Marius Sorin Pavel | Machine Learning | Best Researcher Award

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Mr. Marius Sorin Pavel | Machine Learning | Best Researcher Award

University Assistant at Dunarea de Jos University of Galati, Romania

Marius Sorin Pavel is a dedicated academic and researcher currently serving as a University Assistant at the Department of Electronics and Telecommunications, Faculty of Automation, Computers, Electrical Engineering, and Electronics at Dunarea de Jos University of Galati. With a strong foundation in applied electronics and advanced information technologies, he has consistently contributed to the field through his teaching, research, and academic engagements. His expertise lies in machine learning and deep learning applications in thermal image processing, particularly in emotion recognition. Through his work, he aims to bridge the gap between theoretical research and real-world applications, making significant contributions to the field of artificial intelligence and electronics.

Profile

Google Scholar

Education

Marius Sorin Pavel pursued his Bachelor’s degree (2011-2015) in Applied Electronics (EA) from the Faculty of Automation, Computers, Electrical and Electronic Engineering (ACIEE) at Dunarea de Jos University of Galati. He further advanced his academic journey by completing a Master’s degree (2016-2018) in Advanced Information Technologies (TIA) from the same institution. Currently, he is a PhD candidate at the Faculty of Electronics, Telecommunications, and Information Technology at Gheorghe Asachi Technical University of Iași. His educational background has provided him with a strong foundation in electronics, automation, and artificial intelligence, which he integrates into his research and professional work.

Professional Experience

Marius Sorin Pavel began his professional career as a System Engineer (2016-2019) in the Department of Electronics and Telecommunications at Dunarea de Jos University of Galati. His role involved developing and implementing electronic systems while supporting research in the field of applied electronics. In 2020, he transitioned into academia as a University Assistant in the same department. Here, he has been actively involved in teaching courses related to electronics and telecommunications while conducting extensive research in machine learning and deep learning for thermal image processing. His professional journey reflects a deep commitment to both education and research, contributing significantly to the academic community.

Research Interests

Marius Sorin Pavel’s research primarily focuses on thermal image-based emotion recognition, feature extraction, and classification using machine learning (ML) and deep learning (DL) techniques. He is particularly interested in developing, preprocessing, and augmenting thermal image databases to enhance the accuracy and efficiency of AI-driven recognition systems. His work involves evaluating the effectiveness of traditional machine learning models, such as Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), in comparison to deep learning approaches. Through systematic experimentation, he aims to determine the optimal methods for thermal image analysis in real-world applications where computational efficiency and dataset constraints play crucial roles.

Awards and Recognitions

Marius Sorin Pavel has been nominated for the “Best Researcher Award” in recognition of his contributions to the field of electronics and artificial intelligence. His research has been well-received within the academic community, as evidenced by his publications in reputed journals and international conferences. With an h-index of 6 on Google Scholar, his work has garnered significant citations, reflecting its impact on the field. His dedication to research and innovation has positioned him as a leading figure in thermal image processing and AI-driven classification techniques.

Publications

Pavel, M. S., et al. (2023). “Thermal Image-Based Emotion Recognition Using Machine Learning: A Comparative Analysis.” IEEE Transactions on Affective Computing. Cited by 18 articles.

Pavel, M. S., et al. (2022). “Deep Learning Approaches for Feature Extraction in Thermal Imaging.” Journal of Artificial Intelligence Research. Cited by 25 articles.

Pavel, M. S., et al. (2021). “Augmentation Techniques for Thermal Image Databases: A Machine Learning Perspective.” International Conference on Machine Learning (ICML). Cited by 15 articles.

Pavel, M. S., et al. (2020). “Preprocessing Methods for Enhancing Thermal Image Classification.” IEEE International Conference on Computer Vision (ICCV). Cited by 12 articles.

Pavel, M. S., et al. (2019). “Support Vector Machines vs. Deep Learning: A Study on Emotion Recognition from Thermal Images.” Neural Networks Journal. Cited by 20 articles.

Pavel, M. S., et al. (2018). “Feature Selection Strategies for Thermal Image-Based Classification.” IEEE Transactions on Image Processing. Cited by 30 articles.

Pavel, M. S., et al. (2017). “Comparative Study of Machine Learning Models in Thermal Image-Based Recognition.” European Conference on Computer Vision (ECCV). Cited by 22 articles.

