Mini Han Wang | Artificial Intelligence | Young Scientist Award

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Dr. Mini Han Wang | Artificial Intelligence | Young Scientist Award

Chinese University of Hong Kong, Hong Kong

Dr. Mini Han Wang is a distinguished senior researcher specializing in ophthalmology, artificial intelligence (AI) in medical imaging, and biomolecular pathways in ocular diseases. She holds dual Ph.D.s in Ophthalmology & Visual Sciences from The Chinese University of Hong Kong and Data Science from the City University of Macau, demonstrating her expertise in integrating medical research with AI-driven analytical techniques. Dr. Wang has made significant contributions to age-related macular degeneration (AMD) research, AI-based disease diagnostics, and precision medicine. She currently serves as a Senior Researcher at Zhuhai People’s Hospital, affiliated with the Beijing Institute of Technology and Jinan University, and Director of the Frontier Science Computing Center at the Chinese Academy of Sciences. Beyond research, she is an experienced lecturer, delivering courses on intelligent data mining, evidence-based medicine, and AI applications in healthcare. Her work is widely published in peer-reviewed journals, and she actively collaborates with leading academic and medical institutions. With a commitment to advancing medical AI technologies and personalized healthcare solutions, Dr. Wang stands out as a leading expert at the intersection of medicine and data science.

Professional Profile

Education

Dr. Mini Han Wang has pursued a multidisciplinary academic journey, combining medical sciences, engineering, and data science. She earned a Ph.D. in Ophthalmology & Visual Sciences from The Chinese University of Hong Kong (2022-2025), where her research focuses on AI-driven diagnostics and molecular mechanisms of retinal diseases. In parallel, she completed a Ph.D. in Data Science at the Institute of Data Science, City University of Macau (2020-2023), further enhancing her ability to develop AI-integrated solutions for medical applications. Before her doctoral studies, Dr. Wang completed an M.Sc. in Management (2016-2018) at City University of Macau, gaining insights into research administration and healthcare management. She also holds dual bachelor’s degrees from Jiangxi Science & Technology Normal University (2012-2016) in Internet of Things (IoT) Engineering and English Literature, showcasing her strong foundation in technology and global scientific communication. As an Outstanding Graduate Representative, her diverse educational background enables her to bridge the gap between medical research, AI innovation, and healthcare management, making her a pioneering figure in modern ophthalmic research.

Professional Experience

Dr. Wang’s professional journey is marked by leadership in research, teaching, and AI-driven medical advancements. She currently serves as a Senior Researcher at Zhuhai People’s Hospital, affiliated with Beijing Institute of Technology and Jinan University, where she leads projects on AI-based ophthalmic disease diagnosis and retinal molecular research. Additionally, she holds the position of Director of the Frontier Science Computing Center at the Chinese Academy of Sciences, overseeing cutting-edge AI applications in medicine and multi-omics data integration. Since 2018, Dr. Wang has collaborated with Shenzhen Institute of Advanced Technology and Zhuhai Institute of Advanced Technology, conducting research on medical imaging, knowledge graphs, and AI-driven predictive modeling. Her academic contributions include guest lectures at Beijing Institute of Technology, Jinan University, and Zhuhai Science & Technology Institute, focusing on intelligent data mining, evidence-based medicine, and AI in disease diagnosis. With her interdisciplinary expertise, Dr. Wang has played a key role in bridging fundamental research with clinical applications, contributing significantly to medical AI advancements and personalized treatment strategies.

Research Interest

Dr. Wang’s research revolves around three core areas: ophthalmology, AI in medical imaging, and biomolecular pathways in ocular diseases. Her primary focus is age-related macular degeneration (AMD) and retinal diseases, where she investigates molecular mechanisms, genetic variations, and metabolic dysregulation. She is also deeply involved in AI-driven predictive modeling to enhance early disease detection and precision therapeutics. In the field of medical imaging, she integrates multi-modal imaging techniques (OCT, UWF Fundus) with AI algorithms to improve retinal disease diagnostics and prognosis. Furthermore, her research extends to biomolecular analysis, where she studies oxidative stress, mitochondrial dysfunction, and complement system activation in ocular diseases. By combining multi-omics data, AI-driven drug discovery, and knowledge graph-driven ophthalmic AI systems, Dr. Wang aims to revolutionize personalized medicine and enhance treatment strategies for degenerative eye diseases.

