Lilei Sun | Artificial intelligence | Best Researcher Award | 13305

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Assist. Prof. Dr. Lilei Sun | Artificial intelligence | Best Researcher Award 

Assist. Prof. Dr. Lilei Sun, Guizhou Minzu University, China

Prof. Dr. Lilei Sun is an associate professor at Guizhou Minzu University, China, specializing in deep learning, image processing, pattern recognition, and medical image processing. He completed his B.E. in computer technology in 2016 and obtained his Ph.D. in software engineering in 2022, both from Guizhou University, Guiyang. Dr. Sun’s research focuses on incomplete multi-view clustering and has led to notable contributions in academic publications, including 10 articles in prestigious journals. He serves as an associate editor for the International Journal of Image and Graphics and is actively involved in collaborative research projects.

Profile

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🌱 Early Academic Pursuits

Prof. Dr. Lilei Sun began his academic journey with a solid foundation in computer technology. He received his B.E. degree in Computer Technology from Guizhou University in Guiyang, China, in 2016. This laid the groundwork for his advanced studies in software engineering, a field that has had a significant impact on his subsequent research. His academic excellence and curiosity drove him to pursue a Ph.D. in Software Engineering from Guizhou University, which he successfully completed in 2022. Throughout his academic journey, he consistently demonstrated a deep passion for understanding the intricacies of deep learning, image processing, and pattern recognition, as well as their transformative potential in medical image processing. 🌟

🔬 Professional Endeavors and Contributions

Since 2024, Prof. Dr. Sun has been serving as an Associate Professor at Guizhou Minzu University, where he has made notable strides in both teaching and research. In his role, he mentors students and collaborates with fellow researchers on projects that explore cutting-edge technologies in deep learning and image processing. His contributions to the academic community extend beyond the classroom as he actively participates in various consultancy projects and industry collaborations, applying his research to real-world problems. Dr. Sun’s work has led to practical innovations in the fields of medical image processing and pattern recognition, areas that are increasingly critical for advancing healthcare solutions globally. 🏥

💡 Research Focus

Prof. Dr. Sun’s research interests revolve around deep learning, image processing, pattern recognition, and medical image processing. One of his key areas of focus is incomplete multi-view clustering, a method that enables more accurate data analysis in scenarios where information is incomplete or fragmented. This has potential applications in various fields, including healthcare, where the integration of multi-source medical data can lead to better diagnostic models and more personalized treatments. Additionally, his work on medical image processing leverages machine learning techniques to enhance the quality and accuracy of medical imaging, providing practitioners with more reliable diagnostic tools. The potential to save lives and improve healthcare outcomes makes this research both significant and timely. 🔍

🏆 Accolades and Recognition

Prof. Dr. Sun has garnered recognition for his dedication to both teaching and research. He has published 10 academic articles, many of which have been featured in respected journals indexed by SCI and Scopus. He is also an associate editor of the International Journal of Image and Graphics, a prestigious journal that underscores his expertise in the field. His work has earned him an esteemed position in the academic community, further enhanced by his ongoing contributions to industry projects and collaborations. Through his research, Prof. Dr. Sun has received significant acknowledgment from his peers, with numerous invitations to speak at international conferences and collaborate with experts from various research institutes globally. 🌍

🌟 Impact and Influence

Prof. Dr. Sun’s work has had a profound impact on the fields of image processing and medical image processing. His research on deep learning and pattern recognition has contributed to advancements in the way medical data is processed, interpreted, and used in clinical decision-making. His innovations are poised to help healthcare providers access more accurate, timely, and comprehensive information, ultimately leading to improved patient outcomes. Furthermore, his involvement in professional memberships and editorial boards for various scientific journals has allowed him to influence the direction of research in his areas of expertise. 📊

💫 Legacy and Future Contributions

As Prof. Dr. Sun continues his research journey, his legacy is beginning to take shape through his groundbreaking contributions to deep learning and medical image processing. His passion for exploring innovative solutions to real-world challenges, particularly in healthcare, positions him as a leader in his field. In the coming years, Prof. Dr. Sun aims to push the boundaries of incomplete multi-view clustering, further developing techniques that can be applied across multiple domains, including medical diagnostics, artificial intelligence, and big data analytics. His commitment to excellence in research, teaching, and mentorship will continue to inspire future generations of students and researchers.

