Adil Sultan | Environmental Modeling | Best Researcher Award

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Mr. Adil Sultan | Environmental Modeling | Best Researcher Award

PhD Student | National Yunlin University of Science and Technology | Taiwan

Adil Sultan is a dynamic researcher and postgraduate scholar in Computer Science and Information Engineering at the National Yunlin University of Science and Technology, Taiwan, with a Bachelor’s degree in Geophysics from Bahria University Islamabad, Pakistan. His interdisciplinary expertise bridges artificial intelligence, computational modeling, and earth sciences, emphasizing intelligent predictive frameworks for environmental and marine ecosystem dynamics. Adil’s research integrates machine learning, fractional calculus, and neurocomputational modeling to address complex ecological and climatological phenomena, with publications in high-impact Q1 journals such as Water Research, Engineering Applications of Artificial Intelligence, and Process Safety and Environmental Protection. He has contributed to advancing predictive neural architectures for modeling plankton dynamics, environmental toxin propagation, and climate-induced marine variations. His professional experience includes seismic data processing at Oil and Gas Development Company Limited, where he applied geophysical modeling and data analytics for subsurface evaluations, alongside earlier roles in communication and technical support at IBEX Global. Recognized for academic excellence and innovation, he ranked among the top three in his master’s program and earned a Bronze Medal for his undergraduate thesis. Adil has presented his work at international conferences, authored multiple manuscripts under review, and actively engages in interdisciplinary research collaborations. His memberships, scholarly achievements, and leadership in applied machine learning for environmental intelligence underscore his commitment to sustainable scientific innovation and global research excellence. His Scopus profile reflects 17 citations, 6 indexed documents, and an h-index of 2.

Profile: Scopus

Featured Publications

Adil Sultan*, Design of a Fractional-Order Environmental Toxin-Plankton System in Aquatic Ecosystems: A Novel Machine Predictive Expedition with Nonlinear Autoregressive Neuroarchitectures. Water Research, Q1, 12.4 I.F.

Adil Sultan*, Bayesian-Regularized Cascaded Neural Networks for Fractional Asymmetric Carbon-Thermal Nutrient-Plankton Dynamics under Global Warming and Climatic Perturbations. Engineering Applications of Artificial Intelligence, Q1, 8.0 I.F.

Adil Sultan*, Intelligent Predictive Networks for Nonlinear Oxygen-Phytoplankton-Zooplankton Coupled Marine Ecosystems under Environmental and Climatic Disruptions. Process Safety and Environmental Protection, Q1, 7.8 I.F.

Adil Sultan*, Prognostication of Zooplankton-Driven Cholera Pathoepidemiological Dynamics: Novel Bayesian-Regularized Deep NARX Neuroarchitecture. Computers in Biology and Medicine, Q1, 6.3 I.F.

Adil Sultan*, Predictive Modeling of Fractional Plankton-Assisted Cholera Propagation Dynamics Using Bayesian Regularized Deep Cascaded Exogenous Neural Networks. Process Safety and Environmental Protection, Q1, 7.8 I.F.

Kia Jahanbin | Deep Transfer Learning | Best Researcher Award

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Dr. Kia Jahanbin | Deep Transfer Learning | Best Researcher Award

Data Analyst | Ministry of Economic Affairs and Finance | Iran

Dr. Kia Jahanbin is a highly accomplished data analyst, software engineer, and academic associated with the Ministry of Economic Affairs and Finance and Islamic Azad University (Firuzkoh Branch). He earned his Ph.D. in Software Engineering from Yazd University, focusing on sentiment analysis using transfer learning for cryptocurrency market forecasting. With over a decade of experience, he has contributed to more than 25 research projects and four major national-level initiatives in financial intelligence and data analytics. His expertise covers deep learning, transfer learning, data and text mining, web mining, and public health data analytics, with his works published in reputed journals such as Knowledge-Based Systems, IEEE Access, International Journal of Intelligent Systems, and Financial Innovation. He has authored two academic books, holds a patent on a Wireless Sensor Network Training Simulator, and actively serves as a reviewer for IEEE Access, Ad Hoc & Sensor Wireless Networks, and Financial Innovation, besides being on the editorial board of Journal La Multiapp (Indonesia). His collaborations with institutions like Yazd University and the University of Windsor (Canada) emphasize his international engagement in AI research. Through his innovative contributions, Dr. Jahanbin has played a crucial role in enhancing data-driven decision-making and digital transformation within Iran’s financial sector, while advancing global knowledge in artificial intelligence and predictive analytics. He has a total of 367 citations, with an h-index of 6 and an i10-index of 5.

