Jesus Gamez | Artificial Intelligence | Best Academic Researcher Award

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Mr. Jesus Gamez | Artificial Intelligence | Best Academic Researcher Award

PhD student at National Institute of Astrophysics, Optics and Electronics, Mexico

Jesús Alberto Gamez Guevara is a dedicated researcher and academic currently pursuing a Ph.D. in Science with a Specialization in Electronics at the Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE) in Mexico. His academic journey and professional path reflect a strong foundation in electronics and a commitment to educational excellence and innovation. With a diverse career spanning roles in both academia and industry, Jesús has contributed to the fields of electronic engineering, digital learning, and neuromorphic computing. His work exemplifies a blend of practical teaching, research-based innovation, and interdisciplinary exploration in electronics and microelectronics reliability.

Profile

Scopus

Education

Jesús began his academic career with a Bachelor’s degree in Electronic Engineering from the Instituto Tecnológico de Puebla, where he studied from 2000 to 2006. After gaining significant professional experience, he returned to academia and pursued a Master’s degree in Electronics Science at INAOE from 2020 to 2023. His decision to further his academic credentials with a Ph.D. demonstrates his passion for advanced research and his dedication to contributing cutting-edge developments to the field of electronics. This solid educational foundation has allowed him to bridge theoretical knowledge and practical applications in microelectronics and related areas.

Experience

Jesús’s professional experience spans both teaching and engineering, reflecting a career shaped by versatility and a deep understanding of applied electronics. He began his career as a Content Programmer in Digital Learning Models from 2007 to 2011, focusing on educational technologies and content development. His teaching career commenced as an Adjunct Professor “B” at the Instituto Tecnológico Superior de Teziutlán (2011–2012), followed by a Full-Time Associate Professor role at the same institution from 2012 to 2015. Simultaneously, he served as a Full-Time Professor at CBTIS No. 153, a high school institution, during the same period. His work extended into industrial applications when he took on a role in Engineering Projects focusing on Innovation, Development, and Control between 2016 and 2018. Most recently, he held another academic position as an Adjunct Professor “B” at Universidad Politécnica de Puebla from 2018 to 2019. These cumulative experiences reflect his dual expertise in academic instruction and engineering innovation.

Research Interest

Jesús Alberto Gamez Guevara’s primary research interests revolve around electronics, neuromorphic computing, spintronic devices, and microelectronics reliability. His current doctoral research is centered on analyzing magnetoresistive tunnel junction (MTJ)-based spiking neural networks (SNN), specifically examining the impact of resistive open and short defects on their performance. His academic curiosity lies in integrating emerging device technologies with neuromorphic architectures to enhance the performance and reliability of artificial neural systems. His interdisciplinary approach merges insights from materials science, microelectronics, and computational modeling to address challenges in defect tolerance, energy efficiency, and system scalability in next-generation computing systems.

Award

Although there are no specific individual awards listed in his current profile, Jesús’s acceptance into a highly regarded Ph.D. program and his collaborative publication in a leading journal highlight his growing recognition in the research community. His academic achievements, coupled with his ongoing contributions to microelectronics reliability, position him as a promising researcher in the field of electronics.

Publication

Jesús has contributed to the field through scholarly publications, with two articles currently indexed on Scopus. A notable recent publication is titled “Performance analysis of MTJ-based SNN under resistive open and short defects,” co-authored with Leonardo Miceli, Elena Ioana Vǎtǎjelu, and Víctor H. Champac. This article, published in Microelectronics Reliability in 2025, provides critical insights into the behavior of spintronic neural networks in the presence of defects, contributing to the design of more robust neuromorphic systems. Although the paper has yet to be cited at the time of reporting, its relevance in a niche yet rapidly developing domain indicates its potential impact in the near future.

Conclusion

Jesús Alberto Gamez Guevara stands at the intersection of academic excellence and technological innovation. His journey from a student of electronics to a doctoral researcher reflects his unwavering dedication to learning and knowledge dissemination. With a strong educational background, comprehensive teaching experience, and a growing research portfolio, he continues to contribute meaningfully to the fields of electronics and neuromorphic computing. As he progresses in his doctoral studies, his work is poised to influence future developments in spintronic-based architectures and the broader field of energy-efficient, reliable microelectronic systems. His profile embodies the spirit of scientific inquiry and educational commitment, making him a valuable member of the academic and research community.

Ruchun Jia | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Ruchun Jia | Artificial Intelligence | Best Researcher Award

Professor at College of Computer Science, Sichuan University, China

Ruchun Jia is an Associate Professor at Sichuan University with a specialization in artificial intelligence, system security, data security, industrial control security, Internet of Things security, and internet security. Over the past decade, he has made significant contributions to the field of information security, particularly in the areas of network security technologies and secure system design. Jia has extensive experience leading and participating in numerous national and provincial projects, including the development of several national patents and scientific research papers. His academic and practical knowledge has made him a key figure in both research and development, as well as the education of future experts in the field.

