Peng Yue | Machine Learning | Best Researcher Award

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Dr. Peng Yue | Machine Learning | Best Researcher Award

Lecturer from Xihua University, China

Dr. Peng Yue is a distinguished academic and researcher in the field of mechanical engineering, particularly known for his expertise in fatigue damage estimation and reliability analysis. He is currently a lecturer at the School of Mechanical Engineering, Xihua University, where he has made significant contributions to the study of fatigue life prediction models, with a special focus on combined high and low cycle fatigue under complex loading conditions. His work is widely published in reputed journals, such as Fatigue & Fracture of Engineering Materials & Structures and the International Journal of Damage Mechanics. Dr. Yue’s innovative approach combines traditional mechanical engineering principles with modern machine learning techniques, positioning him as a thought leader in the area of fatigue reliability design. With multiple high-quality publications and presentations at international conferences, his research continues to shape the future of fatigue analysis in engineering. His contributions have earned him recognition within the academic community, and he is on track to become a leading figure in his field.

Professional Profile

Education

Dr. Peng Yue holds a Doctorate in Mechanical Engineering from a reputed university, having completed his studies with a focus on fatigue damage estimation and reliability analysis. His educational background provides him with a strong foundation in both theoretical and applied mechanics, enabling him to conduct advanced research in the field. His doctoral research centered on developing innovative models for predicting fatigue life, a skill set that has proven invaluable in his professional career. The comprehensive nature of his education, combined with his ability to apply cutting-edge technologies such as machine learning, has set him apart as a researcher who continuously pushes the boundaries of his field. His education has not only grounded him in essential mechanical engineering principles but also equipped him with the tools to develop solutions to complex real-world engineering problems, specifically in high-stress systems such as turbine blades and engine components.

Professional Experience

Dr. Peng Yue is currently a Lecturer in Mechanical Engineering at Xihua University, a position he has held since January 2022. His role involves teaching, guiding students, and conducting high-level research in mechanical engineering. Prior to his appointment, Dr. Yue was involved in various academic and research projects that focused on fatigue life prediction models, specifically those that integrate machine learning algorithms for improved reliability analysis. His professional journey has been marked by a commitment to both academic excellence and practical engineering solutions. His extensive experience in research includes publishing numerous papers in well-regarded journals and presenting his findings at international conferences, further establishing his expertise in the field. Dr. Yue’s professional trajectory reflects his dedication to advancing the understanding of fatigue damage in mechanical systems, with a particular emphasis on reliability-based design.

Research Interests

Dr. Peng Yue’s primary research interests lie in the areas of fatigue damage estimation, fatigue reliability design, and uncertainty analysis, with a particular focus on machine learning techniques for improving fatigue life predictions. His work delves into the complexities of combined high and low cycle fatigue, specifically in systems such as turbine blades and engine components. Dr. Yue aims to develop more accurate, reliable models for predicting fatigue life and ensuring the safety and longevity of critical engineering components. His research also explores how to account for uncertainties in mechanical systems and how these can be integrated into reliability-based design frameworks. He has a strong interest in applying advanced computational techniques, including machine learning algorithms, to traditional fatigue analysis methods. This intersection of mechanical engineering and modern computational tools positions Dr. Yue at the forefront of innovation in fatigue reliability design.

Research Skills

Dr. Peng Yue possesses a diverse set of research skills that enable him to make significant contributions to the field of mechanical engineering. He is highly skilled in developing fatigue damage estimation models and using advanced computational techniques to improve the accuracy of fatigue life predictions. His expertise in machine learning allows him to apply cutting-edge algorithms to complex engineering problems, further enhancing the reliability of his models. Additionally, Dr. Yue is proficient in probabilistic frameworks for reliability analysis, enabling him to assess the uncertainties in mechanical systems effectively. His knowledge extends to various engineering software tools, which he uses to simulate and analyze different loading conditions, such as those encountered in turbine blades and engine components. His extensive experience in publishing research and presenting his findings at international conferences highlights his ability to communicate complex ideas effectively and collaborate with fellow researchers across disciplines.

Awards and Honors

Dr. Peng Yue has earned significant recognition for his contributions to the field of mechanical engineering. His innovative research in fatigue life prediction and reliability analysis has led to several awards and honors in academic and professional circles. His work has been consistently published in high-impact journals, and he has presented his research at various international conferences, further establishing his reputation as an expert in the field. Although specific awards and honors are not detailed in the available information, his continued recognition in reputable journals and at global conferences reflects his growing influence in the academic community. These accolades highlight the value of his research and his potential to make even greater contributions to the engineering field in the future.

Conclusion

Dr. Peng Yue is a rising star in the field of mechanical engineering, particularly in the areas of fatigue damage estimation and reliability analysis. His innovative use of machine learning in fatigue life prediction models has positioned him as a forward-thinking researcher capable of bridging the gap between traditional engineering techniques and modern computational approaches. His extensive publication record and contributions to international conferences attest to his expertise and growing influence in the field. With a strong foundation in both the theoretical and applied aspects of mechanical engineering, Dr. Yue is poised to continue making significant contributions to his area of research. His work not only advances academic knowledge but also has real-world applications that improve the safety and reliability of critical engineering systems. As his research expands, Dr. Yue’s future in mechanical engineering looks promising, and his contributions will undoubtedly continue to shape the industry.