Profile: Google Scholar

Featured Publications

Kia Jahanbin*, Sentiment analysis using transfer learning for cryptocurrency market forecasting. Ph.D. Thesis, Yazd University.

Kia Jahanbin*, Deep learning-based hybrid framework for cryptocurrency prediction using social media sentiment. Knowledge-Based Systems, 2024, 302, 112345.

Kia Jahanbin, Predictive modeling of epidemic outbreaks using AI-driven web mining and sentiment analysis. IEEE Access, 2023, 11, 65789–65798.

Kia Jahanbin, Financial data analytics and intelligent forecasting through transfer learning techniques. International Journal of Intelligent Systems, 2023, 38(7), 14562–14579.

Kia Jahanbin*, A deep transfer learning model for cryptocurrency market behavior forecasting. Financial Innovation, Accepted.

Assoc. Prof. Dr. Krzysztof Stepien | Signal & Image Processing | Best Researcher Award

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Assoc. Prof. Dr. Krzysztof Stepien | Signal & Image Processing | Best Researcher Award

Head of Department of Metrology and Modern Manufacturing | Kielce University of Technology | Poland

Assoc. Prof. Krzysztof Stępień is a distinguished researcher and academic leader at the Department of Metrology and Modern Manufacturing, Kielce University of Technology, specializing in precision engineering, geometrical metrology, and surface texture analysis. He earned his Master of Science and Doctor of Science degrees in Mechatronics and Mechanical Engineering from Kielce University of Technology, where his doctoral research focused on cylindricity measurement errors using the V-block method. He later obtained his habilitation from the Warsaw University of Technology for pioneering work on new methods for measuring and evaluating form deviations of rotating elements. Throughout his academic career, he has held multiple leadership roles, including Head of the Department of Metrology and Modern Manufacturing, Head of the Institute of Technological Measuring Systems, and Head of the Laboratory of Computer-Aided Measurements of Geometrical Quantities, contributing significantly to advancing metrological research and education. His research focuses on form and surface metrology, signal processing in measurement systems, and adaptive measurement methods, with publications in top journals such as Precision Engineering, Measurement Science and Technology, and the International Journal of Advanced Manufacturing Technology. Prof. Stępień’s contributions have been widely recognized through professional honors, research collaborations, and editorial and scientific committee memberships, reflecting his commitment to innovation and excellence in modern manufacturing metrology.

Profile: ORCID

Featured Publications

Stępień, K.*, Algorithm for sensor nonlinearity compensation in measurements of geometric deviations of rotating elements with variable diameter. Precision Engineering, Accepted.

Janecki, D., Stępień, K.*, & Adamczak, S., Adaptive cylindricity measurements with the use of circumferential section strategy. Int. J. Adv. Manuf. Technol., 2024, 132, 585–600.

Stępień, K., In situ measurement of cylindricity—Problems and solutions. Precision Engineering, 2014, 38(3), 697–701.

Janecki, D., Stępień, K., & Adamczak, S., Sphericity measurements by the radial method: I. Mathematical fundamentals. Meas. Sci. Technol., 2016, 27(1), 015005.

Zhang-Peng Tian | Data-Driven Decision Analysis | Best Researcher Award

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Zhang-Peng Tian | Data-Driven Decision Analysis | Best Researcher Award

Associate professor | China University of Mining and Technology | China

Zhang-peng Tian, Ph.D., is an Associate Professor and Head of the Master’s Program in Management Science and Engineering at the School of Economics and Management, China University of Mining and Technology. He earned his Ph.D. and M.E. in Management Science and Engineering from Central South University and a B.E. in Electronic Commerce from Tianjin Chengjian University. Dr. Tian has extensive experience in teaching undergraduate and postgraduate courses, leading national research projects, and contributing as a principal investigator on multiple grants focused on decision-making theory, social network analysis, and data-driven consensus models. His research specializes in data-driven decision analysis, preference learning, and multi-criteria group decision-making, with over 40 publications in top international and Chinese journals, including IEEE Transactions on Fuzzy Systems, Information Fusion, and Applied Soft Computing. He is a council member of national academic associations, serves as a reviewer for leading journals such as Tourism Management, Decision Support Systems, and IEEE Transactions, and regularly participates in prestigious conferences. Dr. Tian has received numerous honors, including recognition for his excellent doctoral dissertation, national and provincial scholarships, and selection into Jiangsu Province’s Double Innovation Doctor program. His academic contributions reflect a commitment to advancing decision science and fostering innovation in information management and engineering applications, making him a distinguished candidate for the Best Researcher Award.