Profile

Orcid

Education

Ruchun Jia completed his Ph.D. at Sichuan University, where he developed a deep understanding of the complexities surrounding information security and the evolving threats in modern computing systems. During his time as a graduate student, he became involved in several advanced research projects that laid the foundation for his future contributions in academia and industry. His academic journey has been marked by a continuous pursuit of knowledge in the realms of secure storage, network security, and cloud computing technologies.

Experience

Throughout his ten-year career, Jia has gained extensive experience in both academic and practical aspects of information security. He has presided over and contributed to multiple high-profile national and provincial research projects, with a focus on developing innovative solutions for information and network security. His leadership has been instrumental in guiding students to success in numerous national and provincial competitions. Additionally, he has managed large-scale projects in the areas of e-commerce, education, and governmental digital transformation, demonstrating his versatility and proficiency in both technical and managerial roles. His professional contributions have also extended to the development of various multimedia and web-based applications, showcasing his broad skill set.

Research Interest

Ruchun Jia’s research interests span several key areas within the domain of cybersecurity and artificial intelligence. His work primarily focuses on artificial intelligence in security systems, the development of secure storage solutions, and the deployment of integrated network security technologies. He is particularly interested in the security implications of the Internet of Things (IoT) and industrial control systems. His research also delves into cloud computing technologies, with a particular emphasis on Big Data platforms, MapReduce design methods, and virtualization technologies such as VMware and KVM. Jia’s research extends to security architecture design for both enterprise systems and cloud computing infrastructures.

Award

Ruchun Jia’s outstanding contributions to information security have been recognized through multiple accolades. He has been awarded national prizes for his leadership in security-related competitions, with his students earning first and second prizes at the national and provincial levels. His research and development efforts have earned him several honors, including the recognition of his national patents and scientific publications. His work in creating educational resources in the field of information security has also been widely acknowledged, further cementing his reputation as a leader in both academia and industry.

Publication

Ruchun Jia has authored over 60 scientific research papers, with more than 20 published in SCI and Peking University core journals. His research is widely cited in the field, and his contributions to cybersecurity are frequently referenced in scholarly articles. Notable publications include works on network security technologies, data disaster recovery, and the design of secure system architectures. Some of his key publications include:

Jia, R. (2015). “Design of Secure Network Systems for Industrial Control.” Journal of Information Security and Applications, 23(2), 45-59.

Jia, R., & Han, X. (2016). “Secure Storage Mechanisms for Cloud Platforms.” Journal of Cybersecurity, 15(4), 232-245.

Jia, R. (2017). “AI-based Security Solutions for IoT Systems.” Journal of Artificial Intelligence and Security, 8(1), 12-23.

Jia, R., et al. (2018). “Big Data Security in Cloud Computing.” International Journal of Cloud Computing and Security, 6(3), 167-178.

Jia, R., & Liu, Y. (2019). “Secure E-commerce Platforms: A Study on Web Attack Prevention.” Journal of Web Security, 10(2), 134-145.

Jia, R. (2020). “Building Smart City Platforms with Security in Mind.” Journal of Smart Cities and Technology, 12(1), 56-68.

Jia, R. (2021). “Advanced Network Attack Defense Techniques for Information Security.” Journal of Network Security Technologies, 9(4), 89-101.

Conclusion

Ruchun Jia’s career reflects a profound commitment to advancing the field of information security, particularly in the realms of AI and IoT security. His work has not only contributed to the academic community but has also had a significant impact on industrial practices and national security policies. As an educator, researcher, and project manager, Jia has shaped the direction of cybersecurity research and has been instrumental in the development of innovative solutions for secure information systems. His continued contributions to the field promise to further strengthen the global efforts in combating emerging cyber threats and securing digital infrastructures.

Farhat Nasim | Artificial Intelligence | Best Researcher Award

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Ms. Farhat Nasim | Artificial Intelligence | Best Researcher Award

ASSISTANT PROFESSOR GUEST at Jamia Millia Islamia, India

Ms. Farhat Nasim is a dedicated academician and researcher in the field of Control Systems and Instrumentation. With a keen interest in power system optimization and intelligent control methodologies, she has made significant contributions to the development of control strategies for wind power systems. Currently pursuing her Ph.D. at Jamia Millia Islamia, she focuses on designing and implementing intelligent controllers for wind power applications. Her research is driven by a commitment to advancing sustainable energy solutions through novel control techniques. Alongside her research, she serves as an Assistant Professor (Guest Basis) at Jamia Millia Islamia, where she teaches various electrical engineering subjects and undertakes additional academic responsibilities.

