Shima Shafiee | Bioinformatics | Best Researcher Award

Published on by Computer Vision Awards

Dr. Shima Shafiee | Bioinformatics | Best Researcher Award

Lecturer at Razi University, Iran

Dr. Shima Shafiee is an Iranian researcher specializing in artificial intelligence, bioinformatics, and computational biology. She has demonstrated remarkable expertise in applying machine learning models to complex biomedical problems, particularly in protein-peptide interaction prediction. With a solid foundation in computer engineering and years of academic and research experience, she has developed predictive models and hybrid algorithms that bridge the gap between computer science and life sciences. Dr. Shafiee’s extensive publication record in internationally recognized journals, her involvement in teaching and interdisciplinary research, and her dedication to lifelong learning and professional development highlight her as an influential and forward-looking scholar. She is currently engaged in postdoctoral research and academic roles, contributing to AI-driven solutions in healthcare and medical data analysis. Her collaborative efforts, teaching experience, and scientific contributions position her as a promising leader in both national and international research communities.

Professional Profile 

Education🎓

Dr. Shima Shafiee holds a Ph.D. in Computer Engineering with a specialization in Computer Systems Architecture from Razi University, where her dissertation focused on learning-based models for predicting protein-peptide binding interactions. Prior to this, she completed her M.Sc. in Computer Science from Tabari University, concentrating on optimization algorithms for the two-dimensional bin packing problem. She earned her B.Sc. in Computer Engineering from Kerman University, with a focus on the role of information technology in cybercrime and money laundering. Her academic path has been marked by a strong interdisciplinary orientation, merging principles of algorithm design, artificial intelligence, and systems engineering. Dr. Shafiee is currently preparing to begin her postdoctoral research at Shahid Bahonar University of Kerman, focusing on advanced machine learning applications in medical image analysis and bioinformatics. Her academic training reflects a rigorous and innovative approach to solving computational challenges in the biological and medical sciences.

Professional Experience📝

Dr. Shima Shafiee has accumulated a broad range of academic and research experience over the years. She has served as a lecturer at Razi University and Shahid Bahonar University of Kerman, teaching subjects such as artificial intelligence, computer programming, fundamentals of computing, and algorithm design. Her professional journey includes roles as a research assistant and educational collaborator on interdisciplinary projects in bioinformatics and medical imaging. She has participated in collaborative initiatives with departments in law and medical sciences, highlighting her cross-disciplinary competence. Dr. Shafiee’s earlier professional activities include internships, secondary school teaching, and work as an educational researcher at Tarbiat Modares University. Beyond academia, she actively delivers presentations and training sessions on AI tools in education, medical science, and government applications. Her career reflects a continuous evolution from foundational computing education to advanced AI-driven research and applications in healthcare and biotechnology.

Research Interest🔎

Dr. Shima Shafiee’s research interests center on artificial intelligence applications in bioinformatics, particularly the prediction and analysis of protein-peptide and protein-protein interactions. Her work leverages machine learning, deep learning, ensemble models, and evolutionary computation to develop predictive tools with real-world implications in biomedical science. She is passionate about creating models that improve the accuracy and efficiency of residue-level interaction predictions, utilizing sequence- and structure-based features. Recently, her research has expanded into medical image processing, AI-based diagnostic support systems, and applications of large language models in biological data analysis. She is also actively exploring segmentation-based algorithms and attention mechanisms for bioinformatic tasks. Her interdisciplinary approach connects computer science, structural biology, and clinical applications, aiming to provide computational insights that aid in drug discovery, diagnostics, and personalized medicine. Dr. Shafiee’s ongoing work represents the forefront of AI-driven solutions in life sciences.

Award and Honor🏆

Throughout her academic career, Dr. Shima Shafiee has received several accolades in recognition of her scholarly excellence and scientific contributions. She was recognized as the top-performing student during her Ph.D. studies and earned third place in her M.Sc. program. One of her early works was selected among the best papers at the 2nd International Congress of Electrical Engineering, Computer Science, and Information Technology. Her research papers have been accepted at notable national and international conferences, and her interdisciplinary contributions have been acknowledged by academic bodies. She has also served as a reviewer for various scientific journals and conferences, including IEEE-related events and bioinformatics journals. Dr. Shafiee is an active member of multiple academic and scientific societies, which reflects her standing within the research community. Her dedication to advancing computational applications in biomedicine has earned her a respected position in her field.

Research Skill🔬

Dr. Shima Shafiee possesses a diverse and well-rounded research skill set, spanning programming, algorithm development, data modeling, and AI tool integration. She is proficient in Python, R, WEKA, SPSS, MATLAB, and BioPython, among other platforms, and applies these tools in both supervised and unsupervised machine learning environments. Her work includes the development of hybrid models combining genetic programming, deep learning, support vector machines, and ensemble techniques for high-precision biomedical predictions. Dr. Shafiee is also skilled in optimization algorithms, having previously worked on solving the two-dimensional bin packing problem using particle swarm optimization. She integrates her technical expertise with domain knowledge in protein chemistry, structural biology, and medical imaging to produce interdisciplinary solutions. In addition to computational methods, she is experienced in academic writing, peer reviewing, and scientific communication. Her technical and analytical capabilities are well-aligned with emerging challenges in bioinformatics and artificial intelligence.

Conclusion💡

Dr. Shima Shafiee is a highly deserving candidate for the Best Researcher Award due to her exceptional contributions to the fields of computational biology, artificial intelligence, and bioinformatics. Her innovative research on protein-peptide interaction prediction, coupled with a strong publication record in prestigious journals and conferences, reflects both scientific rigor and societal relevance, particularly in advancing biomedical research. With her interdisciplinary focus, commitment to academic excellence, and proactive engagement in teaching, reviewing, and professional development, Dr. Shafiee exemplifies the qualities of a forward-thinking researcher. As she continues to expand her work into medical image processing and international collaborations, she holds immense potential for future leadership and transformative impact in science and technology.

Publications Top Noted✍

  • Title: SPPPred: Sequence-based protein-peptide binding residue prediction using genetic programming and ensemble learning
    Authors: S. Shafiee, A. Fathi, G. Taherzadeh
    Year: 2022
    Citations: 12

  • Title: Prediction of protein–peptide-binding amino acid residues regions using machine learning algorithms
    Authors: S. Shafiee, A. Fathi
    Year: 2021
    Citations: 6

  • Title: Combination of genetic programming and support vector machine-based prediction of protein-peptide binding sites with sequence and structure-based features
    Authors: S. Shafiee, A. Fathi
    Year: 2021
    Citations: 6

  • Title: Prediction of protein–peptide binding residues using classification algorithms
    Authors: S. Shafiee, A. Fathi, F. Abdali-Mohammadi
    Year: 2020
    Citations: 6

  • Title: A Review of the Uses of Artificial Intelligence in Protein Research
    Authors: S. Shafiee, A. Fathi, F. Abdali-Mohammadi
    Year: 2019
    Citations: 5

  • Title: DP-site: A dual deep learning-based method for protein-peptide interaction site prediction
    Authors: S. Shafiee, A. Fathi, G. Taherzadeh
    Year: 2024
    Citations: 2

  • Title: Protein-peptide interaction region residues prediction using a generative sampling technique and ensemble deep learning-based models
    Authors: S. Shafiee, A. Fathi, G. Taherzadeh
    Year: 2025

  • Title: Integrating Structural Information: Comparing Classification-Based and Segmentation-Based Predictors in Bioinformatics (Case Study: Protein-Peptide Region Residue-Level Interaction)
    Authors: S. Shafiee, A. Fathi, F. Safari
    Year: 2025

  • Title: Leveraging a Structure-Based and Learning-Based Predictor Using Various Feature Groups in Bioinformatics (Case Study: Protein-Peptide Region Residue-Level Interaction)
    Authors: S. Shafiee, A. Fathi
    Year: 2024

  • Title: Application of Learning-Based Models in Predicting of Protein-Peptide Binding Interactions
    Authors: S. Shafiee
    Year: 2024

  • Title: Application of Combined Decision Trees Function for Optimization Issues
    Authors: A. Fathi, S. Shafiee
    Year: 2017

Shiming Ge | AI Safety | Best Researcher Award

Published on by Computer Vision Awards

Prof. Shiming Ge | AI Safety | Best Researcher Award

Professor at Chinese Academy of Sciences, China

Dr. Shiming Ge is a prominent researcher and academic leader in the field of artificial intelligence, with specializations in AI safety, multimedia security, visual understanding, and privacy-preserving machine learning. He serves as a Professor and the Head of the Intelligent Multimedia Security Lab at the Institute of Information Engineering, Chinese Academy of Sciences (CAS). Dr. Ge has made significant contributions to areas such as deepfake detection, robust learning from noisy data, and adversarial machine learning. His work bridges the gap between theoretical AI foundations and real-world applications, addressing modern technological challenges. With over 100 research papers in top-tier journals and conferences, he is recognized both nationally and internationally. He has also successfully led numerous national and industry-funded research projects. Dr. Ge is a Senior Member of IEEE and plays an active role in mentoring young researchers, shaping the future of AI research through leadership, innovation, and global academic engagement.

Professional Profile 

Education🎓

Dr. Shiming Ge pursued his higher education through an accelerated track, showcasing exceptional academic performance from an early stage. He earned his Ph.D. in Electronic Engineering from the University of Science and Technology of China (USTC) in 2008. Before his doctoral studies, he completed his bachelor’s degree in Electronic Engineering from USTC as part of an elite early-excellence program that allowed him to graduate one year ahead of schedule. His Ph.D. research focused on digital image recovery, laying a solid technical foundation that would later underpin his work in computer vision, machine learning, and multimedia analysis. His advanced education provided him with strong theoretical and practical expertise, which he has successfully applied in tackling pressing issues such as visual security, data privacy, and adversarial robustness. Dr. Ge’s academic journey reflects not only his intellectual capacity but also his long-term commitment to innovation and academic excellence in artificial intelligence.

Professional Experience📝

Dr. Shiming Ge currently holds the position of Professor and leads the Intelligent Multimedia Security Lab at the Institute of Information Engineering, Chinese Academy of Sciences (CAS). Over the years, he has taken on several prominent roles within the academic and industrial research community, contributing to both theoretical advancements and real-world applications. He has served as the principal investigator for more than 20 national and industrial research projects, including those funded by China’s National Key R&D Program and companies such as Siemens and Alibaba. His leadership in research and project management has yielded numerous successful outcomes in AI and multimedia security. Beyond project execution, he also teaches graduate-level courses, such as “AI Safety” and “Introduction to Deep Learning,” and actively mentors young researchers. His students have gone on to win prestigious awards such as the CAS President’s Award. His professional journey highlights a balanced dedication to academic innovation, industry collaboration, and talent development.