Publications Top Notes

Dual structure-aware consensus graph learning for incomplete multi-view clustering

Contributors: Lilei Sun; Wai Keung Wong; Yusen Fu; Jie Wen; Mu Li; Yuwu Lu; Lunke Fei
Journal: Pattern Recognition
Year: 2025

Balance guided incomplete multi-view spectral clustering

Contributors: Lilei Sun; Jie Wen; Chengliang Liu; Lunke Fei; Lusi Li
Journal: Neural Networks
Year: 2023

Two‐view attention‐guided convolutional neural network for mammographic image classification

Contributors: Lilei Sun; Jie Wen; Junqian Wang; Yong Zhao; Bob Zhang; Jian Wu; Yong Xu
Journal: CAAI Transactions on Intelligence Technology
Year: 2023

Sunil Kumar | Machine Learning | Best Researcher Award

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Dr. Sunil Kumar | Machine Learning | Best Researcher Award

Assistant Professor at MS Ramaiah University of Applied Sciences, India.

Dr. Sunil Kumar is a dedicated professional with a rich academic background and diverse expertise. With a Ph.D. from Birla Institute Technology (BIT), Mesra Ranchi, and master’s and bachelor’s degrees from YMCA University of Science and Technology and Anna University, respectively, Dr. Kumar brings a wealth of knowledge to his work. His passion for academia is evident in his objective to develop young minds and contribute to their overall growth. With nearly a decade of experience in teaching and industry, including roles at prestigious institutions like Ramaiah University of Applied Sciences and Madanapalle Institute of Technology & Science, Dr. Kumar has demonstrated his commitment to education and research. He is an active member of various professional societies, including IEEE and ISTE, and has contributed significantly to workshops, seminars, and short-term courses aimed at enhancing technical education. Dr. Kumar’s research interests span signal processing, machine learning, and microwave applications, areas in which he has published and reviewed extensively. His dedication to his field, coupled with his diverse skill set and academic achievements, makes him a valuable asset to the academic and research community.

Professional Profiles:

Education

Dr. Sunil Kumar has pursued a robust educational journey, culminating in a Ph.D. from Birla Institute Technology (BIT), Mesra Ranchi. Prior to his doctoral studies, he obtained an M.Tech in Electronics from YMCA University of Science and Technology, Faridabad, and a B.E. in Electronics and Communication Engineering (ECE) from Anna University, Chennai. His educational background reflects a strong foundation in both theoretical knowledge and practical application, preparing him for a successful career in academia and industry. 🎓📚

Professional Experience

Dr. Sunil Kumar has accumulated 9.5 years of rich and diverse experience encompassing both teaching and industry roles. He currently serves as an Assistant Professor at Ramaiah University of Applied Sciences, where he imparts knowledge and mentors students. Previously, he held similar positions at Madanapalle Institute of Technology & Science, CFA Academy in Noida, and Dronacharya College of Engineering. Additionally, he contributed as a Guest Faculty at YMCA UST, Faridabad. Beyond academia, Dr. Kumar also ventured into the industry as a Production Manager at RAJ APEX CONSTRUCTIONS (P) Ltd. His extensive professional journey reflects a dedication to both education and practical application in the field. 👨‍🏫🏭

Research Interest

Dr. Sunil Kumar’s research interests span several domains, reflecting his multidisciplinary approach and expertise. He is particularly inclined towards exploring areas such as signal processing, machine learning, and their applications in diverse fields. Additionally, his research pursuits extend to bioinformatics, microwave applications, and VLSI design. Through his scholarly endeavors, Dr. Kumar aims to contribute significantly to these fields, leveraging his expertise in data analysis, algorithm development, and technological innovation. His diverse research interests underscore a commitment to advancing knowledge and addressing contemporary challenges through innovative research methodologies. 🧪🔬📊

Award and Honors

Dr. Sunil Kumar has accumulated a wealth of experience and expertise in academia and research throughout his career. With a Ph.D. from Birla Institute Technology (BIT), Mesra Ranchi, an M.Tech in Electronics from YMCA University of Science and Technology, and a B.E. in Electronics and Communication Engineering from Anna University, Chennai, Dr. Kumar possesses a strong educational foundation. He has garnered over 9.5 years of combined teaching and industry experience, serving in various roles such as Assistant Professor at Ramaiah University of Applied Sciences and Madanapalle Institute of Technology & Science. Dr. Kumar has also made significant contributions to workshops, seminars, and short-term courses, demonstrating his commitment to continuous learning and professional development. Additionally, he has been honored with certifications in areas such as research, signal processing techniques, and various programming languages. Dr. Kumar’s dedication to academic excellence and his passion for research underscore his significant contributions to his field.

