Dr. Cong Guo | Computer Science | Best Researcher Award Published on 30/09/202430/09/2024 by Shen Awards Dr. Cong Guo | Computer Science | Best Researcher Award Nurse Practitioner at UNC Blue Ridge, United States. Cong Guo, who earned his master’s degree in 2024 from the School of Computer and Information Engineering at Henan University, is currently pursuing a PhD in Computer Science and Technology at Zhejiang Normal University. His research specializes in machine learning and pattern recognition, fields that are increasingly relevant in today’s data-driven landscape. Guo has made significant contributions to the field, as evidenced by his publications, including a novel feature selection framework for incomplete data and a method for iterative missing value imputation based on feature importance. These works demonstrate his innovative approach to addressing common challenges in data science. While his academic background and publication record are impressive, expanding his publication scope and enhancing networking opportunities could further elevate his research impact. With his solid foundation and commitment to advancing knowledge in machine learning, Cong Guo is a promising candidate for recognition as a leading researcher. Profile: ORCID Profile Scopus Profile Education Cong Guo received his master’s degree in 2024 from the School of Computer and Information Engineering at Henan University, where he laid a strong foundation in computer science principles and research methodologies. His academic journey has been characterized by a focus on machine learning and pattern recognition, reflecting his passion for harnessing data to solve complex problems. Currently, Cong is pursuing his Ph.D. at the School of Computer Science and Technology at Zhejiang Normal University, further enhancing his expertise in these cutting-edge fields. His educational experiences have equipped him with essential skills in data analysis, algorithm development, and statistical modeling, which are critical for his research. Throughout his studies, Cong has demonstrated a commitment to academic excellence and innovation, making significant strides in understanding and improving feature selection and data imputation techniques. His educational background positions him as a promising researcher in the rapidly evolving landscape of computer science. Professional Experiences Cong Guo has demonstrated significant commitment to his academic and professional development in the field of computer science. He obtained his master’s degree from the School of Computer and Information Engineering at Henan University in 2024, where he developed a solid foundation in computer science principles and applications. Currently, he is pursuing his PhD at the School of Computer Science and Technology at Zhejiang Normal University, focusing on machine learning and pattern recognition. During his studies, Guo has engaged in research projects that involve innovative approaches to data analysis, particularly in handling incomplete datasets and missing value imputation. His publications in reputable journals reflect his dedication to advancing knowledge in his field. Additionally, his collaborative work with fellow researchers highlights his ability to contribute effectively to team-oriented projects, enhancing his experience and understanding of complex computational problems. This combination of academic rigor and research experience positions Guo as a promising researcher in computer science. Research Interests Cong Guo’s research interests lie primarily in the fields of machine learning and pattern recognition, where he aims to develop innovative algorithms and frameworks to address real-world challenges in data analysis. His work focuses on enhancing feature selection and imputation techniques, particularly in the context of incomplete datasets, which are common in many applications. By investigating novel approaches to handle missing data, Cong seeks to improve the accuracy and efficiency of machine learning models. Additionally, he is interested in exploring the broader implications of machine learning across various domains, such as healthcare, finance, and environmental science. Cong’s passion for advancing knowledge in these areas drives his commitment to research that not only contributes to theoretical advancements but also has practical applications that can benefit society. Through his ongoing doctoral studies and collaborative projects, he aims to further explore the intersections of machine learning and real-world problem-solving. Research Skills Cong Guo possesses a robust set of research skills that enhance his capabilities in machine learning and pattern recognition. His proficiency in feature selection and data imputation techniques demonstrates a strong analytical mindset, enabling him to address complex challenges in handling incomplete datasets effectively. Guo is adept at employing various machine learning algorithms and tools, which allows him to develop innovative frameworks that optimize data analysis processes. His experience in collaborative research, evidenced by his co-authored publications, showcases his ability to work effectively in teams, share ideas, and contribute to collective goals. Additionally, Guo’s familiarity with statistical methods and computational techniques underpins his research, ensuring that his findings are both rigorous and applicable. His commitment to continuous learning and adaptation to emerging trends in technology further solidifies his expertise, making him a valuable asset in advancing the field of computer science and information engineering. Award and Recognition Cong Guo has distinguished himself in the field of machine learning and pattern recognition, earning recognition for his innovative research contributions. He completed his master’s degree in 2024 at the School of Computer and Information Engineering, Henan University, where he developed a strong foundation in computational methodologies. Currently pursuing his PhD at Zhejiang Normal University, Cong has co-authored impactful publications, including “A novel feature selection framework for incomplete data” and “Iterative missing value imputation based on feature importance,” which have been well-received in reputable journals. His research not only addresses critical challenges in data science but also demonstrates his potential to influence future advancements in the field. Cong’s commitment to academic excellence and his collaborative spirit have garnered him respect among peers and mentors alike, positioning him as a promising candidate for the Best Researcher Award. His ongoing efforts are indicative of a bright future in research and innovation. Conclusion Cong Guo exhibits a promising trajectory in research, with a strong academic foundation and relevant publications in machine learning and pattern recognition. His commitment to advancing the field is evident in his current work. By broadening his publication efforts and enhancing his professional network, he can significantly improve his contributions to research. Given his strengths and potential for growth, Cong Guo is a suitable candidate for the Best Researcher Award. Publication Top Notes A novel feature selection framework for incomplete data Authors: Cong Guo Year: 2024 Journal: Chemometrics and Intelligent Laboratory Systems DOI: 10.1016/j.chemolab.2024.105193 Iterative missing value imputation based on feature importance Authors: Cong Guo Year: 2024 Journal: Knowledge and Information Systems DOI: 10.1007/s10115-024-02159-7 KNCFS: Feature selection for high-dimensional datasets based on improved random multi-subspace learning Authors: Cong Guo Year: 2024 Journal: PLOS ONE DOI: 10.1371/journal.pone.0296108
Cong Guo’s research interests lie primarily in the fields of machine learning and pattern recognition, where he aims to develop innovative algorithms and frameworks to address real-world challenges in data analysis. His work focuses on enhancing feature selection and imputation techniques, particularly in the context of incomplete datasets, which are common in many applications. By investigating novel approaches to handle missing data, Cong seeks to improve the accuracy and efficiency of machine learning models. Additionally, he is interested in exploring the broader implications of machine learning across various domains, such as healthcare, finance, and environmental science. Cong’s passion for advancing knowledge in these areas drives his commitment to research that not only contributes to theoretical advancements but also has practical applications that can benefit society. Through his ongoing doctoral studies and collaborative projects, he aims to further explore the intersections of machine learning and real-world problem-solving.
Cong Guo’s research interests lie primarily in the fields of machine learning and pattern recognition, where he aims to develop innovative algorithms and frameworks to address real-world challenges in data analysis. His work focuses on enhancing feature selection and imputation techniques, particularly in the context of incomplete datasets, which are common in many applications. By investigating novel approaches to handle missing data, Cong seeks to improve the accuracy and efficiency of machine learning models. Additionally, he is interested in exploring the broader implications of machine learning across various domains, such as healthcare, finance, and environmental science. Cong’s passion for advancing knowledge in these areas drives his commitment to research that not only contributes to theoretical advancements but also has practical applications that can benefit society. Through his ongoing doctoral studies and collaborative projects, he aims to further explore the intersections of machine learning and real-world problem-solving.
Cong Guo’s research interests lie primarily in the fields of machine learning and pattern recognition, where he aims to develop innovative algorithms and frameworks to address real-world challenges in data analysis. His work focuses on enhancing feature selection and imputation techniques, particularly in the context of incomplete datasets, which are common in many applications. By investigating novel approaches to handle missing data, Cong seeks to improve the accuracy and efficiency of machine learning models. Additionally, he is interested in exploring the broader implications of machine learning across various domains, such as healthcare, finance, and environmental science. Cong’s passion for advancing knowledge in these areas drives his commitment to research that not only contributes to theoretical advancements but also has practical applications that can benefit society. Through his ongoing doctoral studies and collaborative projects, he aims to further explore the intersections of machine learning and real-world problem-solving.
Cong Guo possesses a robust set of research skills that enhance his capabilities in machine learning and pattern recognition. His proficiency in feature selection and data imputation techniques demonstrates a strong analytical mindset, enabling him to address complex challenges in handling incomplete datasets effectively. Guo is adept at employing various machine learning algorithms and tools, which allows him to develop innovative frameworks that optimize data analysis processes. His experience in collaborative research, evidenced by his co-authored publications, showcases his ability to work effectively in teams, share ideas, and contribute to collective goals. Additionally, Guo’s familiarity with statistical methods and computational techniques underpins his research, ensuring that his findings are both rigorous and applicable. His commitment to continuous learning and adaptation to emerging trends in technology further solidifies his expertise, making him a valuable asset in advancing the field of computer science and information engineering.
