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