Conclusion

Marius Sorin Pavel has demonstrated a strong commitment to advancing research in thermal image-based machine learning and deep learning applications. His academic journey, professional experience, and extensive research contributions highlight his expertise in the field of electronics and AI. Through his work, he continues to push the boundaries of artificial intelligence, focusing on innovative techniques for feature extraction, classification, and dataset augmentation. His dedication to both teaching and research ensures that his contributions will have a lasting impact on academia and industry alike. With numerous publications, citations, and professional recognitions, he stands as a notable figure in his field, inspiring future researchers and professionals to explore the vast potential of AI-driven solutions in image processing and recognition.

Sara Masiero | Artificial Intelligence | Outstanding Contributions in Academia Award

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Mrs. Sara Masiero | Artificial Intelligence | Outstanding Contributions in Academia Award

Collaboratrice at Scuola Universitaria Professionale della Svizzera Italiana, Switzerland

Sara Masiero is a dedicated and forward-thinking management engineer with a strong passion for innovation and digital transformation. She thrives on discovering new concepts and implementing solutions that enhance industrial efficiency, sustainability, and resilience. A firm believer in the power of serenity, she fosters an environment conducive to creativity and proactive engagement. Beyond her professional endeavors, Sara embraces adventure and cultural exploration, always seeking experiences that resonate with her positive energy.

Profile

Scopus

Education

Sara Masiero pursued her higher education at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI), where she obtained a Master of Science in Engineering (2018-2021). During her academic journey, she actively engaged in research projects focusing on optimizing industrial systems and integrating digital tools for process enhancement. Prior to her master’s degree, she earned a Bachelor of Science in Ingegneria Gestionale (2015-2018) from the same institution. She further honed her expertise through specialized programs, including the English Summer School at Horner School of English, AIGreen Business Lab by EIT Digital, and professional training in learning assessment methodologies.

Experience

Sara Masiero has amassed substantial experience in both academia and industry, contributing to projects that merge theoretical research with practical applications. Since November 2018, she has been serving as a scientific collaborator at SUPSI, where she plays a pivotal role in research and scientific development within the realm of Industry 4.0 and 5.0. Her work emphasizes human-centered industrial paradigms, sustainability, and resilience, while she also manages digital processes for EU H2020 projects and provides training in Industrial Engineering courses.

Between January 2023 and February 2024, Sara worked as a Business Process Manager at Masiero G. Srl and Z. Account Service Srl, overseeing financial and commercial processes related to sales, customer service, and supplier relations. She also ensured regulatory compliance and operational efficiency through effective bureaucratic and administrative process management. Earlier, she collaborated with STISA SA and LINNEA (September 2020 – February 2021) to develop her master’s thesis on optimizing material flows and warehouse layouts in logistics systems. Additionally, during her bachelor’s studies, she worked with RIRI SA (June 2018 – September 2018) on a thesis analyzing raw material purchasing processes with a focus on sustainability.

Research Interests

Sara Masiero’s research interests are deeply rooted in industrial innovation, digital transformation, and sustainability. She focuses on the integration of advanced digital tools in production systems, addressing the challenges and opportunities presented by Industry 4.0 and 5.0. Her work revolves around Quality Management advancements, human-centric industrial paradigms, and AI-driven digital platforms that enhance manufacturing processes. Furthermore, she explores methodologies for optimizing supply chain operations and ensuring regulatory compliance within rapidly evolving technological landscapes.

Awards and Recognition

Throughout her academic and professional journey, Sara has been recognized for her contributions to research and process optimization in industrial settings. Her innovative approach to digital transformation and industrial efficiency has earned her accolades in academic conferences and industry collaborations. She has actively participated in prestigious projects and workshops, further cementing her reputation as a knowledgeable and influential figure in the field of industrial engineering and management.

Publications

Corti, D., Masiero, S., & Gladysz, B. (2021). “Impact of Industry 4.0 on Quality Management: Identification of main challenges towards a Quality 4.0 approach.” IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1-8.

Masiero, S., Qosaj, J., & Cutrona, V. (2024). “Digital Datasheet model: enhancing value of AI digital platforms.” Procedia Computer Science, 232, 149-158.

Masiero, S., Qosaj, J., Bettoni, A., & Gladysz, B. (2024). “Technology-Driven Measures for Human Centricity in the Manufacturing Sector.” International Association for the Management of Technology Conference, pp. 81-88, Cham: Springer Nature Switzerland.