Profile: ORCID

Featured Publications

Tian Zhang-peng*, Xu Fu-xin, Ma Wei-min, Analysis of coalition stability based on graph model under power asymmetry. Syst. Eng. Theory Pract., 2024, 44(7), 2309-2324.

Tian Zhang-peng, Xu Fu-xin, Nie Ru-xin*, Wang Xiao-kang, Wang Jian-qiang, An adaptive consensus model for multi-criteria sorting under linguistic distribution group decision making considering decision-makers' attitudes. Inf. Fusion, 2024, 108, 102406.

Yang Yu, Tian Zhang-peng, Lin Jun*, Strategic outsourcing in reverse logistics: Neutrosophic integrated approach with a hierarchical and interactive quality function deployment. Appl. Soft Comput., 2024, 152, 111256.

Ma Wei-min, Gong Kai-xin*, Tian Zhang-peng, Heterogeneous large-scale group decision making with subgroup leaders: An application to the green supplier selection. J. Oper. Res. Soc., 2023, 74(6): 1570-1586.

Tian Zhang-peng, Liang He-ming, Nie Ru-xin*, Wang Xiao-kang, Wang Jian-qiang, Data-driven multi-criteria decision support method for electric vehicle selection. Comput. Ind. Eng., 2023, 177: 109061.

Tian Zhang-peng, Xu Fu-xin, Nie Ru-xin*, Wang Xiao-kang, Wang Jian-qiang, Linguistic single-valued neutrosophic multi-criteria group decision making based on personalized individual semantics and consensus. Informatica, 2023, 34(2): 387-413.

Tian Zhang-peng, Liang He-ming, Nie Ru-xin*, Wang Jian-qiang, An integrated multi-granular distributed linguistic decision support framework for low-carbon tourism attraction evaluation. Curr. Issues Tourism, 2023, 26(6): 977-1002.

Nie Ru-xin, Chin Kwai Sang, Tian Zhang-peng*, Wang Jian-qiang, Zhang Hong-yu, Exploring dynamic effects on classifying service quality attributes under the impacts of COVID-19 with evidence from online reviews. Int. J. Contemp. Hosp. Manage., 2023, 35(1): 159-185.

Wang Xiao-kang, Hou Wen-hui, Zhang Hong-yu, Wang Jian-qiang, Goh Mark, Tian Zhang-peng, Shen Kai-wen, KDE-OCSVM model using Kullback-Leibler divergence to detect anomalies in medical claims. Expert Syst. Appl., 2022, 200: 117056.

Assoc. Prof. Dr. Le Yang | Neuromorphic Engineering | Best Researcher Award

Published on by Electrical Engineering

Assoc. Prof. Dr. Le Yang | Neuromorphic Engineering | Best Researcher Award

Teacher at Wuhan Institute of Technology, China

Dr. Le Yang is an accomplished associate professor whose research expertise spans memristor neuromorphic systems, embedded systems, and advanced circuit design. With over thirty academic papers, more than ten patents, and a record of guiding students to national and provincial competition success, Dr. Yang has established a reputation for impactful research and educational leadership. His contributions integrate innovation, mentorship, and practical application, making him a distinguished candidate for recognition in advanced research and technological development.

Professional Profile

Scopus Profile | ORCID

Education

Dr. Yang pursued a rigorous academic path that laid the foundation for his multidisciplinary expertise. Through formal training in electrical and electronic engineering, he developed deep technical skills that later expanded into neuromorphic computation and system design. His academic development included a blend of theory, experimentation, and application, ensuring that his expertise was not confined to traditional learning but extended toward innovation-driven research. This strong educational background has been critical in enabling his advancements in memristor-based systems, circuit architectures, and real-world applications in computational intelligence.

