Bhavesh Kataria | AI and Machine Learning | AI & Machine Learning Award

Published on by AI Data Scientist

Dr. Bhavesh Kataria | AI and Machine Learning | AI & Machine Learning Award

Post-Doctoral Fellow at Emory University | United States

Dr. Bhavesh Kataria is a highly accomplished academician, researcher, and innovator in Computer Engineering, recognized globally for his leadership in Artificial Intelligence, Machine Learning, and Digital Image Processing. His professional journey spans academia and research institutions across India and the United States, including his role at Emory University, where he contributes to advanced AI-driven healthcare analytics and digital pathology solutions. With a Ph.D. focused on Optical Character Recognition of Sanskrit Manuscripts using Convolutional Neural Networks, Dr. Kataria has combined technical precision with deep domain expertise to address challenges in multilingual text recognition and medical imaging. His scholarly portfolio includes numerous publications in reputed international journals, multiple granted patents, and several authored books covering cutting-edge topics in AI, cloud computing, and web technologies. An active member of prestigious organizations such as IEEE and ACM, he serves on editorial boards of international journals and as a reviewer for globally recognized publishers like Springer Nature and Science Publishing Group. He has also chaired sessions and reviewed Ph.D. theses, contributing significantly to the academic ecosystem. Dr. Kataria’s pioneering innovations, such as AI-based network visualization tools, smart teaching devices, and healthcare monitoring systems, underscore his commitment to translational research and practical AI applications. Honored with awards including the Best Researcher Award and Teaching Excellence Award, he exemplifies a blend of scholarly excellence, innovation, and mentorship. His dedication to advancing intelligent systems and promoting interdisciplinary research continues to inspire global collaboration in emerging computational technologies.

Profiles: Scopus | ORCID

Featured Publications

Kataria, B., & Jethva, H. B. (2024, September 30). Decentralized security mechanisms for AI-driven wireless networks: Integrating blockchain and federated learning.

Kataria, B. (2024, June 2). Automated detection of tuberculosis using deep learning algorithms on chest X-rays.

Shivadekar, S., Kataria, B., Hundekari, S., Wanjale, K., Balpande, V. P., & Suryawanshi, R. (2023). Deep learning based image classification of lungs radiography for detecting COVID-19 using a deep CNN and ResNet 50.

Shivadekar, S., Kataria, B., Limkar, S., Wagh, K., Lavate, S., & Mulla, R. (2023, June 15). Design of an efficient multimodal engine for preemption and post-treatment recommendations for skin diseases via a deep learning-based hybrid bioinspired process.

Kataria, B., Jethva, H. B., Shinde, P. V., Banait, S. S., Shaikh, F., & Ajani, S. (2023, April 30). SLDEB: Design of a secure and lightweight dynamic encryption bio-inspired model for IoT networks.

Mr. Sachin Pandey | AI Data Science | AI & Machine Learning Award

Published on by AI Data Scientist

Mr. Sachin Pandey | AI Data Science | AI & Machine Learning Award

Head of Data Engineering and Data Science, Oracle Corporation, United States

Mr. Sachin Pandey is an accomplished data scientist and engineering professional whose expertise bridges the domains of artificial intelligence, data management, and enterprise analytics. With more than thirteen years of progressive experience, Mr. Sachin Pandey currently serves as the Head of Data Engineering and Data Science at Oracle Corporation, where he leads multidisciplinary teams in the development of intelligent data infrastructures, machine learning solutions, and scalable MLOps frameworks. He previously contributed his expertise as Head of Data Science at Walmart US, overseeing large-scale analytical transformations that enhanced predictive decision systems and optimized data-driven strategies across global business operations. Mr. Sachin Pandey’s academic foundation is rooted in a Master of Science in Management Information Systems from the University of Illinois at Chicago – Liautaud Graduate School of Business, where he developed a strong grounding in business intelligence, data visualization, and statistical computing. He earned his Bachelor of Technology in Electronics and Telecommunication Engineering from the Vivekananda Education Society’s Institute of Technology, Mumbai, where his technical acumen and analytical thinking shaped his approach to applied data research. His research interests include machine learning algorithms, deep learning optimization, big data analytics, AI-based automation, and data governance, focusing on how scalable AI systems can transform decision-making and industry practices. Mr. Sachin Pandey has published and co-authored peer-reviewed papers in internationally recognized journals and conference proceedings indexed by Scopus and IEEE, including notable contributions in areas of image detection, intelligent automation, and cloud-based analytics. His most cited work, “Smoke and Fire Detection” is recognized for advancing the use of AI models in safety and monitoring systems, reflecting his commitment to practical applications of data science for societal benefit. In addition to research, he possesses exceptional skills in Python programming, Spark, Airflow, data modeling, ELT/ETL frameworks, MLFlow, and cloud analytics platforms such as Power BI, Tableau, and Alteryx, complemented by a deep understanding of optimization, data governance, and model versioning techniques.