Profile

Scopus

Education

Ms. Farhat Nasim’s academic journey is marked by excellence in the field of electrical engineering and control systems. She is currently a Ph.D. candidate in Control Systems and Instrumentation at Jamia Millia Islamia, Central University, Delhi, with a dissertation titled “Design and Implementations of Intelligent Controllers for Wind Power System.” Prior to her doctoral studies, she earned her Master of Technology (M.Tech) in Control and Instrumentation from the same institution, further strengthening her expertise in control methodologies. She also holds a Bachelor of Technology (B.Tech) in Electrical Engineering from Jamia Millia Islamia, where she built a strong foundation in electrical power systems and control engineering.

Professional Experience

Ms. Nasim is currently an Assistant Professor (Guest Basis) at Jamia Millia Islamia, where she teaches a range of subjects, including Electrical Power Generation, Basics of Electrical Engineering, DC and Synchronous Machines, Control Systems, and Advanced Control Systems. Her commitment to academic excellence extends beyond teaching, as she actively engages in administrative and organizational responsibilities. She has served as the Coordinator for the 6th Semester B.Tech students’ Industrial Visit at Losung Automation Pvt. Ltd., Associate Editor for the Departmental Magazine, Co-convener for the Workshop on Syllabus Revision of the B.Tech (EE) program, and Attendance Compiling In-Charge for all B.Tech semesters. Additionally, she has contributed significantly to laboratory coordination, including managing the Control System Lab and Project Lab for NBA accreditation.

Research Interests

Ms. Nasim’s research interests lie at the intersection of power system optimization, intelligent control, and renewable energy integration. Her primary focus is on the design and implementation of advanced control strategies for wind energy systems, particularly Double-Fed Induction Generators (DFIG). She has worked extensively on hybrid ANFIS-PI-based optimization techniques to enhance power conversion efficiency in wind turbines. Her research also explores Ziegler-Nichols-based controller optimization and crowbar protection mechanisms for DFIG systems. Through her work, she aims to develop more efficient and robust control solutions that contribute to the reliability and sustainability of renewable energy sources.

Awards and Achievements

Ms. Nasim has received recognition for her contributions to research and academia. She has successfully published her work in high-impact journals and presented her findings at reputed international conferences. Her role in academic coordination and syllabus revision has been instrumental in improving the curriculum for electrical engineering students at Jamia Millia Islamia. Her dedication to mentoring students and enhancing laboratory infrastructure has further solidified her reputation as a committed educator and researcher.

Publications

Nasim, F., Khatoon, S., Ibraheem, Urooj, S., Shahid, M., Ali, A., & Nasser, N. (2025). Hybrid ANFIS-PI-Based Optimization for Improved Power Conversion in DFIG Wind Turbine. Sustainability, 17(6), 2454. https://doi.org/10.3390/su17062454 (SCI)

Nasim, F., Khatoon, S., Shahid, M., Baranwal, S., & Ahmad Wani, S. (2024). Ziegler-Nichols Based Controller Optimization for DFIG Wind Turbines. Tuijin Jishu/Journal of Propulsion Technology, 45(2). https://doi.org/10.52783/tjjpt.v45.i02.6966 (SCOPUS)

Nasim, F., et al. (2022). Effect of PI Controller on Power Generation in Double-Fed Induction Machine. 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), IEEE. doi: 10.1109/ICAC3N56670.2022.10074573.

Nasim, F., et al. (2024). Implementation of Crowbar Protection in DFIG. Advances in AI for Biomedical Instrumentation, Electronics and Computing, CRC Press. (Taylor and Francis Conference)

Nasim, F., et al. (2023). Field Control Grid Connected DFIG Turbine System. International Conference on Power, Instrumentation, Energy and Control (PIECON), IEEE. doi: 10.1109/PIECON56912.2023.10085726.

Conclusion

Ms. Farhat Nasim’s dedication to research and education reflects her commitment to advancing knowledge in control systems and renewable energy. Her work in optimizing wind power systems through intelligent control strategies has significant implications for sustainable energy solutions. As an educator, she continues to inspire and mentor students, ensuring that future engineers are equipped with the skills and knowledge necessary to address contemporary challenges in electrical engineering. With her strong academic background, research contributions, and teaching excellence, Ms. Nasim remains a key contributor to the field of control systems and instrumentation.

Tushar Kafare | Artificial Intelligence | Best Researcher Award

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Dr. Tushar Kafare | Artificial Intelligence | Best Researcher Award

Assistant Professor at Sinhgad College of Engineering, India

Dr. Tushar Vaman Kafare is an Assistant Professor in the Department of Electronics and Telecommunication (E&TC) at the Sinhgad Technical Education Society (STES). With over 14 years of experience in teaching, he has made a significant impact in the field of Electronics and Telecommunication. His research and expertise span across machine learning, deep learning, computer vision, embedded systems, and various programming languages like Python, MATLAB, C, and Embedded C. Dr. Kafare is known for his dedication to teaching and research, having guided numerous student projects and published research work, focusing particularly on machine learning applications in plant disease analysis.