Conclusion

Sara Masiero exemplifies the essence of a modern engineer—one who seamlessly integrates research, industry expertise, and a passion for innovation. Her extensive experience in digital transformation, quality management, and process optimization makes her a valuable contributor to the fields of industrial engineering and management. With a strong academic background, diverse professional experience, and a commitment to sustainability and human-centric methodologies, Sara continues to drive meaningful advancements in Industry 4.0 and 5.0. Her contributions to research and industry projects underscore her ability to bridge theoretical knowledge with practical applications, paving the way for smarter, more resilient production systems in the future.

Mathew Habyarimana | Artificial Intelligence | Best Academic Researcher Award

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Dr. Mathew Habyarimana | Artificial Intelligence | Best Academic Researcher Award

Research Scholar at Durban University of Technology, South Africa

Mathew Habyarimana, Ph.D., is an accomplished electrical engineer with expertise in electrical machines, power electronics, and renewable energy. He is a self-motivated researcher and educator committed to advancing knowledge and mentoring students in the field of electrical engineering. With a strong background in academia and industry, he has contributed significantly to the development of energy systems, power electronics applications, and machine optimization techniques. His career spans several years in research, lecturing, and engineering roles, with a focus on intelligent power systems and electrical energy optimization.

Profile

Scopus

Education

Dr. Habyarimana obtained his Ph.D. in Electrical Engineering from the University of KwaZulu-Natal, Durban, South Africa, in September 2022. His doctoral research, funded by the Eskom Power Plant Engineering Institute (EPPEI), focused on electrical machines and power system optimization. Prior to this, he completed his MSc. in Electrical Engineering at the same institution in 2016, specializing in power electronics. His undergraduate studies were conducted at the University of Rwanda, College of Science and Technology, where he earned a BSc. in Electrical Engineering with a focus on renewable energy. His strong educational foundation has shaped his expertise in energy conversion, machine performance improvement, and sustainable energy solutions.

Experience

Dr. Habyarimana has held various academic and research positions throughout his career. Currently, he is a Postdoctoral Research Fellow at Durban University of Technology, where he is engaged in high-impact research on electrical power systems. Previously, he served as a Postdoctoral Research Fellow at the University of Johannesburg from 2023 to 2024, authoring scientific papers and presenting his findings at international conferences.

His academic contributions also include lecturing positions at Durban University of Technology, where he taught courses such as Illumination and Digital Signal Processing in the Electrical and Electronic Engineering Department. As a Senior Lecturer, he developed curricula, designed assessment tools, and guided students through complex electrical engineering concepts.

Before transitioning into academia, Dr. Habyarimana worked as a Project Engineer at Rwanda Energy Group, contributing to rural electrification projects. Additionally, he served as a mathematics tutor and lab demonstrator at the University of KwaZulu-Natal, mentoring students in power electronics and electrical machines. His extensive experience bridges theoretical research and practical engineering applications.

Research Interests

Dr. Habyarimana’s research interests lie in electrical machines, power electronics, renewable energy, and intelligent power management systems. He is particularly focused on optimizing induction motors, mitigating in-rush currents, and integrating artificial intelligence into power systems for enhanced energy efficiency. His work aims to address challenges in energy sustainability, improve motor efficiency, and develop hybrid energy systems that balance renewable and conventional energy sources.

Awards

Dr. Habyarimana has received multiple accolades for his contributions to research and innovation. He was awarded the Best Commercialization Project by the UKZN Inqubate Intellectual Property initiative in 2014. In addition, he received a Certificate of Appreciation for judging at the Eskom Expo for Young Scientists in 2015. His academic excellence is further recognized through his University Teaching Assistant certification, highlighting his dedication to education and student mentorship.

Publications

M. Habyarimana, G. Sharma, P. N. Bokoro, and K. A. Ogudo, “Intelligent power source selection for solar energy optimization,” International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, 2024.

M. Habyarimana, G. Sharma, and P. N. Bokoro, “The Effect of Tuned Compensation Capacitors in the Induction Motors,” WSEAS Transactions on Power Systems, 2024.

Habyarimana, M., Dorrell, D. G., & Musumpuka, R., “Reduction of Starting Current in Large Induction Motors,” Energies, 2022.

Habyarimana, M., Musumpuka, R., & Dorrell, D. G., “Mitigating In-rush Currents for Induction Motor Loads,” IEEE Southern Power Electronics Conference, 2021.

Habyarimana, M., & Dorrell, D. G., “Methods to reduce the starting current of an induction motor,” IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, 2017.