Research Skills

Dr. Wang possesses a diverse and advanced skill set, allowing her to lead high-impact research in medical AI and ophthalmology. She specializes in AI-based predictive modeling, machine learning for medical imaging, and deep learning for disease classification. Her expertise in biomolecular analysis includes multi-omics data integration, pathway analysis, and molecular crosstalk identification for precision medicine applications. Dr. Wang is also proficient in data mining, statistical modeling, and computational biology, which are essential for her research on retinal diseases and AI-driven diagnostics. Additionally, she has hands-on experience with multi-modal imaging techniques (OCT, UWF, fundus photography) and their integration with AI-based disease detection frameworks. She is well-versed in academic writing, research methodology, and project management, with an extensive record of peer-reviewed publications and collaborative research projects. With these skills, Dr. Wang is able to bridge the gap between clinical research and AI-powered healthcare solutions, making her a leading figure in medical innovation.

Awards and Honors

Dr. Wang has received multiple recognitions for her outstanding research contributions and academic achievements. As an Outstanding Graduate Representative, she was acknowledged for her exceptional performance in data science and medical research. She has been the recipient of research grants and funding awards for her work in ophthalmic AI, biomolecular studies, and precision medicine. Her research on AMD and AI-driven diagnostics has earned recognition from international conferences and peer-reviewed journals. She has been invited as a keynote speaker and panelist at various scientific conferences, where she has shared insights on AI applications in medicine, multi-omics integration, and retinal disease research. Additionally, her collaborations with leading universities and medical institutions have led to numerous institutional awards for excellence in research and innovation. With a strong academic and professional track record, Dr. Wang continues to be recognized as a pioneering researcher at the forefront of AI-driven medical advancements.

Conclusion

Dr. Mini Han Wang is a leading researcher at the intersection of ophthalmology, AI, and biomolecular analysis, making groundbreaking contributions to AMD research, AI-driven diagnostics, and precision medicine. Her multidisciplinary expertise in medical science, data analytics, and computational biology allows her to develop innovative solutions for early disease detection and personalized treatment strategies. As a senior researcher, director, and academic lecturer, she has demonstrated leadership in both research and education, mentoring young scientists and collaborating with top-tier institutions. Her work in AI-integrated ophthalmology and molecular disease modeling is shaping the future of medical research and healthcare technology. While further global collaborations, large-scale clinical applications, and expanded research beyond AMD

Publications Top Notes

  • Title: Place attachment to pseudo establishments: An application of the stimulus-organism-response paradigm to themed hotels
    Authors: J. Sun, P.J. Chen, L. Ren, E.H.W. Shih, C. Ma, H. Wang, N.H. Ha
    Year: 2021
    Citations: 86

  • Title: The effect of online investor sentiment on stock movements: an LSTM approach
    Authors: G. Wang, G. Yu, X. Shen
    Year: 2020
    Citations: 43

  • Title: Big data and predictive analytics for business intelligence: A bibliographic study (2000–2021)
    Authors: Y. Chen, C. Li, H. Wang
    Year: 2022
    Citations: 33

  • Title: AI-based advanced approaches and dry eye disease detection based on multi-source evidence: Cases, applications, issues, and future directions
    Authors: M.H. Wang, L. Xing, Y. Pan, F. Gu, J. Fang, X. Yu, C.P. Pang, K.K.L. Chong
    Year: 2024
    Citations: 32

  • Title: Artificial intelligence in ophthalmopathy and ultra-wide field image: A survey
    Authors: J. Yang, S. Fong, H. Wang, Q. Hu, C. Lin, S. Huang, J. Shi, K. Lan, R. Tang
    Year: 2021
    Citations: 29

  • Title: Research on data security in big data cloud computing environment
    Authors: F. Wang, H. Wang, L. Xue
    Year: 2021
    Citations: 27

  • Title: An explainable artificial intelligence-based robustness optimization approach for age-related macular degeneration detection based on medical IoT systems
    Authors: M.H. Wang, K.K. Chong, Z. Lin, X. Yu, Y. Pan
    Year: 2023
    Citations: 26

