A. F. M. Shahen Shah | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr A. F. M. Shahen Shah | Artificial Intelligence | Best Researcher Award

Associate Professor at Yildiz Technical University, Turkey

Assoc. Prof. Dr. A. F. M. Shahen Shah is a distinguished academic and researcher in the Department of Electronics and Communication Engineering at Yildiz Technical University, Turkey. He is recognized as one of the World’s Top 2% Scientists by Stanford University and Elsevier (2023-2024), reflecting his exceptional contributions to research and academia. With extensive experience in teaching, project management, and interdisciplinary research, Dr. Shah’s work primarily focuses on next-generation communication systems, artificial intelligence, and disaster-resilient technologies. His leadership in multiple funded projects and innovative research underscores his commitment to advancing the field of electronics and communication engineering.

Professional Profile

Education

Dr. Shah completed his Ph.D. in Electronics and Communication Engineering at Yildiz Technical University in 2020, earning a CGPA of 3.75 and receiving a prestigious Gold Medal at ITEX. He holds a Master’s degree in Information Technology from the University of Dhaka, Bangladesh, where he ranked third in his batch with a CGPA of 3.85. His academic journey began with a Bachelor’s in Electronics and Telecommunication Engineering from Daffodil International University, Bangladesh, graduating at the top of his class with a CGPA of 3.96. His academic achievements highlight his unwavering commitment to excellence in learning and research.

Professional Experience

Dr. Shah’s professional career encompasses both academia and industry. He is currently an Associate Professor at Yildiz Technical University, where he has been teaching advanced undergraduate and graduate courses since 2021. He previously served as an Assistant Professor at Istanbul Gelisim University, specializing in wireless communication and artificial neural networks. Before transitioning to academia, Dr. Shah gained valuable industry experience as an IT professional in leading banks in Bangladesh, managing critical operations and support systems. His diverse career trajectory combines academic rigor with practical expertise, enabling him to bridge theory and real-world applications effectively.

Research Interests

Dr. Shah’s research interests lie in the realms of next-generation wireless communication systems, artificial intelligence, vehicular ad hoc networks (VANETs), and UAV-based disaster communication systems. He is particularly passionate about exploring the integration of intelligent reflecting surfaces and fluid antenna systems for 6G communication. His work also includes developing deep learning models for real-time sign language recognition and designing mobility-aware cooperative MAC protocols for VANETs. Dr. Shah’s innovative approach to addressing real-world challenges through advanced communication technologies reflects his dedication to impactful and forward-thinking research.

Research Skills

Dr. Shah possesses a diverse set of research skills, including expertise in designing and analyzing wireless communication systems, MIMO antenna systems, and deep learning-based applications. He is proficient in project management, having led multiple high-impact projects funded by TÜBİTAK and YTÜ-BAP. His technical expertise extends to developing and simulating advanced communication protocols, integrating artificial intelligence into communication systems, and optimizing network performance. With a strong foundation in programming, data analysis, and mathematical modeling, Dr. Shah excels in delivering innovative solutions to complex engineering problems.

Awards and Honors

Dr. Shah’s illustrious career has earned him several accolades, including recognition among the World’s Top 2% Scientists by Stanford University and Elsevier. He was awarded a Gold Medal in the 32nd ITEX for his outstanding Ph.D. research. Additionally, his academic excellence during his undergraduate and master’s studies earned him top rankings in his class. Dr. Shah’s consistent record of achievements in both research and academics highlights his profound impact on the field of electronics and communication engineering.

Conclusion 🤝

Assoc. Prof. Dr. A. F. M. Shahen Shah is a strong contender for the Best Researcher Award due to his remarkable academic credentials, global recognition, and leadership in innovative projects. With increased emphasis on publishing in high-impact journals, pursuing patents, and engaging broader audiences, he has the potential to further solidify his reputation as a leading researcher. His interdisciplinary expertise and proven project management skills make him an outstanding candidate for this prestigious recognition.