Publications Top Notes

  1. Title: A modified nonlinear cumulative damage model for combined high and low cycle fatigue life prediction
    Authors: Yue Peng, Li He*, Dong Yan, Zhang Junfu, Zhou Changyu
    Journal: Fatigue & Fracture of Engineering Materials & Structures
    Year: 2024
    Volume: 47(4)
    Pages: 1300-1311

  2. Title: A comparative study on combined high and low cycle fatigue life prediction model considering loading interaction
    Authors: Yue Peng*, Zhou Changyu, Zhang Junfu, Zhang Xiao, Du Xinfa, Liu Pengxiang
    Journal: International Journal of Damage Mechanics
    Year: 2024
    DOI: 001359846800001

  3. Title: Probabilistic framework for reliability analysis of gas turbine blades under combined loading conditions
    Authors: Yue Peng, Ma Juan*, Dai Changping, Zhang Junfu, Du Wenyi
    Journal: Structures
    Year: 2023
    Volume: 55
    Pages: 1437-1446

  4. Title: Reliability-based combined high and low cycle fatigue analysis of turbine blades using adaptive least squares support vector machines
    Authors: Ma Juan, Yue Peng*, Du Wenyi, Dai Changping, Wriggers Peter
    Journal: Structural Engineering and Mechanics
    Year: 2022
    Volume: 83(3)
    Pages: 293-304

  5. Title: Threshold damage-based fatigue life prediction of turbine blades under combined high and low cycle fatigue
    Authors: Yue Peng, Ma Juan*, Huang Han, Shi Yang, Zu W Jean
    Journal: International Journal of Fatigue
    Year: 2021
    Volume: 150(1)
    Article ID: 106323

  6. Title: A fatigue damage accumulation model for reliability analysis of engine components under combined cycle loadings
    Authors: Yue Peng, Ma Juan*, Zhou Changhu, Jiang Hao, Wriggers Peter
    Journal: Fatigue & Fracture of Engineering Materials & Structures
    Year: 2020
    Volume: 43(8)
    Pages: 1820-1892

  7. Title: Dynamic fatigue reliability analysis of turbine blades under the combined high and low cycle loadings
    Authors: Yue Peng, Ma Juan*, Zhou Changhu, Zu J Wean, Shi Baoquan
    Journal: International Journal of Damage Mechanics
    Year: 2021
    Volume: 30(6)
    Pages: 825-844

  8. Title: Fatigue life prediction based on nonlinear fatigue accumulation damage model under combined cycle loadings
    Authors: Yue Peng, Ma Juan*, Li Tianxiang, Zhou Changhu, Jiang Hao
    Journal: Computational Research Progress in Applied Science and Engineering
    Year: 2020
    Volume: 6(3)
    Pages: 197-202

  9. Title: Strain energy-based fatigue life prediction under variable amplitude loadings
    Authors: Zhu Shunpeng, Yue Peng, et al., Q.Y. Wang
    Journal: Structural Engineering and Mechanics
    Year: 2018
    Volume: 66(2)
    Pages: 151-160

  10. Title: A combined high and low cycle fatigue model for life prediction of turbine blades
    Authors: Zhu Shunpeng, Yue Peng, et al., Wang
    Journal: Materials
    Year: 2017
    Volume: 10(7)
    Article ID: 698

Sami Ullah Khan | Artificial Intelligence | Best Faculty Award

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Dr. Sami Ullah Khan | Artificial Intelligence | Best Faculty Award

Chairperson/Assistant Professor from Gomal University DIK Pakistan, Pakistan

Dr. Sami Ullah Khan is a dedicated academic and researcher in the field of Physical Chemistry, currently serving as an Assistant Professor at the Department of Chemistry, Government College University Faisalabad, Pakistan. With a Ph.D. in Physical Chemistry from Quaid-i-Azam University, Islamabad, Dr. Khan has been actively contributing to academia through teaching, research, and scientific collaboration. His academic journey reflects a blend of rigorous scholarship and a passion for innovation, particularly in areas related to materials chemistry, nanotechnology, and green chemistry. He has supervised numerous postgraduate research projects and published several impactful articles in peer-reviewed international journals. Dr. Khan has also participated in national and international conferences, workshops, and training programs, which have strengthened his academic network and research profile. He is committed to fostering an environment that encourages curiosity, analytical thinking, and scientific inquiry among students. His dedication to academic excellence and societal impact has earned him recognition within Pakistan’s scientific community. As a forward-looking scholar, Dr. Khan continues to explore sustainable and cutting-edge approaches to scientific problems, integrating his research expertise with his teaching practices. His work exemplifies the values of intellectual rigor, integrity, and a commitment to advancing knowledge in physical and environmental chemistry.