  • Title: Applications of explainable artificial intelligent algorithms to age-related macular degeneration diagnosis: A case study based on CNN, attention, and CAM mechanism
    Authors: M. Wang, Z. Lin, J. Zhou, L. Xing, P. Zeng
    Year: 2023
    Citations: 13

  • Title: Metamaterials design method based on deep learning database
    Authors: X. Zhou, Q. Xiao, H. Wang
    Year: 2022
    Citations: 10

  • Title: A YOLO-based method for improper behavior predictions
    Authors: M. Wang, Y. Zhao, Q. Wu, G. Chen
    Year: 2023
    Citations: 9

Liangyu Yin | Artificial Intelligence | Best Researcher Award

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Dr. Liangyu Yin | Artificial Intelligence | Best Researcher Award

Research Professor at Xinqiao Hospital, Army Medical University, China

Dr. Liangyu Yin is an accomplished academic and researcher specializing in clinical nutrition, epidemiology, and artificial intelligence. He has made significant contributions to understanding cancer nutrition and malnutrition, particularly in oncology patients. His expertise spans the intersection of nutrition, cancer biology, and advanced machine learning methodologies. With numerous publications in prestigious journals such as Journal of Cachexia Sarcopenia Muscle, American Journal of Clinical Nutrition, and Clinical Nutrition, Dr. Yin is recognized as a thought leader in his field. He is currently a Research Professor at the Department of Nephrology, Xinqiao Hospital, Army Medical University, where he continues to advance research on cancer cachexia, nutritional interventions, and artificial intelligence applications. His work is aimed at improving patient outcomes, especially for cancer patients, by utilizing innovative research methods, including AI-driven diagnostics and predictive models for malnutrition and cancer prognosis.

Professional Profile

Education:

Dr. Liangyu Yin’s educational journey is marked by a strong foundation in medicine and nutrition. He earned his Ph.D. in Nutrition and Food Hygiene from Army Medical University in 2022, following a Master of Medicine in Nutrition and Food Hygiene from Chongqing Medical University in 2012. His academic journey began with a Bachelor of Arts degree in English, specializing in Biomedical English, from Chongqing Medical University. This diverse educational background has provided him with a robust understanding of both medical and nutritional sciences, which he applies in his research. His ongoing contributions reflect his dedication to bridging clinical nutrition with the latest advancements in artificial intelligence and cancer epidemiology.

Professional Experience:

Dr. Liangyu Yin’s professional experience spans several prestigious roles in academic research, clinical settings, and health science institutions. He currently serves as a Research Professor in the Department of Nephrology at Xinqiao Hospital, Army Medical University. Previously, he held positions as an Associate Research Professor at both Daping Hospital and Southwest Hospital within the Army Medical University, focusing on cancer epidemiology, nutrition, and artificial intelligence. Dr. Yin began his research career as a Research Assistant at the Institute of Hepatobiliary Surgery, Southwest Hospital, where he worked on cancer biology and non-coding RNA. His long-standing career at Army Medical University has contributed to the development of novel methodologies and interventions in clinical nutrition and cancer treatment. His expertise in epidemiology, nutrition, and AI has shaped the direction of his research in improving patient care outcomes.

Research Interests:

Dr. Liangyu Yin’s primary research interests lie at the intersection of clinical nutrition, cancer epidemiology, and artificial intelligence. His work focuses on understanding the role of malnutrition in cancer progression, with a particular emphasis on cancer cachexia, a complex metabolic syndrome associated with cancer. Dr. Yin is dedicated to developing predictive models and AI-driven solutions to identify and address malnutrition in cancer patients, improving patient outcomes and survival rates. His research also investigates non-coding RNA and its role in cancer biology, with a focus on its potential applications in cancer treatment. Through his interdisciplinary approach, combining machine learning with clinical nutrition, Dr. Yin aims to revolutionize cancer care by improving diagnosis, prognosis, and nutritional interventions in clinical practice.

Research Skills:

Dr. Liangyu Yin possesses a diverse set of research skills, enabling him to conduct cutting-edge investigations in the fields of clinical nutrition, cancer epidemiology, and artificial intelligence. His proficiency in utilizing machine learning models to predict and diagnose malnutrition in cancer patients demonstrates his technical expertise. Additionally, Dr. Yin’s deep understanding of cancer biology, especially cancer cachexia and non-coding RNA, is critical to his work. His research skills also extend to conducting large-scale cohort studies and multicenter analyses, as evidenced by his numerous publications. Moreover, his ability to integrate AI with clinical nutrition research allows him to pioneer innovative solutions in medical diagnostics and patient care, making him a leader in his field.

