Karimeh Ata | Artificial Intelligence | Best Researcher Award Posted on 12/09/202412/09/2024 by ScienceFather 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 Posted on 06/09/202406/09/2024 by ScienceFather 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