Research Interest🔎

Dr. Shiming Ge’s research interests span several high-impact domains within artificial intelligence, with a central focus on AI safety, multimedia security, visual understanding, adversarial machine learning, and privacy-preserving techniques. He is particularly known for his work on deepfake detection, robust learning under noisy conditions, and trustworthy machine learning—topics that are increasingly critical in today’s digital age. He has also contributed to the development of federated learning frameworks and few-shot learning models, enabling decentralized and efficient AI applications. His research addresses both theoretical challenges and practical implementations, emphasizing the societal importance of trustworthy and secure AI systems. Dr. Ge is also exploring the interpretability of neural networks to improve transparency and accountability in automated decision-making. His work has not only advanced the academic field but also had tangible impacts on real-world technologies, such as surveillance, identity verification, and online content moderation, making his research highly relevant and applicable.

Award and Honor🏆

Dr. Shiming Ge has received multiple prestigious awards in recognition of his groundbreaking research in artificial intelligence and multimedia security. He was honored with the IEEE TMM Best Journal Paper Award in 2025, underscoring the quality and impact of his published work. In addition, he is a recipient of the Wu Wenjun Artificial Intelligence Science and Technology Award, one of China’s most distinguished recognitions in the field of AI. His research team has earned top rankings in international competitions, including the CVPR’20 Anti-UAV Challenge and the ICCV’19 VisDrone-SOT Challenge, reflecting his capacity to translate research into high-performing systems. Dr. Ge’s students have also gained accolades under his mentorship, such as the CAS President’s Award and recognition as Outstanding Graduates of Beijing, further highlighting his excellence in academic supervision. These awards collectively affirm his leadership, innovation, and sustained contributions to advancing AI technologies globally.

Research Skill🔬

Dr. Shiming Ge possesses a comprehensive and multidisciplinary research skillset that enables him to lead innovative projects across multiple AI domains. He is proficient in deep learning frameworks such as TensorFlow and PyTorch, and has extensive experience in designing robust neural network architectures for tasks like object detection, face recognition, and anomaly detection. His technical expertise extends to adversarial training, federated learning, and privacy-preserving machine learning. Dr. Ge also excels in multimedia data analysis, image forensics, and deepfake identification techniques. Beyond technical implementation, he is highly skilled in hypothesis-driven research design, scientific writing, and cross-functional project coordination. His strong grasp of statistical modeling, algorithm optimization, and system-level integration has enabled the successful deployment of AI models in both academic and industry contexts. Furthermore, he has demonstrated a capacity for mentoring, peer reviewing, and leading multidisciplinary teams, making him a valuable contributor to collaborative and large-scale research environments.

Conclusion💡

Publications Top Noted✍

  • Title: Detecting masked faces in the wild with LLE-CNNs
    Authors: S. Ge, J. Li, Q. Ye, Z. Luo
    Year: 2017
    Citations: 576

  • Title: Selective-supervised contrastive learning with noisy labels
    Authors: S. Li, X. Xia, S. Ge, T. Liu
    Year: 2022
    Citations: 300

  • Title: Low-resolution face recognition in the wild via selective knowledge distillation
    Authors: S. Ge, S. Zhao, C. Li, J. Li
    Year: 2018
    Citations: 236

  • Title: Occluded face recognition in the wild by identity-diversity inpainting
    Authors: S. Ge, C. Li, S. Zhao, D. Zeng
    Year: 2020
    Citations: 130

  • Title: Student network learning via evolutionary knowledge distillation
    Authors: K. Zhang, C. Zhang, S. Li, D. Zeng, S. Ge
    Year: 2021
    Citations: 105

  • Title: Detecting Deepfake Videos with Temporal Dropout 3DCNN
    Authors: D. Zhang, C. Li, F. Lin, D. Zeng, S. Ge
    Year: 2021
    Citations: 99

  • Title: Estimating noise transition matrix with label correlations for noisy multi-label learning
    Authors: S. Li, X. Xia, H. Zhang, Y. Zhan, S. Ge, T. Liu
    Year: 2022
    Citations: 98

  • Title: Accurate temporal action proposal generation with relation-aware pyramid network
    Authors: J. Gao, Z. Shi, G. Wang, J. Li, Y. Yuan, S. Ge, X. Zhou
    Year: 2020
    Citations: 97

  • Title: Domain adaptive attention learning for unsupervised person re-identification
    Authors: Y. Huang, P. Peng, Y. Jin, Y. Li, J. Xing
    Year: 2020
    Citations: 89

  • Title: Look through masks: Towards masked face recognition with de-occlusion distillation
    Authors: C. Li, S. Ge, D. Zhang, J. Li
    Year: 2020
    Citations: 87

  • Title: Efficient low-resolution face recognition via bridge distillation
    Authors: S. Ge, S. Zhao, C. Li, Y. Zhang, J. Li
    Year: 2020
    Citations: 82

  • Title: Predicting aesthetic score distribution through cumulative Jensen-Shannon divergence
    Authors: X. Jin, L. Wu, X. Li, S. Chen, S. Peng, J. Chi, S. Ge, C. Song, G. Zhao
    Year: 2018
    Citations: 82

  • Title: Bootstrapping multi-view representations for fake news detection
    Authors: Q. Ying, X. Hu, Y. Zhou, Z. Qian, D. Zeng, S. Ge
    Year: 2023
    Citations: 80

  • Title: VisDrone-SOT2019: The vision meets drone single object tracking challenge results
    Authors: D. Du, P. Zhu, L. Wen, X. Bian, H. Ling, Q. Hu, J. Zheng, T. Peng, X. Wang, …
    Year: 2019
    Citations: 75

  • Title: ILGNet: Inception modules with connected local and global features for efficient image aesthetic quality classification using domain adaptation
    Authors: X. Jin, L. Wu, X. Li, X. Zhang, J. Chi, S. Peng, S. Ge, G. Zhao, S. Li
    Year: 2019
    Citations: 73

  • Title: Attentive Deep Stitching and Quality Assessment for 360 Omnidirectional Images
    Authors: J. Li, Y. Zhao, W. Ye, K. Yu, S. Ge
    Year: 2019
    Citations: 63

  • Title: Aesthetic attributes assessment of images
    Authors: X. Jin, L. Wu, G. Zhao, X. Li, X. Zhang, S. Ge, D. Zou, B. Zhou, X. Zhou
    Year: 2019
    Citations: 59

  • Title: The 3rd anti-UAV workshop & challenge: Methods and results
    Authors: J. Zhao, J. Li, L. Jin, J. Chu, Z. Zhang, J. Wang, J. Xia, K. Wang, Y. Liu, …
    Year: 2023
    Citations: 53

  • Title: Deepfake video detection with spatiotemporal dropout transformer
    Authors: D. Zhang, F. Lin, Y. Hua, P. Wang, D. Zeng, S. Ge
    Year: 2022
    Citations: 51

  • Title: Deepfake video detection via predictive representation learning
    Authors: S. Ge, F. Lin, C. Li, D. Zhang, W. Wang, D. Zeng
    Year: 2022
    Citations: 50

Duy-Dong Le | Federated Learning | Best Researcher Award

Published on by Computer Vision Awards

Mr. Duy-Dong Le | Federated Learning | Best Researcher Award

Lecturer at UEH Campus Vinh Long, Vietnam

Mr. Duy-Dong Le is a dedicated academic and researcher in the field of Computer Science, currently serving as a faculty member at UEH University, Vietnam. He is affiliated with the Vinh Long Campus, where he teaches subjects such as Data Structures and Algorithms, Web Programming, Data Science, and IT for Business. His work bridges teaching, research, and community engagement. Over the years, he has led multiple research projects in deep learning, artificial intelligence, and federated learning with applications in public health, agriculture, and environmental monitoring. Beyond academic duties, Mr. Le plays a significant leadership role as head of the Association for Information Processing and as the leader of an AI research team. He is also an active organizer of educational and community development initiatives in the Mekong Delta. His scholarly contributions and civic involvement highlight his commitment to advancing both research and societal well-being through technology and education.

Professional Profile 

Education🎓

Mr. Duy-Dong Le began his academic journey at Vinh Long Technical and Teacher Training College, where he completed his undergraduate studies. He went on to earn a master’s degree in Computer Science from the University of Information Technology under Vietnam National University, Ho Chi Minh City. During his postgraduate studies, he developed expertise in deep learning, particularly focusing on the application of the Standard Adaptive Model (SAM) for predicting test performance. His thesis work in this domain was later published in conference proceedings and academic journals. Currently, he is pursuing his doctoral studies at the Industry University of Ho Chi Minh City, with a focus on advanced applications of artificial intelligence in areas such as federated learning and data-driven decision-making. His academic path reflects a consistent dedication to learning and advancing knowledge in computer science, while also integrating his research work with practical, community-centered challenges in education, healthcare, and environmental sustainability.

Professional Experience📝

Mr. Le currently holds a teaching and research position at UEH University’s Vinh Long Campus, where he has been actively involved in academic and institutional development. In his professional capacity, he teaches various undergraduate courses in computer science and information technology. His experience spans not only classroom instruction but also academic mentorship, project supervision, and curriculum development. Mr. Le leads the campus AI research team and serves as the head of the Association for Information Processing. He also contributes significantly to organizing events, contests, and workshops aimed at digital transformation, sustainable development, and community learning. His efforts have led to the successful execution of initiatives like the UEH Global Project, which provides free educational infrastructure to teachers and students across the Mekong Delta. His professional journey is marked by a unique combination of educational excellence, technical leadership, and service to the community, reflecting his multifaceted role as a teacher, researcher, and innovator.

Research Interest🔎

Mr. Duy-Dong Le’s research interests lie primarily in the fields of artificial intelligence, deep learning, and federated learning, with a strong focus on solving practical problems in education, agriculture, public health, and environmental sustainability. He has conducted research projects applying AI to predict agricultural prices, forecast COVID-19 transmission rates, and analyze air quality using collaborative machine learning models. He is particularly interested in the intersection of AI and social impact, often designing systems tailored for regional needs in Vietnam, especially within the Mekong Delta. His current studies involve exploring privacy-preserving learning techniques, such as federated learning, to handle distributed data more securely. Mr. Le’s research portfolio is further enhanced by interdisciplinary collaborations and a commitment to community-focused technology deployment. His work reflects a vision of using data science not just for theoretical exploration but as a practical tool for societal transformation and decision-making in both urban and rural contexts.