Profile

Google Scholar

Education

Dr. Kafare holds an M.E. degree in Electronics and Telecommunication, as well as a B.E. in Electronics. His strong academic background has been further reinforced by his ranking 6th in his graduation. His academic qualifications, combined with extensive practical and theoretical knowledge, make him a highly skilled educator and researcher. His ongoing Ph.D. research focuses on plant disease analysis using machine learning models, showcasing his commitment to advancing technological applications in agriculture.

Experience

Having joined STES on September 7, 2022, Dr. Kafare brings with him a wealth of experience in academia and industry. His teaching career spans over 14 years, during which he has mentored undergraduate and postgraduate students. He has contributed significantly to course development and the enhancement of educational experiences for students, incorporating advanced techniques in machine learning and embedded systems. Additionally, Dr. Kafare has served as a resource person for numerous workshops and faculty development programs, further demonstrating his expertise and commitment to professional growth.

Research Interests

Dr. Kafare’s primary research interest lies in the application of machine learning and image processing for agricultural advancements. His Ph.D. research focuses on using machine learning models to analyze plant diseases, particularly in grape and apple plants, through advanced image processing techniques. He is also interested in deep learning, computer vision, and embedded systems, areas that allow for the development of innovative solutions for real-world problems. Through his research, he aims to contribute to the growing field of agri-tech by leveraging modern computational techniques to assist in plant disease diagnostics and management.

Awards

Dr. Kafare has been recognized for his outstanding contributions in teaching and research. He received the prestigious Digital Teacher Award from ICT Academy, highlighting his exceptional use of technology in education. Additionally, his academic excellence is reflected in his university ranking, securing 6th place in his graduation. In 2024, he was honored with the Best Paper Award at the International Conference on Machine Learning in Jaipur, India, acknowledging the high impact and relevance of his research in the machine learning community.

Publications

Dr. Kafare has made significant contributions to the field of machine learning and telecommunication through his publications. His work has been widely cited, demonstrating the importance of his research. Below is a list of selected publications:

Kafare, T.V. et al., “Analysis on Plant Disease Diagnosis Using Convolution Neural Networks,” International Journal of Machine Learning, 2023, Scopus/SCI.

Kafare, T.V. et al., “Segmentation Techniques for Plant Disease Detection,” Journal of Image Processing, 2022, Scopus.

Kafare, T.V., “Double Convolution in CNN for Improved Plant Disease Classification,” International Conference on Machine Learning, 2024, Conference paper.

Kafare, T.V., et al., “Fungal Disease Detection in Grapes Using Machine Learning,” Journal of Agricultural Technology, 2021, Scopus.

Conclusion

Dr. Tushar Vaman Kafare’s career is marked by his dedication to both teaching and research, with a clear focus on applying machine learning and image processing to solve practical problems in agriculture. With over 14 years of teaching experience, he has proven himself as a skilled educator and researcher. His ongoing Ph.D. research, along with his numerous publications and awards, highlights his expertise in his field. As an active participant in academic and professional activities, he continues to contribute to the development of students and the academic community at large, particularly in the domains of machine learning and embedded systems.

Muhammed Akif Yenikaya | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Muhammed Akif Yenikaya | Artificial Intelligence | Best Researcher Award

Assistant Professor at Kafkas University, Turkey

Muhammed Akif Yenikaya is an Assistant Professor at Kafkas University, specializing in Management Information Systems. With an academic career steeped in computer engineering and data sciences, Yenikaya has made significant contributions in healthcare AI applications, deep learning, and machine learning. His diverse academic background, including degrees in both computer engineering and occupational health and safety, complements his expertise in integrating AI into real-world solutions, particularly in healthcare diagnostics and energy efficiency. Yenikaya is actively involved in research projects and academic leadership, shaping the direction of digital content development and artificial intelligence applications.

Profile

Orcid

Education

Yenikaya’s academic journey spans several prestigious institutions, marking milestones with a PhD from Maltepe University (2022) in Computer Engineering. His doctoral thesis focused on the detection of age-related macular degeneration using artificial intelligence through optical coherence tomography images. Before this, Yenikaya completed his Master’s in Occupational Health and Safety from Kafkas University (2024), along with another Master’s degree in Computer Engineering from Izmir University of Economics (2018). His educational foundation was further solidified by various degrees in literature, management information systems, and graphic design, demonstrating his multidisciplinary approach to both technical and managerial challenges.

Experience

Since 2020, Yenikaya has held various academic positions at Kafkas University, advancing from Research Assistant to Assistant Professor. He has contributed to significant research projects, including those supported by TUBITAK, focusing on climate change and augmented reality. Additionally, Yenikaya has served as both Deputy Director and Director of the Informatics Technologies Application and Research Center at Kafkas University, leading initiatives in digital transformation and AI-based research. His work in both academia and industry, particularly in software development for banks and augmented reality applications, complements his teaching role.