Cong Guo exhibits a promising trajectory in research, with a strong academic foundation and relevant publications in machine learning and pattern recognition. His commitment to advancing the field is evident in his current work. By broadening his publication efforts and enhancing his professional network, he can significantly improve his contributions to research. Given his strengths and potential for growth, Cong Guo is a suitable candidate for the Best Researcher Award.
Cong Guo exhibits a promising trajectory in research, with a strong academic foundation and relevant publications in machine learning and pattern recognition. His commitment to advancing the field is evident in his current work. By broadening his publication efforts and enhancing his professional network, he can significantly improve his contributions to research. Given his strengths and potential for growth, Cong Guo is a suitable candidate for the Best Researcher Award.
Karimeh Ata | Artificial Intelligence | Best Researcher Award Published on 12/09/202412/09/2024 by Shen Awards Dr. Karimeh Ata | Artificial Intelligence | Best Researcher Award Researcher at UPM, Jordan Dr. Karimeh Ata is a Computer and Artificial Intelligence Engineering Ph.D. candidate at Universiti Putra Malaysia (UPM), specializing in deep learning and big data analytics for urban mobility and vehicle flow optimization. With a strong academic foundation, she holds a Master’s degree in Computer Engineering and Embedded Systems from UPM and a Bachelor’s degree in Computer Engineering from Fahad Bin Sultan University, Saudi Arabia, where she graduated with first-class honors. Dr. Ata’s research focuses on solving complex problems using advanced algorithms like Dijkstra’s and Ant Colony Optimization, contributing to various high-impact projects. In addition to her academic achievements, she has experience as an AI trainer and lecturer, and her work is highlighted by numerous publications in top-tier journals and conferences. Proficient in technologies like Microsoft Azure, GIS, Python, and Raspberry Pi, Dr. Ata is committed to driving innovation in the fields of artificial intelligence and computer engineering. Profile Google Scholar Profile Education Dr. Karimeh Ata is currently pursuing her Ph.D. in Computer Engineering and Artificial Intelligence at Universiti Putra Malaysia (UPM), with an expected completion in June 2024. Her doctoral research focuses on traffic flow prediction using deep learning and big data analysis, and she has maintained an outstanding GPA of 4.00 throughout her studies. Prior to this, she earned a Master of Computer Engineering and Embedded Systems from UPM in 2019, where she addressed challenges in vehicle navigation and parking optimization using algorithms like Dijkstra’s and Ant Colony Optimization, achieving a GPA of 3.57. Dr. Ata holds a Bachelor of Computer Engineering from Fahad Bin Sultan University (FBSU) in Saudi Arabia, where she graduated with first-class honors and a GPA of 4.91, also receiving the Prince Fahad Bin Sultan Scholarship for academic excellence. Professional Experience Dr. Karimeh Ata has a diverse range of professional experience in the fields of artificial intelligence and computer engineering. From December 2018 to January 2020, she served as an Artificial Intelligence Trainer at Hass Resources Corporation in Malaysia, where she supervised and trained teams on AI applications in education. In early 2019, she was a member of the Technical Committee for the Symposium on Control Systems and Signal Processing in Malaysia, bringing together experts to discuss advancements in AI, signal processing, and control systems. Dr. Ata has also contributed to academia as a Computer Engineering Lecturer at Universiti Putra Malaysia (UPM) from November 2022 to September 2023, where she designed and delivered courses on subjects such as Programming Fundamentals, Digital Logic Design, and Machine Learning, while also supervising laboratory sessions. Additionally, she worked as a Research Assistant at UPM from July 2021 to October 2022, where she ensured the quality, integrity, and security of research data and guided teams in preparing findings for top-tier journals and conferences. Dr. Ata’s professional experience highlights her leadership in project management, research ethics, and AI integration. Research Interest Dr. Karimeh Ata’s research interests focus on leveraging advanced technologies to address complex challenges in urban mobility, traffic flow optimization, and artificial intelligence. Her work primarily centers around deep learning and big data analytics, with a particular emphasis on traffic flow prediction and vehicle optimization. She has explored algorithms such as Dijkstra’s and Ant Colony Optimization to calculate the shortest paths and improve transportation efficiency in urban environments. Additionally, Dr. Ata is interested in applying AI-driven solutions to enhance brain stroke detection, lithium iron phosphate battery electrode performance, and spatial-temporal traffic flow prediction through multi-layer models. Her research aims to innovate in fields like smart transportation systems, deep learning, and AI for real-world problem-solving. Research Skills Dr. Karimeh Ata possesses extensive research skills in deep learning, big data analytics, and artificial intelligence, with a focus on solving complex problems in urban mobility and traffic flow optimization. She is proficient in designing and implementing deep learning models for traffic prediction and vehicle flow using large datasets to ensure accuracy. Dr. Ata has expertise in optimizing algorithms such as Dijkstra’s and Ant Colony Optimization to calculate efficient paths in transportation networks. Her research capabilities extend to developing innovative AI models for brain stroke detection and lithium battery performance evaluation, along with spatial-temporal data analysis using advanced machine learning techniques like CNN-GRU and dynamic KNN-Bi-LSTM. Dr. Ata’s skills reflect a deep understanding of integrating AI into real-world applications. Award and Recognition Dr. Karimeh Ata has been recognized for her academic excellence and contributions to research in the fields of computer engineering and artificial intelligence. She was awarded the prestigious Prince Fahad Bin Sultan Scholarship during her undergraduate studies for her outstanding academic performance, graduating with a first honor distinction. Additionally, her research work has been acknowledged through notable publications in top-tier journals, reflecting her deep expertise in areas such as traffic flow prediction and smart indoor parking systems. Dr. Ata’s achievements underscore her commitment to advancing the field of AI and computer engineering through innovative research and impactful projects. Conclusion Given Dr. Karimeh Ata’s strong academic background, innovative research contributions, and extensive skills in AI and big data, she is a suitable candidate for the Best Researcher Award. Her work not only demonstrates technical proficiency but also showcases her ability to solve complex, real-world problems, making a significant impact in the field of AI and computer engineering. Publications Top Notes Title: Smart Indoor Parking System Based on Dijkstra’s Algorithm Authors: K.M. Ata, A.C. Soh, A. Ishak, H. Jaafar, N. Khairuddin Cited By: 19 Year: 2019 Title: Performance Evaluation of Two Mobile Ad-hoc Network Routing Protocols: Ad-hoc On-Demand Distance Vector Dynamic Source Routing Authors: J. Alamri, A.S. Al-Johani, K.I. Ata Cited By: 13 Year: 2020 Title: Radio Frequency Identification (RFID) Indoor Parking Control System Authors: H.M.M. El-Hageen, K. Ibrahim, M. Ata, A. Chesoh, H. Jaafar Cited By: 3 Year: 2017 Title: A Smart Guidance Indoor Parking System Based on Dijkstra’s Algorithm and Ant Colony Algorithm Authors: K.I. Ata, A.C. Soh, A.J. Ishak, H. Jaafar Cited By: 1 Year: 2020 Title: Investigation of Loading Variation Effect on Lithium Iron Phosphate Battery Electrodes Using Long Short Term Memory Authors: K.A.A. Md Azizul Hoque, Mohd Khair Hassan, Muhesh Dhaarwind, Abdulrahman Hajjo Year: 2024 Title: Enhancing Brain Stroke Detection: A Novel Deep Neural Network with Weighted Binary Cross Entropy Training Authors: A.N. Qasim, S. Alani, S.N. Mahmood, S.S. Mohammed, D.A. Aziz, K.I.M. Ata Year: 2024 Title: Guidance System Based on Dijkstra-Ant Colony Algorithm with Binary Search Tree for Indoor Parking System Authors: H.J. K. Ibrahim Ata, A. Che Soh, A.J. Ishak Year: 2021