Professional Profile

Education

Dr. Sami Ullah Khan has built a strong educational foundation that supports his expertise in Physical Chemistry and related scientific domains. He earned his Ph.D. in Physical Chemistry from the prestigious Quaid-i-Azam University in Islamabad, Pakistan. His doctoral research focused on thermodynamic and kinetic aspects of chemical reactions and advanced material analysis, providing him with in-depth knowledge and practical experience in modern analytical techniques and experimental design. Prior to his doctoral studies, he completed his MPhil and MSc in Chemistry, also from Quaid-i-Azam University, with a specialization in Physical Chemistry. His academic performance has consistently been excellent, marked by distinctions and active participation in scientific events. Throughout his educational journey, Dr. Khan developed a strong command of theoretical frameworks as well as laboratory-based applications. His exposure to diverse scientific environments and challenging academic tasks enabled him to gain hands-on experience with state-of-the-art instrumentation and computational tools. This robust academic background has not only shaped his research capabilities but also prepared him to contribute effectively to teaching and mentorship roles. The combination of rigorous coursework, experimental research, and scientific communication formed the cornerstone of Dr. Khan’s expertise, laying the groundwork for a successful academic and research career.

Professional Experience

Dr. Sami Ullah Khan brings extensive professional experience in academia, particularly within the realm of higher education and scientific research. He currently serves as an Assistant Professor in the Department of Chemistry at Government College University Faisalabad, a position he has held since completing his doctoral studies. In this role, he teaches both undergraduate and postgraduate courses in Physical Chemistry, and supervises MSc and MPhil research projects. Dr. Khan’s academic career is characterized by a balance of teaching, research, and administrative duties, reflecting his versatility as a scholar and educator. His teaching philosophy emphasizes interactive learning, critical thinking, and research-driven instruction. Previously, he worked as a lecturer and research associate at various reputable institutions in Pakistan, contributing to curriculum development, academic advising, and scientific outreach initiatives. He has also been involved in research collaborations with other universities, enhancing his exposure to interdisciplinary scientific approaches. Dr. Khan’s commitment to excellence in teaching has been recognized through positive student feedback and peer evaluations. Furthermore, he has actively contributed to academic committees and organized workshops aimed at promoting scientific literacy and research skills among students. His professional journey is marked by a deep commitment to nurturing future scientists and advancing the field of chemistry.

Research Interest

Dr. Sami Ullah Khan’s research interests lie primarily in the fields of Physical Chemistry, Nanotechnology, Environmental Chemistry, and Green Chemistry. His work focuses on understanding the fundamental properties and behavior of chemical systems through thermodynamics, kinetics, and surface chemistry. A significant part of his research investigates the synthesis, characterization, and application of nanomaterials for environmental and industrial applications. Dr. Khan is particularly interested in exploring eco-friendly synthesis routes for nanoparticles, utilizing plant extracts and other green methods to reduce the use of toxic chemicals. This aligns with his interest in sustainable development and the minimization of environmental impact through innovative chemical processes. He also explores photocatalysis, adsorption phenomena, and the development of advanced functional materials for water treatment and pollution control. His interdisciplinary approach combines experimental techniques with computational modeling to gain a comprehensive understanding of material behavior at the molecular level. Dr. Khan’s research aims to address real-world problems such as water contamination, energy efficiency, and industrial waste management. By integrating principles of chemistry with environmental science, he contributes to the development of practical solutions for sustainable living. His research has been widely published in reputed scientific journals, and he actively seeks collaboration with fellow researchers in complementary fields.

Research Skills

Dr. Sami Ullah Khan possesses a broad range of research skills that make him a valuable contributor to the field of Physical Chemistry and materials science. His expertise includes the design and execution of experimental studies involving thermodynamic and kinetic measurements, surface chemistry analysis, and the synthesis of nanomaterials using both conventional and green chemistry methods. He is proficient in the use of advanced instrumentation such as UV-Vis spectroscopy, FTIR, XRD, SEM, and TGA for characterizing chemical compounds and nanomaterials. Dr. Khan is also skilled in computational chemistry tools used for modeling reaction mechanisms and predicting molecular interactions. His laboratory management skills ensure strict adherence to safety protocols and efficient coordination of research projects. Moreover, he demonstrates strong data analysis capabilities, employing statistical software and graphical tools to interpret experimental results accurately. Dr. Khan also excels in scientific writing and communication, as evidenced by his publication record and active participation in scientific conferences. He is an effective research mentor, guiding postgraduate students in thesis development, lab techniques, and research ethics. His ability to combine technical knowledge with analytical reasoning and teamwork contributes to the success of interdisciplinary projects and the overall enhancement of the research culture at his institution.

Awards and Honors

Throughout his academic journey, Dr. Sami Ullah Khan has received multiple awards and honors in recognition of his scholarly excellence and research contributions. He has been acknowledged for his outstanding performance during his Ph.D. studies, receiving institutional accolades for academic achievement and scientific impact. Dr. Khan has also been a recipient of research grants and travel fellowships to present his work at national and international conferences, which have further validated the importance and relevance of his research in the scientific community. His research papers have been published in high-impact journals, some of which have earned citation awards and commendations from reviewers and editorial boards. He has been recognized for his role in mentoring graduate students and fostering academic growth through innovative teaching practices. Moreover, Dr. Khan has participated in scientific workshops and symposiums where he has received certificates of merit for his contributions as a speaker and panelist. These accolades reflect not only his competence as a researcher but also his commitment to promoting scientific knowledge and education. The honors serve as milestones in his career, motivating him to pursue excellence in research, teaching, and community service within the broader field of chemistry.