Publication Top Notes

  1. Survey and performance evaluation of multiple access schemes for next-generation wireless communication systems
    Authors: AFMS Shah, AN Qasim, MA Karabulut, H Ilhan, MB Islam
    Year: 2021
    Citations: 91
    Published in: IEEE Access 9, 113428-113442
  2. A survey from 1G to 5G including the advent of 6G: Architectures, multiple access techniques, and emerging technologies
    Authors: AFMS Shah
    Year: 2022
    Citations: 65
    Published in: 2022 IEEE 12th Annual Computing and Communication Workshop and Conference
  3. Internet of things and wireless sensor networks for smart agriculture applications-a survey
    Authors: MN Mowla, N Mowla, AFMS Shah, K Rabie, T Shongwe
    Year: 2023
    Citations: 62
    Published in: IEEE Access
  4. A survey on cooperative communication in wireless networks
    Authors: AFMS Shah, MS Islam
    Year: 2014
    Citations: 60
    Published in: International Journal of Intelligent Systems and Applications 6 (7), 66-78
  5. A secured privacy-preserving multi-level blockchain framework for cluster-based VANET
    Authors: AFMS Akhter, M Ahmed, AFMS Shah, A Anwar, A Zengin
    Year: 2021
    Citations: 55
    Published in: Sustainability 13 (1), 400
  6. CB-MAC: A novel cluster-based MAC protocol for VANETs
    Authors: AFM Shahen Shah, H Ilhan, U Tureli
    Year: 2019
    Citations: 53
    Published in: IET Intelligent Transport Systems 13 (4), 587-595
  7. RECV-MAC: A novel reliable and efficient cooperative MAC protocol for VANETs
    Authors: AFM Shahen Shah, H Ilhan, U Tureli
    Year: 2019
    Citations: 43
    Published in: IET Communications 13 (16), 2541-2549
  8. Inspecting VANET with various critical aspects–a systematic review
    Authors: MA Karabulut, AFMS Shah, H Ilhan, ASK Pathan, M Atiquzzaman
    Year: 2023
    Citations: 41
    Published in: Ad Hoc Networks, 103281
  9. A blockchain-based emergency message transmission protocol for cooperative VANET
    Authors: M Ahmed, N Moustafa, AFMS Akhter, I Razzak, E Surid, A Anwar, …
    Year: 2021
    Citations: 38
    Published in: IEEE Transactions on Intelligent Transportation Systems 23 (10), 19624-19633
  10. A blockchain-based authentication protocol for cooperative vehicular ad hoc network
    Authors: AFMS Akhter, M Ahmed, AFMS Shah, A Anwar, ASM Kayes, A Zengin
    Year: 2021
    Citations: 37
    Published in: Sensors 21 (4), 1273

 

Farhad Soleimanian Gharehchopogh | Artificial Intelligent | Best Researcher Award

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Assoc. Prof. Dr. Farhad Soleimanian Gharehchopogh | Artificial Intelligent | Best Researcher Award

Dean of Faculty at Urmia Branch, Islamic Azad University, Iran

Dr. Farhad Soleimanian Gharehchopogh is a distinguished academic with a profound background in computer science and software engineering. He is renowned for his contributions to machine learning, artificial intelligence, and computational intelligence. His research focuses on solving complex problems using evolutionary algorithms and optimization techniques. Dr. Soleimanian is also an active participant in academic circles, serving on the editorial boards of several prestigious journals and regularly presenting his findings at international conferences. With numerous publications in high-impact journals, he has significantly influenced his field. His dedication to research and education has earned him accolades, making him a respected figure among peers and students alike.

Professional Profile

Education

Dr. Farhad Soleimanian Gharehchopogh holds a Ph.D. in Computer Science, specializing in Software Engineering from Urmia University, Iran. His doctoral research focused on advanced optimization techniques and their applications in artificial intelligence. Prior to his Ph.D., he completed a Master of Science in Software Engineering at Islamic Azad University, Tabriz Branch, where he developed a strong foundation in programming, data structures, and algorithm design. He earned his Bachelor of Science in Computer Science from Islamic Azad University, Urmia Branch, where he first explored his interest in computational intelligence. His academic journey has been characterized by a consistent focus on deepening his understanding of complex computational systems.

Professional Experience

Dr. Farhad Soleimanian Gharehchopogh has held various academic positions throughout his career, contributing to the growth of computer science education and research. He has served as an Assistant Professor at Islamic Azad University, Urmia Branch, where he taught undergraduate and graduate courses in software engineering and computer science. In addition to teaching, he has supervised numerous master’s and Ph.D. students, guiding their research in areas like machine learning and optimization algorithms. He has also collaborated with international researchers on various projects, aiming to solve real-world problems using advanced computational methods. His professional experience is marked by a commitment to fostering innovation in both academic and practical applications of computer science.

Research Interest

Dr. Soleimanian’s research interests are centered around machine learning, artificial intelligence, and computational optimization. He is particularly interested in developing new algorithms for data mining, evolutionary computing, and swarm intelligence. His work often explores how optimization techniques, such as genetic algorithms, particle swarm optimization, and ant colony optimization, can be applied to solve complex problems in various fields. Additionally, he is passionate about deep learning and its applications in pattern recognition, natural language processing, and image analysis. Dr. Soleimanian continually seeks to advance the field through innovative research, aiming to bridge the gap between theoretical concepts and practical implementations.