Dr. Karimeh Ata is a Computer and Artificial Intelligence Engineering Ph.D. candidate at Universiti Putra Malaysia (UPM), specializing in deep learning and big data analytics for urban mobility and vehicle flow optimization. With a strong academic foundation, she holds a Master’s degree in Computer Engineering and Embedded Systems from UPM and a Bachelor’s degree in Computer Engineering from Fahad Bin Sultan University, Saudi Arabia, where she graduated with first-class honors. Dr. Ata’s research focuses on solving complex problems using advanced algorithms like Dijkstra’s and Ant Colony Optimization, contributing to various high-impact projects. In addition to her academic achievements, she has experience as an AI trainer and lecturer, and her work is highlighted by numerous publications in top-tier journals and conferences. Proficient in technologies like Microsoft Azure, GIS, Python, and Raspberry Pi, Dr. Ata is committed to driving innovation in the fields of artificial intelligence and computer engineering. Profile Google Scholar Profile Education Dr. Karimeh Ata is currently pursuing her Ph.D. in Computer Engineering and Artificial Intelligence at Universiti Putra Malaysia (UPM), with an expected completion in June 2024. Her doctoral research focuses on traffic flow prediction using deep learning and big data analysis, and she has maintained an outstanding GPA of 4.00 throughout her studies. Prior to this, she earned a Master of Computer Engineering and Embedded Systems from UPM in 2019, where she addressed challenges in vehicle navigation and parking optimization using algorithms like Dijkstra’s and Ant Colony Optimization, achieving a GPA of 3.57. Dr. Ata holds a Bachelor of Computer Engineering from Fahad Bin Sultan University (FBSU) in Saudi Arabia, where she graduated with first-class honors and a GPA of 4.91, also receiving the Prince Fahad Bin Sultan Scholarship for academic excellence. Professional Experience Dr. Karimeh Ata has a diverse range of professional experience in the fields of artificial intelligence and computer engineering. From December 2018 to January 2020, she served as an Artificial Intelligence Trainer at Hass Resources Corporation in Malaysia, where she supervised and trained teams on AI applications in education. In early 2019, she was a member of the Technical Committee for the Symposium on Control Systems and Signal Processing in Malaysia, bringing together experts to discuss advancements in AI, signal processing, and control systems. Dr. Ata has also contributed to academia as a Computer Engineering Lecturer at Universiti Putra Malaysia (UPM) from November 2022 to September 2023, where she designed and delivered courses on subjects such as Programming Fundamentals, Digital Logic Design, and Machine Learning, while also supervising laboratory sessions. Additionally, she worked as a Research Assistant at UPM from July 2021 to October 2022, where she ensured the quality, integrity, and security of research data and guided teams in preparing findings for top-tier journals and conferences. Dr. Ata’s professional experience highlights her leadership in project management, research ethics, and AI integration. Research Interest Dr. Karimeh Ata’s research interests focus on leveraging advanced technologies to address complex challenges in urban mobility, traffic flow optimization, and artificial intelligence. Her work primarily centers around deep learning and big data analytics, with a particular emphasis on traffic flow prediction and vehicle optimization. She has explored algorithms such as Dijkstra’s and Ant Colony Optimization to calculate the shortest paths and improve transportation efficiency in urban environments. Additionally, Dr. Ata is interested in applying AI-driven solutions to enhance brain stroke detection, lithium iron phosphate battery electrode performance, and spatial-temporal traffic flow prediction through multi-layer models. Her research aims to innovate in fields like smart transportation systems, deep learning, and AI for real-world problem-solving. Research Skills Dr. Karimeh Ata possesses extensive research skills in deep learning, big data analytics, and artificial intelligence, with a focus on solving complex problems in urban mobility and traffic flow optimization. She is proficient in designing and implementing deep learning models for traffic prediction and vehicle flow using large datasets to ensure accuracy. Dr. Ata has expertise in optimizing algorithms such as Dijkstra’s and Ant Colony Optimization to calculate efficient paths in transportation networks. Her research capabilities extend to developing innovative AI models for brain stroke detection and lithium battery performance evaluation, along with spatial-temporal data analysis using advanced machine learning techniques like CNN-GRU and dynamic KNN-Bi-LSTM. Dr. Ata’s skills reflect a deep understanding of integrating AI into real-world applications. Award and Recognition Dr. Karimeh Ata has been recognized for her academic excellence and contributions to research in the fields of computer engineering and artificial intelligence. She was awarded the prestigious Prince Fahad Bin Sultan Scholarship during her undergraduate studies for her outstanding academic performance, graduating with a first honor distinction. Additionally, her research work has been acknowledged through notable publications in top-tier journals, reflecting her deep expertise in areas such as traffic flow prediction and smart indoor parking systems. Dr. Ata’s achievements underscore her commitment to advancing the field of AI and computer engineering through innovative research and impactful projects. Conclusion Given Dr. Karimeh Ata’s strong academic background, innovative research contributions, and extensive skills in AI and big data, she is a suitable candidate for the Best Researcher Award. Her work not only demonstrates technical proficiency but also showcases her ability to solve complex, real-world problems, making a significant impact in the field of AI and computer engineering. Publications Top Notes Title: Smart Indoor Parking System Based on Dijkstra’s Algorithm Authors: K.M. Ata, A.C. Soh, A. Ishak, H. Jaafar, N. Khairuddin Cited By: 19 Year: 2019 Title: Performance Evaluation of Two Mobile Ad-hoc Network Routing Protocols: Ad-hoc On-Demand Distance Vector Dynamic Source Routing Authors: J. Alamri, A.S. Al-Johani, K.I. Ata Cited By: 13 Year: 2020 Title: Radio Frequency Identification (RFID) Indoor Parking Control System Authors: H.M.M. El-Hageen, K. Ibrahim, M. Ata, A. Chesoh, H. Jaafar Cited By: 3 Year: 2017 Title: A Smart Guidance Indoor Parking System Based on Dijkstra’s Algorithm and Ant Colony Algorithm Authors: K.I. Ata, A.C. Soh, A.J. Ishak, H. Jaafar Cited By: 1 Year: 2020 Title: Investigation of Loading Variation Effect on Lithium Iron Phosphate Battery Electrodes Using Long Short Term Memory Authors: K.A.A. Md Azizul Hoque, Mohd Khair Hassan, Muhesh Dhaarwind, Abdulrahman Hajjo Year: 2024 Title: Enhancing Brain Stroke Detection: A Novel Deep Neural Network with Weighted Binary Cross Entropy Training Authors: A.N. Qasim, S. Alani, S.N. Mahmood, S.S. Mohammed, D.A. Aziz, K.I.M. Ata Year: 2024 Title: Guidance System Based on Dijkstra-Ant Colony Algorithm with Binary Search Tree for Indoor Parking System Authors: H.J. K. Ibrahim Ata, A. Che Soh, A.J. Ishak Year: 2021
Saeed Mohsen Abosreea | Artificial Intelligence Engineering | Best Researcher Award Published on 06/09/202406/09/2024 by Shen Awards Dr. Saeed Mohsen Abosreea | Artificial Intelligence Engineering | Best Researcher Award Clinical Associate Professor at Department of Otorhinolaryngology-Head and Neck Surgery Yongin Severance Hospital, Yonsei University College of Medicine, South Korea Dr. Saeed Mohsen Abosreea Hassan is an Assistant Professor of Electronics and Communications Engineering with a Ph.D. from Ain Shams University, Cairo. He has extensive academic experience, currently serving at King Salman International University. His research focuses on cutting-edge areas like deep learning, IoT, and wearable devices, with applications in healthcare and smart systems. Dr. Saeed has published 24 papers in reputable journals such as IEEE Access and Multimedia Tools and Applications, achieving an h-index of 10. His work spans various interdisciplinary fields, including Industry 4.0, human activity recognition, and energy harvesting systems. In addition to his research, he has supervised numerous student projects and contributed significantly to teaching advanced courses in electronics and AI. His contributions to both academia and industry make him a versatile researcher poised for continued impact in technological innovation and healthcare systems. Profile ORCID Profile Education Saeed Mohsen Abosreea Hassan holds a Ph.D. in Electronics and Communications Engineering from Ain Shams University, Cairo, Egypt, completed between 2017 and 2020. His doctoral research focused on the design and implementation of hybrid energy harvesting systems for medical wearable sensor nodes, demonstrating his expertise in cutting-edge healthcare technology. Prior to this, Saeed earned his Master’s degree in Electronics and Communications Engineering from the same institution, where he worked on the development of an electroencephalogram (EEG) system, further advancing his specialization in medical applications of electronics. He completed his undergraduate studies at Thebes Higher Institute of Engineering, Cairo, from 2008 to 2013, graduating with honors, earning an overall grade of “Excellent” and a GPA of 3.6/4.0. His strong educational background has provided him with a solid foundation in both theoretical and practical aspects of electronics, communications, and their applications in healthcare and industry. Professional Experience Saeed Mohsen Abosreea Hassan is an accomplished Assistant Professor in Electronics and Communications Engineering, currently serving at King Salman International University since September 2022. Prior to this role, he held a full-time Assistant Professor position at Al-Madinah Higher Institute for Engineering and Technology from April 2021 to August 2022. He also served as a part-time Assistant Professor at Ain Shams University from July 2021 to September 2021, where he contributed to cutting-edge research and advanced teaching methodologies. Before transitioning to academia, Saeed gained extensive