAMMU

As a Project Associate-II for a DRDO project at IIT Jammu, Richa works on cutting-edge computer science projects that contribute to national development. Her role involves harnessing deep learning and AI methodologies for real-world applications, reflecting her commitment to both academic advancement and practical innovation.

📚 PUBLISHED AUTHOR IN AI AND HEALTHCARE

Richa Vij has authored multiple impactful publications in high-profile journals such as Metabolic Brain Disease and Computers and Electrical Engineering. Her research has focused on leveraging deep learning techniques to advance the early detection of diseases like Alzheimer's and diabetic retinopathy, contributing significantly to medical imaging and AI diagnostic systems.

🔬 PIONEERING RETINAL DISEASE DIAGNOSIS

Her Ph.D. research at SMVDU, Katra, under the supervision of Dr. Sakshi Arora, centers on using hybrid deep transfer learning-based algorithms to analyze retinal images for systemic disease detection. This work aims to enhance the performance of segmentation and classification models, positioning her research at the intersection of healthcare and advanced AI technologies.

📊 INNOVATIVE M.TECH RESEARCH

Richa’s M.Tech dissertation focused on "Robust Human Face Tracking and Recognition in Video Frames." By developing a novel face recognition model using the AdaBoost algorithm and K-means clustering, she addressed key challenges in face recognition, such as pose variation and occlusion, further showcasing her expertise in machine learning and pattern recognition.

🧠 FOCUS ON DIABETIC RETINOPATHY AND ALZHEIMER’S

Her work is particularly influential in the early diagnosis of Diabetic Retinopathy and Alzheimer’s disease through retinal imaging. By utilizing AI-based models for retinal vessel segmentation, her contributions are paving the way for improved diagnostic frameworks, with potential applications in clinical environments for early and accurate disease detection.

🌟 COMMITMENT TO AI-DRIVEN HEALTHCARE

Richa’s dedication to advancing AI-driven healthcare solutions is reflected in her ongoing research and teaching. She strives to bridge the gap between technology and healthcare by developing intelligent systems that can assist practitioners in diagnosing complex diseases, ultimately contributing to better patient outcomes and more efficient clinical workflows.

NOTABLE PUBLICATION

Title: A systematic review on diabetic retinopathy detection using deep learning techniques
Authors: R. Vij, S. Arora
Journal: Archives of Computational Methods in Engineering
Year: 2023

Title: A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification
Authors: R. Vij, S. Arora
Journal: Multimedia Tools and Applications
Year: 2023

Title: Computer vision with deep learning techniques for neurodegenerative diseases analysis using neuroimaging: a survey
Authors: R. Vij, S. Arora
Journal: International Conference on Innovative Computing and Communications
Year: 2022

Title: A systematic survey of advances in retinal imaging modalities for Alzheimer’s disease diagnosis
Authors: R. Vij, S. Arora
Journal: Metabolic Brain Disease
Year: 2022

Title: A survey on various face detecting and tracking techniques in video sequences
Authors: R. Vij, B. Kaushik
Journal: 2019 International Conference on Intelligent Computing and Control Systems
Year: 2019

Jiaming Zhong – Artificial intelligence – Best Researcher Award

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Jiaming Zhong - Artificial intelligence - Best Researcher Award

Wuyi university - China

AUTHOR PROFILE

SCOPUS

📚 SCIENTIFIC RESEARCH ACHIEVEMENTS

Jiaming Zhong has made significant contributions to the fields of video classification and tactile sensing. His groundbreaking papers include "Exploring Cross-video Matching for Few-shot Video Classification via Dual-Hierarchy Graph Neural Network Learning," published in Image and Vision Computing, and "Text-guided Graph Temporal Modeling for Few-Shot Video Classification," featured in Engineering Applications of Artificial Intelligence. These studies, published in top-tier journals, highlight Zhong's innovative approaches in utilizing graph neural networks and multimodal models for advanced video analysis and classification.

🛠️ PATENTS AND TECHNOLOGICAL INNOVATIONS

Zhong holds several patents that showcase his expertise in developing practical solutions for various technological challenges. His patents include methods for video anomaly classification, chip defect detection, and mobile robot obstacle avoidance. These patents reflect his commitment to translating theoretical research into tangible technological advancements that address real-world problems.

🔬 PROJECT EXPERIENCE: PEEL RECOGNITION

In a project focused on the precise identification of Chenpi years using a multimodal model, Zhong's work involved designing lightweight modules and fine-tuning models to achieve high recognition accuracy. His use of the CLIP multimodal model for feature extraction led to a remarkable 99% accuracy in recognizing Chenpi years with limited sample data. This project, detailed on GitHub, demonstrates his proficiency in applying advanced machine learning techniques to practical problems.

🎥 PROJECT EXPERIENCE: FEW-SHOT VIDEO CLASSIFICATION

Zhong's research in video behavior classification involved addressing challenges related to data scarcity and model capabilities. Collaborating with Macau University of Science and Technology and Wuyi University, he developed a dual-hierarchy graph neural network that significantly improved classification performance through cross-video frame matching. This innovative approach was published in Image and Vision Computing and showcased Zhong's ability to enhance model performance through sophisticated temporal modeling.

🔍 PROJECT EXPERIENCE: MULTIMODAL REPRESENTATION LEARNING

In a project focused on multimodal video behavior analysis, Zhong led efforts to develop a novel framework for self-supervised learning using multimodal data. This project, supported by a 500,000 RMB research grant, involved developing a text-guided feature optimization module and a query text token learning mechanism. His research aimed to leverage multimodal knowledge to improve the classification performance of few-shot video behaviors, with results published in top journals.

📈 IMPACTFUL RESEARCH AND PUBLICATIONS

Zhong's work has significantly impacted the fields of video classification and sensor technology. His papers in renowned journals and his patents contribute to advancing the understanding and application of these technologies. His research not only addresses current challenges but also paves the way for future innovations in these areas.

🏆 ACKNOWLEDGEMENTS AND RECOGNITION

Zhong's contributions to scientific research and technology have earned him recognition within the academic and professional communities. His innovative work in video classification and sensor technology continues to influence the field and inspire further research and development.

NOTABLE PUBLICATION

Ultra-sensitive and stable All-Fiber iontronic tactile sensors under high pressure for human movement monitoring and rehabilitation assessment
Authors: K. Ma, D. Su, B. Qin, Y. Xin, X. He
Year: 2024
Journal: Chemical Engineering Journal

Real-time citrus variety detection in orchards based on complex scenarios of improved YOLOv7
Authors: F. Deng, J. Chen, L. Fu, J. Li, N. Li
Year: 2024
Journal: Frontiers in Plant Science

Exploring cross-video matching for few-shot video classification via dual-hierarchy graph neural network learning
Authors: F. Deng, J. Zhong, N. Li, D. Wang, T.L. Lam
Year: 2023
Journal: Image and Vision Computing