Profiles: Google Scholar | Orcid 

Featured Publications

  • Gharge, S., Birla, S., Pandey, S., Dargad, R., & Pandita, R. (2013). Smoke and fire detection. International Journal of Advanced Research in Computer and Communication Engineering, 2(6). Cited by: 16

  • Singh, A., & Pandey, S. (2014). Advanced Centralised RTO System for Traffic Data Automation. International Journal of Emerging Technology and Advanced Engineering, 4(5). Cited by: 9

  • Pandey, S. (2015). Intelligent Data Governance Using Cloud-based Frameworks. International Journal of Data Science and Analytics, 3(2). Cited by: 11

  • Pandey, S., & Birla, S. (2016). Optimization of Machine Learning Pipelines for Enterprise Analytics. Proceedings of the IEEE International Conference on Computational Intelligence. Cited by: 7

  • Pandey, S. (2019). Scalable AI Systems for Predictive Data Engineering. Journal of Artificial Intelligence Research and Applications, 10(4). Cited by: 13

Dr. Santosh Jagtap | AI and ML | Microsoft AI Award

Published on by AI Data Scientist

Dr. Santosh Jagtap | AI and ML | Microsoft AI Award

Assistant Professor, Prof. Ramkrishna More Arts, Commerce & Science College, India

Dr. Santosh Jagtap, Assistant Professor at Prof. Ramkrishna More College (Autonomous), is a highly accomplished researcher and academic in the fields of Artificial Intelligence (AI) and Cybersecurity, with extensive expertise in applying AI to smart agriculture, healthcare security, IoT-enabled educational systems, and AI-driven safety solutions. Dr. Jagtap holds advanced academic qualifications and has developed a distinguished research profile that emphasizes practical applications of emerging technologies to address societal challenges. His work integrates machine learning, blockchain, IoT, and real-time data processing, producing innovative solutions in areas such as intelligent irrigation systems, plant disease detection, AI-based emotion recognition for safety alerts, and secure healthcare frameworks. Over his career, Dr. Jagtap has contributed significantly to international research projects and collaborative studies, producing high-impact publications in reputed journals and conference proceedings, such as Materials Today: Proceedings, international conferences on electronics, computing, and applied AI. He has also been recognized for innovation through patent awards, notably for AI-based plant disease identification systems, reflecting his focus on technology transfer and real-world impact. Dr. Jagtap has played an active role in mentoring students, guiding research projects, and participating in professional networks that foster academic and technological growth. He has demonstrated a consistent record of research excellence, with a total of 78 citations across 4 Scopus-indexed publications and an h-index of 3, reflecting the growing impact of his work.

Profile: GOOGLE SCHOLAR | SCOPUS

Featured Publications

  • Jagtap, S. T., Phasinam, K., Kassanu. (2022). Towards application of various machine learning techniques in agriculture. Materials Today: Proceedings, 51, 793–797. 70 citations.

  • Jagtap, S. T., Thakar, (2021). A framework for secure healthcare system using blockchain and smart contracts. Second International Conference on Electronics and Sustainable Technologies. 22 citations.

  • Jagtap, S. T., Jagdale, K. C., & Thakar, C. M. (2023). Identification of plant disease device using artificial intelligence. IN Patent 391523-001. –

  • Pratiksha Bhise, D. S. J., & Jagtap, S. T. (2024). AI-driven emergency response system for women’s safety using real-time location and heart rate monitoring. IJRPR. –

  • Keskar,  A., Jagtap, S. T., et al. (2021). Big data preprocessing frameworks: Tools and techniques. Design Engineering, 1738–1746.

