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.

Seyed Abolfazl Aghili | Artificial Intelligence | Best Review Paper Award

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Dr. Seyed Abolfazl Aghili | Artificial Intelligence | Best Review Paper Award

Lecturer at Iran university of science and technology, Iran

Seyed Abolfazl Aghili is a dedicated researcher in the field of Civil Engineering, specializing in Construction Engineering and Management. With a strong academic foundation and expertise in artificial intelligence applications for engineering systems, he has contributed significantly to the field through research on resiliency, risk management, and sustainability. His work integrates advanced computational methods with real-world construction challenges, aiming to enhance project decision-making and system efficiency.

Profile

Orcid

Education

Seyed Abolfazl Aghili pursued his Ph.D. in Civil Engineering with a focus on Construction Engineering and Management at the Iran University of Science and Technology (IUST) from 2019 to 2024. His doctoral research explored a framework for determining the long-term resilience of hospital air conditioning systems using artificial intelligence under the guidance of Dr. Mostafa Khanzadi. Prior to his Ph.D., he completed his M.Sc. in Civil Engineering at IUST (2013-2015), investigating employee selection methods in construction firms to optimize hiring processes. He obtained his B.Sc. in Civil Engineering from Isfahan University of Technology (2009-2013), focusing on structural analysis and design in his graduation project.

Experience

Throughout his academic career, Aghili has actively contributed to construction engineering through extensive research and project management. His expertise extends to applying machine learning and deep learning methodologies to engineering challenges, particularly in resilience assessment and risk management. He has also engaged in various industry-oriented projects involving Building Information Modeling (BIM) and decision-making systems for project managers. His academic background is complemented by hands-on experience in technical software such as MS Project, AutoCAD, and Primavera Risk Analysis, which enhances his ability to analyze and implement effective construction management strategies.

Research Interests

Aghili’s research spans multiple interdisciplinary domains, including machine learning and deep learning methods in construction engineering, resiliency, Building Information Modeling (BIM), human resource management in construction, decision-making systems for project managers, risk management, sustainability, and lean construction. His studies aim to optimize construction processes, enhance project resilience, and promote sustainable engineering practices.

Awards and Honors

  • Ranked 5th among 2200 participants in the Nationwide University Entrance Exam for Ph.D. in Iran (2019).
  • Ranked 2nd among all Construction Management students at Iran University of Science and Technology (2013-2015).
  • Ranked 220th among 32,663 participants (Top 1%) in the Nationwide University Entrance Exam for the M.Sc. program in Iran (2013).

Publications

“Artificial Intelligence Approaches to Energy Management in HVAC Systems: A Systematic Review.” Journal of Buildings, Vol. 15, No. 7 (2025): 1008.

“Data-driven approach to fault detection for hospital HVAC system.” Journal of Smart and Sustainable Built Environment, ahead-of-print (2024).

“Feasibility Study of Using BIM in Construction Site Decision Making in Iran.” International Conference on Civil Engineering, Architecture and Urban Infrastructure, July 2015, Tabriz, Iran.

“Review of Digital Imaging Technology in Safety Management in the Construction Industry.” 1st National Conference on Development of Civil Engineering, Architecture, Electricity and Mechanical in Iran, December 2014.

“The Role of Insurance Companies in Managing the Crisis After Earthquake.” 1st National Congress of Engineering, Construction and Evaluation of Development Projects, May 2013, Gorgan, Iran.

“The Need for a New Approach to Pre-crisis and Post-crisis Management of Earthquake.” 1st National Conference on Seismology and Earthquake, February 2013, Yazd, Iran.

Conclusion

Seyed Abolfazl Aghili is a distinguished academic and researcher whose contributions to the field of construction engineering focus on integrating artificial intelligence with resiliency assessment and decision-making in project management. His work has been recognized in high-impact journals and conferences, demonstrating his commitment to advancing the construction industry. Through his research and professional endeavors, he continues to shape the future of sustainable and resilient engineering systems.

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.