Award and Honor🏆

Mr. Duy-Dong Le has earned recognition for both his academic contributions and community engagement. His research work has been presented at various national and international conferences, including the FAIR and FDSE conferences, and published in prestigious outlets such as Springer and the MDPI Algorithms Journal. He has played a key role in several provincial and institutional research projects that have received acknowledgment for their innovation and practical relevance. Additionally, his leadership in educational outreach—especially the UEH Global Project—has been commended for supporting thousands of students and educators during the COVID-19 pandemic through free access to online learning platforms and digital tools. As an organizer and presenter of workshops and STEM events, he has significantly contributed to raising awareness about digital transformation and sustainable development in the region. His honors reflect not only his scholarly impact but also his sustained efforts to bridge academia with real-world community needs.

Research Skill🔬

Mr. Le possesses a strong set of research skills that span programming, data modeling, and applied machine learning. He is proficient in programming languages such as Python, PHP, and Java, and commonly uses platforms like Google Colab for deploying deep learning models using TensorFlow. His technical expertise includes predictive modeling, algorithm development, and system integration for real-world applications. In his recent projects, he has implemented AI-based frameworks for public health forecasting and federated learning systems for environmental monitoring. He is also skilled in preparing research manuscripts, presenting at conferences, and collaborating with both local and international research partners. His ability to connect theoretical models with practical solutions showcases a balanced research profile. Moreover, his experience in organizing and translating for academic events has strengthened his communication and cross-functional skills. Collectively, Mr. Le’s research abilities position him as a capable and innovative scholar committed to advancing knowledge and addressing real-life challenges through technology.

Conclusion💡

Mr. Duy-Dong Le is highly deserving of the Best Researcher Award due to his consistent and impactful contributions to applied computer science and artificial intelligence, particularly in addressing region-specific challenges in agriculture, health, and education. His research has not only led to peer-reviewed publications in respected journals and conferences but also translated into tangible benefits for local communities, such as predictive systems for crop pricing and pandemic modeling. Beyond academia, his leadership in organizing educational outreach, promoting STEM initiatives, and supporting digital access in underserved areas highlights his deep commitment to societal advancement. With ongoing doctoral studies and an expanding research portfolio, Mr. Le demonstrates strong potential to become a leading academic and thought leader in his field, both nationally and internationally.

Publications Top Noted✍

  • Title: Further Evaluation of Prompting Tactics for Establishing Intraverbal Responding in Children with Autism
    Authors: ET Ingvarsson, DD Le
    Year: 2011
    Citations: 77

  • Title: Insights into Multi-Model Federated Learning: An Advanced Approach for Air Quality Index Forecasting
    Authors: DD Le, AK Tran, MS Dao, KC Nguyen-Ly, HS Le, XD Nguyen-Thi, …
    Year: 2022
    Citations: 25

  • Title: Leverage Boosting and Transformer on Text-Image Matching for Cheap Fakes Detection
    Authors: TV La, MS Dao, DD Le, KP Thai, QH Nguyen, TK Phan-Thi
    Year: 2022
    Citations: 11

  • Title: Proposed Distance and Entropy Measures of Picture Fuzzy Sets in Decision Support Systems
    Authors: H Van Pham, KP Thai, QH Nguyen, DD Le, TT Le, TXD Nguyen, …
    Year: 2023
    Citations: 9

  • Title: Federated Learning in Smart Agriculture: An Overview
    Authors: DD Le, MS Dao, AK Tran, TB Nguyen, HG Le-Thi
    Year: 2023
    Citations: 8

  • Title: Federated Learning for Air Quality Index Prediction: An Overview
    Authors: DD Le, AK Tran, MS Dao, MSH Nazmudeen, VT Mai, NH Su
    Year: 2022
    Citations: 5

  • Title: Modeling Transmission Rate of COVID-19 in Regional Countries to Forecast Newly Infected Cases in a Nation by the Deep Learning Method
    Authors: LD Dong, VT Nguyen, DT Le, MV Tiep, VT Hien, PP Huy, PT Hieu
    Year: 2021
    Citations: 5

  • Title: Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs
    Authors: PT Nguyen, MS Dao, MA Riegler, RU Kiran, TT Dang, DD Le, …
    Year: 2023
    Citations: 4

  • Title: CrossHeteroFL: Cross-Stratified Sampling Composition-Fitting to Federated Learning for Heterogeneous Clients
    Authors: VP Tinh, HH Son, DNM Dang, NH Nam, DD Le, TB Nguyen, TQ Pham, …
    Year: 2024
    Citations: 2

  • Title: Joint Federated Learning Using Deep Segmentation and the Gaussian Mixture Model for Breast Cancer Tumors
    Authors: PD Lam, VP Tinh, DD Le, NH Nam, TA Khoa
    Year: 2024
    Citations: 2

  • Title: Violence Detection in Videos Based on CNN Feature for ConvLSTM2D
    Authors: TD Trinh, TS Vu-Ngoc, LT Le-Nhi, DD Le, TB Nguyen, TB Pham
    Year: 2024
    Citations: 2

  • Title: A Survey of Model Compression and Its Feedback Mechanism in Federated Learning
    Authors: DD Le, AK Tran, TB Pham, TN Huynh
    Year: 2024
    Citations: 2

  • Title: Correlation-Based Weighted Federated Learning with Multimodal Sensing and Knowledge Distillation: An Application on a Real-World Benchmark Dataset
    Authors: DD Le, DT Huynh, PT Bao
    Year: 2025
    Citations: 1

  • Title: A Novel Solution for Energy-Saving and Lifetime-Maximizing of LoRa Wireless Mesh Networks
    Authors: HH Son, VP Tinh, DNM Dang, BT Duyen, DD Le, TT Dang, QH Nguyen, …
    Year: 2024
    Citations: 1

  • Title: Latency-Aware Split Learning Optimization via Genetic Algorithms
    Authors: LH Trung, TY Nguyen, DD Le, TT Dang, TA Khoa
    Year: 2025

  • Title: FedNolowe: A Normalized Loss-Based Weighted Aggregation Strategy for Robust Federated Learning in Heterogeneous Environments
    Authors: DD Le, N Huynh-Tuong, AK Tran, MS Dao, PT Bao
    Year: 2025

  • Title: SMixSL: The Smashed-Mixture Technique for Split Learning with Localizable Features
    Authors: VP Tinh, TA Khoa, PD Lam, NH Nam, DNM Dang, DD Le, TT Dang, …
    Year: 2025

  • Title: Factors Affecting the Adoption of Information Technology in Medium and Small Enterprises: A Case Study in Mekong Delta, Vietnam
    Authors: TL Nguyen-Thi, DD Le, KC Nguyen-Ly, TT Nguyen, MSH Nazmudeen
    Year: 2024

Jabar Habashi | Hyperspectral Data Processing | Best Researcher Award

Published on by Computer Vision Awards

Dr. Jabar Habashi | Hyperspectral Data Processing | Best Researcher Award

Student at University of Kentucky, Iran

Mr. Jabar Habashi is a dedicated researcher specializing in remote sensing, mineral exploration, and geospatial data science. With a strong academic background and technical expertise, he has contributed to the advancement of hyperspectral image analysis, AI-driven geological mapping, and environmental impact assessment. His work bridges the fields of Earth sciences and data science, focusing on developing predictive models for resource estimation and sustainable land use. Jabar has co-authored over 12 publications in reputed international journals such as ISPRS Journal of Photogrammetry and Remote Sensing and Remote Sensing (MDPI), and presented at leading conferences. He has also served as a reviewer for peer-reviewed journals, demonstrating engagement with the scientific community. Currently working as a Data Scientist at Scan Miner Solutions, Jabar combines practical industry insight with strong research acumen. His interdisciplinary approach and commitment to impactful science make him a promising figure in global geoscience and Earth observation research.

Professional Profile 

Education🎓

Jabar Habashi holds a Master of Science in Mining Exploration Engineering from Sahand University of Technology, Iran, completed in 2023. His MSc thesis focused on multispectral data classification using Hyperion satellite data, highlighting the role of remote sensing in mineral exploration. He previously earned a Bachelor of Science in Mineral Engineering from Malayer University in 2019, where his undergraduate research involved processing and analyzing magnetometric data from the Nadushan region in Yazd, Iran. Throughout his academic journey, Jabar was recognized for his academic excellence, earning full scholarships and achieving high ranks in national entrance examinations. His education provided a strong foundation in mining and geological sciences, with a focus on satellite data processing and geospatial analysis. He also gained valuable teaching experience as a teaching assistant in various laboratories, including mineralogy, petrology, and cartography, which reflects his active engagement with both theoretical and hands-on aspects of Earth sciences.

Professional Experience📝

Jabar Habashi is currently working as a Data Scientist at Scan Miner Solutions (since July 2024), where he applies advanced data analytics and remote sensing techniques to solve real-world challenges in the mining and exploration industry. His previous hands-on experience includes internships at the Department of Industry, Mining, and Trade of Sonqor County, and Gelali Iron Mine in Qorveh, Iran, where he gained insights into mining operations and data interpretation. During his academic years, he held several teaching assistant positions at Malayer University, supporting laboratory sessions in descriptive mineralogy, optical mineralogy, petrology, and cartography. These roles not only strengthened his technical and instructional skills but also laid a foundation for future academic contributions. His combination of fieldwork, laboratory training, and data-driven industry experience allows him to connect theoretical research with practical applications. This balanced background equips Jabar with the multidisciplinary insight essential for innovation in geoscience and mining technologies.

Research Interest🔎

Jabar Habashi’s research interests span a broad array of topics within remote sensing, mineral exploration, and environmental geoscience. He is particularly focused on hyperspectral image analysis, multisource data fusion, and AI-driven geological mapping. His work aims to automate mineral target recognition using deep learning, optimize alteration mineral detection, and advance predictive modeling for resource estimation. He is also passionate about hydrologic modeling, LiDAR data processing, climate change analysis, and geohazard assessment, reflecting a strong commitment to sustainable Earth system science. In recent studies, he has explored Antarctic terrains using PRISMA hyperspectral data and contributed to mapping projects in semi-arid and mountainous regions. His interest in mine closure and post-mining land use demonstrates a forward-thinking approach to environmental reclamation and sustainable mining practices. By integrating satellite data, field observations, and AI, Jabar aims to develop tools that enhance decision-making in exploration, environmental monitoring, and climate-sensitive resource management.

Award and Honor🏆

Jabar Habashi has received multiple awards and honors in recognition of his academic excellence and research capabilities. He earned a national rank that secured him admission to Malayer University with a full undergraduate scholarship in the mathematics branch. Later, he achieved a top national ranking in the MSc entrance exam for Mining Exploration Engineering, which granted him a full scholarship at Sahand University of Technology. These distinctions reflect his strong academic caliber and dedication to scholarly achievement. In addition to his academic honors, Jabar has contributed to the scientific community by reviewing manuscripts for respected journals such as Remote Sensing Applications: Society and Environment, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), and Earth Science Informatics. These contributions highlight his growing reputation in the fields of Earth observation and geospatial research. His ongoing commitment to excellence and collaboration makes him a distinguished researcher in his discipline.