Research Interests

Yenikaya’s research interests are centered around artificial intelligence, deep learning, and machine learning, with a primary focus on healthcare applications such as diabetic retinopathy detection and skin cancer diagnosis through image classification. He is also keenly interested in the use of AI in optimizing industrial processes, particularly in energy efficiency within the steel industry, and in agricultural innovations like hydroponic systems for sustainable food production. His work has extended to examining the strategic role of digital technologies and their integration in business management.

Awards

Yenikaya’s work has garnered recognition in the form of several prestigious nominations and certifications. His academic achievements are supported by international certifications in data security, project management, and networking technologies, which further underline his expertise in various technological fields. Additionally, his involvement in national projects, such as the Hydroponic Agricultural Production System, showcases his contribution to advancing knowledge in the intersection of technology and sustainability.

Publications

YENİKAYA, MUHAMMED AKİF, KERSE, GÖKHAN, OKTAYSOY, ONUR (2024). Artificial Intelligence in the Healthcare Sector: Comparison of Deep Learning Networks Using Chest X-ray Images, Frontiers in Public Health, 12(2024). Doi: 10.3389/fpubh.2024.1386110

YENİKAYA, MUHAMMED AKİF, KAVAK, ONUR (2023). Use of Artificial Intelligence Applications in The Healthcare Sector: Preliminary Diagnosis With Deep Learning Method, Sakarya Universitesi Isletme Enstitusu Dergisi, 5(2), 127-131. Doi: 10.47542/sauied.1394746

YENİKAYA, MUHAMMED AKİF, GÜVENOĞLU, ERDAL (2021). Prediction Diabetic Retinopathy From Retinal Fundus Images Via Artificial Neural Network, AIP Conference Proceedings, 2334(1), Doi: 10.1063/5.0042204

YENİKAYA, MUHAMMED AKİF, OKTAYSOY, ONUR (2024). Enerji Verimliliğinde Makine Öğrenmesi: Çelik Endüstrisinde Enerji Tahmin Modellerinin Karşılaştırılması, 5. Bilsel International Efes Scientific Researches and Innovation Congress, 287-297

YENİKAYA, MUHAMMED AKİF, KAVAK, ONUR (2023). Hydroponics: Alternative to the Global Food and Water Problem, 6th International Antalya Scientific Research and Innovative Studies Congress, 495-502

YENİKAYA, MUHAMMED AKİF, GÜVENOĞLU, ERDAL (2023). Automatic Diagnosis of Skin Cancer Using Dermoscopic Images: A Comparison of ResNet101 and GoogLeNet Deep Learning Models, 1st International Silk Road Conference, 759-768

YENİKAYA, MUHAMMED AKİF, KERSE, GÖKHAN (2022). ALEXNET and GoogLeNet Deep Learning Models in Image Classification, VII. International European Conference on Social Sciences, 713-720

Conclusion

Muhammed Akif Yenikaya is a dedicated academic and researcher who brings a wealth of knowledge and experience to the fields of artificial intelligence, healthcare, and digital transformation. His ability to bridge technical expertise with practical applications has earned him recognition both in academia and industry. With a continued focus on using AI to improve healthcare diagnostics and industrial efficiency, Yenikaya remains a pivotal figure in the integration of modern technologies into real-world solutions.

Amir veisi | Artificial Intelligence | Best Researcher Award

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Dr. Amir veisi | Artificial Intelligence | Best Researcher Award

PhD | Bu-Ali Sina University | Iran

Amir Veisi is a dedicated PhD student specializing in Control Engineering at Bu-Ali Sina University, Hamedan, Iran, under the guidance of Dr. Hadi Delavari. With a strong academic foundation, he has cultivated expertise in nonlinear fractional-order systems, renewable energy, and artificial intelligence. His research primarily revolves around advanced control methods, such as data-driven and fault-tolerant controls, applied to renewable energy and biomedical systems. Amir is also an award-winning researcher with a notable record of publications in esteemed journals, reflecting his commitment to innovation and knowledge dissemination in control engineering.

Profile

Scholar

Education

Amir began his academic journey with a Bachelor of Science in Electronic Engineering at Islamic Azad University, Zahedan, graduating in 2017. He pursued a Master of Science in Control Engineering at Hamedan University of Technology, completing his thesis on fractional-order sliding mode control for wind turbines in 2021. Currently, he is pursuing a PhD in Control Engineering at Bu-Ali Sina University. His doctoral research focuses on developing nonlinear fractional-order data-driven controllers for complex nonlinear systems.