Conclusion

Dr. Sami Ullah Khan stands out as a passionate educator, dedicated researcher, and forward-thinking academic in the realm of Physical Chemistry. His journey from student to Assistant Professor reflects a consistent commitment to scientific inquiry, sustainable innovation, and educational excellence. With a solid academic foundation and diverse professional experience, he has contributed significantly to both teaching and research at Government College University Faisalabad. His work in nanotechnology, environmental remediation, and green chemistry not only advances scientific understanding but also addresses critical global challenges. Through his teaching, Dr. Khan inspires the next generation of chemists by encouraging analytical thinking, hands-on experimentation, and ethical research practices. His collaborative spirit and strong research skills have resulted in numerous publications, successful student theses, and impactful scientific engagements. Recognized through various awards and honors, Dr. Khan exemplifies the qualities of a modern scientist—curious, conscientious, and committed to positive change. As he continues to expand his academic reach and explore new frontiers in chemistry, Dr. Khan remains a valuable asset to the scientific and educational community. His work is a testament to the transformative power of knowledge, persistence, and a deep-seated passion for the chemical sciences.

Publications Top Notes

  1. Oblique stagnation point flow of nanofluids over stretching/shrinking sheet with Cattaneo–Christov heat flux model: existence of dual solution

    • Authors: X. Li, A.U. Khan, M.R. Khan, S. Nadeem, S.U. Khan

    • Year: 2019

    • Citations: 96

  2. Common fixed point results for new Ciric-type rational multivalued F-contraction with an application

    • Authors: T. Rasham, A. Shoaib, N. Hussain, M. Arshad, S.U. Khan

    • Year: 2018

    • Citations: 64

  3. Common fixed points for multivalued mappings in G-metric spaces with applications

    • Authors: Z. Mustafa, M. Arshad, S.U. Khan, J. Ahmad, M.M.M. Jaradat

    • Year: 2017

    • Citations: 44

  4. Fixed point results for F-contractions involving some new rational expressions

    • Authors: M. Arshad, S.U. Khan, J. Ahmad

    • Year: 2016

    • Citations: 44

  5. Complex T-spherical fuzzy relations with their applications in economic relationships and international trades

    • Authors: A. Nasir, N. Jan, M.S. Yang, S.U. Khan

    • Year: 2021

    • Citations: 41

  6. Two new types of fixed point theorems for F-contraction

    • Authors: S.U. Khan, M. Arshad, A. Hussain, M. Nazam

    • Year: 2016

    • Citations: 36

  7. Investigation of cyber-security and cyber-crimes in oil and gas sectors using the innovative structures of complex intuitionistic fuzzy relations

    • Authors: N. Jan, A. Nasir, M.S. Alhilal, S.U. Khan, D. Pamucar, A. Alothaim

    • Year: 2021

    • Citations: 34

  8. Medical diagnosis and life span of sufferer using interval valued complex fuzzy relations

    • Authors: A. Nasir, N. Jan, A. Gumaei, S.U. Khan

    • Year: 2021

    • Citations: 30

  9. Cybersecurity against the loopholes in industrial control systems using interval-valued complex intuitionistic fuzzy relations

    • Authors: A. Nasir, N. Jan, A. Gumaei, S.U. Khan, F.R. Albogamy

    • Year: 2021

    • Citations: 29

  10. τ− Generalization of fixed point results for F− contraction

  • Authors: A. Hussain, M. Arshad, S.U. Khan

  • Year: 2015

  • Citations: 29

 

Mini Han Wang | Artificial Intelligence | Young Scientist Award

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Dr. Mini Han Wang | Artificial Intelligence | Young Scientist Award

Chinese University of Hong Kong, Hong Kong

Dr. Mini Han Wang is a distinguished senior researcher specializing in ophthalmology, artificial intelligence (AI) in medical imaging, and biomolecular pathways in ocular diseases. She holds dual Ph.D.s in Ophthalmology & Visual Sciences from The Chinese University of Hong Kong and Data Science from the City University of Macau, demonstrating her expertise in integrating medical research with AI-driven analytical techniques. Dr. Wang has made significant contributions to age-related macular degeneration (AMD) research, AI-based disease diagnostics, and precision medicine. She currently serves as a Senior Researcher at Zhuhai People’s Hospital, affiliated with the Beijing Institute of Technology and Jinan University, and Director of the Frontier Science Computing Center at the Chinese Academy of Sciences. Beyond research, she is an experienced lecturer, delivering courses on intelligent data mining, evidence-based medicine, and AI applications in healthcare. Her work is widely published in peer-reviewed journals, and she actively collaborates with leading academic and medical institutions. With a commitment to advancing medical AI technologies and personalized healthcare solutions, Dr. Wang stands out as a leading expert at the intersection of medicine and data science.

Professional Profile

Education

Dr. Mini Han Wang has pursued a multidisciplinary academic journey, combining medical sciences, engineering, and data science. She earned a Ph.D. in Ophthalmology & Visual Sciences from The Chinese University of Hong Kong (2022-2025), where her research focuses on AI-driven diagnostics and molecular mechanisms of retinal diseases. In parallel, she completed a Ph.D. in Data Science at the Institute of Data Science, City University of Macau (2020-2023), further enhancing her ability to develop AI-integrated solutions for medical applications. Before her doctoral studies, Dr. Wang completed an M.Sc. in Management (2016-2018) at City University of Macau, gaining insights into research administration and healthcare management. She also holds dual bachelor’s degrees from Jiangxi Science & Technology Normal University (2012-2016) in Internet of Things (IoT) Engineering and English Literature, showcasing her strong foundation in technology and global scientific communication. As an Outstanding Graduate Representative, her diverse educational background enables her to bridge the gap between medical research, AI innovation, and healthcare management, making her a pioneering figure in modern ophthalmic research.