experience as a Teaching Assistant at Thebes Academy from September 2013 to March 2021. Throughout his career, he has demonstrated expertise in various fields such as deep learning, IoT systems, and medical wearable sensor technologies. His diverse academic roles, combined with his active involvement in research, student supervision, and curriculum development, highlight his commitment to advancing education and innovation in engineering. Research Interest Saeed Mohsen Abosreea Hassan’s research interests focus on cutting-edge technologies in electronics, communications, and artificial intelligence. His work spans deep learning models, machine learning algorithms, and their applications in human activity recognition, smart healthcare systems, and Internet of Things (IoT) technologies. A significant portion of his research is dedicated to the development of energy harvesting systems for wearable medical sensor nodes, which has the potential to revolutionize real-time healthcare monitoring. He is also passionate about the use of neural networks and convolutional neural networks (CNNs) for the detection of brain tumors, Alzheimer’s disease, and other medical conditions through medical imaging techniques. His focus on Industry 4.0 and smart city networks highlights his commitment to advancing technologies that enhance both industrial automation and urban living. Saeed’s research integrates theoretical advancements with practical applications, particularly in healthcare and smart environments. Research Skills Saeed Mohsen Abosreea Hassan possesses a diverse and advanced set of research skills that span multiple fields of electronics, communications, and deep learning. He is proficient in AI tools such as TensorFlow, PyTorch, Keras, and Scikit-Learn, which he uses for developing machine learning and deep learning models. His expertise in embedded systems, IoT, and smart healthcare technologies is reflected in his research on wearable sensor nodes and energy harvesting systems. He is skilled in programming languages like Python, MATLAB, and Embedded C, essential for his work in developing algorithms and systems for medical and industrial applications. Additionally, Saeed is experienced in electronic circuit and layout design using tools like Proteus, LT-spice, and NI Multisim. His research extends into data acquisition systems, neural networks, and signal processing, particularly in healthcare applications such as brain tumor detection and human activity recognition, showcasing his multidisciplinary research proficiency. Award and Recognition Dr. Saeed Mohsen Abosreea Hassan, an accomplished Assistant Professor in Electronics and Communications Engineering, has made significant strides in the fields of deep learning, IoT, and wearable healthcare technologies. He holds a Ph.D. from Ain Shams University and has published 24 research papers, with an impressive h-index of 10 on Google Scholar. His work has been featured in prestigious journals, including IEEE Access, highlighting his contributions to Industry 4.0, smart healthcare systems, and energy harvesting technologies. Dr. Saeed’s research has been recognized for its practical applications in healthcare, with innovations like self-powered medical wearable sensors. His expertise has also earned him opportunities to present at international conferences and collaborate with top-tier researchers globally. As an emerging leader in his field, Dr. Saeed’s work continues to push the boundaries of technology and healthcare, positioning him as a distinguished researcher dedicated to advancing science and improving lives. Conclusion Saeed Mohsen Abosreea Hassan is a well-qualified candidate for the Best Researcher Award. His strong academic foundation, multidisciplinary research, and publication record make him a strong contender. By expanding his international collaborations, focusing on high-impact research, and pursuing more patents or grants, he could significantly increase his research impact and standing in the academic community. His work in healthcare and energy harvesting aligns with global trends, making his contributions both timely and impactful. Publication Top Notes Title: Human Activity Recognition Using K-Nearest Neighbor Machine Learning Algorithm Authors: S Mohsen, A Elkaseer, SG Scholz Year: 2021 Citations: 63 Title: Industry 4.0-Oriented Deep Learning Models for Human Activity Recognition Authors: Saeed Mohsen, Ahmed Elkaseer, Steffen G. Scholz Year: 2021 Citations: 46 Title: A Self-Powered Wearable Wireless Sensor System Powered by a Hybrid Energy Harvester for Healthcare Applications Authors: S Mohsen, A Zekry, K Youssef, M Abouelatta Year: 2021 Citations: 41 Title: Machine Learning and Deep Learning Techniques for Driver Fatigue and Drowsiness Detection: A Review Authors: S Abd El-Nabi, W El-Shafai, ES M. El-Rabaie, K F. Ramadan, S Mohsen Year: 2023 Citations: 25 Title: Brain Tumor Classification Using Hybrid Single Image Super-Resolution Technique with ResNext101_32x8d and VGG19 Pre-Trained Models Authors: S Mohsen, AM Ali, ESM El-Rabaie, A Elkaseer, SG Scholz, AMA Hassan Year: 2023 Citations: 22 Title: Recognition of Human Activity Using GRU Deep Learning Algorithm Authors: S Mohsen Year: 2023 Citations: 18 Title: An Autonomous Wearable Sensor Node for Long-Term Healthcare Monitoring Powered by a Photovoltaic Energy Harvesting System Authors: S Mohsen, A Zekry, K Youssef, M Abouelatta Year: 2020 Citations: 15 Title: On Architecture of Self-Sustainable Wearable Sensor Node for IoT Healthcare Applications Authors: S Mohsen, A Zekry, K Youssef, M Abouelatta Year: 2021 Citations: 13 Title: EEG-Based Human Emotion Prediction Using an LSTM Model Authors: S Mohsen, AG Alharbi Year: 2021 Citations: 12 Title: A Self-Powered Wearable Sensor Node for IoT Healthcare Applications Authors: S Mohsen, A Zekry, M Abouelatta, K Youssef Year: 2020 Citations: 12
Dr. Saeed Mohsen Abosreea Hassan is an Assistant Professor of Electronics and Communications Engineering with a Ph.D. from Ain Shams University, Cairo. He has extensive academic experience, currently serving at King Salman International University. His research focuses on cutting-edge areas like deep learning, IoT, and wearable devices, with applications in healthcare and smart systems. Dr. Saeed has published 24 papers in reputable journals such as IEEE Access and Multimedia Tools and Applications, achieving an h-index of 10. His work spans various interdisciplinary fields, including Industry 4.0, human activity recognition, and energy harvesting systems. In addition to his research, he has supervised numerous student projects and contributed significantly to teaching advanced courses in electronics and AI. His contributions to both academia and industry make him a versatile researcher poised for continued impact in technological innovation and healthcare systems. Profile ORCID Profile Education Saeed Mohsen Abosreea Hassan holds a Ph.D. in Electronics and Communications Engineering from Ain Shams University, Cairo, Egypt, completed between 2017 and 2020. His doctoral research focused on the design and implementation of hybrid energy harvesting systems for medical wearable sensor nodes, demonstrating his expertise in cutting-edge healthcare technology. Prior to this, Saeed earned his Master’s degree in Electronics and Communications Engineering from the same institution, where he worked on the development of an electroencephalogram (EEG) system, further advancing his specialization in medical applications of electronics. He completed his undergraduate studies at Thebes Higher Institute of Engineering, Cairo, from 2008 to 2013, graduating with honors, earning an overall grade of “Excellent” and a GPA of 3.6/4.0. His strong educational background has provided him with a solid foundation in both theoretical and practical aspects of electronics, communications, and their applications in healthcare and industry. Professional Experience Saeed Mohsen Abosreea Hassan is an accomplished Assistant Professor in Electronics and Communications Engineering, currently serving at King Salman International University since September 2022. Prior to this role, he held a full-time Assistant Professor position at Al-Madinah Higher Institute for Engineering and Technology from April 2021 to August 2022. He also served as a part-time Assistant Professor at Ain Shams University from July 2021 to September 2021, where he contributed to cutting-edge research and advanced teaching methodologies. Before transitioning to academia, Saeed gained extensive experience as a Teaching Assistant at Thebes Academy from September 2013 to March 2021. Throughout his career, he has demonstrated expertise in various fields such as deep learning, IoT systems, and medical wearable sensor technologies. His diverse academic roles, combined with his active involvement in research, student supervision, and curriculum development, highlight his commitment to advancing education and innovation in engineering. Research Interest Saeed Mohsen Abosreea Hassan’s research interests focus on cutting-edge technologies in electronics, communications, and artificial intelligence. His work spans deep learning models, machine learning algorithms, and their applications in human activity recognition, smart healthcare systems, and Internet of Things (IoT) technologies. A significant portion of his research is dedicated to the development of energy harvesting systems for wearable medical sensor nodes, which