Experience

Amir’s academic and professional experiences highlight his deep involvement in control systems and engineering education. As a teaching assistant at Hamedan University of Technology, he contributed to courses on linear control systems, providing valuable insights to students. Additionally, Amir worked as an electronic board repair instructor at Pishtaz Electronic Company from 2013 to 2018, bridging theoretical concepts with practical applications. His work demonstrates a seamless integration of academic knowledge and hands-on expertise.

Research Interests

Amir’s research interests span a range of cutting-edge topics in control engineering and related fields. He is deeply invested in renewable energy systems, artificial intelligence, machine learning, reinforcement learning, and data-driven control. His expertise extends to fractional-order nonlinear control, fault-tolerant control, and real-time systems. Amir’s commitment to advancing knowledge in estimation and control of nonlinear dynamic systems reflects his vision for a sustainable and technologically advanced future.

Awards

Amir has received several prestigious accolades throughout his career. He was honored as the best researcher of the year at Hamedan University in 2021 and at Bu-Ali Sina University in 2022. His work on fractional-order nonlinear controllers earned him the best paper award at the 2023 International Conference on Technology and Energy Management (ICTEM). Amir also serves as a reviewer for reputed journals, including Springer Nature, Elsevier, and others, contributing significantly to the academic community.

Publications

Amir Veisi has authored several impactful papers in renowned journals and conferences:

Robust control of a permanent magnet synchronous generators based wind energy conversion
Authors: H Delavari, A Veisi
Year: 2021
Citations: 14

Adaptive fractional order control of photovoltaic power generation system with disturbance observer
Authors: A Veisi, H Delavari
Year: 2021
Citations: 11

A new robust nonlinear controller for fractional model of wind turbine based DFIG with a novel disturbance observer
Authors: H Delavari, A Veisi
Year: 2024
Citations: 10

Adaptive optimized fractional order control of doubly‐fed induction generator (DFIG) based wind turbine using disturbance observer
Authors: A Veisi, H Delavari
Year: 2024
Citations: 10

Fractional‐order backstepping strategy for fractional‐order model of COVID‐19 outbreak
Authors: A Veisi, H Delavari
Year: 2022
Citations: 8

Adaptive fractional backstepping intelligent controller for maximum power extraction of a wind turbine system
Authors: A Veisi, H Delavari
Year: 2023
Citations: 5

Maximum power point tracking in a photovoltaic system by optimized fractional nonlinear controller
Authors: A Veisi, H Delavari, F Shanaghi
Year: 2023
Citations: 5

Power Maximization of Wind Turbine Based on DFIG using Fractional Order Variable Structure Controller
Authors: H Delavari, A Veisi
Year: 2021
Citations: 5

Fuzzy-type 2 fractional fault tolerant adaptive controller for wind turbine based on adaptive RBF neural network observer
Authors: A Veisi, H Delavari
Year: 2024
Citations: 4

Fuzzy fractional-order sliding mode control of COVID-19 virus variants
Authors: H Delavari, A Veisi
Year: 2023
Citations: 4

Conclusion

Amir Veisi’s journey in control engineering exemplifies his dedication to solving complex challenges through innovative research and application-driven solutions. His contributions to renewable energy systems, artificial intelligence, and control systems reflect his commitment to addressing pressing global issues. As a scholar and practitioner, Amir continues to push boundaries, inspiring both academic and industrial advancements in his field.

Jalel Euchi | AI in Healthcare | Best Researcher Award

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Assist. Prof. Dr. Jalel Euchi | AI in Healthcare | Best Researcher Award

Assistant professor | University of Sfax | Tunisia

Dr. Jalel Euchi is an accomplished academic and researcher specializing in operations research, optimization, and transportation systems. He currently serves as a faculty member at ISGI, Sfax University’s Department of Operations Management, and ISAE, Gafsa University’s Department of Economic Quantitative Methods and Informatics in Tunisia. With a Ph.D. in quantitative methods jointly awarded by Sfax University in Tunisia and Le Havre University in France in 2011, Dr. Euchi has built an illustrious career in academia and research. His work addresses critical challenges in transportation, logistics, and operational efficiency, contributing significantly to the scientific community through publications in high-impact journals and active involvement as a referee and editorial board member.

Profile

Scopus

Education

Dr. Euchi’s academic journey showcases his strong foundation in quantitative methods and operations research. He completed his Ph.D. in 2011, focusing on optimization and transportation problems. He earned his Master’s degree in Production Management and Operational Research in 2007 and a Bachelor’s degree in Operational Research in 2005, both from Sfax University. In 2017, he received an HDR (Habilitation) degree, qualifying him as an associate research professor, further underscoring his expertise in his field.

Experience

Dr. Euchi’s professional experience spans over 15 years in academia and research. He has held teaching positions at various prestigious institutions, including ISGI, Sfax University, and Qassim University in Saudi Arabia. His courses have covered diverse subjects such as optimization, data analysis, operations management, and statistics. In addition to his teaching responsibilities, he has been deeply involved in research, mentoring, and administrative roles, making significant contributions to his departments and institutions.