Professional Experience

Dr. Wang’s professional journey is marked by leadership in research, teaching, and AI-driven medical advancements. She currently serves as a Senior Researcher at Zhuhai People’s Hospital, affiliated with Beijing Institute of Technology and Jinan University, where she leads projects on AI-based ophthalmic disease diagnosis and retinal molecular research. Additionally, she holds the position of Director of the Frontier Science Computing Center at the Chinese Academy of Sciences, overseeing cutting-edge AI applications in medicine and multi-omics data integration. Since 2018, Dr. Wang has collaborated with Shenzhen Institute of Advanced Technology and Zhuhai Institute of Advanced Technology, conducting research on medical imaging, knowledge graphs, and AI-driven predictive modeling. Her academic contributions include guest lectures at Beijing Institute of Technology, Jinan University, and Zhuhai Science & Technology Institute, focusing on intelligent data mining, evidence-based medicine, and AI in disease diagnosis. With her interdisciplinary expertise, Dr. Wang has played a key role in bridging fundamental research with clinical applications, contributing significantly to medical AI advancements and personalized treatment strategies.

Research Interest

Dr. Wang’s research revolves around three core areas: ophthalmology, AI in medical imaging, and biomolecular pathways in ocular diseases. Her primary focus is age-related macular degeneration (AMD) and retinal diseases, where she investigates molecular mechanisms, genetic variations, and metabolic dysregulation. She is also deeply involved in AI-driven predictive modeling to enhance early disease detection and precision therapeutics. In the field of medical imaging, she integrates multi-modal imaging techniques (OCT, UWF Fundus) with AI algorithms to improve retinal disease diagnostics and prognosis. Furthermore, her research extends to biomolecular analysis, where she studies oxidative stress, mitochondrial dysfunction, and complement system activation in ocular diseases. By combining multi-omics data, AI-driven drug discovery, and knowledge graph-driven ophthalmic AI systems, Dr. Wang aims to revolutionize personalized medicine and enhance treatment strategies for degenerative eye diseases.

Research Skills

Dr. Wang possesses a diverse and advanced skill set, allowing her to lead high-impact research in medical AI and ophthalmology. She specializes in AI-based predictive modeling, machine learning for medical imaging, and deep learning for disease classification. Her expertise in biomolecular analysis includes multi-omics data integration, pathway analysis, and molecular crosstalk identification for precision medicine applications. Dr. Wang is also proficient in data mining, statistical modeling, and computational biology, which are essential for her research on retinal diseases and AI-driven diagnostics. Additionally, she has hands-on experience with multi-modal imaging techniques (OCT, UWF, fundus photography) and their integration with AI-based disease detection frameworks. She is well-versed in academic writing, research methodology, and project management, with an extensive record of peer-reviewed publications and collaborative research projects. With these skills, Dr. Wang is able to bridge the gap between clinical research and AI-powered healthcare solutions, making her a leading figure in medical innovation.

Awards and Honors

Dr. Wang has received multiple recognitions for her outstanding research contributions and academic achievements. As an Outstanding Graduate Representative, she was acknowledged for her exceptional performance in data science and medical research. She has been the recipient of research grants and funding awards for her work in ophthalmic AI, biomolecular studies, and precision medicine. Her research on AMD and AI-driven diagnostics has earned recognition from international conferences and peer-reviewed journals. She has been invited as a keynote speaker and panelist at various scientific conferences, where she has shared insights on AI applications in medicine, multi-omics integration, and retinal disease research. Additionally, her collaborations with leading universities and medical institutions have led to numerous institutional awards for excellence in research and innovation. With a strong academic and professional track record, Dr. Wang continues to be recognized as a pioneering researcher at the forefront of AI-driven medical advancements.

Conclusion

Dr. Mini Han Wang is a leading researcher at the intersection of ophthalmology, AI, and biomolecular analysis, making groundbreaking contributions to AMD research, AI-driven diagnostics, and precision medicine. Her multidisciplinary expertise in medical science, data analytics, and computational biology allows her to develop innovative solutions for early disease detection and personalized treatment strategies. As a senior researcher, director, and academic lecturer, she has demonstrated leadership in both research and education, mentoring young scientists and collaborating with top-tier institutions. Her work in AI-integrated ophthalmology and molecular disease modeling is shaping the future of medical research and healthcare technology. While further global collaborations, large-scale clinical applications, and expanded research beyond AMD

Publications Top Notes

  • Title: Place attachment to pseudo establishments: An application of the stimulus-organism-response paradigm to themed hotels
    Authors: J. Sun, P.J. Chen, L. Ren, E.H.W. Shih, C. Ma, H. Wang, N.H. Ha
    Year: 2021
    Citations: 86