Research Skill🔬

Jabar Habashi possesses a wide range of technical and analytical research skills essential for modern geoscientific inquiry. He is proficient in ENVI, ArcGIS Pro, Surfer, MATLAB, and Geosoft Oasis Montaj, with specialization in magnetometry and remote sensing software. He has advanced command of Python programming, particularly for image classification, data preprocessing, and deep learning applications. Jabar’s skill set extends to satellite image fusion, LiDAR and PALSAR data processing, and the integration of field and geospatial datasets for exploration and environmental modeling. He has experience in multispectral and hyperspectral classification, geohazard analysis, and mine site monitoring, showcasing his versatility in both theoretical and applied research. Additionally, he holds multiple certifications in GIS, remote sensing, and Python from Coursera. His analytical capabilities, combined with a deep understanding of geological systems and AI integration, enable him to address complex challenges in mineral exploration and Earth system science with precision and innovation.

Conclusion💡

Mr. Jabar Habashi is highly deserving of the Best Researcher Award for his outstanding contributions to remote sensing, mineral exploration, and geospatial data science. His innovative research—spanning hyperspectral image analysis, deep learning applications in mineral mapping, and environmental impact assessment—has significantly advanced the field while addressing pressing global challenges such as sustainable resource management and climate resilience. With a strong record of international publications, active peer-review service, and technical excellence, he exemplifies the qualities of a dedicated and impactful researcher. As he continues to expand his scholarly reach and takes on greater leadership roles, Mr. Habashi is well-positioned to become a prominent figure in Earth observation and environmental geoscience research on a global scale.

Publications Top Noted✍

  • Title: PRISMA hyperspectral remote sensing data for mapping alteration minerals in Sar-e-Châh-e-Shur region, Birjand, Iran
    Authors: J. Habashi, H. Jamshid Moghadam, M. Mohammady Oskouei, A.B. Pour, et al.
    Year: 2024
    Citations: 13

  • Title: Optimizing alteration mineral detection: A fusion of multispectral and hyperspectral remote sensing techniques in the Sar-e-Chah-e Shur, Iran
    Authors: J. Habashi, M.M. Oskouei, H.J. Moghadam, A.B. Pour
    Year: 2024
    Citations: 10

  • Title: Classification of ASTER Data by Neural Network to Mapping Alterations Related to Copper and Iron Mineralization in Birjand
    Authors: J. Habashi, M.M. Oskouei, H. Jamshid Moghadam
    Year: 2024
    Citations: 5

  • Title: Remote Sensing for Geophysicists
    Authors: M. Gupta
    Year: 2025
    Citations: 1

  • Title: 19 Mineral Remote Identification Sensing Data Using
    Authors: A.B. Pour, S. Niroomand, R. Lavaei, S. Mirzaee, J. Habashi, H.J. Moghadam
    Year: 2025
    Citations: 1

  • Title: Integration of ASTER imagery and field data for chromite exploration in the Eastern Khoy Ophiolite Complex, NW Iran
    Authors: G. Nabatian, A. Songjian, A.B. Pour, F. Abdollahi, J. Habashi
    Year: 2025
    Citations: 1

  • Title: Recurrent-spectral convolutional neural networks (RecSpecCNN) architecture for hyperspectral lithological classification optimization
    Authors: S. Hajaj, A.E. Harti, A.B. Pour, Y. Khandouch, N. Benaouiss, M. Hashim, J. Habashi, et al.
    Year: 2025
    Citations: 1

  • Title: Revealing critical mineralogical insights in extreme environments using deep learning technique on hyperspectral PRISMA satellite imagery: Dry Valleys, South Victoria Land, Antarctica
    Authors: J. Habashi, A.B. Pour, A.M. Muslim, A.M. Afrapoli, J.K. Hong, Y. Park, A. Almasi, et al.
    Year: 2025

  • Title: Mineral Identification Using Remote Sensing Data
    Authors: A.B. Pour, S. Niroomand, R. Lavaei, S. Mirzaee, J. Habashi, H.J. Moghadam
    Year: 2025

  • Title: Advancing Planetary Sustainability Through Lithological Mapping: ASTER Remote Sensing in Antarctica’s Dry Valleys
    Authors: K. Riaz, A.B. Pour, A.M. Muslim, S. Khurram, J. Habashi

Rohan Duppala | Machine Learning | Young Researcher Award

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Mr. Rohan Duppala | Machine Learning | Young Researcher Award

Student at VIT-AP University, India

Rohan Duppala is an emerging researcher and technologist with a strong foundation in artificial intelligence, machine learning, deep learning, and natural language processing. As a final-year B.Tech Computer Science student at VIT-AP University, he has demonstrated exceptional research capabilities, developing innovative solutions in healthcare, education, and smart transportation. Rohan has published multiple papers in reputed journals, including Scientific Reports and MDPI, and has worked on diverse AI-driven projects, from infant cry classification and Alzheimer’s detection to generative AI-based educational tools. His ability to integrate advanced AI models with real-world applications reflects a rare combination of academic rigor and practical insight. In addition to academic work, Rohan has engaged with leading technologies like Gemini, Llama 3, and Weights & Biases, earning several certifications and accolades. With a forward-thinking mindset and a passion for impactful research, he aspires to contribute meaningfully to global challenges through AI and interdisciplinary innovation.

Professional Profile 

Education🎓

Rohan Duppala is currently pursuing his Bachelor of Technology in Computer Science and Engineering at VIT-AP University in Andhra Pradesh. His education has provided a rigorous grounding in core computer science principles while enabling him to explore advanced technologies such as artificial intelligence, machine learning, and natural language processing. Prior to his undergraduate studies, he completed his intermediate education in the Mathematics, Physics, and Chemistry (MPC) stream at Narayana Junior College in Visakhapatnam. He also completed his schooling at Sri Chaitanya School in Palasa, Andhra Pradesh. Throughout his academic journey, Rohan has demonstrated consistent excellence and a strong inclination toward analytical thinking and computational problem-solving. This solid educational background has laid the foundation for his research endeavors and technical accomplishments in AI, edge computing, and intelligent systems.

Professional Experience📝

Rohan gained hands-on industry experience as an IoT Specialist at Prayana Electric between June and August 2024. During his tenure, he was instrumental in integrating IoT solutions into electric bicycles, leveraging microcontrollers, GPS, and LoRa technologies to enable real-time monitoring and smart navigation. He also spearheaded the development of a pothole detection system, optimized specifically for edge deployment on Raspberry Pi Pico, seamlessly integrating it into the e-bike ecosystem. This professional experience not only expanded his understanding of smart transportation and embedded systems but also allowed him to apply theoretical AI knowledge to practical, scalable solutions. Rohan’s work at Prayana Electric reflects his ability to bridge the gap between academic research and industry requirements, highlighting his skills in system design, data analysis, and sensor integration. His initiative, problem-solving abilities, and adaptability in a real-world setting underscore his potential as a well-rounded researcher and future technology leader.

Research Interest🔎

Rohan Duppala’s research interests lie at the intersection of artificial intelligence, healthcare, education, and smart systems. He is particularly focused on building explainable and ethically sound AI systems that can be deployed in real-world settings. His work in medical diagnostics, including projects on Alzheimer’s and Parkinson’s disease detection using deep learning, underscores a commitment to socially impactful research. Rohan also explores Generative AI and Large Language Models, applying them to applications such as automated script evaluation, infant care, and educational feedback systems. His interest in edge AI and IoT-enabled smart devices reflects a drive to create scalable, efficient, and context-aware solutions for real-time environments. By combining transformer models, retrieval augmented generation (RAG), signal processing, and explainable AI (XAI) techniques, Rohan aims to push the boundaries of intelligent automation in human-centric domains. His interdisciplinary approach and ethical consideration make his research both innovative and future-ready.

Award and Honor🏆

Rohan Duppala has been recognized for his academic and technical excellence with several awards and honors. He received a Certificate of Honor from OpenCV University in recognition of his outstanding performance in deep learning applications and project execution. Additionally, he was awarded a unique NFT (Non-Fungible Token) honor from The Hashgraph Association, celebrating his innovation and engagement in the decentralized technology community. His publications in reputed platforms such as Scientific Reports (Nature) and MDPI further reflect the quality and impact of his research. These accolades highlight not only his technical achievements but also his ability to stand out in competitive, global academic and developer communities. His participation in specialized bootcamps, advanced workshops, and certificate programs offered by Google, AWS, Cisco, and Weights & Biases has further solidified his position as an accomplished early-career researcher poised for excellence in AI-driven innovation.

Research Skill🔬

Rohan Duppala possesses an advanced and versatile skill set tailored for research in artificial intelligence and related domains. He is proficient in Python and Java, with hands-on experience in building custom convolutional neural networks, implementing transformer models, and deploying real-time deep learning systems on edge devices like Raspberry Pi. His practical expertise spans data preprocessing, hyperparameter tuning, model evaluation, explainable AI (XAI) techniques like LIME and Saliency Maps, and fine-tuning large models like LLaMA 3 8b using LoRA. He has also worked with tools such as Hugging Face Transformers, Weights & Biases, and Streamlit for model development and deployment. Rohan is skilled in retrieval augmented generation (RAG), multimodal data processing, and prompt engineering. His ability to combine AI techniques with IoT, computer vision, and NLP enables him to develop interdisciplinary solutions. These research skills, backed by strong implementation and critical thinking abilities, make him a technically mature and innovation-ready researcher.

Conclusion💡

Rohan Duppala is a highly deserving candidate for the Best Researcher Award, owing to his exceptional drive, technical acumen, and impactful research contributions at an early stage of his academic journey. His work spans critical societal applications—from medical diagnostics using deep learning to educational tools powered by large language models—demonstrating both depth and relevance. With peer-reviewed publications in reputed journals like Scientific Reports and MDPI, and hands-on innovation in smart technologies and AI systems, he has already laid a strong foundation for a distinguished research career. Given his dedication, continuous learning, and visionary approach to solving real-world problems, Rohan holds immense potential for future leadership in the fields of artificial intelligence and intelligent healthcare systems.

