Kia Jahanbin | Deep Transfer Learning | Best Researcher Award

Published on by Electrical Engineering

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.

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.