Chokri Aloui | Operations Research and Statistical Optimization | Research Excellence Award

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Dr. Chokri Aloui | Operations Research and Statistical Optimization | Research Excellence Award

University of Sousse | Tunisia

Dr. Chokri Aloui is an Assistant Professor at the Faculty of Economics and Management, University of Sousse, and a researcher at the Laboratory of Research in Innovation Management and Sustainable Development, Sousse Higher Institute of Management, specializing in microeconomics, industrial organization, platform and network economics, environmental economics, and the economic appraisal of development projects. He holds a Ph.D. in Economics from Sousse University, preceded by a Master’s degree from Tunisia Polytechnic School and a Bachelor’s degree from Jendouba University, and his academic trajectory reflects a consistent focus on network externalities, two-sided markets, competition, and digital economy dynamics. His teaching portfolio spans industrial economics, microeconomics at various levels, game theory, markets and strategies, development project appraisal, and business simulation, demonstrating broad expertise across applied and theoretical microeconomics. His research contributions include influential works on platform capacity sharing, congestion pricing, net neutrality, corporate social responsibility in two-sided platforms, and environmental certification within international trade, published in journals such as Economic Modelling, Networks and Spatial Economics, Managerial and Decision Economics, the International Review of Economics, and The Manchester School. Across his scholarly output, he has produced multiple peer-reviewed articles and maintains an active presence on platforms such as Google Scholar and ResearchGate. His Scopus profile reports approximately 43 citations, an h-index of 3, and a set of documents reflecting his ongoing research productivity. Overall, Chokri Aloui stands out as a researcher whose work integrates rigorous modeling with practical economic policy implications, contributing meaningfully to the understanding of digital markets, innovation, environmental responsibility, and development-oriented economic assessments.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Oleg Selyugin | Big Data and Statistical Analytics | Big Data Analytics Award

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Dr. Oleg Selyugin | Big Data and Statistical Analytics | Big Data Analytics Award

Joint Institute for Nuclear Research | Russia

Dr. Oleg Selyugin is a Russian empirical and theoretical physicist with a distinguished career in high-energy hadron scattering and the structure of hadrons. After completing his studies at the Physics Department of Moscow State University, he joined the Joint Institute for Nuclear Research (JINR), first as a probationer and researcher at the Laboratory of Nuclear Problems, and later at the Bogoliubov Theoretical Laboratory (BTL), where he now serves as a leading scientist. At BTL, he earned his Ph.D. with the thesis “High energy elastic hadron-hadron scattering in a wide momentum transfer region,” and later obtained his Doctor of Physics and Mathematics degree with the thesis “The structure of high-energy amplitude of the elastic hadron-hadron scattering in the diffraction region.” Dr Selyugin has been recognized with multiple International Prizes of JINR for his outstanding contributions to polaron physics, hadron physics, and high-energy physics. His primary research interests include the structure of hadrons (PDFs, GPDs, form-factors), phenomenology of high-energy physics (differential cross sections, spin phenomena), models of extra dimensions (d-brane gravity), and nonlinear effects. He has been actively involved in interpreting experimental results from the CERN LHC, particularly the TOTEM and ATLAS Collaborations. His theoretical work integrates electromagnetic and gravitational form-factors derived from novel t-dependent GPDs, as well as soft and cross-even pomeron contributions, within dispersion-relation-based frameworks. With over 180 scientific papers, Dr Selyugin has made a profound and lasting impact on the understanding of elastic hadron scattering at high energies. Although specific bibliometric indicators such as 17 h-index, 78 documents, and 815 citations vary across databases, his scientific influence is widely recognized within the international physics community. He continues his pioneering research at JINR in Dubna, Russia.

Profiles: Scopus | Orcid

Featured Publications

Selyugin, O. V. (2024). Unified description of elastic hadron scattering at low and high energies. Physics of Atomic Nuclei, 87(S2), S349–S362.