Awards and Honors:

Dr. Liangyu Yin has received numerous accolades and honors for his contributions to clinical nutrition and cancer research. His work has been consistently recognized in prestigious academic journals, and his research has influenced global medical practices regarding nutrition in cancer care. Dr. Yin’s expertise in combining artificial intelligence with nutrition science has earned him several recognitions for innovation in healthcare. He is a highly regarded researcher within the medical and scientific community, regularly invited to present his findings at international conferences and to collaborate on advanced research projects. His commitment to improving cancer patient outcomes through his interdisciplinary research has made him a prominent figure in his field.

Conclusion:

Liangyu Yin is an outstanding candidate for the Best Researcher Award. His research in clinical nutrition, cancer epidemiology, and the innovative use of artificial intelligence sets him apart as a leader in his field. His work has made significant strides in understanding malnutrition and cancer cachexia, with implications for improving patient care. By expanding the scope of his research and enhancing the real-world application of his findings, he has the potential to make an even greater impact on global health. Therefore, he is highly deserving of this award, and his future contributions will continue to shape the field of clinical nutrition and cancer care.

Publication Top Notes:

  1. Early prediction of severe acute pancreatitis based on improved machine learning models
    • Authors: Li, L., Yin, L., Chong, F., Wang, Y., Xu, H.
    • Journal: Journal of Army Medical University
    • Year: 2024
    • Volume: 46(7)
    • Pages: 753–759
  2. Association of possible sarcopenia with all-cause mortality in patients with solid cancer: A nationwide multicenter cohort study
    • Authors: Yin, L., Song, C., Cui, J., Shi, H., Xu, H.
    • Journal: Journal of Nutrition, Health and Aging
    • Year: 2024
    • Volume: 28(1)
    • Article ID: 100023
    • Citations: 3
  3. Comment on: “Triceps skinfold-albumin index significantly predicts the prognosis of cancer cachexia: A multicentre cohort study” by Yin et al. – the authors reply
    • Authors: Yin, L., Cui, J., Lin, X., Shi, H., Xu, H.
    • Journal: Journal of Cachexia, Sarcopenia and Muscle
    • Year: 2023
    • Volume: 14(6)
    • Pages: 2993–2994
  4. Comparison of the performance of the GLIM criteria, PG-SGA and mPG-SGA in diagnosing malnutrition and predicting survival among lung cancer patients: A multicenter study
    • Authors: Huo, Z., Chong, F., Yin, L., Shi, H., Xu, H.
    • Journal: Clinical Nutrition
    • Year: 2023
    • Volume: 42(6)
    • Pages: 1048–1058
    • Citations: 6
  5. Ensemble learning system to identify nutritional risk and malnutrition in cancer patients without weight loss information
    • Authors: Yin, L., Liu, J., Liu, M., Shi, H., Xu, H.
    • Journal: Science China Life Sciences
    • Year: 2023
    • Volume: 66(5)
    • Pages: 1200–1203
  6. Kruppel-like Factors 3 Regulates Migration and Invasion of Gastric Cancer Cells Through NF-κB Pathway
    • Authors: Liang, X., Feng, Z., Yan, R., Lu, H., Zhang, L.
    • Journal: Alternative Therapies in Health and Medicine
    • Year: 2023
    • Volume: 29(2)
    • Pages: 64–69
    • Citations: 1
  7. Triceps skinfold–albumin index significantly predicts the prognosis of cancer cachexia: A multicentre cohort study
    • Authors: Yin, L., Cui, J., Lin, X., Shi, H., Xu, H.
    • Journal: Journal of Cachexia, Sarcopenia and Muscle
    • Year: 2023
    • Volume: 14(1)
    • Pages: 517–533
    • Citations: 5

 

 

Farhad Soleimanian Gharehchopogh | Artificial Intelligent | Best Researcher Award

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Assoc. Prof. Dr. Farhad Soleimanian Gharehchopogh | Artificial Intelligent | Best Researcher Award

Dean of Faculty at Urmia Branch, Islamic Azad University, Iran

Dr. Farhad Soleimanian Gharehchopogh is a distinguished academic with a profound background in computer science and software engineering. He is renowned for his contributions to machine learning, artificial intelligence, and computational intelligence. His research focuses on solving complex problems using evolutionary algorithms and optimization techniques. Dr. Soleimanian is also an active participant in academic circles, serving on the editorial boards of several prestigious journals and regularly presenting his findings at international conferences. With numerous publications in high-impact journals, he has significantly influenced his field. His dedication to research and education has earned him accolades, making him a respected figure among peers and students alike.