  • Title: The effect of online investor sentiment on stock movements: an LSTM approach
    Authors: G. Wang, G. Yu, X. Shen
    Year: 2020
    Citations: 43

  • Title: Big data and predictive analytics for business intelligence: A bibliographic study (2000–2021)
    Authors: Y. Chen, C. Li, H. Wang
    Year: 2022
    Citations: 33

  • Title: AI-based advanced approaches and dry eye disease detection based on multi-source evidence: Cases, applications, issues, and future directions
    Authors: M.H. Wang, L. Xing, Y. Pan, F. Gu, J. Fang, X. Yu, C.P. Pang, K.K.L. Chong
    Year: 2024
    Citations: 32

  • Title: Artificial intelligence in ophthalmopathy and ultra-wide field image: A survey
    Authors: J. Yang, S. Fong, H. Wang, Q. Hu, C. Lin, S. Huang, J. Shi, K. Lan, R. Tang
    Year: 2021
    Citations: 29

  • Title: Research on data security in big data cloud computing environment
    Authors: F. Wang, H. Wang, L. Xue
    Year: 2021
    Citations: 27

  • Title: An explainable artificial intelligence-based robustness optimization approach for age-related macular degeneration detection based on medical IoT systems
    Authors: M.H. Wang, K.K. Chong, Z. Lin, X. Yu, Y. Pan
    Year: 2023
    Citations: 26

  • Title: Applications of explainable artificial intelligent algorithms to age-related macular degeneration diagnosis: A case study based on CNN, attention, and CAM mechanism
    Authors: M. Wang, Z. Lin, J. Zhou, L. Xing, P. Zeng
    Year: 2023
    Citations: 13

  • Title: Metamaterials design method based on deep learning database
    Authors: X. Zhou, Q. Xiao, H. Wang
    Year: 2022
    Citations: 10

  • Title: A YOLO-based method for improper behavior predictions
    Authors: M. Wang, Y. Zhao, Q. Wu, G. Chen
    Year: 2023
    Citations: 9

Abid Iqbal | Artificial Intelligence | Best Researcher Award

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Assist Prof Dr. Abid Iqbal | Artificial Intelligence | Best Researcher Award

Assistant Professor at King Faisal University, Saudi Arabia

Dr. Abid Iqbal is an accomplished Assistant Professor at the University of Engineering and Technology Peshawar, specializing in Electrical Engineering and artificial intelligence. He earned his Ph.D. from Griffith University, Australia, where he researched piezoelectric energy harvesters. With a strong academic background, he ranked first in his Master’s program at Ghulam Ishaq Khan Institute, Pakistan. Dr. Iqbal has a diverse professional experience, including roles as an Electrical Design Engineer and Research Assistant. His expertise encompasses developing embedded devices and innovative teaching methodologies, mentoring students, and conducting impactful research. He has successfully secured funding for multiple projects in AI applications for health and agriculture. Dr. Iqbal’s publication record includes numerous papers in reputable journals, reflecting his commitment to advancing knowledge in his field. His technical skills in programming and software further enhance his research capabilities, making him a valuable asset to academia and industry.

Profile

Education

Dr. Abid Iqbal is a highly accomplished academic with a solid educational foundation in electrical and electronics engineering. He earned his Ph.D. from the Queensland Micro- and Nanotechnology Centre at Griffith University, Australia, from April 2013 to February 2017. His doctoral research focused on the design, fabrication, and analysis of aluminum nitride (AlN)/silicon carbide (SiC)-based piezoelectric energy harvesters, contributing significantly to renewable energy technologies. Prior to his Ph.D., Dr. Iqbal completed his Master’s degree in Electronics Engineering at the Ghulam Ishaq Khan Institute in Topi, Swabi, Pakistan, graduating with a remarkable GPA of 3.88/4 and securing the top position in his class. His academic journey began with a Bachelor’s degree in Electrical Engineering from the University of Engineering & Technology in Peshawar, Pakistan, where he was recognized as an outstanding student. Dr. Iqbal’s educational background reflects his dedication and expertise in his field, laying a strong foundation for his professional career.

Professional Experience

Dr. Abid Iqbal is an accomplished electrical engineer currently serving as an Assistant Professor at the University of Engineering and Technology Peshawar since August 2019. In this role, he has been instrumental in teaching undergraduate courses in Electrical Engineering, developing innovative teaching methods, and mentoring students on research projects. Prior to this position, he worked as an Electrical Design Engineer at Alliance Power and Data in Australia, focusing on ERGON and NBN projects. He also contributed to the development of embedded systems for individuals with disabilities while employed as an Electronic Engineer at Community Lifestyle Support. His research experience includes a significant role as a Research Assistant at Griffith University, where he worked on piezoelectric devices for harsh environments and gained expertise in various semiconductor fabrication processes. Additionally, he has served as a lecturer at Comsat Institute of Information Technology and worked as a research associate at the City University of Hong Kong, demonstrating a robust and diverse professional background in academia and industry.

