Mohamed Reda Shoeib | Artificial Intelligence | Best Researcher Award Posted on 29/06/202402/07/2024 by sciencefather 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: ORCID Profile 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 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 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 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 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 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 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 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 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 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 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
Morteza Karimian Kelishadrokhi | Artificial Intelligence | Best Researcher Award Posted on 18/06/202419/06/2024 by sciencefatherLeave a Comment on Morteza Karimian Kelishadrokhi | Artificial Intelligence | Best Researcher Award Mr. Morteza Karimian Kelishadrokhi | Artificial Intelligence | Best Researcher Award Deep Learning Researcher at Islamic Azad University (IAU) Najafabad Branch, Iran. Morteza Karimian Kelishadrokhi is a distinguished researcher specializing in artificial intelligence and data science, holding a master’s degree in Artificial Intelligence from Islamic Azad University (IAU), Najafabad Branch, Iran. He excels in deep learning, focusing on EEG signal analysis and time series classification. Morteza is recognized for his development of advanced neural network architectures, including memory-augmented models, to enhance the classification of brain activities. His research extends to signal processing and computer vision, where he explores novel techniques for content-based image retrieval and real-time data analysis. Morteza’s contributions significantly advance AI methodologies, impacting both academic research and practical applications across various industries. His dedication and expertise position him as a key figure in the field, bridging theoretical advancements with tangible solutions in artificial intelligence and data-driven technologies. Professional Profiles: Scopus Profile ORCID Profile Google Scholar Education Morteza Karimian Kelishadrokhi holds a Master of Science in Artificial Intelligence from the Islamic Azad University (IAU), Najafabad Branch, Iran, graduating in 2024. He excelled academically, achieving first place among students of the computer faculty with an outstanding GPA of 19.42 out of 20.00. His dedication and excellence were recognized by the dean, who honored him as the “Top Scientific Student.” Morteza also holds a Bachelor of Science in Computer Science from the same institution, graduating in 2021. During his undergraduate studies, he secured first place in the academic year 2019-2020 and second place in 2018-2019. Additionally, he served as a Teaching Assistant for the “Deep Learning Course” in both the Fall and Winter semesters of 2021 and 2022. Morteza’s educational journey highlights his exceptional academic performance and strong focus on artificial intelligence and deep learning. Professional Experience Morteza Karimian Kelishadrokhi is a Deep Learning Researcher and Senior Data Analyst at the Islamic Azad University (IAU), Najafabad Branch, Iran. He coordinates with industries and participates in workshops, such as the “Application of Deep Learning in the Industry.” Morteza has contributed to various research projects, including the development of a Security Framework for AI-Enhanced Microarchitectural Analysis and a Multi-Class EEG Brain Activity Classification system using the TD-MANN architecture. He has served as a Teaching Assistant for the “Deep Learning Course” in the Fall and Winter semesters of 2021 and 2022, where he designed course materials and assisted students with projects. Morteza’s work also extends to consultancy and industry projects, applying his expertise in AI, machine learning, and data analytics to solve real-world problems. His professional journey is marked by innovative research and practical applications in artificial intelligence. Research Interest Morteza Karimian Kelishadrokhi’s research interests span several cutting-edge domains within artificial intelligence and data science. He is particularly focused on deep learning, exploring its applications in time series classification and EEG signal analysis. His work aims to develop advanced neural network architectures, such as memory-augmented neural networks, to improve the classification of brain activities. Additionally, Morteza is interested in signal processing and computer vision, seeking innovative solutions for content-based image retrieval and real-time data analysis. His research contributes to the advancement of AI methodologies and their practical implementation in various scientific and industrial applications. Award and Honors Morteza Karimian Kelishadrokhi has received multiple accolades for his academic excellence and contributions to the field of artificial intelligence. In 2022, he was honored and awarded by the dean of the Islamic Azad University, Najafabad branch, as the “Top Scientific Student.” He secured the 1st place position in the academic years 2019-2020 and 2018-2019, and 2nd place in the academic year 2018-2019. Additionally, Morteza has served as a teaching assistant for the “Deep Learning Course” during the winter semester of 2022 and the fall semester of 2021. He also played a key role in coordinating with industries and participating as a speaker in the workshop “Application of Deep Learning in the Industry” in 2022. Research Skills Morteza Karimian Kelishadrokhi possesses a diverse skill set in research methodologies and advanced data analysis. His expertise includes developing and implementing deep learning models, particularly for EEG signal classification and time series analysis. He is proficient in designing neural network architectures, including memory-augmented neural networks, and applying machine learning algorithms to solve complex problems. Morteza is skilled in signal processing techniques and has experience with computer vision applications, such as content-based image retrieval. His research capabilities extend to conducting comprehensive literature reviews, data preprocessing, and statistical analysis, ensuring the integrity and reliability of his findings. Additionally, he is adept at using various programming languages and tools essential for AI research, including Python, TensorFlow, and MATLAB.
Yalan Ye | Artificial Intelligence | Best Researcher Award Posted on 12/06/2024 by sciencefatherLeave a Comment on Yalan Ye | Artificial Intelligence | Best Researcher Award 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: Scopus Profile ORCID Profile 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 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 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 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 Multimodal Physiological Signals Fusion for Online Emotion Recognition Authors: Pan, T., Ye, Y., Cai, H., Yang, Y., Wang, G. Year: 2023 Citations: 0 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 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 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 Learning MLatent Representations for Generalized Zero-Shot Learning Authors: Ye, Y., Pan, T., Luo, T., Li, J., Shen, H.T. Year: 2023 Citations: 5 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 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