Professional Profile

Education

Dr. Farhad Soleimanian Gharehchopogh holds a Ph.D. in Computer Science, specializing in Software Engineering from Urmia University, Iran. His doctoral research focused on advanced optimization techniques and their applications in artificial intelligence. Prior to his Ph.D., he completed a Master of Science in Software Engineering at Islamic Azad University, Tabriz Branch, where he developed a strong foundation in programming, data structures, and algorithm design. He earned his Bachelor of Science in Computer Science from Islamic Azad University, Urmia Branch, where he first explored his interest in computational intelligence. His academic journey has been characterized by a consistent focus on deepening his understanding of complex computational systems.

Professional Experience

Dr. Farhad Soleimanian Gharehchopogh has held various academic positions throughout his career, contributing to the growth of computer science education and research. He has served as an Assistant Professor at Islamic Azad University, Urmia Branch, where he taught undergraduate and graduate courses in software engineering and computer science. In addition to teaching, he has supervised numerous master’s and Ph.D. students, guiding their research in areas like machine learning and optimization algorithms. He has also collaborated with international researchers on various projects, aiming to solve real-world problems using advanced computational methods. His professional experience is marked by a commitment to fostering innovation in both academic and practical applications of computer science.

Research Interest

Dr. Soleimanian’s research interests are centered around machine learning, artificial intelligence, and computational optimization. He is particularly interested in developing new algorithms for data mining, evolutionary computing, and swarm intelligence. His work often explores how optimization techniques, such as genetic algorithms, particle swarm optimization, and ant colony optimization, can be applied to solve complex problems in various fields. Additionally, he is passionate about deep learning and its applications in pattern recognition, natural language processing, and image analysis. Dr. Soleimanian continually seeks to advance the field through innovative research, aiming to bridge the gap between theoretical concepts and practical implementations.

Research Skills

Dr. Farhad Soleimanian Gharehchopogh possesses a wide array of research skills that make him a leader in computational intelligence and software engineering. He has extensive experience in developing and implementing optimization algorithms, leveraging his expertise in evolutionary computing and metaheuristics. Proficient in programming languages such as Python, MATLAB, and C++, he applies these skills to simulate and analyze complex models. Dr. Soleimanian is also skilled in statistical analysis and data visualization, enabling him to derive meaningful insights from large datasets. His ability to collaborate effectively with other researchers and his strong analytical mindset have allowed him to make significant contributions to his field.

Awards and Honors

Dr. Soleimanian’s excellence in research and education has been recognized with several awards and honors throughout his career. He has received accolades for his high-quality research papers presented at international conferences and published in peer-reviewed journals. His contributions to the field have been acknowledged with best paper awards and recognition from academic societies. He has also been honored for his outstanding teaching and mentoring, guiding students towards academic and professional success. Dr. Soleimanian’s dedication to advancing computer science and his commitment to academic excellence have made him a recipient of numerous prestigious awards, highlighting his impact in both research and education.

Conclusion

Dr. Farhad Soleimanian Gharehchopogh is a strong candidate for the Best Researcher Award, given his extensive research output, mentorship of graduate students, and recognition among the top-cited scientists globally. His consistent contributions to the academic and research community, particularly in computer engineering, make him well-suited for this award. Addressing the minor areas for improvement, such as updating student mentorship records and highlighting recent publications, would further solidify his application.

