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
Fahd Alharithi | Artificial Intelligence | Best Researcher Award Posted on 31/08/202431/08/2024 by ScienceFather 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