Publications Top Noted✍

  • Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques
    Authors: D. Rohan, G.P. Reddy, Y.V.P. Kumar, K.P. Prakash, C.P. Reddy
    Year: 2025
    Citations: 4

  • Title: A Custom Convolutional Neural Network Model-Based Bioimaging Technique for Enhanced Accuracy of Alzheimer’s Disease Detection
    Authors: P. Reddy G., S.M.A. Kareem, Y.V.P. Kumar, P.P. Kasaraneni, M. Janapati
    Year: 2025
    Citations: 1

  • Title: Artificial intelligence-based effective detection of Parkinson’s disease using voice measurements
    Authors: G. Pradeep Reddy, D. Rohan, Y.V.P. Kumar, K.P. Prakash, M. Srikanth
    Year: 2024
    Citations: 1

Young-Min Shon | Epilepsy Surgery | Excellence in Research

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Prof. Young-Min Shon | Epilepsy Surgery | Excellence in Research

Professor at Samsung Medical Center, South Korea

Dr. Young-Min Shon is a distinguished neurologist and neuroscientist, internationally recognized for his expertise in epilepsy, neuromodulation, and translational neuroscience. As a professor and director at Samsung Medical Center and Sungkyunkwan University School of Medicine, he has been instrumental in pioneering non-invasive and implantable neuromodulation therapies for drug-resistant epilepsy. His research integrates clinical neurology with advanced biomedical engineering, focusing on technologies such as deep brain stimulation, EEG analysis, focused ultrasound, and wearable sensors. With a prolific academic portfolio, he has published over 40 high-impact papers and contributed extensively to Korean and international neurological societies. His leadership and multidisciplinary approach have earned him national accolades and positions of influence across research institutions and professional committees. Committed to advancing smart healthcare systems and patient-centered neurotechnologies, Dr. Shon continues to drive innovation that bridges laboratory discoveries with real-world clinical applications, making a profound impact on neuroscience and patient care.

Professional Profile 

Education🎓 

Dr. Shon began his medical journey at Seoul National University, where he completed his MD in 1993. He later pursued specialized training in neurology, completing his residency at Samsung Medical Center from 1997 to 2001. His strong interest in research and epilepsy led him to further academic excellence, culminating in a Ph.D. in Neurology from Sungkyunkwan University in 2006. He continued to deepen his clinical skills and academic exposure through a fellowship at Samsung Medical Center and later as a Visiting Professor at the Mayo Clinic in the U.S., one of the world’s leading medical institutions. His educational foundation is further strengthened by multiple certifications in Good Clinical Practice (GCP) from 2007 to 2023, reflecting his commitment to clinical research ethics and standards. Dr. Shon’s education reflects a blend of rigorous academic training and international exposure, positioning him as a highly qualified expert in clinical neurology and neuroscience research.

Professional Experience📝

Dr. Shon’s professional journey spans over two decades of clinical, academic, and leadership excellence. He is currently a Professor of Neurology and Director at Samsung Medical Center and Chair of the Medical Engineering Center at Sungkyunkwan University School of Medicine. Previously, he served as a professor and associate professor at Seoul St. Mary’s Hospital, part of the Catholic University of Korea, where he developed his clinical and research expertise in epilepsy. He has also held a prestigious Visiting Professorship at the Mayo Clinic, USA. Since beginning his academic career as an instructor in 2003, Dr. Shon has taken on progressively influential roles in both hospital and university settings. His leadership at the Smart Healthcare Research Institute and Biomedical Engineering Research Center has helped drive interdisciplinary research and innovation. Throughout his career, he has actively led research teams, supervised clinical trials, and mentored future clinicians and researchers in neurology and neuroscience.

Research Interest🔎

Dr. Shon’s research interests are deeply rooted in the clinical management and technological advancement of refractory epilepsy. He focuses on the development and clinical translation of neuromodulation techniques such as deep brain stimulation (DBS), focused ultrasound, and vagus nerve stimulation. His research also emphasizes multimodal neuroimaging, EEG signal processing, and closed-loop seizure control systems. In recent years, he has expanded into wearable biomedical sensors and AI-based diagnostics, collaborating across neurology, biomedical engineering, and computer science. His translational approach bridges preclinical studies, device development, and clinical trials to create practical, patient-centered neurotherapies. Additionally, Dr. Shon is interested in exploring the neurovascular and electrophysiological mechanisms underlying seizure disorders, contributing to better-targeted treatments. His vision is to combine engineering innovation with neurological care to transform the lives of patients with intractable epilepsy, making his research not only scientifically groundbreaking but also socially impactful.

Award and Honor🏆

Dr. Young-Min Shon has earned recognition for his significant contributions to neurology and epilepsy research, most notably receiving the Best Research Award from the Korean Epilepsy Society in 2006. His dedication to advancing epilepsy care through both clinical and translational research has earned him respected positions on the research boards of the Korean Epilepsy Society (KES), Korean Neurological Association (KNA), and Korean Academy of Sleep Medicine (KASM). He has also held roles such as Planning Director and Academic Board Member for KES, reflecting his influence in shaping national research agendas. Furthermore, his contributions have been recognized through editorial appointments, including serving as Co-editor for the Journal of Epilepsy Research. His involvement in numerous national committees highlights the esteem he holds in Korea’s neurological community. These honors underscore his leadership, integrity, and commitment to improving neurological health through innovation and collaborative advancement in science and medicine.

Research Skill🔬

Dr. Shon possesses a comprehensive skill set that spans clinical, technical, and analytical domains in neuroscience and biomedical research. He is highly skilled in intracranial EEG interpretation, seizure localization, and neuroimaging modalities like SPECT, fMRI, and PET. His engineering-oriented capabilities include the design and application of neuromodulation systems, such as DBS, low-intensity focused ultrasound, and wireless stimulation devices. He has deep expertise in clinical trial design, adhering to GCP standards, and is experienced in integrating machine learning algorithms into EEG and heart rate variability analyses for disease prediction. Dr. Shon is proficient in both preclinical animal studies and human translational research, often employing multimodal biomarker integration to enhance diagnostic precision. His ability to collaborate with engineers, neuroscientists, and clinicians has enabled the development of closed-loop seizure control technologies. Collectively, these interdisciplinary research skills make Dr. Shon a leader in transforming complex clinical challenges into actionable and innovative therapeutic solutions.

Conclusion💡

Dr. Young-Min Shon is a highly deserving candidate for the Best Researcher Award due to his exceptional contributions to the fields of epileptology, neuromodulation, and translational neuroscience. His groundbreaking work on advanced treatments for intractable epilepsy—ranging from deep brain stimulation and focused ultrasound to real-time EEG diagnostics—has significantly improved patient outcomes and set new standards in neurological care. With a robust academic background, leadership in prestigious medical institutions, and a strong publication record in high-impact journals, he exemplifies research excellence and clinical innovation. Looking ahead, Dr. Shon is well-positioned to lead next-generation neurotechnological advancements, foster global collaborations, and continue shaping the future of smart healthcare and personalized 

Publications Top Noted✍

  • Title: A 10-V-Tolerant Dual-Mode Neural Stimulation System With Self-Sustaining Dynamic Supply and Error-Resilient Digital Stimulus Odometer
    Authors: Kyeongho Eom, Han-Sol Lee, Seung-Beom Ku, Joonghoon Kang, Hyungjin Jung, Taewoo Kim, Jaesoon Joo, Taekyung Kim, Young-Min Shon, Hyung-Min Lee
    Year: 2025
    Citation: IEEE Journal of Solid-State Circuits, DOI: 10.1109/JSSC.2025.3542022

  • Title: Electroencephalogram Changes After Virtual Reality-Based Cognitive Behavioral Therapy for Panic Disorder
    Authors: Hyewon Park, Heekyung Hwang, Minjung Kim, Haein Park, Imhong Jeon, Young-Min Shon, SungJun Hong, Deokjong Lee
    Year: 2025
    Citation: Journal of Korean Medical Science, DOI: 10.3346/jkms.2025.40.e185

  • Title: Hippocampal Deep Brain Stimulation for Drug-Resistant Epilepsy: Insights from Bilateral Temporal Lobe and Posterior Epilepsy Cases
    Authors: Seung Ho Choo, Hea Ree Park, Seunghoon Lee, Jung-Il Lee, Eun Yeon Joo, Dae-Won Seo, Seung Bong Hong, Young-Min Shon
    Year: 2025
    Citation: Seizure: European Journal of Epilepsy, DOI: 10.1016/j.seizure.2024.11.018

  • Title: Transcutaneous Auricular Vagus Nerve Stimulation Can Alter Autonomic Function and Induce an Antiepileptic Effect in Pentylenetetrazol-Induced Seizures in Rats
    Authors: Eunmi Choi, Taekyung Kim, Sung Jun Hong, Taewoo Kim, Minhee Kang, Young-Min Shon, Eunkyoung Park
    Year: 2024
    Citation: IEEE Access, DOI: 10.1109/ACCESS.2024.3393984

  • Title: A Multi-Channel Neural Recording System With Neural Spike Scan and Adaptive Electrode Selection for High-Density Neural Interface
    Authors: Han-Sol Lee, Kyeongho Eom, Minju Park, Seung-Beom Ku, Kwonhong Lee, Taewoo Kim, Taekyung Kim, Young-Min Shon, Hangue Park, Hyung-Min Lee
    Year: 2023
    Citation: IEEE Transactions on Circuits and Systems I, DOI: 10.1109/TCSI.2023.3268686

  • Title: Exploring Autonomic Alterations During Seizures in Temporal Lobe Epilepsy: Insights from a Heart-Rate Variability Analysis
    Authors: Sung-Min You, Baek-Hwan Cho, Hyo-Eun Bae, Young-Kyun Kim, Jae-Rim Kim, Soo-Ryun Park, Young-Min Shon, Dae-Won Seo, In-Young Kim
    Year: 2023
    Citation: Journal of Clinical Medicine, DOI: 10.3390/jcm12134284

  • Title: Deep Brain Stimulation of the Anterior Nuclei of the Thalamus Can Alleviate Seizure Severity and Induce Hippocampal GABAergic Neuronal Changes in a Pilocarpine-Induced Epileptic Mouse Brain
    Authors: Sungjun Bae, Hyun-Kyoung Lim, Yoonyi Jeong, Seong-Gi Kim, Sung-Min Park, Young-Min Shon, Minah Suh
    Year: 2022
    Citation: Cerebral Cortex, DOI: 10.1093/cercor/bhac033

  • Title: Prediction of the Responsiveness to Vagus-Nerve Stimulation in Patients With Drug-Resistant Epilepsy via Directed-Transfer-Function Analysis of Their Perioperative Scalp EEGs
    Authors: Dongyeop Kim, Taekyung Kim, Yoonha Hwang, Chae Young Lee, Eun Yeon Joo, Dae-Won Seo, Seung Bong Hong, Young-Min Shon
    Year: 2022
    Citation: Journal of Clinical Medicine, DOI: 10.3390/jcm11133695