has the potential to revolutionize real-time healthcare monitoring. He is also passionate about the use of neural networks and convolutional neural networks (CNNs) for the detection of brain tumors, Alzheimer’s disease, and other medical conditions through medical imaging techniques. His focus on Industry 4.0 and smart city networks highlights his commitment to advancing technologies that enhance both industrial automation and urban living. Saeed’s research integrates theoretical advancements with practical applications, particularly in healthcare and smart environments. Research Skills Saeed Mohsen Abosreea Hassan possesses a diverse and advanced set of research skills that span multiple fields of electronics, communications, and deep learning. He is proficient in AI tools such as TensorFlow, PyTorch, Keras, and Scikit-Learn, which he uses for developing machine learning and deep learning models. His expertise in embedded systems, IoT, and smart healthcare technologies is reflected in his research on wearable sensor nodes and energy harvesting systems. He is skilled in programming languages like Python, MATLAB, and Embedded C, essential for his work in developing algorithms and systems for medical and industrial applications. Additionally, Saeed is experienced in electronic circuit and layout design using tools like Proteus, LT-spice, and NI Multisim. His research extends into data acquisition systems, neural networks, and signal processing, particularly in healthcare applications such as brain tumor detection and human activity recognition, showcasing his multidisciplinary research proficiency. Award and Recognition Dr. Saeed Mohsen Abosreea Hassan, an accomplished Assistant Professor in Electronics and Communications Engineering, has made significant strides in the fields of deep learning, IoT, and wearable healthcare technologies. He holds a Ph.D. from Ain Shams University and has published 24 research papers, with an impressive h-index of 10 on Google Scholar. His work has been featured in prestigious journals, including IEEE Access, highlighting his contributions to Industry 4.0, smart healthcare systems, and energy harvesting technologies. Dr. Saeed’s research has been recognized for its practical applications in healthcare, with innovations like self-powered medical wearable sensors. His expertise has also earned him opportunities to present at international conferences and collaborate with top-tier researchers globally. As an emerging leader in his field, Dr. Saeed’s work continues to push the boundaries of technology and healthcare, positioning him as a distinguished researcher dedicated to advancing science and improving lives. Conclusion Saeed Mohsen Abosreea Hassan is a well-qualified candidate for the Best Researcher Award. His strong academic foundation, multidisciplinary research, and publication record make him a strong contender. By expanding his international collaborations, focusing on high-impact research, and pursuing more patents or grants, he could significantly increase his research impact and standing in the academic community. His work in healthcare and energy harvesting aligns with global trends, making his contributions both timely and impactful. Publication Top Notes Title: Human Activity Recognition Using K-Nearest Neighbor Machine Learning Algorithm Authors: S Mohsen, A Elkaseer, SG Scholz Year: 2021 Citations: 63 Title: Industry 4.0-Oriented Deep Learning Models for Human Activity Recognition Authors: Saeed Mohsen, Ahmed Elkaseer, Steffen G. Scholz Year: 2021 Citations: 46 Title: A Self-Powered Wearable Wireless Sensor System Powered by a Hybrid Energy Harvester for Healthcare Applications Authors: S Mohsen, A Zekry, K Youssef, M Abouelatta Year: 2021 Citations: 41 Title: Machine Learning and Deep Learning Techniques for Driver Fatigue and Drowsiness Detection: A Review Authors: S Abd El-Nabi, W El-Shafai, ES M. El-Rabaie, K F. Ramadan, S Mohsen Year: 2023 Citations: 25 Title: Brain Tumor Classification Using Hybrid Single Image Super-Resolution Technique with ResNext101_32x8d and VGG19 Pre-Trained Models Authors: S Mohsen, AM Ali, ESM El-Rabaie, A Elkaseer, SG Scholz, AMA Hassan Year: 2023 Citations: 22 Title: Recognition of Human Activity Using GRU Deep Learning Algorithm Authors: S Mohsen Year: 2023 Citations: 18 Title: An Autonomous Wearable Sensor Node for Long-Term Healthcare Monitoring Powered by a Photovoltaic Energy Harvesting System Authors: S Mohsen, A Zekry, K Youssef, M Abouelatta Year: 2020 Citations: 15 Title: On Architecture of Self-Sustainable Wearable Sensor Node for IoT Healthcare Applications Authors: S Mohsen, A Zekry, K Youssef, M Abouelatta Year: 2021 Citations: 13 Title: EEG-Based Human Emotion Prediction Using an LSTM Model Authors: S Mohsen, AG Alharbi Year: 2021 Citations: 12 Title: A Self-Powered Wearable Sensor Node for IoT Healthcare Applications Authors: S Mohsen, A Zekry, M Abouelatta, K Youssef Year: 2020 Citations: 12
Saeed Mohsen Abosreea Hassan holds a Ph.D. in Electronics and Communications Engineering from Ain Shams University, Cairo, Egypt, completed between 2017 and 2020. His doctoral research focused on the design and implementation of hybrid energy harvesting systems for medical wearable sensor nodes, demonstrating his expertise in cutting-edge healthcare technology. Prior to this, Saeed earned his Master’s degree in Electronics and Communications Engineering from the same institution, where he worked on the development of an electroencephalogram (EEG) system, further advancing his specialization in medical applications of electronics. He completed his undergraduate studies at Thebes Higher Institute of Engineering, Cairo, from 2008 to 2013, graduating with honors, earning an overall grade of “Excellent” and a GPA of 3.6/4.0. His strong educational background has provided him with a solid foundation in both theoretical and practical aspects of electronics, communications, and their applications in healthcare and industry.
Saeed Mohsen Abosreea Hassan is an accomplished Assistant Professor in Electronics and Communications Engineering, currently serving at King Salman International University since September 2022. Prior to this role, he held a full-time Assistant Professor position at Al-Madinah Higher Institute for Engineering and Technology from April 2021 to August 2022. He also served as a part-time Assistant Professor at Ain Shams University from July 2021 to September 2021, where he contributed to cutting-edge research and advanced teaching methodologies. Before transitioning to academia, Saeed gained extensive experience as a Teaching Assistant at Thebes Academy from September 2013 to March 2021. Throughout his career, he has demonstrated expertise in various fields such as deep learning, IoT systems, and medical wearable sensor technologies. His diverse academic roles, combined with his active involvement in research, student supervision, and curriculum development, highlight his commitment to advancing education and innovation in engineering. Research Interest Saeed Mohsen Abosreea Hassan’s research interests focus on cutting-edge technologies in electronics, communications, and artificial intelligence. His work spans deep learning models, machine learning algorithms, and their applications in human activity recognition, smart healthcare systems, and Internet of Things (IoT) technologies. A significant portion of his research is dedicated to the development of energy harvesting systems for wearable medical sensor nodes, which has the potential to revolutionize real-time healthcare monitoring. He is also passionate about the use of neural networks and convolutional neural networks (CNNs) for the detection of brain tumors, Alzheimer’s disease, and other medical conditions through medical imaging techniques. His focus on Industry 4.0 and smart city networks highlights his commitment to advancing technologies that enhance both industrial automation and urban living. Saeed’s research integrates theoretical advancements with practical applications, particularly in healthcare and smart environments. Research Skills Saeed Mohsen Abosreea Hassan possesses a diverse and advanced set of research skills that span multiple fields of electronics, communications, and deep learning. He is proficient in AI tools such as TensorFlow, PyTorch, Keras, and Scikit-Learn, which he uses for developing machine learning and deep learning models. His expertise in embedded systems, IoT, and smart healthcare technologies is reflected in his research on wearable sensor nodes and energy harvesting systems. He is skilled in programming languages like Python, MATLAB, and Embedded C, essential for his work in developing algorithms and systems for medical and industrial applications. Additionally, Saeed is experienced in electronic circuit and layout design using tools like Proteus, LT-spice, and NI Multisim. His research extends into data acquisition systems, neural networks, and signal processing, particularly in healthcare applications such as brain tumor detection and human activity recognition, showcasing his multidisciplinary research proficiency. Award and Recognition Dr. Saeed Mohsen Abosreea Hassan, an accomplished Assistant Professor in Electronics and Communications Engineering, has made significant strides in the fields of deep learning, IoT, and wearable healthcare technologies. He holds a Ph.D. from Ain Shams University and has published 24 research papers, with an impressive