Dr. Baha Ihnaini | Computer Science | Outstanding Contribution Award
Assistant Professor at Wenzhou-Kean University, China
Dr. Baha Ihnaini is an accomplished academic and researcher in computer science with expertise spanning artificial intelligence, data science, machine learning, and natural language processing. His scholarly work has addressed significant challenges in sentiment analysis, medical diagnostics, disease prediction, and misinformation detection, with publications in respected journals and international conferences. Notably, he has contributed to developing Arabic lexicons for sentiment analysis, enhancing AI-driven healthcare solutions, and advancing transfer learning models for predictive analytics. Alongside his research, Dr. Ihnaini has demonstrated a strong commitment to teaching, covering a wide range of computer science courses and mentoring students in senior projects. His service on academic committees and involvement in curriculum development highlight his leadership and dedication to institutional growth. With a record of impactful research, effective teaching, and professional service, Dr. Ihnaini stands out as a valuable contributor to his field and a strong candidate for academic recognition.
Professional Profile
Google Scholar | Scopus Profile | ORCID Profile
Education
Dr. Baha Ihnaini holds a Ph.D. in Computer Science with a specialization in Data Science from Universiti Utara Malaysia, where his research focused on developing an expandable Arabic lexicon and sentiment analysis rules for social media text. He also earned a Master of Science in Management Information Systems from The Arab Academy for Banking and Financial Sciences, equipping him with a strong foundation in both technical and managerial aspects of information technology. His academic journey began with a Bachelor’s degree in Computer Engineering from Philadelphia University in Jordan, providing him with comprehensive knowledge of hardware, software, and systems engineering. This multidisciplinary educational background has enabled Dr. Ihnaini to integrate advanced computational methods with practical problem-solving approaches, particularly in the areas of machine learning, natural language processing, and data-driven applications. His academic credentials reflect a balance between rigorous technical expertise and applied research in emerging fields of computing.
Experience
Dr. Ihnaini has accumulated rich professional experience as an educator, researcher, and academic leader across several institutions. He currently serves as an Assistant Professor of Computer Science at Wenzhou-Kean University, where he teaches a broad range of courses, from foundational programming and systems to advanced research in computer science. His roles extend beyond teaching, as he actively contributes to academic committees focused on curriculum development, faculty hiring, and student support. Prior to this, he served as an Adjunct Professor at Al Ain University and BTEC Abu Dhabi, as well as a Lecturer at Al Khawarizmi International College, where he guided student projects and curriculum design. Earlier in his career, he worked as a Research Officer at Universiti Utara Malaysia’s InterNetWorks Research Lab, where he played a pivotal role in advancing sentiment analysis for Arabic text. Collectively, his experience reflects a strong commitment to research, teaching excellence, and academic service.
Research Focus
Dr. Ihnaini’s research focuses on artificial intelligence, machine learning, data science, and natural language processing, with an emphasis on solving real-world problems through intelligent systems. A significant part of his work has concentrated on Arabic sentiment analysis, where he developed innovative linguistic resources and computational models to improve text classification accuracy. His recent research extends to medical AI, including predictive modeling for diseases such as diabetic retinopathy, Alzheimer’s disease, and vitamin D deficiency, showcasing his ability to merge computer science with healthcare applications. He has also contributed to the advancement of fake news detection, stock market prediction using sentiment data, and multimodal semantic similarity. His interdisciplinary approach highlights the versatility of data-driven methods and their societal impact, from enhancing healthcare systems to improving digital communication analysis. Through these diverse but interconnected research directions, Dr. Ihnaini continues to contribute to both theoretical advancements and practical innovations in computer science.
Award and Honor
Dr. Ihnaini has earned recognition for his scholarly contributions through research publications in reputable international journals and conferences, with several works indexed in Scopus and well-regarded platforms. His achievements include developing novel computational methods for sentiment analysis, interdisciplinary research in healthcare prediction models, and the application of advanced machine learning techniques to real-world problems. His role as a key contributor to collaborative projects with international researchers further reflects the recognition of his expertise and the impact of his work. Beyond research, his commitment to education and service has been acknowledged within the institutions he has served, particularly through his involvement in curriculum innovation and student mentorship. While formal distinctions are highlighted through his publication record and conference participation, his career trajectory itself demonstrates consistent recognition as a capable scholar and educator. His growing research visibility and international collaborations continue to strengthen his candidacy for prestigious awards and honors.