Murtaza Hussain | Artificial Intelligence | Best Researcher Award

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Mr. Murtaza Hussain | Artificial Intelligence | Best Researcher Award

PhD Research Scholar at Xi’an Jiaotong University, Singapore

Murtaza Hussain is a dedicated doctoral researcher in applied economics at Xi’an Jiaotong University, focusing on the dynamic intersections of innovation, environmental sustainability, and digital transformation. With an international academic background spanning Pakistan and China, he has cultivated a global perspective in addressing critical economic challenges. His research integrates cutting-edge methodologies to explore how financial constraints and digital orientation influence corporate sustainability and innovation. Passionate about interdisciplinary collaboration, he aims to contribute meaningful insights to the evolving landscape of applied economics, ensuring that businesses and policymakers are equipped with strategic frameworks to drive sustainable growth.

Profile

Orcid

Education

Murtaza Hussain is currently pursuing a Ph.D. in Applied Economics at Xi’an Jiaotong University, where he works under the guidance of Associate Professor Dr. Shaohua Yang. His doctoral research explores the impact of digital transformation on corporate green innovation, particularly in the Chinese market. Prior to his Ph.D., he earned a Master of Audit degree from Nanjing Audit University in 2020, supervised by Dr. Chien-Yu Huang. His master’s studies provided him with strong analytical skills in financial auditing and corporate governance. Earlier in his academic journey, he completed a Bachelor of Science in Economics from Quaid-e-Azam University in Pakistan in 2014, solidifying his foundational understanding of economic theory and policy analysis.

Experience

Throughout his academic and professional career, Murtaza Hussain has engaged in extensive research on corporate sustainability, financial constraints, and digital transformation. He has conducted empirical studies using large-scale panel data to analyze firm behavior and policy impacts. His expertise extends to statistical modeling, data analysis, and econometric techniques using software such as Stata and EViews. Beyond academia, he has participated in several research collaborations focusing on corporate governance, artificial intelligence, and regulatory frameworks. Additionally, he has held leadership roles, including serving as a Recreational Coordinator and a committee member for international students at Nanjing Audit University, where he facilitated academic and cultural exchange initiatives.

Research Interests

Murtaza Hussain’s research interests lie at the confluence of digital transformation, financial constraints, and corporate green innovation. He examines how emerging technologies, particularly artificial intelligence, drive corporate sustainability and strategic decision-making. His work also investigates the role of regulatory policies in shaping CEO compensation structures and corporate misconduct, with a special focus on state-owned enterprises. By integrating theoretical perspectives with empirical analysis, he aims to contribute policy-relevant research that informs both academia and industry on sustainable economic practices.

Awards

Murtaza Hussain has received numerous academic scholarships and recognitions for his contributions to research and leadership. In 2021, he was awarded the prestigious China Belt and Road University Scholarship by Xi’an Jiaotong University. He also received the Chinese Government Scholarship through the China Scholarship Council in 2018. His excellence in postgraduate studies was recognized by Nanjing Audit University, where he was honored as an Excellent Postgraduate of the School of International Exchange in 2020. Additionally, he was a recipient of the Higher Education Commission’s FATA & Balochistan Scholarship in Pakistan, further demonstrating his academic merit and dedication.

Publications

How Digital Orientation Drives Green Innovation: Financial Constraints as a Mediator in Chinese A-Share Firms – Baltic Journal of Management, 2025 (Yang, S., Hussain, M., Maqsood, U.S., Younas, M.W., Zahid, R.M.A.)

Evaluating Corporate Environmental Performance in the Context of Artificial Intelligence: The Contingent Roles of Ownership Type and External Monitoring – Business Strategy and the Environment, 2025 (S. Wang, Y. Yong, M. Hussain, U.S. Maqsood, R.M.A. Zahid)

Regulating CEO Compensation: A Remedy for Corporate Misconducts in China’s State-Owned Enterprises – Borsa Istanbul Review, 2024 (U.S. Maqsood, Q. Li, H. Hussain, M. Hussain, R.M.A. Zahid)

Tapping into the Green Potential: The Power of Artificial Intelligence Adoption in Corporate Green Innovation Drive – Business Strategy and the Environment, 2024 (Hussain, M., Yang, S., Maqsood, U.S., Zahid, R.M.A.)

The Role of Artificial Intelligence in Corporate Digital Strategies: Evidence from China – Kybernetes, 2024 (Yang, S., Hussain, M., Ammar Zahid, R.M., Maqsood, U.S.)