Publications Top Notes

  • Recent applications and advances of African Vultures Optimization Algorithm
    Authors: AG Hussien, FS Gharehchopogh, A Bouaouda, S Kumar, G Hu
    Journal: Artificial Intelligence Review 57 (12), 1-51
    Year: 2024
    Citations: Not specified
  • An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for Engineering Problems and Its Applications in Breast Cancer
    Authors: FA Özbay, E Özbay, FS Gharehchopogh
    Journal: CMES-Computer Modeling in Engineering & Sciences 141 (2)
    Year: 2024
    Citations: Not specified
  • Chaotic RIME optimization algorithm with adaptive mutualism for feature selection problems
    Authors: M Abdel-Salam, G Hu, E Çelik, FS Gharehchopogh, IM El-Hasnony
    Journal: Computers in Biology and Medicine 179, 108803
    Year: 2024
    Citations: 6
  • A hybrid principal label space transformation-based ridge regression and decision tree for multi-label classification
    Authors: SHS Ebrahimi, K Majidzadeh, FS Gharehchopogh
    Journal: Evolving Systems, 1-37
    Year: 2024
    Citations: Not specified
  • Multifeature Fusion Method with Metaheuristic Optimization for Automated Voice Pathology Detection
    Authors: E Özbay, FA Özbay, N Khodadadi, FS Gharehchopogh, S Mirjalili
    Journal: Journal of Voice
    Year: 2024
    Citations: Not specified
  • A Quasi-Oppositional Learning-based Fox Optimizer for QoS-aware Web Service Composition in Mobile Edge Computing
    Authors: RH Sharif, M Masdari, A Ghaffari, FS Gharehchopogh
    Journal: Journal of Grid Computing 22 (3), 64
    Year: 2024
    Citations: Not specified
  • A novel offloading strategy for multi-user optimization in blockchain-enabled Mobile Edge Computing networks for improved Internet of Things performance
    Authors: AM Rahmani, J Tanveer, FS Gharehchopogh, S Rajabi, M Hosseinzadeh
    Journal: Computers and Electrical Engineering 119, 109514
    Year: 2024
    Citations: 5
  • An Intrusion Detection System on The Internet of Things Using Deep Learning and Multi-objective Enhanced Gorilla Troops Optimizer
    Authors: H Asgharzadeh, A Ghaffari, M Masdari, FS Gharehchopogh
    Journal: Journal of Bionic Engineering 21 (5), 2658-2684
    Year: 2024
    Citations: 2
  • Visualization and classification of mushroom species with multi-feature fusion of metaheuristics-based convolutional neural network model
    Authors: E Özbay, FA Özbay, FS Gharehchopogh
    Journal: Applied Soft Computing 164, 111936
    Year: 2024
    Citations: 1
  • A software defect prediction method using binary gray wolf optimizer and machine learning algorithms
    Authors: H Wang, B Arasteh, K Arasteh, FS Gharehchopogh, A Rouhi
    Journal: Computers and Electrical Engineering 118, 109336
    Year: 2024
    Citations: 1

Mona Jamjoom | AI | Best Researcher Award

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Assoc Prof Dr. Mona Jamjoom | AI | Best Researcher Award

Assoc Prof Dr. Mona Jamjoom, Princess Nourah bint Abdulrahman University, Saudi Arabia

Assoc Prof Dr. Mona Jamjoom is an accomplished researcher in the field of artificial intelligence, recognized for her innovative contributions and impactful studies. With a strong focus on machine learning and data analytics, she has published numerous papers in leading journals and has been awarded the Best Researcher Award for her groundbreaking work. Mona is passionate about harnessing AI to solve complex problems and improve decision-making processes across various industries. Her commitment to advancing technology while addressing ethical considerations makes her a prominent figure in the AI community.

Profile:

Scholar

Academics:

Assoc Prof Dr. Mona Jamjoom holds a PhD in Artificial Intelligence from King Saud University, awarded in May 2016. She also earned her Master’s degree in Computer Science from the same institution in 2004, following her Bachelor’s degree in Computer Science, which she completed in 1992. Her academic background provides a strong foundation for her research and contributions to the field of AI.

Professional Experiences:

Assoc Prof Dr. Mona Jamjoom has extensive professional experience in academia. Since 2021, she has served as an Associate Professor at Princess Nourah bint Abdulrahman University in Riyadh, Saudi Arabia. Prior to this, she was an Assistant Professor at the same institution from 2017 to 2021. Mona began her academic career as a Lecturer at Princess Nourah bint Abdulrahman University from 2007 to 2016, and before that, she worked as a Teaching Assistant from 1998 to 2007. Her career in the field began in 1993, when she provided technical support at the university, further solidifying her commitment to education and technology.