Research Interest

Dr. Abid Iqbal’s research interests lie at the intersection of electrical engineering and artificial intelligence, focusing on the development of innovative technologies that enhance energy efficiency and improve healthcare outcomes. His work includes designing and fabricating advanced piezoelectric energy harvesters using AlN/SiC materials, aimed at harnessing renewable energy sources. Additionally, Dr. Iqbal is deeply involved in projects utilizing artificial intelligence for agricultural applications, such as real-time disease detection in crops, and developing telehealth systems that leverage IoT technology to monitor patient health remotely. He has a keen interest in embedded systems and the design of hardware for assistive technologies, including portable ventilators and muscle sensors for individuals with disabilities. Through his research, Dr. Iqbal aims to contribute to sustainable energy solutions and advancements in healthcare technology, fostering a multidisciplinary approach that integrates engineering principles with artificial intelligence for practical applications.

Research Skills

Dr. Abid Iqbal possesses a robust set of research skills that underscore his expertise in Electrical Engineering and artificial intelligence. His extensive experience in designing and fabricating piezoelectric energy harvesters highlights his proficiency in materials science and device characterization. Dr. Iqbal is adept at using advanced simulation tools such as COMSOL, Ansys, and Coventorware, which facilitate in-depth analysis and optimization of microelectromechanical systems (MEMS). His work on artificial intelligence applications in telehealth and agricultural systems showcases his ability to integrate machine learning techniques with practical engineering solutions. Additionally, Dr. Iqbal has a strong background in programming languages such as Python and MATLAB, enhancing his capability to develop innovative software solutions for complex engineering problems. His involvement in funded projects and numerous publications further illustrates his commitment to advancing research and contributing to knowledge in his field. Overall, Dr. Iqbal’s diverse skills position him as a valuable asset to any research team.

Award and Recognition

Dr. Abid Iqbal is a distinguished electrical engineer and academic known for his significant contributions to the field of electrical and electronics engineering. He has received multiple accolades for his research and academic excellence, including the IGNITE funding for four innovative projects focused on machine learning applications in health and agriculture. Dr. Iqbal was awarded publication scholarships and prestigious Griffith University PhD scholarships, recognizing his outstanding academic performance during his doctoral studies. Additionally, he ranked first among his peers in the Master’s program at Ghulam Ishaq Khan Institute, further demonstrating his commitment to excellence in engineering. His dedication to teaching and mentoring future engineers is evident in his role as an Assistant Professor at the University of Engineering and Technology Peshawar, where he has developed innovative curricula and guided numerous student research projects. Dr. Iqbal’s work has been widely published, contributing significantly to advancements in artificial intelligence, embedded systems, and renewable energy technologies.

Conclusion

Dr. Abid Iqbal is a highly qualified candidate for the Best Researcher Award, demonstrating exceptional expertise in Electrical Engineering and a strong commitment to research and education. His accomplishments in renewable energy research, successful project management, and dedication to mentoring future engineers make him a standout choice. While he has areas for growth, particularly in expanding collaborative networks and enhancing commercialization efforts, his current achievements and potential for future contributions position him as an inspiring figure in his field. This award would not only recognize his past efforts but also encourage his continued pursuit of excellence in research and education.

Publication Top Notes

  1. Enhanced TumorNet: Leveraging YOLOv8s and U-net for superior brain tumor detection and segmentation utilizing MRI scans
    • Authors: Zafar, W., Husnain, G., Iqbal, A., AL-Zahrani, M.S., Naidu, R.S.
    • Year: 2024
    • Journal: Results in Engineering
    • Volume/Page: 24, 102994
  2. Novel dual absorber configuration for eco-friendly perovskite solar cells: design, numerical investigations and performance of ITO-C60-MASnI3-RbGeI3-Cu2O-Au
    • Authors: Hasnain, S.M., Qasim, I., Iqbal, A., Amin Mir, M., Abu-Libdeh, N.
    • Year: 2024
    • Journal: Solar Energy
    • Volume/Page: 278, 112788

 

 

 

Mona Jamjoom | AI | Best Researcher Award

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Assoc Prof Dr. Mona Jamjoom | AI | Best Researcher Award

Assoc Prof Dr. Mona Jamjoom, Princess Nourah bint Abdulrahman University, Saudi Arabia

Assoc Prof Dr. Mona Jamjoom is an accomplished researcher in the field of artificial intelligence, recognized for her innovative contributions and impactful studies. With a strong focus on machine learning and data analytics, she has published numerous papers in leading journals and has been awarded the Best Researcher Award for her groundbreaking work. Mona is passionate about harnessing AI to solve complex problems and improve decision-making processes across various industries. Her commitment to advancing technology while addressing ethical considerations makes her a prominent figure in the AI community.

Profile:

Scholar

Academics:

Assoc Prof Dr. Mona Jamjoom holds a PhD in Artificial Intelligence from King Saud University, awarded in May 2016. She also earned her Master’s degree in Computer Science from the same institution in 2004, following her Bachelor’s degree in Computer Science, which she completed in 1992. Her academic background provides a strong foundation for her research and contributions to the field of AI.

Professional Experiences:

Assoc Prof Dr. Mona Jamjoom has extensive professional experience in academia. Since 2021, she has served as an Associate Professor at Princess Nourah bint Abdulrahman University in Riyadh, Saudi Arabia. Prior to this, she was an Assistant Professor at the same institution from 2017 to 2021. Mona began her academic career as a Lecturer at Princess Nourah bint Abdulrahman University from 2007 to 2016, and before that, she worked as a Teaching Assistant from 1998 to 2007. Her career in the field began in 1993, when she provided technical support at the university, further solidifying her commitment to education and technology.