Conclusion

Murtaza Hussain is an emerging scholar in applied economics, committed to advancing research at the intersection of digital transformation, corporate sustainability, and regulatory frameworks. His academic journey from Pakistan to China reflects his adaptability and global outlook, making him a valuable contributor to interdisciplinary research. Through his extensive publication record and scholarship achievements, he continues to shape the discourse on economic innovation and sustainability. With a strong foundation in empirical research and policy analysis, he remains dedicated to bridging the gap between academia and industry, offering solutions to contemporary economic challenges.

Anna Pokrovskaya | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Anna Pokrovskaya | Artificial Intelligence | Best Researcher Award

Ph.D. in Law at Peoples’ Friendship University of Russia, Russia

Anna Pokrovskaya is a dedicated legal professional and researcher specializing in intellectual property law, with extensive experience in patent practices and international legal frameworks. She is currently pursuing her Ph.D. in Law at the Peoples’ Friendship University of Russia, focusing on civil law, procedure, and private international law. Over the years, she has contributed significantly to academia, legal research, and intellectual property management through various roles in leading institutions and organizations. Her work encompasses research, legal consultancy, and publication activities, making her a prominent voice in the legal field.

Profile

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Education

Anna Pokrovskaya holds multiple degrees in law and intellectual property management. She earned her Bachelor of Laws (LLB) from the Peoples’ Friendship University of Russia, specializing in international law. She further pursued her Master’s degree in Intellectual Property Management at Bauman Moscow State Technical University. Additionally, she completed an LLM in Intellectual Property Law at the University of Turin, a joint program with WIPO. Continuing her studies, she is currently completing another LLM in Intellectual Property Law at Tongji University in Shanghai, also in collaboration with WIPO. Her academic journey demonstrates her commitment to understanding global legal perspectives and contributing to legal scholarship.

Experience

Anna has held various roles in prominent institutions. She worked as a Leading Specialist at the Federal Institute of Industrial Property (FIPS), where she contributed to enhancing awareness about intellectual property publication opportunities. She later served as a Lawyer specializing in labor law at LLC Brunel Russia. Since 2020, she has been working as an Expert in Patent Practice at the IP Center “Skolkovo,” dealing with national phase patent applications and collaborating with international clients. In 2024, she joined the Peoples’ Friendship University of Russia as a Research Assistant, contributing to grant projects and academic research. She is set to become an Assistant at the same university in 2025.

Research Interests

Anna’s research interests focus on intellectual property rights, intermediary liability, copyright infringement, and legal frameworks governing e-commerce platforms. She explores how AI influences intellectual property protection and enforcement on digital marketplaces. Her work extends to comparative legal studies, analyzing trademark and copyright laws in different jurisdictions, including Russia, China, and the European Union. Through her research, she seeks to develop effective legal mechanisms to address contemporary intellectual property challenges in digital and cross-border environments.

Awards

Anna has received several grants and academic recognitions. She is a recipient of the RUDN Development Programme “Priority-2030” grant, supporting postgraduate research potential. In 2024, she secured funding under the Russian Science Foundation Grant for research on procedural mechanisms for suppressing online copyright infringements. Additionally, she won individual financial support for participating in international and Russian scientific and technical events. She has also been awarded grants from the Presidential Program and RUDN University for her contributions to the field of intellectual property law.

Publications

Pokrovskaya, A. (2022). “Trademark Infringement on E-commerce Sites.” International Scientific Legal Forum in memory of Prof. V.K. Puchinsky.

Pokrovskaya, A. (2023). “Liability for Trademark Infringement on e-Commerce Marketplaces.” International Journal of Law in Changing World.

Pokrovskaya, A. (2023). “The Distribution of Liability in Trademark Infringement on E-commerce Marketplaces.” Fifth IP & Innovation Researchers of Asia Conference.

Pokrovskaya, A. (2024). “AI-driven Disruption: Trademark Infringement on E-commerce Marketplaces in China.” Russian Law Journal.

Pokrovskaya, A. (2024). “Principles of Intermediaries’ Liability in the Online Environment: The Issue of Online Self-Regulation.” BIO Web of Conferences.

Pokrovskaya, A. (2024). “Protection of Trademark Rights on E-commerce Platforms: An Updated Outlook.” Journal of Comprehensive Business Administration Research.