Kuruba Chandrakala | Machine Learning and Statistics | Best Researcher Award

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Dr. Kuruba Chandrakala | Machine Learning and Statistics | Best Researcher Award

Siddhartha Academy of Higher Education | India

Dr. Kuruba Chandrakala is an emerging researcher in the domains of computer vision, deep learning, and medical image processing, currently serving as Assistant Professor (Selection Grade) in the CSE department at Siddhartha Academy of Higher Education, Vijayawada. She earned her Ph.D. from NIT Tiruchirappalli, preceded by M.Tech in Computer Science and Engineering with distinction from JNTU Kakinada and B.Tech in the same discipline from JNTU Anantapur. She has qualified both NET and APSET examinations. Her professional trajectory includes roles as Head of Department (CSE-AIML) at Vignan’s Nirula Institute of Technology & Science for Women and previous teaching appointments at VNITSW and SITAM, along with industry experience as a System Engineer with Tata Consultancy Services. Her publication record comprises five Scopus indexed papers, four of which are in SCIE journals, two IEEE conference papers, and one book chapter; she also holds one patent. Her Scopus metrics include an h-index of 4, 10 documents, and 150 citations. Her research has addressed areas such as diabetic retinopathy segmentation, robust blood vessel detection, and image enhancement through deep learning architectures. She teaches courses including Deep Learning, Machine Learning, Big Data Analytics, Cloud Computing, and programming in C, C++, Java, and Python. She has earned numerous certifications from NPTEL, Coursera, Microsoft, IBM, and Wipro and received awards such as the NPTEL Discipline Star and Wipro Project Excellence Award. Her leadership and mentoring roles include serving as a mentor for Wipro TalentNext, nodal officer for Microsoft Upskilling and APSCHE virtual internship programs, and coordinator for various hackathons. She is a life member of professional bodies such as CSI, ISTE, IAENG, and IET, and has delivered several invited and guest lectures, contributing significantly to academic excellence and research advancement.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Chandrakala, K., & Gopalan, N. P. (2025). 3DECNN: A novel method for segmentation of diabetic retinopathy in retinal fundus images using 3D-edge CNN. Neural Computing and Applications.

Kuruba, C., Sharmila, S. K., Mounika, V., Aswini, D., & Poojitha, G. (2023). Three layered security model to prevent credit card fraud using LBPH and CNN-ResNet architecture. International Conference on Hybrid Intelligent Systems, 422–428.

Dharmaiah, K., Mebarek-Oudina, F., Sreenivasa Kumar, M., & Chandra Kala. (2023). Nuclear reactor application on Jeffrey fluid flow with Falkner-Skan factor, Brownian and thermophoresis, non-linear thermal radiation impacts past a wedge. Journal of the Indian Chemical Society, 100(2), 117.

Kuruba, C., & Gopalan, N. P. (2023). Robust blood vessel detection with image enhancement using relative intensity order transformation and deep learning. Biomedical Signal Processing and Control, 86, 105195.

Kuruba, C., Pushpalatha, N., Ramu, G., Suneetha, I., Kumar, M. R., & Harish, P. (2023). Data mining and deep learning-based hybrid health care application. Applied Nanoscience, 13(3), 2431–2437.

Ching Chih Tsai | Fuzzy Statistics and Uncertainty Modelling | Best Researcher Award

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Prof. Ching Chih Tsai | Fuzzy Statistics and Uncertainty Modelling | Best Researcher Award