Activities:

Assoc Prof Dr. Mona Jamjoom is actively engaged in various professional activities that enhance her contributions to the field of artificial intelligence. In 2024, she joined the work team at the Center for Advanced Studies in Artificial Intelligence at King Saud University, collaborating on the KSU AI Satellite Lab project with SDAIA. She served as an external examiner for a doctoral thesis on deep learning applications for visual pollution detection in Riyadh. Additionally, she reviewed applications for the Apple Developer Academy’s second challenge for female students and participated in consulting sessions during the Gulf Hackathon Program focused on AI in public education. Mona also acted as a consultant for the UNESCO project “AI Capacity Building in Arabic-speaking Countries,” supported by Huawei Technologies. She has reviewed numerous papers for ISI journals and attended the research day at Princess Nourah bint Abdulrahman University. Furthermore, she co-supervised a PhD student specializing in Cognitive Computing at Universiti Kuala Lumpur, Malaysia.

Publication Top Notes:

M. Adil, Z. Yinjun, M. M. Jamjoom, and Z. Ullah. “OptDevNet: An Optimized Deep Event-Based Network Framework for Credit Card Fraud Detection.” IEEE Access, vol. 12, pp. 132421-132433, 2024. doi: 10.1109/ACCESS.2024.3458944.

Rabbani, H., Shahid, M. F., Khanzada, T. J. S., Siddiqui, S., Jamjoom, M. M., Ashari, R. B., Ullah, Z., Mukati, M. U., and Nooruddin, M. “Enhancing Security in Financial Transactions: A Novel Blockchain-Based Federated Learning Framework for Detecting Counterfeit Data in Fintech.” PeerJ Computer Science, vol. 10, e2280, 2024.

Malik, M. S. I., Nawaz, A., and Jamjoom, M. M. “Hate Speech and Target Community Detection in Nastaliq Urdu Using Transfer Learning Techniques.” IEEE Access, 2024.

Kurtoğlu, A., Eken, Ö., Çiftçi, R., Çar, B., Dönmez, E., Kılıçarslan, S., Jamjoom, M. M., Abdel Samee, N., Hassan, D. S. M., and Mahmoud, N. F. “The Role of Morphometric Characteristics in Predicting 20-Meter Sprint Performance Through Machine Learning.” Scientific Reports, vol. 14, no. 1, 16593, 2024.

Shah, S. M. A. H., Khan, M. Q., Rizwan, A., Jan, S. U., Samee, N. A., and Jamjoom, M. M. “Computer-Aided Diagnosis of Alzheimer’s Disease and Neurocognitive Disorders with Multimodal Bi-Vision Transformer (BiViT).” Pattern Analysis and Applications, vol. 27, no. 3, 76, 2024.

Ishtiaq, A., Munir, K., Raza, A., Samee, N. A., Jamjoom, M. M., and Ullah, Z. “Product Helpfulness Detection with Novel Transformer Based BERT Embedding and Class Probability Features.” IEEE Access, 2024.

Abbas, M. A., Munir, K., Raza, A., Samee, N. A., Jamjoom, M. M., and Ullah, Z. “Novel Transformer Based Contextualized Embedding and Probabilistic Features for Depression Detection from Social Media.” IEEE Access, 2024.

Elhadad, A., Jamjoom, M., and Abulkasim, H. “Reduction of NIFTI Files Storage and Compression to Facilitate Telemedicine Services Based on Quantization Hiding of Downsampling Approach.” Scientific Reports, vol. 14, no. 1, 5168, 2024.

Malik, M. S. I., Younas, M. Z., Jamjoom, M. M., and Ignatov, D. I. “Categorization of Tweets for Damages: Infrastructure and Human Damage Assessment Using Fine-Tuned BERT Model.” PeerJ Computer Science, vol. 10, e1859, 2024.

Malik, M. S. I., Nawaz, A., Jamjoom, M. M., and Ignatov, D. I. “Effectiveness of ELMo Embeddings and Semantic Models in Predicting Review Helpfulness.” Intelligent Data Analysis, (Preprint), 1-21, 2023.