Pokrovskaya, A. (2024). “Infringement of Intellectual Property Rights on E-commerce Trading Platforms.” Eurasian Law Journal.

Conclusion

Anna Pokrovskaya’s contributions to the field of intellectual property law are remarkable, combining academic research, practical expertise, and international collaboration. Her work on trademark and copyright infringement on digital platforms is highly relevant in today’s rapidly evolving technological landscape. With her ongoing research, publications, and involvement in academic and legal discussions, she continues to shape the discourse on intellectual property rights and their enforcement in the digital age.

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.

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.

Anvesh Reddy Minukuri | Artificial Intelligence | Data Scientist of the Year Award

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Mr. Anvesh Reddy Minukuri | Artificial Intelligence | Data Scientist of the Year Award

Senior Lead at Jpmorgan Chase, United States

Anvesh Reddy Minukuri is a highly experienced data science and artificial intelligence professional with over twelve years of experience in IT, specializing in full-stack modeling, data mining, marketing analytics, big data, AI/ML, and visualization. With a keen focus on developing advanced AI-driven solutions, he has played a pivotal role in optimizing large-scale machine learning models, particularly in the domain of large language models (LLMs). His expertise spans across predictive modeling, customer retention frameworks, deep learning applications, and AI-driven decision-making. Currently, he serves as a Senior Lead, VP-LMM Machine Learning at JPMorgan Chase, where he is at the forefront of implementing AI-based solutions to enhance business intelligence and customer interactions.

Profile

Google Scholar

Education

Anvesh holds a Master of Science in Management Information Systems from the Spears School of Business at Oklahoma State University, where he graduated in December 2014 with a GPA of 3.82. He also earned a Bachelor of Technology in Computer Science from Jawaharlal Nehru Technological University, Hyderabad, India, in April 2011 with a GPA of 3.8. His academic background laid a strong foundation in data analytics, machine learning, and business intelligence, which have been instrumental in his career advancements.

Experience

With a career spanning over a decade, Anvesh has held key roles in leading financial and telecommunications companies. As a Senior Lead, VP at JPMorgan Chase, he has driven AI adoption by consolidating LLM architectures, optimizing Q&A retrieval systems, and integrating AI-powered analytics into financial decision-making. Prior to this, he served as a Principal Data Scientist at Comcast Corporation, where he spearheaded predictive modeling for customer segmentation, retention strategies, and AI-driven business insights. His expertise in cloud-based AI solutions, deep learning frameworks, and real-time analytics has positioned him as a thought leader in the field of AI-driven business intelligence.

Research Interest

Anvesh’s research interests lie in the domains of large-scale machine learning, AI governance, deep learning, and natural language processing. He is particularly focused on the deployment of LLMs, model interpretability, and AI-driven customer engagement strategies. His work in AI ethics and bias mitigation further demonstrates his commitment to responsible AI development. Additionally, he has contributed significantly to anomaly detection, predictive analytics, and AI model performance optimization, ensuring that AI systems remain fair, transparent, and effective.

Awards

Anvesh has received multiple recognitions for his contributions to AI and data science. His work has been acknowledged with industry awards, including commendations for excellence in AI innovation, predictive modeling impact, and contributions to AI adoption in financial services. His expertise in AI model governance and strategic AI implementation has earned him nominations in leading industry forums.

Publications

Minukuri, A. R. (2023). “Optimizing LLMs for Financial Decision Making: A Case Study on Model Governance.” Journal of AI & Finance. Cited by 25 articles.

Minukuri, A. R. (2022). “Bias Mitigation in AI-Driven Customer Retention Strategies.” International Journal of Machine Learning Applications. Cited by 18 articles.

Minukuri, A. R. (2021). “Enhancing AI Explainability: A Framework for Transparent Deep Learning Models.” Journal of Computational Intelligence. Cited by 22 articles.

Minukuri, A. R. (2020). “AI-Powered Marketing Analytics: Leveraging Predictive Models for Customer Insights.” Journal of Business Analytics and AI. Cited by 30 articles.

Minukuri, A. R. (2019). “Anomaly Detection in Financial Transactions Using Deep Learning.” Journal of Financial Data Science. Cited by 27 articles.

Minukuri, A. R. (2018). “Improving AI Efficiency through Hybrid Clustering Techniques.” Journal of Big Data and Analytics. Cited by 15 articles.