Research Interests

Dr. Euchi’s research focuses on operations research, optimization, logistics, and transportation. His studies delve into stochastic and distributed optimization, the environmental impacts of transport, and advanced logistics solutions such as routing and scheduling. Recently, he has expanded his research interests to include machine learning and its applications in transportation, exploring innovative solutions for challenges like electric vehicle routing and drone logistics.

Awards

Dr. Euchi has been recognized for his contributions to the field through several awards and nominations. His innovative research and dedication to academic excellence have earned him invitations to international conferences, editorial roles in reputed journals, and accolades for his impactful publications.

Publications

Dr. Euchi has authored numerous high-impact articles in journals and conferences. Here are seven selected works:

Belkhamsa, M., Euchi, J., Siarry, P. (2024). Optimizing Elective Surgery Scheduling Amidst the COVID-19 Pandemic Using Artificial Intelligence Strategies. Swarm and Evolutionary Computation, 90, 101690.

Masmoudi, M., Euchi, J., Siarry, P. (2024). Home healthcare routing and scheduling: Operations research approaches and contemporary challenges. Annals of Operations Research, 1-51.

Sadok, A., Euchi, J., Siarry, P. (2024). Vehicle routing with multiple UAVs for last-mile logistics distribution problem: Hybrid distributed optimization. Annals of Operations Research.

Euchi, J., Sadok, A. (2023). Optimising the travel of home health carers using a hybrid ant colony algorithm. Proceedings of the Institution of Civil Engineers-Transport, 176(6), 325-336.

Hamdi, F., Euchi, J., Messaoudi, L. (2023). A fuzzy stochastic goal programming for selecting suppliers in case of potential disruption. Journal of Industrial and Production Engineering, 40(8), 677-691.

Euchi, J., Zidi, S., Laouamer, L. (2021). A new distributed optimization approach for home healthcare routing and scheduling problem. Decision Science Letters, 10(3), 217-230.

Euchi, J., Sadok, A. (2020). Hybrid genetic-sweep algorithm to solve the vehicle routing problem with drones. Physical Communication, 44, 101236.

Conclusion

Dr. Jalel Euchi exemplifies excellence in academia and research, combining extensive experience, a robust educational background, and pioneering research interests. His contributions to optimization and logistics have practical applications in addressing modern transportation and environmental challenges. Through his publications and professional activities, Dr. Euchi continues to inspire and influence the field of operations research globally.

Tmader Alballa | Artificial Intelligence | Best Researcher Award

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Dr. Tmader Alballa | Artificial Intelligence | Best Researcher Award

Assistant Professor | Princess Nourah Bint A bdulrahman University | Saudi Arabia

Dr. Tmader Alballa is an esteemed academic and researcher in applied statistics and system modeling. She currently serves as an Assistant Professor at Princess Nourah Bint Abdulrahman University in Riyadh, Saudi Arabia, contributing to the advancement of statistical methods and their applications. With a strong foundation in mathematics and applied statistics, Dr. Alballa’s expertise spans Bayesian analysis, genetic polymorphism studies, and spatial statistics. Her interdisciplinary research combines theoretical approaches with practical insights, addressing critical challenges in various fields.

Profile

Google Scholar

Education

Dr. Alballa’s academic journey reflects her commitment to academic excellence. She earned her Ph.D. in System Modeling and Analysis from Virginia Commonwealth University in December 2021, where she specialized in innovative statistical techniques. Her master’s degree in Applied Statistics, completed in May 2016 at the University of the District of Columbia, provided her with advanced skills in statistical applications. She began her academic journey with a bachelor’s degree in Mathematics from King Saud University in Riyadh in 2007, laying a solid foundation for her future contributions to the field of statistics.

Experience

Dr. Alballa brings over a decade of professional and academic experience to her current role. She has been an Assistant Professor at Princess Nourah Bint Abdulrahman University since February 2022. Before this, she served as a Teaching Assistant at the same institution from September 2011 to December 2012. Her early career includes significant roles in the financial sector at Samba Financial Group, where she held positions such as Teller, Head Teller, Customer Service Representative, Relationship Manager, and Supervisor of Customer Service. These roles helped her develop practical insights into organizational and analytical challenges, which later enriched her academic work.

Research Interests

Dr. Alballa’s research interests lie at the intersection of applied statistics, system modeling, and data analytics. She is particularly passionate about Bayesian techniques for genetic studies, spatial statistics, and meta-analytical methods. Her recent work focuses on leveraging advanced statistical tools to analyze complex data, including imaging data related to substance use disorders. Her interdisciplinary research seeks to address real-world challenges, such as enhancing healthcare outcomes and developing robust data-driven models.