Research Skills

Dr. Farhad Soleimanian Gharehchopogh possesses a wide array of research skills that make him a leader in computational intelligence and software engineering. He has extensive experience in developing and implementing optimization algorithms, leveraging his expertise in evolutionary computing and metaheuristics. Proficient in programming languages such as Python, MATLAB, and C++, he applies these skills to simulate and analyze complex models. Dr. Soleimanian is also skilled in statistical analysis and data visualization, enabling him to derive meaningful insights from large datasets. His ability to collaborate effectively with other researchers and his strong analytical mindset have allowed him to make significant contributions to his field.

Awards and Honors

Dr. Soleimanian’s excellence in research and education has been recognized with several awards and honors throughout his career. He has received accolades for his high-quality research papers presented at international conferences and published in peer-reviewed journals. His contributions to the field have been acknowledged with best paper awards and recognition from academic societies. He has also been honored for his outstanding teaching and mentoring, guiding students towards academic and professional success. Dr. Soleimanian’s dedication to advancing computer science and his commitment to academic excellence have made him a recipient of numerous prestigious awards, highlighting his impact in both research and education.

Conclusion

Dr. Farhad Soleimanian Gharehchopogh is a strong candidate for the Best Researcher Award, given his extensive research output, mentorship of graduate students, and recognition among the top-cited scientists globally. His consistent contributions to the academic and research community, particularly in computer engineering, make him well-suited for this award. Addressing the minor areas for improvement, such as updating student mentorship records and highlighting recent publications, would further solidify his application.

Publications Top Notes

  • Recent applications and advances of African Vultures Optimization Algorithm
    Authors: AG Hussien, FS Gharehchopogh, A Bouaouda, S Kumar, G Hu
    Journal: Artificial Intelligence Review 57 (12), 1-51
    Year: 2024
    Citations: Not specified
  • An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for Engineering Problems and Its Applications in Breast Cancer
    Authors: FA Özbay, E Özbay, FS Gharehchopogh
    Journal: CMES-Computer Modeling in Engineering & Sciences 141 (2)
    Year: 2024
    Citations: Not specified
  • Chaotic RIME optimization algorithm with adaptive mutualism for feature selection problems
    Authors: M Abdel-Salam, G Hu, E Çelik, FS Gharehchopogh, IM El-Hasnony
    Journal: Computers in Biology and Medicine 179, 108803
    Year: 2024
    Citations: 6
  • A hybrid principal label space transformation-based ridge regression and decision tree for multi-label classification
    Authors: SHS Ebrahimi, K Majidzadeh, FS Gharehchopogh
    Journal: Evolving Systems, 1-37
    Year: 2024
    Citations: Not specified
  • Multifeature Fusion Method with Metaheuristic Optimization for Automated Voice Pathology Detection
    Authors: E Özbay, FA Özbay, N Khodadadi, FS Gharehchopogh, S Mirjalili
    Journal: Journal of Voice
    Year: 2024
    Citations: Not specified
  • A Quasi-Oppositional Learning-based Fox Optimizer for QoS-aware Web Service Composition in Mobile Edge Computing
    Authors: RH Sharif, M Masdari, A Ghaffari, FS Gharehchopogh
    Journal: Journal of Grid Computing 22 (3), 64
    Year: 2024
    Citations: Not specified
  • A novel offloading strategy for multi-user optimization in blockchain-enabled Mobile Edge Computing networks for improved Internet of Things performance
    Authors: AM Rahmani, J Tanveer, FS Gharehchopogh, S Rajabi, M Hosseinzadeh
    Journal: Computers and Electrical Engineering 119, 109514
    Year: 2024
    Citations: 5
  • An Intrusion Detection System on The Internet of Things Using Deep Learning and Multi-objective Enhanced Gorilla Troops Optimizer
    Authors: H Asgharzadeh, A Ghaffari, M Masdari, FS Gharehchopogh
    Journal: Journal of Bionic Engineering 21 (5), 2658-2684
    Year: 2024
    Citations: 2
  • Visualization and classification of mushroom species with multi-feature fusion of metaheuristics-based convolutional neural network model
    Authors: E Özbay, FA Özbay, FS Gharehchopogh
    Journal: Applied Soft Computing 164, 111936
    Year: 2024
    Citations: 1
  • A software defect prediction method using binary gray wolf optimizer and machine learning algorithms
    Authors: H Wang, B Arasteh, K Arasteh, FS Gharehchopogh, A Rouhi
    Journal: Computers and Electrical Engineering 118, 109336
    Year: 2024
    Citations: 1

Ali Ghandi | Artificial intelligence | Best Researcher Award

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Ali Ghandi | Artificial intelligence | Best Researcher Award

PhD, Sharif University of Technology, Iran.