  • Title: Evolution of Magnetic Resonance Imaging Features in Cerebral Parenchyma From Prolonged Focal Status Epilepticus: A Case Study
    Authors: Sung Chul Lim, Jung Hee Cho, Young-Min Shon
    Year: 2022
    Citation: Encephalitis, DOI: 10.47936/encephalitis.2021.00171

  • Title: Lateralizing Characteristics of Morphometric Changes to Hippocampus and Amygdala in Unilateral Temporal Lobe Epilepsy With Hippocampal Sclerosis
    Authors: Hyunjin Jo, Jeongsik Kim, Dongyeop Kim, Yoonha Hwang, Daewon Seo, Seungbong Hong, Young-Min Shon
    Year: 2022
    Citation: Medicina, DOI: 10.3390/medicina58040480

 

Qi Li | Human-Computer Interaction | Women Researcher Award

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Prof. Qi Li | Human-Computer Interaction | Women Researcher Award

Professor at Capital Normal University, China

Professor Qi Li is a renowned scholar in psychology, currently serving as a Professor and Doctoral Supervisor at the School of Psychology, Capital Normal University, and a part-time Associate Researcher at the Chinese Academy of Sciences. He is also the Director of the Institute of Basic Psychology and Deputy Director of a Scientific Research Innovation Team. With visiting experience at Stanford University, he brings a strong global perspective. Professor Li has made pioneering contributions in the fields of cognitive neuroscience and adolescent mental health, integrating traditional psychological methods with cutting-edge technologies like EEG, brain imaging, and behavioral modeling. His research has led to the creation of impactful platforms like the Mental Health Assessment and Early Warning System and the Qihuang Angel Platform, serving over 100,000 individuals nationwide. With over 110 peer-reviewed publications and leadership in 20+ national projects, Professor Qi Li exemplifies excellence in research, innovation, and societal impact.

Professional Profile 

Education🎓

Professor Qi Li holds a robust academic background in psychology and neuroscience, which laid the foundation for his influential research career. Although specific degree details are not listed in the provided profile, his academic and research affiliations with prestigious institutions such as the Chinese Academy of Sciences and Stanford University indicate a high level of scholarly achievement and global academic engagement. His continuous progression from Assistant Researcher to Associate Researcher and ultimately to Professor and Doctoral Supervisor reflects both his academic competence and research leadership. His education has empowered him to lead interdisciplinary work that combines psychology, cognitive neuroscience, and behavioral science with practical application in education and healthcare. The depth and breadth of his academic training are also evident in his ability to mentor PhD students, spearhead large-scale national projects, and develop research-backed mental health assessment tools. His educational journey reflects a strong commitment to academic excellence and innovation.

Professional Experience📝

Professor Qi Li’s professional trajectory is marked by steady advancement and impactful leadership. He began his career in 2011 as an Assistant Researcher at the Institute of Psychology, Chinese Academy of Sciences, progressing to Associate Researcher and Doctoral Supervisor by 2014. In 2021, he joined Capital Normal University as a Professor and Director of the Institute of Basic Psychology, while maintaining part-time research responsibilities at the Chinese Academy of Sciences. Additionally, he serves as Deputy Director of a major Scientific Research Innovation Team and holds numerous committee and advisory roles across educational and psychological institutions in China. His multidisciplinary leadership spans clinical, educational, and research settings. Notably, he has developed two national platforms addressing adolescent behavior and mental health, collaborated on major government-funded R&D projects, and published extensively in international journals. His dual engagement in academic research and real-world application highlights his broad professional impact.

Research Interest🔎

Professor Qi Li’s research focuses on the neural mechanisms of cognitive control and reward, especially as they relate to adolescent behavior, internet addiction, emotional regulation, and decision-making. His work examines how cognitive and affective systems interact in developing brains, using tools such as EEG, brain imaging, behavioral experiments, and data modeling. A major focus of his research is the early warning, assessment, and correction of problem behaviors in children and adolescents. He has pioneered interdisciplinary platforms that integrate psychological theory, educational practice, and digital technology to address behavioral health challenges in schools. His studies have explored social comparison, fairness decision-making, and the brain’s response to motivation and learning contexts. Additionally, he leads projects that combine neuroscience with traditional Chinese medicine in mental health interventions. His work stands at the frontier of basic and applied psychological science, aiming to translate theoretical insights into scalable and effective behavioral interventions.

Award and Honor🏆

Although specific individual awards are not directly listed in the provided profile, Professor Qi Li’s nationally funded research projects, editorial roles, software copyrights, and leadership in high-impact platforms are indicative of substantial recognition and honor in his field. His appointment as a Visiting Scientist at Stanford University, Editorial Board Member of Frontiers, and reviewer for top-tier journals such as Neuroimage and Cerebral Cortex reflect his respected standing in the international academic community. Additionally, he holds key positions in national academic committees, such as Vice Chairman of the Traditional Chinese Medicine Nursing Committee and Expert Steering Committee for Psychological Correction in Drug Rehabilitation. The wide adoption of his mental health assessment platform in over 50 institutions nationwide showcases the trust and acknowledgment his work has earned. His sustained research funding from the National Natural Science Foundation of China and other prestigious bodies further attests to his excellence and credibility.

Research Skill🔬

Professor Qi Li possesses a comprehensive and multidisciplinary research skill set that bridges psychology, cognitive neuroscience, education, and digital health. He is adept in questionnaire design, behavioral experiment protocols, electroencephalography (EEG), functional brain imaging, and computational data modeling. These skills have enabled him to explore the intricacies of cognitive control, reward processing, emotion regulation, and adolescent behavioral patterns. His ability to analyze complex brain-behavior relationships has been instrumental in developing real-time, technology-driven assessment and correction systems. He also excels in platform development, evidenced by the large-scale deployment of his behavioral intervention platforms in schools and healthcare systems. His proficiency in software development is supported by multiple software copyrights for psychological assessment and intervention tools. Furthermore, his leadership in interdisciplinary projects highlights strong skills in project management, team coordination, and inter-institutional collaboration. Altogether, Professor Li’s technical and analytical expertise underscores his capability as a top-tier researcher in psychology and behavioral science.

Conclusion💡

Professor Qi Li is highly suitable and deserving of the Best Researcher Award. His track record exemplifies academic excellence, innovation in psychological assessment and intervention, real-world impact, and national-level leadership in both research and education. While opportunities for enhanced globalization and commercialization exist, his contribution to mental health, cognitive neuroscience, and student behavioral development is both timely and transformative.

Publications Top Noted✍

  1. Title: Core Symptoms and Symptom Relationships of Problematic Internet Use in Children and Adolescents: A Network Analysis
    Authors: Qi Li, Hui Zhou, Guangteng Meng, Jing Xiao, Kesong Hu, Ping Wei, Jinpeng Wang, Mei Du, Xun Liu
    Year: 2025
    Citation (DOI): https://doi.org/10.1007/s11469-025-01455-9

  2. Title: Patients with Methamphetamine Use Disorder Show Highly Utilized Proactive Inhibitory Control and Intact Reactive Inhibitory Control with Long-Term Abstinence
    Authors: Weine Dai, Hui Zhou, Arne Møller, Ping Wei, Kesong Hu, Kezhuang Feng, Jie Han, Qi Li, Xun Liu
    Year: 2022
    Citation (DOI): https://doi.org/10.3390/brainsci12080974

  3. Title: The Roles of Risk Perception, Negative Emotions and Perceived Efficacy in the Association Between COVID-19 Infection Cues and Preventive Behaviors: A Moderated Mediation Model (Preprint)
    Authors: Guangteng Meng, Xiaoyan Yuan, Ya Zheng, Kesong Hu, Qi Li, Bibing Dai, Xun Liu
    Year: 2021
    Citation (DOI): https://doi.org/10.2196/preprints.32930

  4. Title: Enhanced Neural Responses in Specific Phases of Reward Processing in Individuals with Internet Gaming Disorder
    Authors: Lingxiao Wang, Guochun Yang, Ya Zheng, Zhenghan Li, Yue Qi, Qi Li, Xun Liu
    Year: 2021
    Citation (DOI): https://doi.org/10.1556/2006.2021.00003

  5. Title: The Impact of Intolerance of Uncertainty on Negative Emotions in COVID-19: Mediation by Pandemic-Focused Time and Moderation by Perceived Efficacy
    Authors: Weine Dai, Guangteng Meng, Ya Zheng, Qi Li, Bibing Dai, Xun Liu
    Year: 2021
    Citation (DOI): https://doi.org/10.3390/ijerph18084189

  6. Title: The Effects of COVID-19 Infection Cues on Preventive Behaviors: Development of a Moderated Mediation Model (Preprint)
    Authors: Guangteng Meng, Xiaoyan Yuan, Qi Li, Bibing Dai, Xun Liu
    Year: 2021
    Citation (DOI): https://doi.org/10.2196/preprints.28986

  7. Title: Dysfunctional Cognitive Control and Reward Processing in Adolescents with Internet Gaming Disorder
    Authors: Qi Li, Yong Wang, Zhong Yang, Weine Dai, Ya Zheng, Yuwei Sun, Xun Liu
    Year: 2020
    Citation (DOI): https://doi.org/10.1111/psyp.13469

 

Yoo Kyung Chang | Learning Technology | Best Researcher Award

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Dr. Yoo Kyung Chang | Learning Technology | Best Researcher Award

Clinical Professor and Academic Director at New York University, United States

Dr. Yoo Kyung Chang is a Clinical Professor and the Academic Director of Emerging Technologies at the Division of Undergraduate Studies, School of Professional Studies, New York University. Her work centers on the intersection of technology and human development, combining theory, design, and practice to explore how digital tools shape cognition, behavior, and learning. Her multidisciplinary research portfolio includes areas such as data-driven design, online learning environments, game-based media literacy education, e-sports training, health behavior support systems, mixed reality (MR), and artificial intelligence (AI). With a commitment to both academic rigor and practical impact, Dr. Chang leverages her expertise to prepare future innovators while leading critical conversations around ethical and developmental implications of emerging technologies. Her leadership roles and research excellence position her as a forward-thinking academic and a valuable contributor to technology-enhanced education and behavioral transformation initiatives.