Prof. Ching Chih Tsai |  National Chung Hsing University | Taiwan

Prof. Ching Chih Tsai is a distinguished academic in electrical engineering and control systems, currently serving as a Life Distinguished Professor at the Department of Electrical Engineering, National Chung Hsing University (NCHU), Taiwan. He earned his Ph.D. from Northwestern University in 1991. Dr. Tsai has held significant leadership roles, including serving as the President of the Chinese Automatic Control Society (CACS), the Robotics Society of Taiwan (RST), and the International Fuzzy Systems Association (IFSA). He has also been a Board of Governors member of IEEE Systems, Man, and Cybernetics Society (SMCS) and is currently the Dean of the College of Electrical Engineering and Computer Science at NCHU. An IEEE Fellow, his research focuses on intelligent control systems, mobile robotics, and automation intelligence. Dr. Tsai has published over 700 technical articles, with more than 20 in the International Journal of Fuzzy Systems since 2005. His recent work includes a 2025 paper on intelligent adaptive formation control for multi-quadrotors, introducing a hybrid controller combining Output Recurrent Fuzzy Broad Learning Systems (ORFBLS), reinforcement learning, and adaptive backstepping sliding mode control. According to Scopus, he has an h-index of 29, with 3,902 citations from 272 documents.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Rospawan, A., Tsai, C.-C., & Hung, C.-C. (2025). Two-layer intelligent learning control using output recurrent fuzzy neural long short-term memory broad learning system with RMSprop. IEEE Access.

Tsai, C.-C., Hung, C.-C., Mao, C.-F., Wu, H.-S., & Chen, C.-H. (2025). Fuzzy neural LSTM-RBLS for fractional-order PID sliding-mode motion control of autonomous mobile robots with four ISID wheels. International Journal of Fuzzy Systems.

Tsai, C.-C., Mao, C.-F., & Hussain, K. (2025). Intelligent adaptive formation control using ORFBLS and reinforcement learning for uncertain tilting multi-quadrotors. International Journal of Fuzzy Systems. =

Rospawan, A., Tsai, C.-C., & Hung, C.-C. (2025). Intelligent MIMO ORFBLS-based setpoint tracking control with its application to temperature control of an industrial extrusion barrel. International Journal of Fuzzy Systems.

Tsai, C.-C., Huang, H.-C., Chen, H.-Y., Hung, C.-C., & Chen, S.-T. (2024). Intelligent collision-free formation control of ball-riding robots using output recurrent broad learning in industrial cyber-physical systems. IEEE Transactions on Industrial Cyber-Physical Systems.

Ahmed A. Ahmed | Regression and Correlation Analysis | Best Researcher Award

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Assoc Prof. Dr. Ahmed A. Ahmed | Regression and Correlation Analysis | Best Researcher Award

Mustansiriyah University | Iraq

Dr. Ahmed A. Ahmed, Ph.D, is a distinguished faculty member in the Civil Engineering Department at Mustansiriyah University, Baghdad, Iraq, with a robust research profile emphasizing new and advanced infrastructure materials, durability of engineering materials, infrastructure sustainability, green building materials, material characterization, corrosion and carbonation assessment, microstructure analysis, and evaluation of concrete deterioration processes and mechanisms. With an h-index of 5, 70 citations, and a ResearchGate score of 83.5, Dr. Ahmed has contributed extensively to the field through numerous high-impact publications, including studies on corrosion resistance of calcium sulfoaluminate cementitious systems, reliability of chloride testing in cementitious systems, and experimental investigations on reinforcing steel behavior under corrosive conditions. Since 2012, he has served as a faculty member at Mustansiriyah University and held leadership roles such as Head of the Training and Development Unit in the Continuing Education Branch and active membership in professional societies including the American Concrete Institute, American Society of Civil Engineers, Iraqi Academic Syndicate, Iraqi Engineers Union, and Iraqi Teachers Union. Dr. Ahmed earned his Ph.D. in Infrastructure Materials with a minor in Statistics from Oregon State University and holds multiple advanced teaching certificates and engineering degrees. He has also presented at international conventions and workshops across the United States and Malaysia, highlighting his commitment to research excellence, pedagogy, and professional development in civil engineering.

Profiles:  Orcid | Scopus

Featured Publications

“Assessing the Impact of Graphene Nanoplatelets Aggregates on the Performance Characteristics of Cement-Based Materials”

“Performance Evaluation of Concrete Masonry Unit Mixtures Incorporating Citric Acid-Treated Corn Stover Ash and Alkalinized Corn Stover Fibers”

“Evaluating the Characteristics of Fibrous Pure Gypsum Containing Chopped Carbon Fiber (CCF)”

“Evaluating the performance of thermomechanically beneficiated fly ash blended mortar”

“Reliability of chloride testing results in cementitious systems containing admixed chlorides”