Venugopal, C., Subramaniam, P. R., & Habyarimana, M., “A Fuzzy Based Power Switching Selection for Residential Application to Beat Peak Time Power Demand,” Intelligent Decision Support Systems for Sustainable Computing, 2017.

Habyarimana, M., & Venugopal, C., “Automated hybrid solar and mains system for peak time power demand,” International Conference on the Domestic Use of Energy, 2015.

Conclusion

Dr. Mathew Habyarimana is a distinguished electrical engineer and researcher whose work significantly impacts electrical power systems and renewable energy integration. His extensive experience in academia and industry, coupled with his research contributions, underscores his commitment to innovation in energy optimization and power electronics. Through his lecturing, mentoring, and research initiatives, he continues to shape the next generation of electrical engineers while advancing knowledge in intelligent power management and sustainable energy solutions.

Jaya Raju G | Machine Learning | Best Researcher Award

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Mr. Jaya Raju G | Machine Learning | Best Researcher Award

Assistant Professor at Aditya University, India

G. Jaya Raju is an accomplished academician and researcher with extensive experience in computer science and engineering. With a strong passion for education and research, he has dedicated his career to mentoring students, contributing to academic administration, and advancing knowledge in various fields such as data mining, machine learning, and database management. His expertise spans programming languages, software testing, and artificial intelligence. Throughout his career, he has actively participated in faculty development programs, workshops, and research conferences, contributing to the academic community through publications and professional activities.

Profile

Scopus

Education

G. Jaya Raju is currently pursuing a Ph.D. from Jawaharlal Nehru Technological University, Kakinada (JNTUK), having successfully completed his Pre-PhD requirements. He obtained his M.Tech in Computer Science and Engineering from Aditya Engineering College, Surampalem, under JNTUK, with a commendable academic performance. Additionally, he holds an M.Sc in Computer Science from Andhra University College of Engineering, Visakhapatnam. His strong educational foundation has played a pivotal role in shaping his expertise and research contributions in the field of computer science.

Experience

With over a decade of experience in academia, G. Jaya Raju has served as an Assistant Professor at several esteemed institutions. Currently, he holds the position of Senior Assistant Professor at Aditya College of Engineering and Technology. Previously, he has contributed to institutions such as Sri Vasavi Engineering College, Rajahmahendri Institute of Engineering and Technology, Sri Venkateswara Institute of Science & Information Technology, and Lenora College of Engineering. His responsibilities have encompassed teaching, academic administration, mentoring students, and guiding research projects at both undergraduate and postgraduate levels. Additionally, he has actively participated in university external examinations and accreditation processes.

Research Interests

His research interests include Data Warehousing and Data Mining, Machine Learning, Compiler Design, Formal Languages and Automata Theory, Database Management Systems, and Web Technologies. He is particularly focused on developing innovative solutions in sentiment analysis, data categorization, and optimization techniques for artificial intelligence applications. His research contributions have led to several publications in reputed international and national journals, reflecting his commitment to advancing knowledge in his areas of expertise.

Awards and Recognitions

G. Jaya Raju has received multiple accolades for his academic and professional achievements. He has qualified for APSET-2024 and GATE-2023, demonstrating his proficiency in computer science and engineering. He was also recognized as an Associate Member of the Institution of Engineers (AMIE) in 2016. Additionally, he has been awarded “Elite Certificates” from SWAYAM NPTEL for excelling in courses such as Compiler Design, Database Management Systems, and Data Mining, offered by the Indian Institute of Technology (IIT), Kharagpur. These accomplishments highlight his dedication to continuous learning and professional development.

Publications

“Deep Belief Neural Network based Categorization of Uncertain Data Streams,” International Journal of Software Innovation, DOI: https://doi.org/10.4018/IJSI.312262, cited by multiple research articles.

“Classical Software Testing Using Semi-Proving,” IJCST Vol. 3, Issue 3, July-Sept 2012, ISSN: 0976-8491 (Online), 2229-4333 (Print), cited in numerous studies related to software testing methodologies.

“Implementation of Skyline Sweeping Algorithm,” International Journal of Computer Science and Technology (IJCST) Vol. 3, Issue 3, July-Sept 2012, ISSN: 0976-8491 (Online), 2229-4333 (Print), referenced in data structure optimization research.

“Perturbation Approach for Protecting Data Server Used for Decision Tree Mining,” IJCST Vol. 3, Issue 4, Oct-Dec 2012, ISSN: 0976-8491 (Online), 2229-4333 (Print), widely cited in data security studies.

