Lei Guan | Machine Learning | Research Excellence Award

Published on by Electrical Engineering

Mr. Lei Guan | Machine Learning | Research Excellence Award

Director | China Academy of Safety Science and Technology | China

Lei Guan is a Director and Professor at the Risk Monitoring and Early Warning Center, China Academy of Safety Science and Technology, with expertise in risk monitoring, early warning systems, artificial intelligence, and industrial safety engineering. He holds a Bachelor’s degree in Materials Science and Master’s and Doctoral degrees in Mechanical Engineering with specialization in precision instruments and safety-related systems. He has led major national and ministerial research programs, directed key laboratories and professional committees, supervised graduate researchers, and provided technical leadership for large-scale industrial and governmental safety initiatives. His research focuses on intelligent work safety systems, industrial internet applications, digital twins, data-driven risk modeling, and emergency management, with sustained contributions through peer-reviewed publications, patents, and standards development. His scholarly impact is reflected in 18 citations, an h-index of 3, and 13 published articles.

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Featured Publications

Numerical simulation of the double pits stress concentration in a curved casing inner surface
W. Yan, L. Guan, Y. Xu, J.G. Deng – Advances in Mechanical Engineering, 9(1) (3 citations)

Safety monitoring and management system for fluid catalytic cracking (FCC) process
L. Fang, Z. Wu, L. Wei, R. Kang, L. Guan – International Conference on Information and Automation (3 citations)

Study on SVM-based Flame Recognition and Fire Warning for Cotton and Linen Warehouses
X. Zhao, S. Hao, L. Guan, Y. Wang, Q. Zhao, D. Lv – IEEE Conference on Advances in Electrical Engineering (2 citations)

Industrial Internet of Things (IIoT) Identity Resolution Techniques: A Review
C. Dai, H. Li, L. Guan, M. Chi – IEEE BigDataSecurity (1 citation)

Soodeh Hosseini | Machine Learning | Research Excellence Award

Published on by Electrical Engineering

Prof. Dr. Soodeh Hosseini | Machine Learning | Research Excellence Award

Corresponding Author | Shahid Bahonar University of Kerman | Iran

Dr. Soodeh Hosseini is an Associate Professor of Computer Science at the Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, specializing in artificial intelligence, machine learning, cybersecurity, and complex networks. She holds a bachelor’s degree in Computer Science, a master’s degree in Computer Engineering with a specialization in software, and a doctorate in Computer Engineering with a focus on software engineering. Her professional experience encompasses extensive academic teaching, supervision of advanced research projects, leadership as head of academic and research units, and active involvement in technology growth centers and science parks, alongside advisory and executive roles in scholarly and innovation-driven initiatives. Her scholarly impact is evidenced by 1,426 citations, an h-index of 23, and an i10-index of 34.

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Featured Publications

A hybrid sine–cosine and golden ratio optimization algorithm for feature selection in intrusion detection systems
M. Maazalahi, S. Hosseini – International Journal of System Assurance Engineering and Management

Analytics and measuring the vulnerability of communities for complex network security
M. Jouyban, S. Hosseini – International Journal of Data Science and Analytics

An Improved Binary Slime Mold Algorithm for Intrusion Detection Systems
M. Khorashadizade, S. Hosseini, M. Jouyban – Concurrency and Computation: Practice and Experience

Aseel Basheer | Machine Learning | Excellence in Research Award

Published on by Electrical Engineering

Dr. Aseel Basheer | Machine Learning | Excellence in Research Award

Postdoc | University of Oklahoma | United States

Aseel Basheer is a Graduate Research Assistant and Ph.D. candidate in Computer Science at the University of Oklahoma, with expertise in machine learning, data science, and large-scale data analytics. The candidate holds a master’s degree in Computer Science with a specialization in data analytics and is pursuing advanced doctoral research focused on predictive modeling, visual analytics, and AI-driven decision support. Professionally, Aseel has contributed to interdisciplinary research projects in public health intelligence and pandemic surveillance, developing AI/ML models, data-driven forecasting systems, and visualization platforms, while also demonstrating academic leadership through teaching, mentoring, and curriculum support in higher education. The candidate’s professional profile is further strengthened by recognized certifications in data analytics, machine learning, healthcare data science, and research rigor, alongside active engagement in scholarly communities. The scholarly impact is reflected through 22 citations, an h-index of 2, and an i10-index of 1.

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Adil Sultan | Environmental Modeling | Best Researcher Award

Published on by Electrical Engineering

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.

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

Published on by Electrical Engineering

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.