h-index of 10 on Google Scholar. His work has been featured in prestigious journals, including IEEE Access, highlighting his contributions to Industry 4.0, smart healthcare systems, and energy harvesting technologies. Dr. Saeed’s research has been recognized for its practical applications in healthcare, with innovations like self-powered medical wearable sensors. His expertise has also earned him opportunities to present at international conferences and collaborate with top-tier researchers globally. As an emerging leader in his field, Dr. Saeed’s work continues to push the boundaries of technology and healthcare, positioning him as a distinguished researcher dedicated to advancing science and improving lives. Conclusion Saeed Mohsen Abosreea Hassan is a well-qualified candidate for the Best Researcher Award. His strong academic foundation, multidisciplinary research, and publication record make him a strong contender. By expanding his international collaborations, focusing on high-impact research, and pursuing more patents or grants, he could significantly increase his research impact and standing in the academic community. His work in healthcare and energy harvesting aligns with global trends, making his contributions both timely and impactful. Publication Top Notes Title: Human Activity Recognition Using K-Nearest Neighbor Machine Learning Algorithm Authors: S Mohsen, A Elkaseer, SG Scholz Year: 2021 Citations: 63 Title: Industry 4.0-Oriented Deep Learning Models for Human Activity Recognition Authors: Saeed Mohsen, Ahmed Elkaseer, Steffen G. Scholz Year: 2021 Citations: 46 Title: A Self-Powered Wearable Wireless Sensor System Powered by a Hybrid Energy Harvester for Healthcare Applications Authors: S Mohsen, A Zekry, K Youssef, M Abouelatta Year: 2021 Citations: 41 Title: Machine Learning and Deep Learning Techniques for Driver Fatigue and Drowsiness Detection: A Review Authors: S Abd El-Nabi, W El-Shafai, ES M. El-Rabaie, K F. Ramadan, S Mohsen Year: 2023 Citations: 25 Title: Brain Tumor Classification Using Hybrid Single Image Super-Resolution Technique with ResNext101_32x8d and VGG19 Pre-Trained Models Authors: S Mohsen, AM Ali, ESM El-Rabaie, A Elkaseer, SG Scholz, AMA Hassan Year: 2023 Citations: 22 Title: Recognition of Human Activity Using GRU Deep Learning Algorithm Authors: S Mohsen Year: 2023 Citations: 18 Title: An Autonomous Wearable Sensor Node for Long-Term Healthcare Monitoring Powered by a Photovoltaic Energy Harvesting System Authors: S Mohsen, A Zekry, K Youssef, M Abouelatta Year: 2020 Citations: 15 Title: On Architecture of Self-Sustainable Wearable Sensor Node for IoT Healthcare Applications Authors: S Mohsen, A Zekry, K Youssef, M Abouelatta Year: 2021 Citations: 13 Title: EEG-Based Human Emotion Prediction Using an LSTM Model Authors: S Mohsen, AG Alharbi Year: 2021 Citations: 12 Title: A Self-Powered Wearable Sensor Node for IoT Healthcare Applications Authors: S Mohsen, A Zekry, M Abouelatta, K Youssef Year: 2020 Citations: 12
Fahd Alharithi | Artificial Intelligence | Best Researcher Award Published on 31/08/202431/08/2024 by Shen Awards Assoc Prof Dr. Fahd Alharithi | Artificial Intelligence | Best Researcher Award Department chair at Taif University, Saudi Arabia Dr. Fahd Saad Alharithi is an accomplished researcher and academic with a Ph.D. in Computer Science from Florida Institute of Technology and extensive experience in both teaching and research. Currently an Assistant Professor at Taif University, his research spans a wide array of topics, including medical data categorization, oil spill detection, COVID-19 diagnosis, and IoT security. Dr. Alharithi has published numerous papers in high-impact journals such as Sensors and Remote Sensing, showcasing his innovative approaches and significant contributions to his field. In addition to his research, he has a strong background in teaching, having served as a lecturer and teaching assistant at various institutions. His involvement in volunteer work and leadership roles further highlights his commitment to community service. While his diverse research and impactful publications are noteworthy, highlighting research grants and awards could strengthen his profile for recognition. Profile ORCID Profile Scopus Profile Education Dr. Fahd Saad Alharithi completed his educational journey with a strong foundation in Computer Science. He earned his Ph.D. from the Florida Institute of Technology, USA, in 2019, where he focused on advanced topics in the field. Prior to that, he obtained his Master of Science degree in Computer Science from the University of New Haven, USA, in 2013. His academic journey began with a Bachelor of Science degree in Computer Science from Taif University, Saudi Arabia, in 2008. This comprehensive educational background, spanning both international and local institutions, has equipped Dr. Alharithi with a robust theoretical and practical understanding of Computer Science, paving the way for his subsequent research and teaching career. His diverse educational experiences contribute significantly to his expertise and innovative approaches in the field. Professional Experience Dr. Fahd Saad Alharithi has garnered extensive experience in academia and education, currently serving as an Assistant Professor in the Computer Science Department at Taif University since 2019. His career began with roles as a Lecturer and Teacher Assistant at Taif University and the University of New Haven, where he honed his teaching and research skills. Dr. Alharithi has also contributed as a Trainer at New Horizons Institute, showcasing his versatility in the field. His professional journey is marked by significant research achievements, including innovative publications in medical data categorization, AI-assisted algorithms, and IoT security. His role extends beyond teaching, encompassing volunteer work with the Hemaya Group and leadership positions like President of the Saudi Student Club. Dr. Alharithi’s career reflects a robust blend of research excellence, educational dedication, and active community involvement. Research Interest Dr. Fahd Saad Alharithi’s research interests primarily focus on advancing computational methods and applications across various domains. His work explores medical data categorization using flexible mixture models, oil spill detection through SAR image analysis, and the development of hybrid convolutional neural network models for diagnosing diseases from chest X-ray images. Dr. Alharithi is also deeply involved in enhancing IoT security with AI-assisted bio-inspired algorithms and addressing environmental challenges through intelligent garbage detection systems. His research extends to secure communication protocols and energy-efficient solutions for sensor networks, demonstrating a strong emphasis on both practical and theoretical advancements. By integrating innovative methodologies such as deep learning and AI, Dr. Alharithi aims to address complex problems in medical imaging, environmental monitoring, and network security, reflecting a broad and impactful approach to computational science. Research Skills Dr. Fahd Saad Alharithi exhibits a robust set of research skills, underscored by his extensive work in computer science and related fields. His proficiency in advanced methodologies, including deep learning, AI-assisted algorithms, and hybrid models, highlights his capacity for innovative problem-solving. Dr. Alharithi’s experience with diverse data types and applications, such as medical data categorization, oil spill detection, and IoT security, demonstrates his ability to tackle complex, interdisciplinary challenges. His strong analytical skills are evident from his impactful publications in high-impact journals like Sensors and Remote Sensing. Additionally, his adeptness in leveraging various computational techniques and his commitment to exploring novel solutions further underscore his research capabilities. Dr. Alharithi’s contributions reflect a deep understanding of both theoretical and practical aspects of his field, positioning him as a skilled researcher with a significant impact on advancing technology and knowledge. Award and Recognition Dr. Fahd Saad Alharithi’s research has garnered considerable recognition within the academic community. He has published extensively in high-impact journals, including Sensors, Remote Sensing, and Computers, Materials & Continua, showcasing his significant contributions to fields such as medical data categorization, oil spill detection, and AI-assisted algorithms. His innovative work, particularly in developing hybrid convolutional neural network models and intelligent systems for garbage detection, underscores his leadership in advancing technology. Although specific awards and formal recognitions are not detailed in his resume, Dr. Alharithi’s influential publications and his role in mentoring and educating future researchers highlight his exceptional impact in computer science. His involvement in volunteer activities and community service further demonstrates his commitment to fostering academic and professional excellence. Conclusion Dr. Taimoor Asim is a strong candidate for the Best Researcher Award due to his substantial contributions to Mechanical Engineering, particularly in fluid dynamics and renewable energy systems. His extensive research experience, leadership roles, and professional achievements make him a noteworthy contender. To strengthen his candidacy, he could