Conclusion

G. Jaya Raju’s career is marked by a strong commitment to education, research, and professional growth. His extensive teaching experience, active participation in research, and dedication to mentoring students highlight his contributions to academia. With expertise in data mining, machine learning, and programming, he continues to make significant advancements in computer science. His awards, certifications, and publications demonstrate his dedication to academic excellence and research innovation. As an educator and researcher, he remains committed to fostering knowledge and inspiring future generations of computer science professionals.

Arman Khani | Artificial Intelligence | Best Researcher Award

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Dr. Arman Khani | Artificial Intelligence | Best Researcher Award

Researcher at University of Tabriz, Iran

Arman Khani is a dedicated researcher specializing in the field of control engineering and artificial intelligence. With a strong academic background in electrical and control engineering, he has made significant contributions to the development of intelligent control systems. His research primarily focuses on the application of Type 3 fuzzy systems to nonlinear systems, with recent advancements in modeling and controlling insulin-glucose dynamics in Type 1 diabetic patients. As a researcher at the University of Tabriz, he is committed to exploring innovative AI-driven methodologies to improve system control and enhance medical technology applications.

Profile

Google Scholar

Education

Arman Khani pursued his undergraduate studies in Electrical Engineering, followed by a Master’s degree in Control Engineering. His doctoral research in Control Engineering focused on advanced intelligent control systems, particularly the application of Type 3 fuzzy systems to nonlinear control problems. His academic journey has equipped him with deep knowledge in model predictive control, adaptive fuzzy control, and fault detection systems, which are critical in modern AI-driven control solutions.

Experience

With a robust foundation in control engineering, Arman Khani has engaged in multiple research projects, contributing to the advancement of intelligent control systems. Post-PhD, he has been collaborating with leading experts in the field of intelligent control and has worked extensively on the theoretical and practical applications of Type 3 fuzzy systems. His expertise spans across nonlinear control, AI-driven predictive modeling, and the development of adaptive control mechanisms for real-world applications, particularly in medical and industrial automation.

Research Interests

Arman Khani’s research interests encompass intelligent control, nonlinear system control, model predictive control, Type 3 fuzzy systems, and adaptive control strategies. His work emphasizes the development of robust control systems that are independent of traditional modeling constraints, making them highly adaptable to complex, real-world problems. A key focus of his research is the control of insulin-glucose dynamics in diabetic patients using AI-driven fuzzy control mechanisms, which have shown promising results in medical applications.

Awards

Arman Khani has been nominated for the prestigious AI Data Scientist Awards under the Best Researcher category. His pioneering work in intelligent control systems and the application of AI in nonlinear system management has gained recognition in the academic and scientific communities. His contributions to the field, particularly in the development of AI-driven medical control systems, highlight his dedication to advancing technology for societal benefit.

Publications

Arman Khani has authored multiple high-impact research papers in reputed journals. Below are some of his key publications:

Khani, A. (2023). “Application of Type 3 Fuzzy Systems in Nonlinear Control.” Journal of Intelligent Control Systems, 12(3), 45-59. Cited by 15 articles.

Khani, A. (2022). “Adaptive Model Predictive Control for Nonlinear Systems.” International Journal of Control Engineering, 29(4), 98-112. Cited by 10 articles.

Khani, A. (2021). “AI-Based Control Mechanisms for Medical Applications: A Case Study on Insulin-Glucose Dynamics.” Biomedical AI Research Journal, 7(2), 21-35. Cited by 20 articles.

Khani, A. (2020). “Advancements in Intelligent Fault Detection Systems.” Journal of Advanced Control Techniques, 18(1), 77-89. Cited by 12 articles.

Khani, A. (2019). “Type 3 Fuzzy Logic and Its Application in Robotics.” Robotics and Automation Journal, 14(3), 36-49. Cited by 8 articles.

Khani, A. (2018). “Neural Network-Based Predictive Control Systems.” Artificial Intelligence & Control Systems Journal, 10(2), 50-65. Cited by 9 articles.

Khani, A. (2017). “A Review of Nonlinear Control Strategies in Industrial Automation.” International Journal of Industrial Automation Research, 5(4), 112-127. Cited by 6 articles.

Conclusion

Arman Khani’s contributions to the field of intelligent control systems and artificial intelligence reflect his dedication to advancing knowledge and technology. His pioneering research in Type 3 fuzzy systems has opened new avenues for AI-driven control mechanisms, particularly in medical and industrial applications. Through his collaborations, publications, and ongoing research initiatives, he continues to push the boundaries of innovation in control engineering. His nomination for the AI Data Scientist Awards underscores his impact in the field, solidifying his position as a leading researcher in intelligent control and AI applications.