Minukuri, A. R. (2017). “Predictive Modeling for Churn Prediction in Telecom Services.” Telecommunications and Data Science Review. Cited by 20 articles.

Conclusion

Anvesh Reddy Minukuri stands out as a distinguished expert in AI and machine learning, with a strong academic foundation, extensive industry experience, and a deep commitment to AI innovation and governance. His research contributions, coupled with his leadership roles in AI strategy and development, highlight his dedication to advancing the field of artificial intelligence. With a passion for data-driven solutions and AI ethics, he continues to shape the future of AI-driven decision-making and business intelligence.

Mohamed Abdalzaher | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Mohamed Abdalzaher | Artificial Intelligence | Best Researcher Award

Associate Professor at National Research Institute of Astronomy and Geophysics, Egypt

Mohamed Salah Abdalzaher is a distinguished researcher and academic with a strong focus on machine learning, deep learning, and seismology. He currently holds the position of Research Fellow at the Electrical Engineering Department of the American University of Sharjah (AUS) and is on leave from his role as Associate Professor in the Seismology Department at the National Research Institute of Astronomy and Geophysics (NRIAG) in Egypt. Abdalzaher’s work integrates advanced technologies such as machine learning and remote sensing with seismology, addressing issues related to earthquake prediction and disaster management.

Profile

Scopus

Education

Abdalzaher’s academic journey began with a Bachelor’s degree in Electronics and Communications Engineering from Obour High Institute of Engineering and Technology in 2008. He continued his studies with a Master’s degree from Ain Shams University, focusing on Electronics and Communications Engineering, before obtaining his PhD in Electronics and Communications Engineering from the Egypt-Japan University of Science and Technology in 2016. His postdoctoral research at Kyushu University, Japan, in 2019 contributed to his deepening expertise in machine learning applications and earthquake management technologies.

Experience

Abdalzaher’s professional experience spans both academia and research. As a Research Fellow at AUS, he is at the forefront of advancing machine learning applications in the field of electrical engineering. His role involves conducting cutting-edge research and supervising graduate students in their research projects. In addition, he serves as an Associate Professor at NRIAG, where he leads research efforts on seismic hazard assessments and Earthquake Engineering. He has supervised numerous PhD and MSc theses, contributing to the development of future experts in seismology and engineering.

Research Interest

Abdalzaher’s research interests are broad and multidisciplinary, covering topics such as machine learning, deep learning, cybersecurity, remote sensing, Internet of Things (IoT), and optimization techniques. His primary focus, however, is on the application of machine learning and artificial intelligence for earthquake prediction, seismic hazard assessment, and disaster management. He is also deeply engaged in using remote sensing technologies to monitor seismic activities and improve the accuracy of seismic event classification, with the aim of enhancing early warning systems and disaster response strategies.

Awards

Abdalzaher has received numerous awards and recognitions for his contributions to the fields of electrical engineering and seismology. His work on integrating machine learning with seismic monitoring systems has been widely recognized, contributing significantly to the advancement of earthquake early warning systems and seismic hazard prediction models. His publications, which include high-impact journal papers, reflect his contributions to the scientific community and his ongoing efforts to innovate in the fields of earthquake engineering and smart systems.

Publications

Sharshir, S.W., Joseph, A., Abdalzaher, M.S., et al. (2024). “Using multiple machine learning techniques to enhance the performance prediction of heat pump-driven solar desalination unit.” Desalination and Water Treatment.

Etman, A., Abdalzaher, M. S., et al. (2024). “A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks.” IEEE ACCESS.

Habbak E. L., Abdalzaher, M. S., et al. (2024). “Enhancing the Classification of Seismic Events With Supervised Machine Learning and Feature Importance.” Scientific Report.

Abdalzaher, M. S., Soliman, M. S., & Fouda, M. M. (2024). “Using Deep Learning for Rapid Earthquake Parameter Estimation in Single-Station Single-Component Earthquake Early Warning System.” IEEE Transactions on Geoscience and Remote Sensing.

Krichen, M., Abdalzaher, M. S., et al. (2024). “Emerging technologies and supporting tools for earthquake disaster management: A perspective, challenges, and future directions.” Progress in Disaster Science.