Professional Profile 

Education🎓

Dr. Yoo Kyung Chang holds a Ph.D. in a relevant discipline that integrates educational technology, cognitive science, and media studies, reflecting her interdisciplinary academic background. Her education has laid a strong foundation for her research focus on human development through the use of emerging technologies. Although specific degree-granting institutions are not provided, her academic trajectory likely includes advanced degrees in fields such as learning sciences, human-computer interaction, or communication and technology. This educational background informs her exploration of metacognition, behavioral psychology, and data-driven environments, which are key themes in her scholarly work. Throughout her academic training, she has developed a comprehensive understanding of how educational technologies affect learners’ cognition and engagement. Her education also supports her capacity to design impactful curricula and lead research initiatives at NYU, fostering innovation at the intersection of learning, media, and technology.

Professional Experience📝

Dr. Yoo Kyung Chang serves as a Clinical Professor and Academic Director of Emerging Technologies at the Division of Undergraduate Studies, School of Professional Studies, New York University. In this dual role, she combines teaching, research leadership, and curriculum development to foster innovation in undergraduate education. Her professional experience spans academia and applied research environments, where she has designed and led initiatives in online learning, data analytics, digital literacy, and immersive technologies. Prior to her current position, she likely held roles in educational research, instructional design, or interdisciplinary technology-focused programs. Her responsibilities include mentoring students, guiding research on cognitive and behavioral development, and overseeing technology integration in educational practices. Dr. Chang’s professional career is marked by a commitment to fostering equity and impact through digital innovation, making her a respected leader in the academic community and a sought-after expert in human-technology interaction.

Research Interest🔎

Dr. Yoo Kyung Chang’s research interests lie at the nexus of emerging technologies and human development. Her current work focuses on the cognitive, affective, and metacognitive dimensions of data-driven design, with applications across online learning, media behavior, and behavioral health. She is particularly interested in how technological environments—such as games, MR, and AI systems—affect users’ learning strategies, decision-making processes, and emotional responses. Her research also explores game-based approaches to media literacy, the design of digital tools for health behavior support, and training systems in competitive e-sports. With a deep commitment to both empirical study and practical application, Dr. Chang examines how technology can be purposefully designed to empower individuals across age groups and contexts. Her interdisciplinary lens bridges psychology, communication, and design, allowing her to contribute meaningful insights into the ethical, developmental, and educational implications of next-generation technologies.

Award and Honor🏆

While specific awards and honors are not detailed in the current biography, Dr. Yoo Kyung Chang’s prestigious position at New York University as a Clinical Professor and Academic Director is itself a reflection of her recognized academic and professional excellence. Her leadership in emerging technologies and her influential research on cognitive and behavioral impacts of technology likely contribute to her recognition within scholarly and professional communities. It is reasonable to infer that she has received institutional commendations, research grants, or teaching awards throughout her academic career, especially given her interdisciplinary focus and innovation in pedagogy and applied research. Participation in national or international conferences, keynote invitations, and leadership roles in research projects further speak to the esteem in which she is held. Documenting specific recognitions would further validate her candidacy for research awards and highlight her sustained contributions to academic and technological advancement.

Research Skill🔬

Dr. Yoo Kyung Chang possesses a diverse and advanced set of research skills tailored to the study of human-technology interaction. Her expertise spans qualitative and quantitative research methods, data-driven design analysis, cognitive and behavioral assessment techniques, and educational technology evaluation. She is skilled in developing and deploying experimental frameworks that explore how digital environments affect learning, emotional engagement, and metacognitive awareness. Her background supports the design of game-based learning interventions, MR/VR environments, AI-driven education systems, and health behavior platforms. Dr. Chang also demonstrates proficiency in human-centered design and user experience (UX) research, enabling her to align technological innovation with developmental needs. Her ability to collaborate across disciplines—integrating insights from psychology, media studies, and design—strengthens her research output and impact. These skills make her not only an accomplished researcher but also a catalyst for meaningful innovation in digital learning and behavioral support technologies.

Conclusion💡

Dr. Yoo Kyung Chang is a highly suitable candidate for the Best Researcher Award based on her innovative and interdisciplinary research portfolio, leadership in academic settings, and her significant contributions to technology-enabled human development. While additional visibility in terms of publications, grants, and international collaborations would enhance her candidacy further, her impactful work across cognitive, behavioral, and technological domains already positions her as a strong and deserving contender.

Publications Top Noted✍

  • Title: How can AR‑enhanced books support early readers? Exploring literacy development through AR design principles

  • Authors: Yoo Kyung Chang, Jullia Lim, Jordan Burkland

  • Year: 2024

  • Citation Count: 1 citation

Sivanagireddy Kalli | Deep Learning for Computer Vision | Academic Excellence Distinction Award

Published on by Computer Vision Awards

Dr. Sivanagireddy Kalli | Deep Learning for Computer Vision | Academic Excellence Distinction Award

Professor at Sridevi Women’s Engineering College, India

Dr. K. Sivanagireddy is a seasoned academician and researcher with over 20 years of experience in teaching, research, and administration. He has served in key academic leadership roles including Dean Academics, Head of Department, and Principal across reputed engineering institutions in Telangana and Andhra Pradesh. His extensive contributions include the publication of more than 60 research papers in SCI, Scopus, and UGC CARE-listed journals, along with participation in over 20 international conferences. He has been a driving force in innovation, holding eight patents—both national and international—and authoring nine technical books. He recently completed a Postdoctoral Fellowship at the University of South Florida (2024) and earned a Ph.D. in Electronics and Communication Engineering from JNTU Hyderabad (2019). His expertise spans areas like VLSI Design, IoT, AI, Embedded Systems, and Medical Image Processing. Recognized nationally and internationally, Dr. Sivanagireddy is also an active member of professional bodies such as IEEE, IAENG, and IAOE.

Professional Profile 

Education🎓

Dr. K. Sivanagireddy has a strong academic foundation rooted in electronics, communication, and embedded systems. He earned his Ph.D. in Electronics and Communication Engineering from Jawaharlal Nehru Technological University, Hyderabad, in 2019. Recently, in 2024, he completed his Postdoctoral Fellowship at the University of South Florida, USA, further enriching his research exposure and global academic outlook. His earlier postgraduate education includes an M.Tech in Embedded Systems from JNTUK, Kakinada (2014), and an M.E in VLSI Design from Vinayaka Missions University, Tamil Nadu (2006). He began his academic journey with a B.Tech in Electronics and Communication Engineering from Bharathidasan University, Tiruchirappalli, in 2002. His education reflects a clear emphasis on digital design, embedded computing, and system optimization, which laid the groundwork for his multifaceted contributions in academia and research. He has also pursued various NPTEL and FDP certifications from top IITs, demonstrating his commitment to lifelong learning and skill enhancement.

Professional Experience📝

Dr. K. Sivanagireddy brings over two decades of professional academic experience, with an emphasis on leadership, research, and teaching. Currently serving as Dean Academics and Professor at Sridevi Women’s Engineering College, Hyderabad since 2019, he previously held the positions of Head of Department and Associate Professor at the same institute. Earlier in his career, he worked at Arjun College of Technology and Science and LITAM, Guntur, where he mentored undergraduate and postgraduate students and handled administrative responsibilities. His contributions extend to coordinating academic accreditations like NAAC and NBA, overseeing student projects, counseling, and organizing technical paper contests. His strategic leadership has helped align institutional goals with academic excellence and research development. With a deep understanding of educational systems, faculty management, and curriculum design, Dr. Sivanagireddy has played a pivotal role in shaping the academic structure of the institutions he served. His professionalism and experience continue to influence engineering education in India.

Research Interest🔎

Dr. Sivanagireddy’s research interests are broad, multidisciplinary, and highly application-oriented. His primary focus lies in Medical Image Processing, Artificial Intelligence, Deep Learning, and IoT-enabled systems, especially for healthcare diagnostics and smart surveillance. He has conducted advanced research in brain tumor detection, cancer classification, heart disease prediction, and autonomous medical devices, often leveraging CNN, LSTM, and hybrid deep learning models. Additionally, his work spans VLSI Design, Embedded Systems, Cybersecurity, Video Surveillance, and Signal Processing, reflecting his versatility. His contributions also extend to developing IoT-integrated intelligent systems, machine learning-based prediction models, and hardware optimization techniques. Many of his projects are focused on societal needs, such as fall detection for the elderly, counterfeit currency detection, and remote health monitoring. His research is rooted in real-world impact, bridging engineering with life sciences and computing. This interdisciplinary approach allows him to explore innovative solutions across both theoretical and applied research domains.

Award and Honor🏆

Dr. K. Sivanagireddy’s scholarly achievements have been widely recognized through multiple national and international honors. He received the International Academic Excellence Award from I2OR in 2022, acknowledging his impactful global research footprint. In 2021, he was conferred with the National Faculty Excellency Award by the International Journal of MC Square Scientific Research, reflecting his outstanding contributions to teaching and innovation. He also earned the National Certificate of Excellence from the Telangana Engineering Colleges Faculty Association in 2020, further emphasizing his role in academic leadership. In addition to these awards, his editorial engagement with the Asian Council of Science Editors and professional memberships with IEEE, IAENG, and IAOE signify his active participation in international scholarly communities. His commitment to excellence, innovation, and quality research has made him a role model in engineering academia, and these accolades underscore his dedication to elevating academic standards at both institutional and national levels.

Research Skill🔬

Dr. Sivanagireddy possesses a diverse and robust set of research skills that span both theoretical modeling and practical application. He is adept in machine learning algorithms, deep learning frameworks, IoT development, and VLSI simulation tools. His proficiency in tools like MATLAB, Python, Verilog, and FPGA platforms has enabled him to develop and deploy intelligent systems for healthcare, security, and automation. He has expertise in image processing techniques, including segmentation, classification, and feature extraction using CNNs, Bi-LSTM, and hybrid models. Additionally, he demonstrates advanced knowledge in medical diagnostics, pattern recognition, and cloud computing integration. His research skillset is not only confined to software but extends to hardware optimization, including CMOS and ASIC design. Through his participation in over 20 conferences and completion of NPTEL certifications from IITs, he maintains up-to-date technical competence. These diverse skills allow him to drive interdisciplinary research, publish impactful papers, and mentor future innovators effectively.

Conclusion💡

Dr. K. Sivanagireddy is highly deserving and well-qualified for the Best Researcher Award. With a prolific publication record, leadership roles, multiple patents, academic books, and contributions to multiple domains in engineering and technology, he stands out as a multidisciplinary scholar and innovator. A stronger emphasis on research impact, international projects, and focused thematic expertise would further elevate his candidacy.