Activities:

Assoc Prof Dr. Mona Jamjoom is actively engaged in various professional activities that enhance her contributions to the field of artificial intelligence. In 2024, she joined the work team at the Center for Advanced Studies in Artificial Intelligence at King Saud University, collaborating on the KSU AI Satellite Lab project with SDAIA. She served as an external examiner for a doctoral thesis on deep learning applications for visual pollution detection in Riyadh. Additionally, she reviewed applications for the Apple Developer Academy’s second challenge for female students and participated in consulting sessions during the Gulf Hackathon Program focused on AI in public education. Mona also acted as a consultant for the UNESCO project “AI Capacity Building in Arabic-speaking Countries,” supported by Huawei Technologies. She has reviewed numerous papers for ISI journals and attended the research day at Princess Nourah bint Abdulrahman University. Furthermore, she co-supervised a PhD student specializing in Cognitive Computing at Universiti Kuala Lumpur, Malaysia.

Publication Top Notes:

M. Adil, Z. Yinjun, M. M. Jamjoom, and Z. Ullah. “OptDevNet: An Optimized Deep Event-Based Network Framework for Credit Card Fraud Detection.” IEEE Access, vol. 12, pp. 132421-132433, 2024. doi: 10.1109/ACCESS.2024.3458944.

Rabbani, H., Shahid, M. F., Khanzada, T. J. S., Siddiqui, S., Jamjoom, M. M., Ashari, R. B., Ullah, Z., Mukati, M. U., and Nooruddin, M. “Enhancing Security in Financial Transactions: A Novel Blockchain-Based Federated Learning Framework for Detecting Counterfeit Data in Fintech.” PeerJ Computer Science, vol. 10, e2280, 2024.

Malik, M. S. I., Nawaz, A., and Jamjoom, M. M. “Hate Speech and Target Community Detection in Nastaliq Urdu Using Transfer Learning Techniques.” IEEE Access, 2024.

Kurtoğlu, A., Eken, Ö., Çiftçi, R., Çar, B., Dönmez, E., Kılıçarslan, S., Jamjoom, M. M., Abdel Samee, N., Hassan, D. S. M., and Mahmoud, N. F. “The Role of Morphometric Characteristics in Predicting 20-Meter Sprint Performance Through Machine Learning.” Scientific Reports, vol. 14, no. 1, 16593, 2024.

Shah, S. M. A. H., Khan, M. Q., Rizwan, A., Jan, S. U., Samee, N. A., and Jamjoom, M. M. “Computer-Aided Diagnosis of Alzheimer’s Disease and Neurocognitive Disorders with Multimodal Bi-Vision Transformer (BiViT).” Pattern Analysis and Applications, vol. 27, no. 3, 76, 2024.

Ishtiaq, A., Munir, K., Raza, A., Samee, N. A., Jamjoom, M. M., and Ullah, Z. “Product Helpfulness Detection with Novel Transformer Based BERT Embedding and Class Probability Features.” IEEE Access, 2024.

Abbas, M. A., Munir, K., Raza, A., Samee, N. A., Jamjoom, M. M., and Ullah, Z. “Novel Transformer Based Contextualized Embedding and Probabilistic Features for Depression Detection from Social Media.” IEEE Access, 2024.

Elhadad, A., Jamjoom, M., and Abulkasim, H. “Reduction of NIFTI Files Storage and Compression to Facilitate Telemedicine Services Based on Quantization Hiding of Downsampling Approach.” Scientific Reports, vol. 14, no. 1, 5168, 2024.

Malik, M. S. I., Younas, M. Z., Jamjoom, M. M., and Ignatov, D. I. “Categorization of Tweets for Damages: Infrastructure and Human Damage Assessment Using Fine-Tuned BERT Model.” PeerJ Computer Science, vol. 10, e1859, 2024.

Malik, M. S. I., Nawaz, A., Jamjoom, M. M., and Ignatov, D. I. “Effectiveness of ELMo Embeddings and Semantic Models in Predicting Review Helpfulness.” Intelligent Data Analysis, (Preprint), 1-21, 2023.

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) 📄🔍

Fahd Alharithi | Artificial Intelligence | Best Researcher Award

Published on by Shen Awards

Assoc Prof Dr. Fahd Alharithi | Artificial Intelligence | Best Researcher Award

Department chair at Taif University, Saudi Arabia

Dr. Fahd Saad Alharithi is an accomplished researcher and academic with a Ph.D. in Computer Science from Florida Institute of Technology and extensive experience in both teaching and research. Currently an Assistant Professor at Taif University, his research spans a wide array of topics, including medical data categorization, oil spill detection, COVID-19 diagnosis, and IoT security. Dr. Alharithi has published numerous papers in high-impact journals such as Sensors and Remote Sensing, showcasing his innovative approaches and significant contributions to his field. In addition to his research, he has a strong background in teaching, having served as a lecturer and teaching assistant at various institutions. His involvement in volunteer work and leadership roles further highlights his commitment to community service. While his diverse research and impactful publications are noteworthy, highlighting research grants and awards could strengthen his profile for recognition.

Profile

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

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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