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

Mohamed Reda Shoeib | Artificial Intelligence | Best Researcher Award

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Dr. Mohamed Reda Shoeib | Artificial Intelligence | Best Researcher Award

Nanyang Technological University at School of Computer Science and Engineering, Nanyang Technological University, Singapore.

Mohamed R. Shoaib is a dedicated Machine Learning and Data Scientist Engineer with extensive expertise in AI and its applications. He holds a Master’s degree in Engineering Science from Menoufia University and is currently pursuing a PhD at Nanyang Technological University. Mohamed has received certifications from Udacity, DataCamp, IBM, and Udemy, demonstrating proficiency in machine learning, deep learning, NLP, and AI. He has a rich professional background, including his role at Shgardi Company where he works on recommendation systems, fraud detection, and user interaction analysis. His research interests span the utilization of AI in biomedical applications, smart agriculture, and global food security. Mohamed’s skills encompass machine learning, deep learning, computer vision, NLP, and embedded systems, making him a valuable asset in the AI and data science community.

Professional Profiles:

Education 🎓

Mohamed R. Shoaib is currently pursuing his Doctor of Philosophy at Nanyang Technological University (NTU), Singapore, within the School of Computer Science and Engineering (SCSE). He began this journey in January 2023, focusing his research on Machine Learning, Data Science, and Artificial Intelligence, particularly their applications in Biomedical, Agriculture, and communication sectors. Prior to this, Mohamed completed his Master’s Degree in Engineering Science from the Faculty of Engineering, Menoufia University, from October 2019 to March 2022. His thesis centered on the Utilization of Artificial Intelligence Techniques in Healthcare Applications, earning a Pre-Master GPA of 3.46/4. Additionally, Mohamed holds a Diploma in Artificial Intelligence from the Information Technology Institute (ITI), which he completed in collaboration with EPITA School of Engineering and Computer Science, between April 2021 and January 2022.

Professional Experience 💼

Mohamed R. Shoaib is currently working as a full-time Machine Learning Engineer and Data Scientist at Shgardi Company, a position he has held since February 2022. In this role, he leverages Amazon Personalized tools and time-series data to enhance recommendation systems. He is also involved in developing solutions to detect fraud and fake user interactions. Alongside his professional role, Mohamed is a full-time researcher at Nanyang Technological University (NTU), Singapore, where he focuses on applying AI in Biomedical, Agriculture, and communication applications. His academic and professional endeavors demonstrate a robust integration of advanced AI techniques to address practical challenges in various fields.

Research Interest 🔍

Mohamed R. Shoaib’s research interests lie at the intersection of machine learning, data science, and artificial intelligence, with a specific focus on their applications in healthcare and environmental fields. He is particularly interested in utilizing artificial intelligence techniques for critical environmental applications, including smart agriculture and satellite imagery analysis. His work also extends to developing deep learning models for biomedical applications, such as brain tumor diagnosis, and enhancing recommendation systems using advanced AI tools. Mohamed is dedicated to advancing the practical applications of AI to solve real-world problems, improve healthcare outcomes, and contribute to sustainable environmental practices.

Awards and Honors 🏆

Mohamed R. Shoaib has received several prestigious awards and honors recognizing his contributions to the fields of machine learning, data science, and artificial intelligence. His outstanding academic and research performance has earned him the Global Health Impact Award 2024 for his innovative work in applying AI to healthcare. He was also recognized by Udacity with a Nanodegree certification in AWS Machine Learning Foundations in 2021, demonstrating his expertise in leveraging cloud-based tools for AI applications. Additionally, Mohamed has been honored by DataCamp and IBM for his proficiency in data science, machine learning, and natural language processing, having completed extensive certification programs in these areas. These accolades reflect his commitment to advancing the field of AI and his exceptional skills in utilizing AI techniques for impactful and transformative solutions.

Research Skills 🧠

Mohamed R. Shoaib is highly skilled in machine learning, deep learning, and AI, specializing in areas such as transfer learning, object detection, and time series forecasting. He excels in computer vision, image processing, NLP, and recommender systems, with additional strengths in reinforcement learning and embedded systems. Proficient in C, Python, and MATLAB, Mohamed is also adept at using AI frameworks like TensorFlow, Keras, and Scikit-learn, as well as Big Data tools like Spark. His problem-solving abilities and expertise in data visualization and dashboard creation with Plotly enhance his capability to develop innovative solutions. Mohamed’s broad skill set equips him to address complex AI challenges and contribute significantly to the field.

Publications

  1. Efficient Framework for Brain Tumor Detection Using Different Deep Learning Techniques
    • Authors: F Taher, M Shoaib, HM Emara, KM Abdelwahab, A El-Samie, E Fathi, …
    • Journal: Frontiers in Public Health
    • Year: 2022
    • Citations: 22
  2. Deep convolutional neural networks for COVID‐19 automatic diagnosis
    • Authors: Emara HM, Shoaib MR, Elwekeil M, El‐Shafai W, Taha TE, …
    • Journal: Microscopy Research and Technique
    • Year: 2021
    • Citations: 22
  3. Hybrid classification structures for automatic COVID-19 detection
    • Authors: MR Shoaib, HM Emara, M Elwekeil, W El-Shafai, TE Taha, AS El-Fishawy, …
    • Journal: Journal of Ambient Intelligence and Humanized Computing
    • Year: 2022
    • Citations: 16
  4. Deepfakes, misinformation, and disinformation in the era of frontier AI, generative AI, and large AI models
    • Authors: MR Shoaib, Z Wang, MT Ahvanooey, J Zhao
    • Conference: 2023 International Conference on Computer and Applications (ICCA)
    • Year: 2023
    • Citations: 13
  5. Simultaneous super-resolution and classification of lung disease scans
    • Authors: HM Emara, MR Shoaib, W El-Shafai, M Elwekeil, EED Hemdan, …
    • Journal: Diagnostics
    • Year: 2023
    • Citations: 13
  6. Efficient deep learning models for brain tumor detection with segmentation and data augmentation techniques
    • Authors: MR Shoaib, MR Elshamy, TE Taha, AS El‐Fishawy, FE Abd El‐Samie
    • Journal: Concurrency and Computation: Practice and Experience
    • Year: 2022
    • Citations: 13
  7. Efficient brain tumor detection based on deep learning models
    • Authors: MR Shoaib, MR Elshamy, TE Taha, AS El-Fishawy, FE Abd El-Samie
    • Journal: Journal of Physics: Conference Series
    • Year: 2021
    • Citations: 13
  8. Automatic modulation classification with 2D transforms and convolutional neural network
    • Authors: HS Ghanem, MR Shoaib, S El‐Gazar, H Emara, W El‐Shafai, …
    • Journal: Transactions on Emerging Telecommunications Technologies
    • Year: 2022
    • Citations: 9
  9. Automated diagnosis of EEG abnormalities with different classification techniques
    • Authors: E Abdellatef, HM Emara, MR Shoaib, FE Ibrahim, M Elwekeil, W El-Shafai, …
    • Journal: Medical & Biological Engineering & Computing
    • Year: 2023
    • Citations: 4
  10. A survey on the applications of frontier AI, foundation models, and large language models to intelligent transportation systems
    • Authors: MR Shoaib, HM Emara, J Zhao
    • Conference: 2023 International Conference on Computer and Applications (ICCA)
    • Year: 2023
    • Citations: 4

 

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