Publications Top Noted✍

  • Title: An effective motion object detection using adaptive background modeling mechanism in video surveillance system
    Authors: SNR Kalli
    Year: 2021
    Citations: 54

  • Title: Early lung cancer prediction using correlation and regression
    Authors: K Sivanagireddy, S Yerram, SSN Kowsalya, SS Sivasankari, J Surendiran, RG Vidhya
    Year: 2022
    Citations: 24

  • Title: Image Compression and reconstruction using a new approach by artificial neural network
    Authors: KSN Reddy, BR Vikram, LK Rao, BS Reddy
    Year: 2012
    Citations: 21

  • Title: A Fast Curvelet Transform Image Compression Algorithm using with Modified SPIHT
    Authors: KSN Reddy, BRS Reddy, G Rajasekhar, KC Rao
    Year: 2012
    Citations: 14

  • Title: A nanoplasmonic branchline coupler for subwavelength wireless networks
    Authors: K Thirupathaiah, KS Reddy, GRS Reddy
    Year: 2021
    Citations: 11

  • Title: Generative Adversarial Networks based Approach for Intrusion Detection System
    Authors: S Kalli, BN Kumar, S Jagadeesh
    Year: 2022
    Citations: 8

  • Title: IMPLEMENTATION OF OBJECT TRACKING AND VELOCITY DETERMINATION
    Authors: SNR Kalli
    Year: 2012
    Citations: 5

  • Title: Image compression by discrete curvelet wrapping technique with simplified SPIHT
    Authors: KSN Reddy, L Rao, P Ravikanth
    Year: 2012
    Citations: 4

  • Title: Identification of criminal & non-criminal faces using deep learning and optimization of image processing
    Authors: K Sivanagireddy, S Jagadeesh, A Narmada
    Year: 2024
    Citations: 3

  • Title: Low memory low complexity image compression using DWT and HS-SPIHT encoder
    Authors: K Sivanagireddy, M Saipravallika, PKC Tejaswini
    Year: 2012
    Citations: 3

  • Title: Reconstruction Using a New Approach By Artificial Neural Network
    Authors: SNRKI Compression
    Year: 2012
    Citations: 3

  • Title: Early Lung Cancer Prediction using Correlation and Regression
    Authors: K Sivanagireddy
    Year: 2022
    Citations: 2

  • Title: Smart Door Lock to Avoid Robberies in ATM
    Authors: VS Reddy, S Kalli, H Gebregziabher, BR Babu
    Year: 2021
    Citations: 2

  • Title: Image Segmentation by Using Modified Spatially Constrained Gaussian Mixture Model
    Authors: S Kalli, BM Bhaskara
    Year: 2016
    Citations: 2

  • Title: Efficient Memory and Low Complexity Image Compression Using DWT with Modified SPIHT Encoder
    Authors: KSN Reddy, VS Reddy, DBR Vikram
    Year: 2012
    Citations: 2

  • Title: Brain Tumor Detection through Image Fusion Using Cross Guided Filter and Convolutional Neural Network
    Authors: MV Srikanth, S Kethavath, S Yerram, SNR Kalli, JB Naik
    Year: 2024
    Citations: 1

  • Title: Autoencoder-based Deep Learning Approach for Intrusion Detection System using Firefly Optimization Algorithms
    Authors: N Kumar Bukka, S Jagadeesh, KS Reddy
    Year: 2024
    Citations: 1

Sudhagar D | Medical Image Classification | Best Researcher Award

Published on by Computer Vision Awards

Dr. D. Sudhagar | Medical Image Classification | Best Researcher Award

Associate Professor at Jerusalem College of Engineering, India

Dr. D. Sudhagar is an accomplished academician and researcher with over 22 years of experience in the field of Computer Science and Engineering. Currently serving as an Associate Professor at Jerusalem College of Engineering, Chennai, he specializes in Big Data Analytics, Data Mining, and Data Science. Dr. Sudhagar holds a Ph.D. in Information and Communication Engineering from Anna University and has published over 15 research articles in reputed national and international journals, along with several conference papers. He is also a recognized innovator with five published patents and two authored textbooks. His contributions span across education, research, curriculum development, and technical mentoring. A committed educator, he has taught a wide range of advanced computing subjects and lab courses. Actively involved in institutional development, he has held multiple administrative and academic roles. Dr. Sudhagar’s consistent academic performance, leadership qualities, and multidisciplinary research make him a strong candidate for national and international recognition.

Professional Profile 

Education🎓 

Dr. D. Sudhagar possesses a rich and multi-layered academic background rooted in Computer Science. He earned his Ph.D. in Information and Communication Engineering from Anna University, Chennai, completing it in 2023. He holds an M.E. in Computer Science and Engineering from Anna University with first-class distinction, showcasing his early commitment to academic excellence. Prior to that, he completed an M.Phil. in Computer Science from Annamalai University and a Master of Computer Applications (MCA) from the University of Madras, also with distinction. His foundational academic journey began with a B.Sc. in Computer Science, also from the University of Madras, where he graduated with first-class honors. His educational progression—from undergraduate to doctoral levels—demonstrates a continuous and focused pursuit of knowledge in computing, data systems, and emerging technologies. These qualifications have provided him with both the theoretical foundation and practical insight needed to excel in research, teaching, and technological innovation.

Professional Experience📝

Dr. D. Sudhagar brings more than two decades of academic and teaching experience in Computer Science, having served in various positions since 2002. He is currently an Associate Professor at Jerusalem College of Engineering, a role he has held since 2015, where he contributes to both teaching and departmental leadership. Prior to this, he served as an Assistant Professor at Arunai Engineering College for over a decade and began his career as a Teaching Faculty at Government Arts College. Throughout his career, Dr. Sudhagar has taught numerous undergraduate and postgraduate courses in cutting-edge areas such as Cyber Security, Big Data, Web Technologies, and Artificial Intelligence. He has also handled various lab courses and mentored numerous student projects. In addition to teaching, he has held significant administrative responsibilities, including NBA/NACC coordinator, academic advisor, symposium organizer, and department in-charge. His professional journey reflects a strong balance of pedagogy, leadership, and technical excellence.

Research Interest🔎

Dr. D. Sudhagar’s research interests lie at the intersection of Big Data Analytics, Data Mining, Data Science, and Artificial Intelligence. His recent works demonstrate a commitment to applying machine learning and deep learning to real-world problems such as medical imaging, smart city systems, IoT-based healthcare, and cybersecurity. Notable research areas include the use of hybrid deep learning models for disease classification, optimization techniques for IoT and smart grid solutions, and advanced clustering methods for high-dimensional data analysis. He has also explored novel applications in paraphrase generation, facial recognition for automation, and secure data deduplication using blockchain. Dr. Sudhagar’s approach is interdisciplinary, blending AI, IoT, and cloud computing to create scalable, intelligent systems. His publications in journals such as Concurrency and Computation and Journal of Intelligent & Fuzzy Systems underscore his expertise in algorithm development, model optimization, and intelligent automation. His forward-looking research continues to address socially relevant technological challenges.

Award and Honor🏆

Dr. D. Sudhagar has received multiple recognitions for his dedication to teaching, research, and academic service. He was awarded the “Best Teacher” honor twice, in the academic years 2017–2018 and 2021–2022, recognizing his overall academic performance and student engagement. He was also named a “Top Performing Mentor” by NPTEL in 2022 for the course “Online Privacy.” In addition, he received accolades for producing 100% pass results in Anna University examinations across multiple years and was consistently appreciated for excellence in academic coordination and conference organization. As a student, he earned the prestigious “Nayudamma Award” and a cash prize for being the best outgoing student during his MCA program. His professional memberships in CSI and IAENG further acknowledge his active participation in global computing communities. Collectively, these awards reflect his consistent excellence, innovative teaching methods, and commitment to student success and academic advancement.

Research Skill🔬

Dr. D. Sudhagar demonstrates a robust set of research skills, especially in the domains of data science, machine learning, and intelligent systems. He is proficient in designing and implementing deep learning models, optimization algorithms, and high-dimensional data analysis methods. His research often involves hybrid AI models, transfer learning, clustering techniques, and feature optimization. He exhibits strong competence in software tools such as Python, TensorFlow, and MATLAB, alongside data platforms like Hadoop and AWS. Dr. Sudhagar’s research also incorporates IoT, cloud computing, and blockchain to build real-time and secure applications. He has published impactful studies on AI-driven medical diagnostics, smart infrastructure, and NLP tools, showcasing his interdisciplinary expertise. His roles as a journal reviewer, editorial board member, and conference presenter further reflect his analytical thinking, peer review capabilities, and academic writing proficiency. His ability to bridge theory with application makes him an asset to the research community.

Conclusion💡

Dr. D. Sudhagar is highly suitable for the Best Researcher Award based on his strong academic experience, solid research output, impactful innovations (patents), and contribution to education and institutional development. His consistent publication record, interdisciplinary innovations, and long-standing commitment to teaching and research make him a commendable candidate.

Publications Top Noted✍

  • Title: Recent Advancement in Prediction and Analyzation of Brain Tumour using the Artificial Intelligence Method
    Authors: RGV Balasubramani Ramesh, Sudhagar Dhandapani, Sanda Sri Harsha, Naheem
    Year: 2023
    Cited by: 7

  • Title: An IoT and Fuzzy aware e-Healthcare system using feature optimization tuned T-CNN with high dimensional data
    Authors: D. Sudhagar, J. Arokia Renjit
    Year: 2023
    Cited by: 2

  • Title: Revolutionizing Data Transmission Efficiency in IoT-Enabled Smart Cities: A Novel Optimization-Centric Approach
    Authors: RGV Sudhagar D, Swapna Saturi, Mukesh Choudhary, Pranav Senthilkumaran, Eric
    Year: 2024
    Cited by: 1

  • Title: Poor and rich dolphin optimization algorithm with modified deep fuzzy clustering for COVID‐19 patient analysis
    Authors: S. Dhandapani, A.R. Jerald Rodriguez
    Year: 2023
    Cited by: 1

  • Title: Transfer Learning With Adam Gold Rush Optimization for Endometrial Disease Classification Using Histopathological Image
    Authors: SKB Sudhagar Dhandapani, Ravikumar Subburam, Pretty Diana Cyril Cyriloose
    Year: 2025

  • Title: AI-Driven Platform for Missing Person Identification
    Authors: AV Sudhagar D, Harini Senthil
    Year: 2025

  • Title: Pneumonia Detection using Deep Learning
    Authors: RLG Sudhagar D, Kanmani Sekar
    Year: 2025

  • Title: Glaucoma Detection using Hybrid Deep Learning
    Authors: KSM Sudhagar D, Jaladhija S
    Year: 2025

  • Title: An Automated Water Tank Management System using IOT
    Authors: AA Sudhagar D, Aswath Narayana. P.V.
    Year: 2025

  • Title: Individuality Traits Projection By Tweets via Myers Briggs Type Indicator using Machine Learning
    Authors: S.D. Ajay.R, Ajith Kumar.C, Deepa Shri.V.U
    Year: 2023