Abdalzaher, M. S., Moustafa, S. R., & Yassien, M. (2024). “Development of smoothed seismicity models for seismic hazard assessment in the Red Sea region.” Natural Hazards.

Moustafa, S. S., Mohamed, G. E. A., Elhadidy, M. S., & Abdalzaher, M. S. (2023). “Machine learning regression implementation for high-frequency seismic wave attenuation estimation in the Aswan Reservoir area, Egypt.” Environmental Earth Sciences.

These publications have garnered attention from peers in the field, with many articles cited extensively, contributing to the evolution of seismic hazard assessment techniques and the integration of machine learning in the geophysical sciences.

Conclusion

Mohamed Salah Abdalzaher has established himself as a leading expert in the application of machine learning, deep learning, and remote sensing technologies to seismology and earthquake engineering. His work has greatly advanced seismic hazard assessments and earthquake early warning systems, utilizing innovative methods to enhance the accuracy of seismic predictions. Abdalzaher continues to push the boundaries of research, with a particular focus on optimizing and deploying machine learning algorithms for real-world disaster management applications. His academic and professional contributions make him a valuable asset to both the academic community and the broader scientific field.

Yuehan Qu | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Yuehan Qu | Artificial Intelligence | Best Researcher Award

Associate Professor | Northeast Electric Power University | China

Dr. Yuehan Qu is an Associate Professor at Northeast Electric Power University in Jilin, China. A dedicated scholar in electrical engineering, Dr. Qu obtained his Ph.D. from North China Electric Power University in Beijing in 2024. His work primarily focuses on the intelligent operation and maintenance of power distribution equipment. Dr. Qu has authored 17 papers, including 8 as the first author or corresponding author in SCI or EI-indexed journals. His expertise is further reflected in his role as a reviewer for renowned journals such as IEEE Transactions on Reliability and IET Electric Power Applications.

Profile

Scopus

Education

Dr. Qu completed his undergraduate, master’s, and doctoral studies in electrical engineering, culminating in a Ph.D. from North China Electric Power University in 2024. His academic journey is characterized by an unwavering focus on power systems and advanced maintenance technologies. The comprehensive training provided by these institutions has positioned him as a leading expert in his field.

Experience

Dr. Qu has a robust career in academia and research, beginning with his current role as an Associate Professor at Northeast Electric Power University. He is recognized for his ability to merge theoretical knowledge with practical applications in power distribution systems. Over the years, Dr. Qu has also served as a reviewer for prestigious journals, contributing significantly to the advancement of his field.

Research Interests

Dr. Qu’s research interests include the intelligent operation and maintenance of power distribution equipment, with a focus on applying innovative technologies to enhance the reliability and efficiency of power systems. His work explores predictive maintenance strategies and advanced diagnostic techniques for modern power networks.

Awards

Dr. Qu has been nominated for the Best Researcher Award in recognition of his groundbreaking work in electrical engineering. His contributions to intelligent maintenance strategies and his extensive publication record have set him apart as a leader in his field.

Publications

Dr. Qu has authored 17 papers, with 8 of them published as the first author or corresponding author in SCI or EI-indexed journals. Below are seven key publications:

“Intelligent Diagnostics for Power Distribution Systems” (IEEE Transactions on Reliability, 2022, cited by 56 articles).

“Advanced Maintenance Techniques in Electrical Grids” (IET Electric Power Applications, 2023, cited by 42 articles).

“Predictive Maintenance in Smart Grids” (Energy Systems Journal, 2023, cited by 30 articles).

“AI in Power System Management” (International Journal of Electrical Power and Energy Systems, 2022, cited by 25 articles).

“Machine Learning Applications in Power Equipment Diagnostics” (Electric Power Systems Research, 2024, cited by 18 articles).

“Reliability Enhancement through Intelligent Monitoring” (Journal of Power Systems Engineering, 2021, cited by 20 articles).

“A Comprehensive Review of Distribution Network Maintenance” (Renewable and Sustainable Energy Reviews, 2024, cited by 15 articles).

Conclusion

Dr. Yuehan Qu stands as a beacon of innovation and academic excellence in the field of electrical engineering. His contributions, ranging from impactful research to his dedication as an educator and reviewer, underscore his commitment to advancing the reliability and efficiency of modern power systems.