Ali Ghandi is an innovative researcher and educator specializing in Artificial Intelligence, particularly in reinforcement learning and generative AI. Currently pursuing his Ph.D. at Sharif University of Technology, he is known for his groundbreaking work that enhances reinforcement learning processes by leveraging side-channel data. Ali’s academic journey began with a B.Sc. in Digital System Design, followed by an M.Sc. in Machine Learning, where he excelled as one of the top students. He has taught courses in Neural Networks and Deep Generative Models, effectively sharing his knowledge with students. His research has been recognized through publications in reputable journals and presentations at significant conferences, such as the Iran Workshop on Communication and Information Theory. Ali’s accomplishments include a top rank in a national entrance exam and membership in Iran’s National Elites Foundation, underscoring his exceptional capabilities and contributions to the field of AI and his commitment to advancing technology for practical applications.

Profile:

 

Education

Ali Ghandi has an impressive academic background in electrical and computer engineering, with a particular focus on Artificial Intelligence. He is currently pursuing a Ph.D. at Sharif University of Technology (SUT) in Tehran, where he is conducting innovative research aimed at improving reinforcement learning processes using side-channel data. Prior to his doctoral studies, Ali earned his Master’s degree in Machine Learning from SUT, where his thesis focused on analyzing IoT systems through location-based data, effectively modeling traffic based on dynamic maps and registered commutes. He completed his Bachelor’s degree in Digital System Design at the same university, where he developed an online coordinate system for managing thermal loads in IoT applications. Throughout his educational journey, Ali has consistently demonstrated academic excellence, evidenced by his top rankings in national examinations and competitive academic events, establishing him as a leading figure among his peers in the field of electrical engineering and AI.

Professional Experiences

Ali Ghandi has an impressive academic background in electrical and computer engineering, with a particular focus on Artificial Intelligence. He is currently pursuing a Ph.D. at Sharif University of Technology (SUT) in Tehran, where he is conducting innovative research aimed at improving reinforcement learning processes using side-channel data. Prior to his doctoral studies, Ali earned his Master’s degree in Machine Learning from SUT, where his thesis focused on analyzing IoT systems through location-based data, effectively modeling traffic based on dynamic maps and registered commutes. He completed his Bachelor’s degree in Digital System Design at the same university, where he developed an online coordinate system for managing thermal loads in IoT applications. Throughout his educational journey, Ali has consistently demonstrated academic excellence, evidenced by his top rankings in national examinations and competitive academic events, establishing him as a leading figure among his peers in the field of electrical engineering and AI.

 

Research skills

Ali Ghandi possesses a strong set of research skills that position him as a leading figure in the field of Artificial Intelligence. His primary focus is on reinforcement learning, where he has developed innovative approaches, such as utilizing side-channel data to enhance the learning process. Ali’s expertise extends to deep generative models, where he explores the potential of generative AI in various applications. Additionally, he is adept at massive data mining, allowing him to extract valuable insights from large datasets, which is crucial in today’s data-driven world. His research also includes analyzing IoT systems, particularly in modeling traffic using location-based data. This multifaceted skill set enables Ali to approach complex problems with a comprehensive perspective, combining theoretical knowledge with practical applications. His ability to publish in reputable journals and present at conferences demonstrates his commitment to advancing the field and contributing to the academic community.

 

Awards And Recoginition

Ali Ghandi has received numerous accolades that underscore his academic excellence and contributions to the field of Artificial Intelligence. He achieved a remarkable 68th rank in a highly competitive university entrance exam, placing him among the top candidates out of 250,000 participants. His outstanding performance in the International A-lympiad, where he ranked third, showcases his proficiency in applied mathematics within a global context. Additionally, Ali has been a member of Iran’s National Elites Foundation since 2013, reflecting his recognition as a leading talent in his field. His academic journey at Sharif University of Technology has been marked by multiple distinctions, including first place among students in his Digital Systems minor and second place among all M.Sc. Electrical Engineering students. These honors highlight Ali’s commitment to excellence in research and education, positioning him as a promising contributor to the advancement of Artificial Intelligence.

Conclusion

In conclusion, Ali Ghandi possesses a solid foundation of academic excellence, innovative research, and early recognition in his field. His focus on advanced topics within AI positions him well for the Best Researcher Award. By addressing areas for improvement, such as increasing the practical impact of his work and expanding his collaborative efforts, Ali can further enhance his candidacy for this prestigious recognition. His commitment to advancing knowledge in AI and machine learning makes him a strong contender for the award.

Publication Top Notes

  • Title: Ex-RL: Experience-based Reinforcement Learning
    Authors: Ghandi, A., Shouraki, S.B., Gholampour, I., Kamranian, A., Riazati, M.
    Year: 2025
    Citation: Information Sciences, 689, 121479 📚🤖
  • Title: Deep ExRL: Experience-Driven Deep Reinforcement Learning in Control Problems
    Authors: Ghandi, A., Shouraki, S.B., Riazati, M.
    Year: 2024
    Citation: 12th Iran Workshop on Communication and Information Theory (IWCIT 2024) 📄🔍

Karimeh Ata | Artificial Intelligence | Best Researcher Award

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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

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

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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

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