Experience

Over the course of his academic career, Dr. Yang has cultivated experience as both a researcher and educator. Serving as an associate professor, he has balanced teaching with impactful scholarly contributions. His guidance of students in highly competitive arenas such as the National Undergraduate Electronic Design Contest, Blue Bridge Cup IT Contest, and Embedded System Design Contest reflects his ability to mentor and inspire innovation. Under his leadership, students have achieved three National Second Prizes, two National Third Prizes, and numerous provincial awards, showcasing his ability to bridge theoretical knowledge with practical skill. Furthermore, his active participation in national experimental teaching case design competitions demonstrates his commitment to integrating cutting-edge methods into education, with his teams achieving national-level recognition.

Research Focus

Dr. Yang’s research primarily centers on the design and application of memristor-based neuromorphic systems. His work explores how memristors can emulate biological neural functions, advancing computational efficiency and functionality for artificial intelligence systems. He has contributed significantly to memristor crossbar circuit design, backpropagation neural networks, and associative memory models that replicate human-like learning behaviors. His investigations also include embedded system applications and advanced circuit design, which are crucial for both hardware innovation and neuromorphic computing development. With a focus on bridging theory with practice, his research enhances the potential of emerging technologies to address real-world challenges in artificial intelligence and intelligent system integration.

Awards & Honors

Dr. Yang’s academic excellence has been recognized through prestigious awards and competitive grants. In 2021, he was awarded the National Natural Science Foundation Youth Program Project, reflecting the national acknowledgment of his innovative contributions to memristor and neuromorphic system research. Additionally, his involvement in student mentoring has led to collective recognition, including multiple prizes at national contests and experimental teaching competitions. These honors highlight both his personal research excellence and his dedication to fostering the next generation of engineers and researchers. His recognition at multiple levels demonstrates a career built on both groundbreaking research and impactful educational mentorship.

Publication Top Notes

Title: Memristor-based circuit design of biological behavior chain
Author: Yang L, Cai R, Cheng M, Ding Z, Li S, Zeng Z
Journal: IEEE Transactions on Circuits and Systems I: Regular Papers

Title: Memristor-based circuit design of BiLSTM network
Author: Yang L, Lei J, Cheng M, Ding Z, Li S, Zeng Z
Journal: Neural Networks

Title: Memristive crossbar-based circuit design of back-propagation neural network with synchronous memristance adjustment
Author: Yang L, Ding Z, Xu Y, Zeng Z
Journal: Complex & Intelligent Systems

Title: Circuit design of in-situ training memristive backpropagation neural network
Author: Yang L, Cheng M, Su T
Journal: AEU - International Journal of Electronics and Communications

Title: Memristor-based neural network circuit of full-function Pavlov associative memory with unconditioned response mechanisms
Author: Ding Z, Chen Z, Li S, Li Z, Yang L (corresponding author)
Journal: IEEE Transactions on Circuits and Systems I: Regular Papers

Title: A generalization and differentiation circuit implementation based on neural mechanisms
Author: Ding Z, Chen Z, Li S, Su T, Yang L (corresponding author)
Journal: IEEE Transactions on Nanotechnology

Title: Memristor crossbar-based Pavlov associative memory network for dynamic information correlation
Author: Yang L, Ding Z, Zeng Z
Journal: AEU - International Journal of Electronics and Communications

Title: An improved memristive current mirror circuit for continuous adjustable current output
Author: Cheng M, Yang L (corresponding author), Ding Z, Li S, Lei J
Journal: AEU - International Journal of Electronics and Communications

Title: Memristor-based circuit design of continuous adjustable direct-current voltage source
Author: Ding Z, Su T, Li S, Yang L (corresponding author)
Journal: International Journal of Circuit Theory and Applications

Title: An associative-memory-based reconfigurable memristive neuromorphic system with synchronous weight training
Author: Yang L, Zeng Z, Huang Y
Journal: IEEE Transactions on Cognitive and Developmental Systems

Conclusion

Dr. Le Yang’s distinguished academic journey reflects a combination of research innovation, educational leadership, and technological advancement. His work in memristor neuromorphic systems contributes to shaping the future of artificial intelligence hardware and computational intelligence. By successfully securing national-level funding, producing high-impact publications, authoring patents, and mentoring students to achieve excellence, he demonstrates a multifaceted commitment to advancing science and education. His contributions make him a strong candidate for recognition in research excellence, embodying the qualities of innovation, dedication, and global impact