Mr. Sonjoy Ranjon Das | Computer Vision | AI & Machine Learning Award

Published on by AI Data Scientist

Mr. Sonjoy Ranjon Das | Computer Vision | AI & Machine Learning Award

Lecturer,  Global Banking School, United Kingdom

Mr. Sonjoy Ranjon Das (FHEA, MIEEE, MBCS) is a Lecturer in Computing at the Global Banking School, UK, PhD Candidate in Computer Science at London Metropolitan University, and an affiliated researcher with the AI & Data Science Research Group at London Metropolitan University. He is an emerging academic with expertise in artificial intelligence, soft biometrics, cybersecurity, and privacy-preserving surveillance frameworks aligned with ethical AI deployment and GDPR compliance. Mr. Sonjoy Ranjon Das earned his MSc in Cyber Security Technology with Distinction from Northumbria University, UK, following an MBA in Management Information Systems and a BSc (Hons) in Computer Science from Leading University, Bangladesh, which provided him with an integrated background in computing, management information systems, and advanced security practices. Professionally, he has served in diverse higher-education lecturing roles across the UK including Elizabeth School of London, New City College, Shipley College, and other institutions, as well as holding the position of Research Associate on the SoftMatrix and Surveillance (SMS) Project at Northumbria University, contributing to cross-disciplinary and international research. Mr. Sonjoy Ranjon Das’s research interests include privacy-preserving multimodal soft biometrics for identity verification, AI-driven covert surveillance, ethical and GDPR-compliant surveillance technologies, and the fusion of biometrics for crowd analytics in public safety and border security. His research skills encompass advanced machine learning and computer vision techniques, data analytics, Python and Java programming, cloud-IoT integration, and full-stack development, supported by proficiency in data visualization tools such as Power BI, Tableau, and MATLAB.

Profile :  GOOGLE SCHOLAR

Featured Publications

  • Das, S. R., Kruti, A., Devkota, R., & Sulaiman, R. B. (2023). Evaluation of machine learning models for credit card fraud detection: A comparative analysis of algorithmic performance and their efficacy. FMDB Transactions on Sustainable Technoprise Letters. 12 citations.

  • Thinesh, M. A., Varmann, S. S., Sharmila, S. L., & Das, S. R. (2023). Detection of credit card fraud using random forest classification model. FMDB Transactions on Sustainable Technologies Letters. 9 citations.

  • Pranav, R. P., Prawin, R. P., Subhashni, R., & Das, S. R. (2023). Enhancing remote sensing with advanced convolutional neural networks: A comprehensive study on advanced sensor design for image analysis and object detection. FMDB Transactions on Sustainable Computer Letters. 8 citations.

  • Das, S. R., Hassan, B., Patel, P., & Yasin, A. (2024). Global soft biometrics in surveillance: Benchmark analysis, open challenges, and recommendations. Multimedia Tools and Applications. 6 citations.

Jesus Gamez | Artificial Intelligence | Best Academic Researcher Award

Published on by AI Data Scientist

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

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

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

Profile

Scopus

Education

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

Experience

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

Research Interest

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

Award

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

Publication

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

Conclusion

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

Ruchun Jia | Artificial Intelligence | Best Researcher Award

Published on by AI Data Scientist

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

Professor at College of Computer Science, Sichuan University, China

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

Profile

Orcid

Education

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

Experience

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

Research Interest

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

Award

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

Publication

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

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

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

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

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

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

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

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

Conclusion

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

Seyed Abolfazl Aghili | Artificial Intelligence | Best Review Paper Award

Published on by AI Data Scientist

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.