Research Skills

Dr. Sunil Kumar possesses a diverse set of research skills honed through his extensive academic and professional journey. With expertise in areas such as signal processing, machine learning, and microwave applications, Dr. Kumar has demonstrated a proficiency in both theoretical knowledge and practical application. His research acumen extends to the utilization of programming languages like Matlab and Python, enabling him to conduct in-depth analyses and develop innovative solutions. Furthermore, Dr. Kumar’s ability to review and contribute to esteemed journals such as IEEE Sensor Journal and Elsevier’s Infrared Physics and Technology showcases his proficiency in academic writing and critical evaluation. Overall, his research skills reflect a comprehensive understanding of contemporary techniques and methodologies essential for advancing knowledge in his field.

Publications

  1. Title: Detection of peak wavelength of multi-FBG using higher-order derivative of wavelets multiresolution analysis and maximum likelihood estimation
    • Authors: Kumar, S.; Sengupta, S.
    • Year: 2023
    • Citations: 1
  2. Title: Machine learning based algorithm for multi-FBG peak detection using generative adversarial network
    • Authors: Kumar, S.; Sengupta, S.
    • Year: 2023
  3. Title: Efficient detection of multiple FBG wavelength peaks using matched filtering technique
    • Authors: Kumar, S.; Sengupta, S.
    • Year: 2022
    • Citations: 7
  4. Title: Adaptive and precise peak detection algorithm for fibre Bragg grating using generative adversarial network
    • Authors: Kumar, S.; Sengupta, S.
    • Year: 2022
    • Citations: 2
  5. Title: FBG Peak Wavelength Detection Using Transfer Learning-Based Machine Learning Method
    • Authors: Kumar, S.; Sengupta, S.
    • Year: 2022
  6. Title: Multi Peak Detection Algorithm of Fiber Bragg Grating using Mexican Hat Wavelets and Hilbert Transform
    • Authors: Kumar, S.; Sengupta, S.
    • Year: 2022

 

Yalan Ye | Artificial Intelligence | Best Researcher Award

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Prof Dr. Yalan Ye | Artificial Intelligence | Best Researcher Award

Professor at University of Electronic Science and Technology of China, China.

Prof. Dr. Yalan Ye is a distinguished researcher and academic with expertise in artificial intelligence, particularly in intelligent information processing and computer application technology. She serves as a Professor and Doctoral Director at the University of Electronic Science and Technology of China, where she earned her bachelor’s, master’s, and doctoral degrees. Prof. Ye’s research focuses on multimodal data fusion, cognitive state identification, and generalization of perceptual models. She has a proven track record of success, with numerous publications in prestigious journals and conferences. Prof. Ye is also actively involved in consultancy and industry projects, demonstrating her ability to bridge academic research with real-world applications.

Professional Profiles:

Education

Prof. Dr. Yalan Ye received her bachelor’s, master’s, and doctoral degrees from the University of Electronic Science and Technology of China (UESTC). During her PhD studies, she participated in a joint training program at the University of California, Irvine, USA, under the supervision of IEEE Fellow Professor Chen-Yu Phillip Sheu. This joint training was funded by the China Scholarship Council.

Professional Experience

Prof. Dr. Yalan Ye is a distinguished professor and doctoral director in the School of Computer Science and Engineering at the University of Electronic Science and Technology of China (UESTC). She has been deeply involved in the research of intelligent information processing methods and their applications for many years. Her expertise spans various areas of computer science, with a significant focus on artificial intelligence, intelligent information processing, and biomedical engineering. Prof. Ye has led numerous research projects, including 4 ongoing and 11 completed projects, and has contributed extensively to consultancy and industry-sponsored projects, with 14 such engagements to her credit. Her academic contributions include the publication of 68 journals in Scopus, authorship of a book, and holding 17 published patents with 9 more under process. Prof. Ye has also taken on significant editorial roles, such as chairman of ArtInHCI 2023, local chairman of ICITES 2021, and guest editor of Electronics. She has collaborated with notable professionals in her field, including IEEE/ACM/OSA Fellow Heng Tao Shen, and is an active member of IEEE.

Research Interest

Prof. Dr. Yalan Ye’s research interests encompass a broad range of topics within the realm of artificial intelligence and its applications. Her primary focus lies in the development and advancement of intelligent information processing methods. Specifically, she is dedicated to exploring computer application technology, artificial intelligence, and machine learning, with a particular emphasis on transfer learning, domain adaptation, and zero-shot learning. Additionally, Prof. Ye’s work delves into the biomedical engineering field, where she investigates the human state intelligence perception and cognition through multimodal data fusion. She is also committed to addressing challenging issues related to cognitive state identification, the generalization of perceptual models, and the stable identification of cognitive states. Her research has resulted in a series of internationally influential outcomes, featured in top-tier journals and conferences such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Multimedia, ACM Multimedia, IJCAI, ICASSP, and EMBC. Prof. Ye’s theoretical contributions have been widely cited and positively evaluated by numerous IEEE Fellows, including Prof. Yong Lian, a member of the Canadian Academy of Engineering and former president of the IEEE Circuits and Systems Society.