focus on broadening his research impact, exploring diverse research areas, and enhancing community engagement related to his work. Overall, Dr. Asim’s profile reflects a high level of expertise and dedication, aligning well with the criteria for the Best Researcher Award. Publications Top Notes Machine learning approaches for advanced detection of rare genetic disorders in whole-genome sequencing Authors: Alzahrani, A.A., Alharithi, F.S. Journal: Alexandria Engineering Journal Year: 2024 Volume: 106, pp. 582–593 IoT-enabled healthcare systems using blockchain-dependent adaptable services Authors: Arul, R., Alroobaea, R., Tariq, U., Alharithi, F.S., Shoaib, U. Journal: Personal and Ubiquitous Computing Year: 2024 Volume: 28(1), pp. 43–57 Citations: 13 A comprehensive cost performance analysis for a QoS-based scheme in network mobility (NEMO) Authors: Hussein, L.F., Abass, I.A.M., Aissa, A.B., Alzahrani, A.A., Alharithi, F.S. Journal: Alexandria Engineering Journal Year: 2023 Volume: 76, pp. 349–360 Citations: 1 Performance Analysis of Machine Learning Approaches in Automatic Classification of Arabic Language Authors: Alharithi, F.S. Journal: Information Sciences Letters Year: 2023 Volume: 12(3), pp. 1563–1578 Citations: 1 A blockchain-based hybrid platform for multimedia data processing in IoT-Healthcare Authors: Taloba, A.I., Elhadad, A., Rayan, A., Alharithi, F.S., Park, C. Journal: Alexandria Engineering Journal Year: 2023 Volume: 65, pp. 263–274 Citations: 74 Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements Authors: Kumar, P., Swarnkar, N.K., Mahela, O.P., Mazon, J.L.V., Alharithi, F.S. Journal: International Transactions on Electrical Energy Systems Year: 2023 Volume: 2023, 6315918 Citations: 1 Energy-Aware Live VM Migration Using Ballooning in Cloud Data Center Authors: Gupta, N., Gupta, K., Qahtani, A.M., Singh, A., Goyal, N. Journal: Electronics (Switzerland) Year: 2022 Volume: 11(23), 3932 Citations: 4 NLP-Based Application for Analyzing Private and Public Banks Stocks Reaction to News Events in the Indian Stock Exchange Authors: Dogra, V., Alharithi, F.S., Álvarez, R.M., Singh, A., Qahtani, A.M. Journal: Systems Year: 2022 Volume: 10(6), 233 Citations: 7 Deep learned BLSTM for online handwriting modeling simulating the Beta-Elliptic approach Authors: Hamdi, Y., Boubaker, H., Rabhi, B., Dhahri, H., Alimi, A.M. Journal: Engineering Science and Technology, an International Journal Year: 2022 Volume: 35, 101215 Citations: 6 A software for thorax images analysis based on deep learning Authors: Almulihi, A.H., Alharithi, F.S., Mechti, S., Alroobaea, R., Rubaiee, S. Book Chapter: Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention Year: 2022 Pages: 1166–1178
Dr. Fahd Saad Alharithi is an accomplished researcher and academic with a Ph.D. in Computer Science from Florida Institute of Technology and extensive experience in both teaching and research. Currently an Assistant Professor at Taif University, his research spans a wide array of topics, including medical data categorization, oil spill detection, COVID-19 diagnosis, and IoT security. Dr. Alharithi has published numerous papers in high-impact journals such as Sensors and Remote Sensing, showcasing his innovative approaches and significant contributions to his field. In addition to his research, he has a strong background in teaching, having served as a lecturer and teaching assistant at various institutions. His involvement in volunteer work and leadership roles further highlights his commitment to community service. While his diverse research and impactful publications are noteworthy, highlighting research grants and awards could strengthen his profile for recognition. Profile ORCID Profile Scopus Profile Education Dr. Fahd Saad Alharithi completed his educational journey with a strong foundation in Computer Science. He earned his Ph.D. from the Florida Institute of Technology, USA, in 2019, where he focused on advanced topics in the field. Prior to that, he obtained his Master of Science degree in Computer Science from the University of New Haven, USA, in 2013. His academic journey began with a Bachelor of Science degree in Computer Science from Taif University, Saudi Arabia, in 2008. This comprehensive educational background, spanning both international and local institutions, has equipped Dr. Alharithi with a robust theoretical and practical understanding of Computer Science, paving the way for his subsequent research and teaching career. His diverse educational experiences contribute significantly to his expertise and innovative approaches in the field. Professional Experience Dr. Fahd Saad Alharithi has garnered extensive experience in academia and education, currently serving as an Assistant Professor in the Computer Science Department at Taif University since 2019. His career began with roles as a Lecturer and Teacher Assistant at Taif University and the University of New Haven, where he honed his teaching and research skills. Dr. Alharithi has also contributed as a Trainer at New Horizons Institute, showcasing his versatility in the field. His professional journey is marked by significant research achievements, including innovative publications in medical data categorization, AI-assisted algorithms, and IoT security. His role extends beyond teaching, encompassing volunteer work with the Hemaya Group and leadership positions like President of the Saudi Student Club. Dr. Alharithi’s career reflects a robust blend of research excellence, educational dedication, and active community involvement. Research Interest Dr. Fahd Saad Alharithi’s research interests primarily focus on advancing computational methods and applications across various domains. His work explores medical data categorization using flexible mixture models, oil spill detection through SAR image analysis, and the development of hybrid convolutional neural network models for diagnosing diseases from chest X-ray images. Dr. Alharithi is also deeply involved in enhancing IoT security with AI-assisted bio-inspired algorithms and addressing environmental challenges through intelligent garbage detection systems. His research extends to secure communication protocols and energy-efficient solutions for sensor networks, demonstrating a strong emphasis on both practical and theoretical advancements. By integrating innovative methodologies such as deep learning and AI, Dr. Alharithi aims to address complex problems in medical imaging, environmental monitoring, and network security, reflecting a broad and impactful approach to computational science. Research Skills Dr. Fahd Saad Alharithi exhibits a robust set of research skills, underscored by his extensive work in computer science and related fields. His proficiency in advanced methodologies, including deep learning, AI-assisted algorithms, and hybrid models, highlights his capacity for innovative problem-solving. Dr. Alharithi’s experience with diverse data types and applications, such as medical data categorization, oil spill detection, and IoT security, demonstrates his ability to tackle complex, interdisciplinary challenges. His strong analytical skills are evident from his impactful publications in high-impact journals like Sensors and Remote Sensing. Additionally, his adeptness in leveraging various computational techniques and his commitment to exploring novel solutions further underscore his research capabilities. Dr. Alharithi’s contributions reflect a deep understanding of both theoretical and practical aspects of his field, positioning him as a skilled researcher with a significant impact on advancing technology and knowledge. Award and Recognition Dr. Fahd Saad Alharithi’s research has garnered considerable recognition within the academic community. He has published extensively in high-impact journals, including Sensors, Remote Sensing, and Computers, Materials & Continua, showcasing his significant contributions to fields such as medical data categorization, oil spill detection, and AI-assisted algorithms. His innovative work, particularly in developing hybrid convolutional neural network models and intelligent systems for garbage detection, underscores his leadership in advancing technology. Although specific awards and formal recognitions are not detailed in his resume, Dr. Alharithi’s influential publications and his role in mentoring and educating future researchers highlight his exceptional impact in computer science. His involvement in volunteer activities and community service further demonstrates his commitment to fostering academic and professional excellence. Conclusion Dr. Taimoor Asim is a strong candidate for the Best Researcher Award due to his substantial contributions to Mechanical Engineering, particularly in fluid dynamics and renewable energy systems. His extensive research experience, leadership roles, and professional achievements make him a noteworthy contender. To strengthen his candidacy, he could focus on broadening his research impact, exploring diverse research areas, and enhancing community engagement related to his work. Overall, Dr. Asim’s profile reflects a high level of expertise and dedication, aligning well with the criteria for the Best Researcher Award. Publications Top Notes Machine learning approaches for advanced detection of rare genetic disorders in whole-genome sequencing Authors: Alzahrani, A.A., Alharithi, F.S. Journal: Alexandria Engineering Journal Year: 2024 Volume: 106, pp. 582–593 IoT-enabled