Farhat Nasim | Artificial Intelligence | Best Researcher Award

Published on by AI Data Scientist

Ms. Farhat Nasim | Artificial Intelligence | Best Researcher Award

ASSISTANT PROFESSOR GUEST at Jamia Millia Islamia, India

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

Profile

Scopus

Education

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

Professional Experience

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

Research Interests

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

Awards and Achievements

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

Publications

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

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

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

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

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

Conclusion

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

Mathew Habyarimana | Artificial Intelligence | Best Academic Researcher Award

Published on by AI Data Scientist

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.

mohammad mohsen sadr | Artificial Intelligence | AI & Machine Learning Award

Published on by AI Data Scientist

Mr. mohammad mohsen sadr | Artificial Intelligence | AI & Machine Learning Award

Assistant Professor of Information Technology at payame noor univercity, Iran

Dr. Mohsen Sadr is a distinguished scholar and industry leader specializing in information science, artificial intelligence, and business technology. With extensive experience in academia, corporate leadership, and research, he has made significant contributions to digital transformation, data science, and machine learning applications. Currently serving as the Vice Chairman and CEO of Navaran Boom Gostar Omid (affiliated with Bank Sepah), he is also an Assistant Professor in the Information Technology Department at Payame Noor University. His work spans across AI-based decision-making, network security, and advanced data analysis, making him a key figure in both academic and professional domains.

profile

scopus

Education

Dr. Sadr has an interdisciplinary academic background, holding a Ph.D. in Information Science. He completed his M.Sc. in Information Technology Engineering at Tarbiat Modares University and earned a B.Sc. in Computer Engineering – Software. Additionally, he pursued a second bachelor’s degree in Law and is currently studying for a master’s degree in Financial Management. His foundational education includes an associate degree in Mathematics from Hamedan.

Experience

Dr. Sadr has held numerous executive and managerial positions in both the public and private sectors. He has served as the CEO and board member of various technology and financial institutions, including Navaran Boom Gostar Omid, RighTel Information Services, and the Financial Technology Services Company of Refah Bank. His leadership extends to the steel, pharmaceutical, and telecommunications industries. Furthermore, he has played a pivotal role in governmental organizations such as Payame Noor University, where he managed IT, public relations, and digital transformation initiatives.

Research Interests

His research primarily focuses on artificial intelligence, machine learning, and digital transformation. Specific interests include fake news detection using deep learning, optimization of wireless sensor networks, webometrics, and knowledge management. He is particularly engaged in the application of AI-driven solutions for decision-making in business and governance, including CRM implementation, sentiment analysis, and network security.

Awards & Recognitions

Dr. Sadr has been recognized for his academic and professional excellence, including:

Outstanding Student Award in Associate Mathematics

Best Lecturer Award at Payame Noor University in 2012

National Best Director Award for exceptional management contributions

Publications

Dr. Sadr has authored several books and research papers in leading journals. Below are some of his notable publications:

Sadr, M.M., & Torkashvand, S. (Year). Coverage Optimization of Wireless Sensor Network Using Learning Automata Techniques. Published in Chemical and Process Engineering.

Sadr, M.M., & Dadstani, M. (Year). Webometrics of Payame Noor University of Iran with Emphasis on Provincial Capital Branches’ Websites. Published in Library Philosophy and Practice.

Sadr, M.M., et al. (Year). A Predictive Model Based on Machine Learning Methods to Recognize Fake Persian News on Twitter. Published in Turkish Journal of Computer and Mathematics Education.

Sadr, M.M., & Akhavan Safar, M. (Year). The Use of LSTM Neural Networks to Detect Fake News on Persian Twitter. Published in Applied Research in Sports Management.

Sadr, M.M., & Asgari, P. (Year). Scientometric Analysis of Research Published in the Journal of Applied Research in Sports Management. Published in Organizational Behavior Management Studies in Sports.

Khani, M., & Sadr, M.M. (Year). A Mapping and Visualization of the Role of Artificial Intelligence in the Sports Industry. Published in Concurrency and Computation: Practice and Experience.

Sadr, M.M., et al. (Year). Deep Reinforcement Learning-Based Resource Allocation in Multi-Access Edge Computing. Published in Transactions on Emerging Telecommunications Technologies.

Conclusion

With his strong academic background, extensive research, publications, AI-driven projects, and contributions to education, Dr. Mohammad Mohsen Sadr is a highly deserving candidate for the Research in AI & Machine Learning Award. His work in fake news detection, deep learning, reinforcement learning, and AI applications in various industries aligns perfectly with the objectives of this prestigious award.