Publication Top Notes
-
Title: A smart healthcare recommendation system for multidisciplinary diabetes patients with data fusion based on deep ensemble learning
Authors: B Ihnaini, MA Khan, TA Khan, S Abbas, MS Daoud, M Ahmad, MA Khan
Year: 2021
Citations: 158 -
Title: Machine Learning Empowered Software Defect Prediction System
Authors: MS Daoud, S Aftab, M Ahmad, MA Khan, A Iqbal, S Abbas, M Iqbal, B Ihnaini
Year: 2022
Citations: 37 -
Title: Stock trend prediction using sentiment analysis
Authors: Q Xiao, B Ihnaini
Year: 2023
Citations: 35 -
Title: Joint channel and multi-user detection empowered with machine learning
Authors: MS Daoud, A Fatima, WA Khan, MA Khan, S Abbas, B Ihnaini, M Ahmad
Year: 2021
Citations: 31 -
Title: Real-time shill bidding fraud detection empowered with fussed machine learning
Authors: WUH Abidi, MS Daoud, B Ihnaini, MA Khan, T Alyas, A Fatima, M Ahmad
Year: 2021
Citations: 28 -
Title: Rider weed deep residual network-based incremental model for text classification using multidimensional features and MapReduce
Authors: HB Abdalla, AM Ahmed, SRM Zeebaree, A Alkhayyat, B Ihnaini
Year: 2022
Citations: 18 -
Title: Presenting and evaluating scaled extreme programming process model
Authors: M Ibrahim, S Aftab, M Ahmad, A Iqbal, BS Khan, M Iqbal, BNS Ihnaini
Year: 2020
Citations: 13 -
Title: Exploring the agile family: A survey
Authors: M Ibrahim, S Aftab, B Bakhtawar, M Ahmad, A Iqbal, N Aziz, MS Javeid, B Ihnaini
Year: 2020
Citations: 12 -
Title: Predicting vitamin D deficiency using optimized random forest classifier
Authors: A Alloubani, B Abuhaija, M Almatari, G Jaradat, B Ihnaini
Year: 2024
Citations: 9 -
Title: Lexicon-based sentiment analysis of Arabic tweets: A survey
Authors: B Ihnaini, M Mahmuddin
Year: 2018
Citations: 9 -
Title: A Severity Grading Framework for Diabetic Retinopathy Detection using Transfer Learning
Authors: S Akhtar, S Aftab, S Kousar, A Rehman, M Ahmad, AQ Saeed, B Ihnaini
Year: 2024
Citations: 8 -
Title: Improving the Quality of e-Commerce Service by Implementing Combination Models with Step-by-Step, Bottom-Up Approach
Authors: BA Hemn, G Chengwei, B Ihnaini
Year: 2021
Citations: 6 -
Title: Sentiment analysis of Song Dynasty classical poetry using fine-tuned large language models: a study with LLMs
Authors: B Ihnaini, W Sun, Y Cai, Z Xu, R Sangi
Year: 2024
Citations: 5 -
Title: A transfer learning based framework for diabetic retinopathy detection using data fusion
Authors: S Akhtar, S Aftab, M Ahmad, B Ihnaini
Year: 2024
Citations: 4 -
Title: Semantic similarity on multimodal data: A comprehensive survey with applications
Authors: B Ihnaini, B Abuhaija, EA Mills, M Mahmuddin
Year: 2024
Citations: 3
Conclusion
Dr. Baha Ihnaini’s publication record reflects a strong and steadily growing research trajectory across diverse yet interconnected fields of computer science, including artificial intelligence, data science, natural language processing, and medical informatics. His works demonstrate both theoretical depth and practical applications, ranging from healthcare prediction models and fraud detection to sentiment analysis and software engineering. The high citation impact of certain publications highlights the relevance and influence of his research in the academic community, while his more recent works indicate an expanding focus on interdisciplinary applications such as healthcare and cultural text analysis using advanced AI techniques. Collectively, his contributions showcase a balance of innovation, collaboration, and societal relevance, positioning him as a researcher whose work is not only academically significant but also impactful in addressing real-world challenges. This combination of influence, diversity, and practical value strengthens his candidacy for recognition through awards and honors.