healthcare systems using blockchain-dependent adaptable services Authors: Arul, R., Alroobaea, R., Tariq, U., Alharithi, F.S., Shoaib, U. Journal: Personal and Ubiquitous Computing Year: 2024 Volume: 28(1), pp. 43–57 Citations: 13 A comprehensive cost performance analysis for a QoS-based scheme in network mobility (NEMO) Authors: Hussein, L.F., Abass, I.A.M., Aissa, A.B., Alzahrani, A.A., Alharithi, F.S. Journal: Alexandria Engineering Journal Year: 2023 Volume: 76, pp. 349–360 Citations: 1 Performance Analysis of Machine Learning Approaches in Automatic Classification of Arabic Language Authors: Alharithi, F.S. Journal: Information Sciences Letters Year: 2023 Volume: 12(3), pp. 1563–1578 Citations: 1 A blockchain-based hybrid platform for multimedia data processing in IoT-Healthcare Authors: Taloba, A.I., Elhadad, A., Rayan, A., Alharithi, F.S., Park, C. Journal: Alexandria Engineering Journal Year: 2023 Volume: 65, pp. 263–274 Citations: 74 Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements Authors: Kumar, P., Swarnkar, N.K., Mahela, O.P., Mazon, J.L.V., Alharithi, F.S. Journal: International Transactions on Electrical Energy Systems Year: 2023 Volume: 2023, 6315918 Citations: 1 Energy-Aware Live VM Migration Using Ballooning in Cloud Data Center Authors: Gupta, N., Gupta, K., Qahtani, A.M., Singh, A., Goyal, N. Journal: Electronics (Switzerland) Year: 2022 Volume: 11(23), 3932 Citations: 4 NLP-Based Application for Analyzing Private and Public Banks Stocks Reaction to News Events in the Indian Stock Exchange Authors: Dogra, V., Alharithi, F.S., Álvarez, R.M., Singh, A., Qahtani, A.M. Journal: Systems Year: 2022 Volume: 10(6), 233 Citations: 7 Deep learned BLSTM for online handwriting modeling simulating the Beta-Elliptic approach Authors: Hamdi, Y., Boubaker, H., Rabhi, B., Dhahri, H., Alimi, A.M. Journal: Engineering Science and Technology, an International Journal Year: 2022 Volume: 35, 101215 Citations: 6 A software for thorax images analysis based on deep learning Authors: Almulihi, A.H., Alharithi, F.S., Mechti, S., Alroobaea, R., Rubaiee, S. Book Chapter: Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention Year: 2022 Pages: 1166–1178
Dr. Fahd Saad Alharithi has garnered extensive experience in academia and education, currently serving as an Assistant Professor in the Computer Science Department at Taif University since 2019. His career began with roles as a Lecturer and Teacher Assistant at Taif University and the University of New Haven, where he honed his teaching and research skills. Dr. Alharithi has also contributed as a Trainer at New Horizons Institute, showcasing his versatility in the field. His professional journey is marked by significant research achievements, including innovative publications in medical data categorization, AI-assisted algorithms, and IoT security. His role extends beyond teaching, encompassing volunteer work with the Hemaya Group and leadership positions like President of the Saudi Student Club. Dr. Alharithi’s career reflects a robust blend of research excellence, educational dedication, and active community involvement. Research Interest Dr. Fahd Saad Alharithi’s research interests primarily focus on advancing computational methods and applications across various domains. His work explores medical data categorization using flexible mixture models, oil spill detection through SAR image analysis, and the development of hybrid convolutional neural network models for diagnosing diseases from chest X-ray images. Dr. Alharithi is also deeply involved in enhancing IoT security with AI-assisted bio-inspired algorithms and addressing environmental challenges through intelligent garbage detection systems. His research extends to secure communication protocols and energy-efficient solutions for sensor networks, demonstrating a strong emphasis on both practical and theoretical advancements. By integrating innovative methodologies such as deep learning and AI, Dr. Alharithi aims to address complex problems in medical imaging, environmental monitoring, and network security, reflecting a broad and impactful approach to computational science. Research Skills Dr. Fahd Saad Alharithi exhibits a robust set of research skills, underscored by his extensive work in computer science and related fields. His proficiency in advanced methodologies, including deep learning, AI-assisted algorithms, and hybrid models, highlights his capacity for innovative problem-solving. Dr. Alharithi’s experience with diverse data types and applications, such as medical data categorization, oil spill detection, and IoT security, demonstrates his ability to tackle complex, interdisciplinary challenges. His strong analytical skills are evident from his impactful publications in high-impact journals like Sensors and Remote Sensing. Additionally, his adeptness in leveraging various computational techniques and his commitment to exploring novel solutions further underscore his research capabilities. Dr. Alharithi’s contributions reflect a deep understanding of both theoretical and practical aspects of his field, positioning him as a skilled researcher with a significant impact on advancing technology and knowledge. Award and Recognition Dr. Fahd Saad Alharithi’s research has garnered considerable recognition within the academic community. He has published extensively in high-impact journals, including Sensors, Remote Sensing, and Computers, Materials & Continua, showcasing his significant contributions to fields such as medical data categorization, oil spill detection, and AI-assisted algorithms. His innovative work, particularly in developing hybrid convolutional neural network models and intelligent systems for garbage detection, underscores his leadership in advancing technology. Although specific awards and formal recognitions are not detailed in his resume, Dr. Alharithi’s influential publications and his role in mentoring and educating future researchers highlight his exceptional impact in computer science. His involvement in volunteer activities and community service further demonstrates his commitment to fostering academic and professional excellence. Conclusion Dr. Taimoor Asim is a strong candidate for the Best Researcher Award due to his substantial contributions to Mechanical Engineering, particularly in fluid dynamics and renewable energy systems. His extensive research experience, leadership roles, and professional achievements make him a noteworthy contender. To strengthen his candidacy, he could focus on broadening his research impact, exploring diverse research areas, and enhancing community engagement related to his work. Overall, Dr. Asim’s profile reflects a high level of expertise and dedication, aligning well with the criteria for the Best Researcher Award. Publications Top Notes Machine learning approaches for advanced detection of rare genetic disorders in whole-genome sequencing Authors: Alzahrani, A.A., Alharithi, F.S. Journal: Alexandria Engineering Journal Year: 2024 Volume: 106, pp. 582–593 IoT-enabled healthcare systems using blockchain-dependent adaptable services Authors: Arul, R., Alroobaea, R., Tariq, U., Alharithi, F.S., Shoaib, U. Journal: Personal and Ubiquitous Computing Year: 2024 Volume: 28(1), pp. 43–57 Citations: 13 A comprehensive cost performance analysis for a QoS-based scheme in network mobility (NEMO) Authors: Hussein, L.F., Abass, I.A.M., Aissa, A.B., Alzahrani, A.A., Alharithi, F.S. Journal: Alexandria Engineering Journal Year: 2023 Volume: 76, pp. 349–360 Citations: 1 Performance Analysis of Machine Learning Approaches in Automatic Classification of Arabic Language Authors: Alharithi, F.S. Journal: Information Sciences Letters Year: 2023 Volume: 12(3), pp. 1563–1578 Citations: 1 A blockchain-based hybrid platform for multimedia data processing in IoT-Healthcare Authors: Taloba, A.I., Elhadad, A., Rayan, A., Alharithi, F.S., Park, C. Journal: Alexandria Engineering Journal Year: 2023 Volume: 65, pp. 263–274 Citations: 74 Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements Authors: Kumar, P., Swarnkar, N.K., Mahela, O.P., Mazon, J.L.V., Alharithi, F.S. Journal: International Transactions on Electrical Energy Systems Year: 2023 Volume: 2023, 6315918 Citations: 1 Energy-Aware Live VM Migration Using Ballooning in Cloud Data Center Authors: Gupta, N., Gupta, K., Qahtani, A.M., Singh, A., Goyal, N. Journal: Electronics (Switzerland) Year: 2022 Volume: 11(23), 3932 Citations: 4 NLP-Based Application for Analyzing Private and Public Banks Stocks Reaction to News Events in the Indian Stock Exchange Authors: Dogra, V., Alharithi, F.S., Álvarez, R.M., Singh, A., Qahtani, A.M. Journal: Systems Year: 2022 Volume: 10(6), 233 Citations: 7 Deep learned BLSTM for online handwriting modeling simulating the Beta-Elliptic approach Authors: Hamdi, Y., Boubaker, H., Rabhi, B., Dhahri, H., Alimi, A.M. Journal: Engineering Science and Technology, an International Journal Year: 2022 Volume: 35, 101215 Citations: 6 A software for thorax images analysis based on deep learning Authors: Almulihi, A.H., Alharithi, F.S., Mechti, S., Alroobaea, R., Rubaiee, S. Book Chapter: Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention Year: 2022 Pages: 1166–1178
Mijanur Rahaman | Machin Learning | Excellence in Research Published on 18/04/2024 by Shen Awards Mr. Mijanur Rahaman | Machin Learning | Excellence in Research Assistant Professor at Bangladesh University of Business and Technology (BUBT), Bangladesh.