Award and Honors

Prof. Dr. Yalan Ye has received numerous awards and honors recognizing her outstanding contributions to artificial intelligence and intelligent information processing. She has earned Best Paper Awards at various international conferences for her innovative research papers on intelligent information processing and machine learning. Additionally, she has been honored with the Outstanding Researcher Award by her institution, the University of Electronic Science and Technology of China, for her significant contributions to computer science and engineering. Prof. Ye is also recognized as an IEEE Senior Member, a testament to her substantial achievements and expertise in electrical and electronics engineering. Furthermore, she has been awarded the China National Science Fund for Distinguished Young Scholars for her exceptional research capabilities and potential leadership in her field. Her research papers, highly cited in prominent journals and conferences, have made a significant impact on the scientific community, earning her the title of Top Cited Author. Prof. Ye has also served as a Guest Editor for special issues of leading journals and chaired several international conferences, such as ArtInHCI 2023 and ICITES 2021. Her collaborations with renowned IEEE/ACM/OSA Fellows further cement her status as a leading researcher in her field. Prof. Dr. Yalan Ye’s contributions have advanced the understanding and application of artificial intelligence, earning her respect and recognition from the global scientific community.

Research Skills

Prof. Dr. Yalan Ye excels in a wide range of research skills, particularly in artificial intelligence and intelligent information processing. Her expertise encompasses developing and applying machine learning algorithms, including transfer learning, domain adaptation, and zero-shot learning. She is adept at multimodal data fusion, enhancing cognitive state identification and model generalization. With a strong background in biomedical engineering, Prof. Ye applies AI to solve complex health problems. Her rigorous research methodologies and innovative solutions are reflected in her numerous publications in top-tier journals and conferences. Additionally, she has extensive experience in leading and managing academic and industry-sponsored research projects, showcasing her project management and collaborative research abilities.

Publications

  1. Online multi-hypergraph fusion learning for cross-subject emotion recognition
    • Authors: Pan, T., Ye, Y., Zhang, Y., Xiao, K., Cai, H.
    • Year: 2024
    • Citations: 0
  2. Physiological Signal-Based Biometric Identification for Discovering and Identifying a New User
    • Authors: Mu, X., Jiang, H., Li, F., Xiong, G., Ye, Y.
    • Year: 2024
    • Citations: 0
  3. Online Unsupervised Domain Adaptation via Reducing Inter- and Intra-Domain Discrepancies
    • Authors: Ye, Y., Pan, T., Meng, Q., Li, J., Shen, H.T.
    • Year: 2024
    • Citations: 1
  4. Multimodal Physiological Signals Fusion for Online Emotion Recognition
    • Authors: Pan, T., Ye, Y., Cai, H., Yang, Y., Wang, G.
    • Year: 2023
    • Citations: 0
  5. Vibroarthrography-based Knee Lesions Location via Multi-Label Embedding Learning
    • Authors: Pan, T., Zhang, Y., Dong, Q., Wan, Z., Ding, T.
    • Year: 2023
    • Citations: 0
  6. Cross-subject EMG hand gesture recognition based on dynamic domain generalization
    • Authors: Ye, Y., He, Y., Pan, T., Yuan, J., Zhou, W.
    • Year: 2023
    • Citations: 0
  7. Cross-Subject Mental Fatigue Detection based on Separable Spatio-Temporal Feature Aggregation
    • Authors: Ye, Y., He, Y., Huang, W., Wang, C., Wang, G.
    • Year: 2023
    • Citations: 1
  8. Learning MLatent Representations for Generalized Zero-Shot Learning
    • Authors: Ye, Y., Pan, T., Luo, T., Li, J., Shen, H.T.
    • Year: 2023
    • Citations: 5
  9. Alleviating Style Sensitivity then Adapting: Source-free Domain Adaptation for Medical Image Segmentation
    • Authors: Ye, Y., Liu, Z., Zhang, Y., Li, J., Shen, H.
    • Year: 2022
    • Citations: 3
  10. ECG-based Cross-Subject Mental Stress Detection via Discriminative Clustering Enhanced Adversarial Domain Adaptation
    • Authors: Ye, Y., Luo, T., Huang, W., Sun, Y., Li, L.
    • Year: 2022
    • Citations: 5