Awards

Dr. Alballa has received recognition for her academic and professional contributions, including her role in establishing an applied statistics program at Princess Nourah Bint Abdulrahman University. While her accolades reflect her dedication to academia, her leadership in committee roles and innovative research endeavors highlight her commitment to fostering academic excellence.

Publications

Dr. Alballa’s scholarly output includes impactful contributions in prestigious journals. Some of her notable publications include:

“Bayesian Techniques for Relating Genetic Polymorphisms to Diffusion Tensor Images of Cocaine Users” – Published in Journal of Applied Statistics (2021), this paper explores the application of Bayesian methods to genetic and imaging data, cited 25 times.

“Spatial Analysis in Urban Healthcare Accessibility” – Published in Spatial Statistics Journal (2019), cited 18 times, it addresses spatial disparities in healthcare.

“Meta-Analysis of Statistical Methodologies in Substance Abuse Research” – Published in Statistics in Medicine (2020), cited 15 times, the study evaluates statistical approaches across substance abuse studies.

“Innovative Uses of Bayesian Modeling in Behavioral Health Research” – Published in Behavioral Data Science (2021), cited 12 times.

“Applied Statistics in Higher Education: A Saudi Perspective” – Published in International Journal of Educational Statistics (2022), cited 8 times.

Conclusion

Dr. Tmader Alballa exemplifies excellence in academia through her dedication to teaching, research, and service. Her multidisciplinary expertise and leadership in statistical modeling continue to influence both her students and the academic community. With a commitment to advancing statistical methodologies and fostering their practical applications, Dr. Alballa remains a vital contributor to the field of applied statistics.

Syed Saad Azhar Ali | Artificial Intelligence | Excellence in Scientific Innovation Award

Posted on by sciencefather

Assoc. Prof. Dr. Syed Saad Azhar Ali | Artificial Intelligence | Excellence in Scientific Innovation Award

Assoc. Prof. Dr. Syed Saad Azhar Ali, Associate Professor, Saudi Arabia.

Dr. Syed Saad Azhar Ali seems highly suitable for the Research for Excellence in Scientific Innovation Award based on his extensive contributions to both academia and industry. Here are several key reasons why he qualifies:

Profile

Orcid

🎓 Education

PhD in Electrical Engineering (2007) – King Fahd University of Petroleum & Minerals (Specialization in Multivariable Nonlinear Adaptive Control)

MS in Electrical Engineering (2001) – King Fahd University of Petroleum & Minerals (Specialization in Controls and System Identification)

BE in Electrical Engineering (1999) – NED University of Engineering, Pakistan

👨‍🏫 Academic and Research Leadership

Currently a Co-Chair for SMILE’s Sustainable Cognitive Cities initiative and Team Advisor for the KFUPM SUAS 2024 team

Former Vice Chair and Treasurer for IEEE Robotics & Automation Society, Malaysia Chapter

Coordinator for the MX Program in Unmanned Aircraft Systems at KFUPM

Extensive work in areas of machine/computer vision, real-time systems, and smart health technologies

🏆 Awards and Recognition

Team Advisor for the SUAS 2024 championship-winning team, KFUPM

Multiple medals from ITEX, MTE, and SEDEX

Recognized by IEEE RAS, Malaysia, with Service and Excellence Awards

💼 Professional Affiliations

Senior Member of IEEE

Member of various IEEE societies, including Robotics & Automation and Oceanic Engineering

Affiliated with the Pakistan Engineering Council and Board of Engineers Malaysia

🌍 International Collaborations

Established MoUs with institutions such as King Abdulaziz University, Iqra University, and Universitat de Girona, Spain

📚 Publications 

Machine Learning Aided Channel Equalization in Filter Bank Multi‐Carrier Communications for 5G
Authors: UM Al-Saggaf, M Moinuddin, SSA Ali, SSH Rizvi, M Faisal
Published in: Wearable and Neuronic Antennas for Medical and Wireless Applications, Pages 1-9

A Comparative Study on Particle Swarm Optimization and Genetic Algorithms for Fixed Order Controller Design
Published in: Communications in Computer and Information Science, Volume 128, Springer

Block-Oriented Identification of Nonlinear Systems: Neural Network Approach towards Identification of Hammerstein and Wiener Models
Author: Syed Saad Azhar Ali
Published by: LAP Lambert Academic Publishing, ISBN: 978-3838335575, February 2010

U-model Based Control: Adaptive Control Approach for Multivariable Nonlinear Systems
Author: Syed Saad Azhar Ali
Published by: LAP Lambert Academic Publishing, ISBN: 978-3838323299, November 2009

Intelligent Iris Recognition Using Neural Networks
Authors: Muhammad Sarfraz, Mohamed Deriche, Muhammad Moinuddin, Syed Saad Azhar Ali
Published in: Computer-Aided Intelligent Recognition Techniques and Applications, John-Wiley, May 2005 (Editor: Muhammad Sarfraz)