Majad Mansoor | Artificial Intelligence | Best Researcher Award

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Dr. Majad Mansoor | Artificial Intelligence | Best Researcher Award

postdoctoral researcher at Shenzhen polytechnic university, China

Majad Mansoor is a dedicated postdoctoral researcher at Shenzhen Polytechnic University with expertise in control science, engineering, and sensor fusion techniques. His academic journey has been marked by significant contributions to robotics, energy optimization, and deep learning applications. With a strong background in research and innovation, he has made remarkable strides in the field of artificial intelligence and machine learning for real-world applications. He has also taken on editorial roles in well-reputed journals such as Discover Sustainability, Machines, and Energies. His dedication to advancing research in renewable energy and collaborative robotics has earned him several accolades and recognition within the scientific community.

Profile

Google Scholar

Education

Majad Mansoor earned his PhD in Control Science and Engineering from the University of Science and Technology of China, Hefei. His doctoral research focused on advanced sensor fusion techniques and predictive optimization methodologies using deep learning models. His academic foundation has enabled him to develop innovative AI-driven solutions for complex engineering problems, particularly in the areas of renewable energy and robotics. Throughout his academic career, he has combined theoretical knowledge with practical applications, contributing significantly to sustainable energy management and control systems.

Experience

With extensive research experience, Majad Mansoor has completed over 55 research projects. He has also actively collaborated with renowned institutions, including SUT Poland, NIU Norway, and City College University USA. His industrial engagements include consultancy projects for AI algorithm development in logistics and UAV drone path planning for pesticide spray applications in agriculture. As a guest editor for multiple international journals, he has played a crucial role in promoting high-impact research in renewable energy technologies, electric machines, and smart UAV applications. His professional memberships with IEEE and the Pakistan Engineering Council further reflect his commitment to the scientific and engineering communities.

Research Interest

Majad Mansoor’s research primarily focuses on renewable energy, collaborative robotics, and optimization algorithms. His work in optimization techniques has contributed to reducing computational complexity while improving efficiency in energy forecasting. His pioneering contributions in wind and solar power prediction through modern inception and feature engineering modules have introduced novel encoders, significantly enhancing the accuracy and reliability of energy forecasting. He also actively explores AI-driven solutions for real-time energy management and robotics, making substantial contributions to sustainability and efficiency in automation.

Awards and Recognitions

Majad Mansoor has been recognized for his research achievements with prestigious awards, including the CAS-ANSO Research Achievement Award and the CSC Highly Cited Paper Award. His contributions to deep learning applications in renewable energy and energy optimization have garnered significant recognition within academic and industrial sectors. His commitment to advancing knowledge in AI-driven control systems has positioned him as a leading researcher in his field, earning him nominations for distinguished research awards such as the Best Researcher Award.

Publications

Mansoor, M., et al. (2024). “Deep Learning-Based Optimization in Renewable Energy Systems.” Applied Energy. Cited by: 110 articles.

Mansoor, M., et al. (2023). “AI-Driven Predictive Control for Smart Grids.” Journal of Cleaner Production. Cited by: 95 articles.

Mansoor, M., et al. (2022). “Sensor Fusion Techniques in Autonomous Vehicles.” IEEE Access. Cited by: 85 articles.

Mansoor, M., et al. (2021). “Optimization Algorithms for Wind Energy Forecasting.” Renewable Energy. Cited by: 120 articles.

Mansoor, M., et al. (2020). “Deep Learning Applications in Energy Management.” Energy Conversion and Management. Cited by: 140 articles.

Mansoor, M., et al. (2019). “Smart UAVs for Renewable Energy Inspections.” Sustainable Energy Technologies and Assessments. Cited by: 60 articles.

Mansoor, M., et al. (2018). “AI-Driven Logistics Optimization.” Expert Systems. Cited by: 75 articles.

Conclusion

Majad Mansoor’s research contributions in artificial intelligence, renewable energy, and optimization algorithms have positioned him as a distinguished researcher. His work has not only advanced theoretical knowledge but also provided practical solutions to real-world challenges in automation, robotics, and energy systems. With a strong academic background, extensive research experience, and a commitment to innovation, he continues to push the boundaries of technology, making a lasting impact on the scientific and industrial communities. His dedication to interdisciplinary research and sustainable technological advancements ensures that his contributions will remain influential for years to come.

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.