Hossein Ghaffarian | Machine Learning | Editorial Board Member

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Dr. Hossein Ghaffarian | Machine Learning | Editorial Board Member 

Assistant Professor | Arak University | Iran

Dr. Hossein Ghaffarian is a distinguished researcher and faculty member in the Department of Computer Engineering at Arak University, Iran, recognized for his expertise in computer networks, intelligent transportation systems (ITS), data mining, and applied artificial intelligence. His academic contributions encompass both theoretical and applied dimensions of wired and wireless network architectures, network security, and quality of service optimization. Dr. Ghaffarian’s scholarly work demonstrates a strong interdisciplinary orientation, bridging computer systems architecture with real-world applications in vehicular ad hoc networks (VANETs), indoor localization, and cloud-based network solutions. He has served in multiple academic and professional capacities, including as IT and Product Manager at Sanaat Yar Afzar Iranian and consultant for Iran’s Ministry of Education and the Electrical Industry Data Committee (Tavanir). His innovative research has earned national recognition, including a Best Paper Award at the IEEE International Conference on Internet of Things and Applications. Dr. Ghaffarian has also contributed to key industrial and governmental projects, such as developing WAN solutions for electrical industries and designing cloud-based monitoring systems. His research achievements are further complemented by his active engagement in academic translation and technical education, with works such as Python Numpy for Beginners and Python Pandas for Beginners (Farsi editions). Dr. Hossein Ghaffarian’s academic impact is reflected in his international research visibility, with 82 citations by 81 documents, 21 publications, and an h-index of 4, underscoring his growing influence in computer engineering and artificial intelligence research.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Ghaffarian, H., Fathy, M., & Soryani, M. (2012). Vehicular ad hoc networks enabled traffic controller for removing traffic lights in isolated intersections based on integer linear programming. IET Intelligent Transport Systems, 6(2), 115–123. Citations: 52

  2. Farahani, B. J., Ghaffarian, H., & Fathy, M. (2009). A fuzzy based priority approach in mobile sensor network coverage. International Journal of Recent Trends in Engineering, 2(1), 138. Citations: 19

  3. Rashvand, H. F., & Chao, H. C. (2013). Dynamic ad hoc networks. Institution of Engineering and Technology. Citations: 18

  4. Parvin, H., Minaei-Bidgoli, B., & Ghaffarian, H. (2011). An innovative feature selection using fuzzy entropy. In International Symposium on Neural Networks (pp. 576–585). Citations: 16

  5. Keramatpour, A., Nikanjam, A., & Ghaffarian, H. (2017). Deployment of wireless intrusion detection systems to provide the most possible coverage in wireless sensor networks without infrastructures. Wireless Personal Communications, 96(3), 3965–3978. Citations: 15

Feng Mao | Cognitive Science | Best Researcher Award

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Assist. Prof. Dr. Feng Mao | Cognitive Science | Best Researcher Award

Associate Professor | Shanghai University of International Business and Economics | China

Assist. Prof. Dr. Feng Mao is a distinguished Associate Professor and Senior Translator at Shanghai University of International Business and Economics with 28 years of higher education experience, specializing in country and area studies, translation studies, and foreign language education. He is currently pursuing a Ph.D. at Shanghai International Studies University (since 2022) and serves as a Master’s supervisor for MA in Linguistics and MTI programs. Over his career, he has led 11 research projects, including a national-level Social Science Fund project, and collaborated with international scholars from Singapore, Canada, the UK, and Germany, reflecting his strong global research network. His professional experience includes mentoring MA students, peer reviewing for SSCI and AHCI journals, editorial committee service, and contributions to national professional assessments, including CATTI examination grading. His research interests focus on translation and interpreting studies, foreign language education and policy, cross-cultural communication, applied linguistics, audiovisual translation, and country and area studies. MAO Feng has developed advanced research skills in big data analysis for textbook and material compilation, literary analysis, audiovisual translation methods, and educational program evaluation, supporting both theoretical and applied projects. He has authored 64 academic publications, including 20 SSCI/AHCI journal papers with 5 in Q1 journals, and published 5 academic monographs and translations totaling over 2 million words, alongside textbooks and review articles that serve thousands of students. He has also contributed to industry and government consultancy projects such as cultural brand promotion and copyright export research. His awards and honors include national-level recognition for professional degree assessment and leadership roles within the China Association for Educational Linguistics and the Translators Association of China. Overall, MAO Feng’s extensive research, teaching, publications, and international collaborations highlight his exceptional academic leadership and ongoing potential to advance translation studies and foreign language education globally. 7 Citations, 8 Documents, h-index 2.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

Mao, F., & Yang, X. (2024). Literary therapy based on positive psychology: Impact on college students’ happiness. Journal of Poetry Therapy, 37(1), 16–34. https://doi.org/[insert DOI] (Citations: 5)

Mao, F., & Liu, S. (2024). Book review: Networked feminism: How digital media makers transformed gender justice movements (R. Clark-Parsons, California, University of California Press, 2022). Feminist Media Studies, 24(2), 404–406. (Citations: 3)

Feng, M., Wenhui, L., Xinle, Y., & Biyu, W. (2022). Romantic narrative in the film The Battle at Lake Changjin. International Journal of English and Comparative Literary Studies, 3(1), 19–27. (Citations: 3)

Feng, M., Quan, L., & Wu, B. (2021). A review on the compilation of college English textbooks in China based on big data. Sino-US English Teaching, 18(3), 60–65. (Citations: 3)

Feng, M., Quan, L., & Biyu, W. (2021). Exploration of the compilation of English learning materials for Chinese college students based on big data under the guidance of complex dynamic theory. International Journal of Linguistics, Literature and Translation, 4(3), 22–32. (Citations: 2)