Dr. Aymen Mosleh Massad Ahmed Al-Hejri is an accomplished researcher and lecturer in Information Technology with expertise in AI, IoT, and cybersecurity ๐ง ๐ป. He holds a Masterโs degree with distinction and has published impactful studies on healthcare tech and smart systems ๐๐ก. With over a decade of teaching experience, he excels in both academia and applied research ๐๐ฌ. Fluent in Arabic and English, he bridges global knowledge with regional relevance ๐๐ฃ๏ธ.
Profile
Scopus Profile
orcid Profile
Google Scholar Profile
๐ Early Academic Pursuits
Dr. Aymen Mosleh Massad Ahmed Al-Hejri began his academic journey with a strong foundation in science, graduating from the Scientific Section at Al-Saeed Educational Complex in 2005 with an impressive 83.36%. He pursued a Bachelorโs Degree in Information Technology from Dhamar University, achieving a “Very Good” grade and a GPA of 84.94%. Demonstrating a commitment to academic excellence, he further completed a Masterโs Degree in Information Technology at BAMU University in India with a Distinction and an outstanding GPA of 92.70%. His early dedication to both formal education and supplemental learningโincluding diplomas in computer maintenance and Englishโlaid the groundwork for a thriving academic and professional career. ๐๐
๐ผ Professional Endeavors
Dr. Al-Hejri has held several prominent teaching positions across Yemeni universities. From 2012 to 2018, he served as a Teaching Assistant at the Faculty of Administrative Sciences and Computing, Al-Bayda University, delivering courses in C++, C#, DBMS, Information Security, and Logic. He also lectured at Al-Jazeera College in Sanaโa (2011โ2014), Al-Saeeda University in Dhamar (2016โ2018), and Al-Razi University in Yarim (2018), teaching diverse subjects ranging from Software Engineering and Web Design to Data Mining. His versatile expertise in both theoretical and practical computing has made him a respected figure in academic circles. ๐ซ๐ก
๐ฌ Contributions and Research Focus On AI in medical
Dr. Al-Hejri has contributed significantly to cutting-edge fields such as Artificial Intelligence, Deep Learning, and the Internet of Things (IoT). His research has tackled real-world challengesโfrom remote patient monitoring systems to cyber-attack defenses in smart homes. He also developed an innovative facilitation system for Arabic-speaking foreigners in India, showcasing his sensitivity to cultural and technological inclusivity. His publications reflect a deep interest in health informatics, particularly in breast cancer diagnosis using ensemble learning and transformer encoders. ๐๐งฌ
๐ Impact and Influence
Dr. Al-Hejriโs work has had a notable impact in both regional and international research communities. His studies on real-time monitoring systems and AI-based medical diagnostics are not only cited in academic journals but also provide technological blueprints for smarter, safer healthcare environments. His unique approach of integrating SDN and deep learning into IoT ecosystems addresses modern cybersecurity needs, positioning him as a thought leader in AI-driven innovation. ๐๐
๐ง Research Skills
Dr. Al-Hejri possesses a well-rounded research toolkit. He is proficient in Python and adept at data analysis, image processing, machine learning, and deep learning. His ability to visualize model accuracy and interpret performance metrics adds substantial value to his research projects. His practical skills in AI are complemented by an academic rigor, making him a strong contributor to interdisciplinary studies. ๐ฅ๏ธ๐
๐
Awards and Honors
Dr. Al-Hejri has consistently demonstrated academic excellence, earning a Distinction for his Master’s studies at BAMU University. His high GPAs and scholarly performance have been recognized throughout his educational journey. Although formal awards are not listed, his achievements in publishing, teaching, and software innovation mark him as a high-performing academic professional. ๐๐
๐๏ธ Legacy and Future Contributions
Dr. Aymen Al-Hejri is poised to continue making transformative contributions to computer science and health informatics. His future work is expected to deepen the integration of AI into real-world systemsโenhancing healthcare, security, and education. With a growing academic footprint and a passion for technological innovation, his legacy will likely be one of impactful, inclusive, and intelligent solutions to global challenges. ๐๐ก
Publications Top Notes
๐ A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-Ray Mammograms
๐ Journal: Biomedicines, Volume 10, Issue 11
๐ข Citations: 46
๐
Year: 2022
๐งฌ๐ก Focus: AI-based breast cancer classification using medical imaging
๐ ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-Ray Breast Images
๐ Journal: Diagnostics, Volume 13, Issue 1
๐ข Citations: 38
๐
Year: 2022
๐ฉบ๐ค Focus: Deep learning ensemble model for breast cancer diagnostics
๐ Using Deep DenseNet with Cyclical Learning Rate to Classify Leukocytes for Leukemia Identification
๐ Journal: Frontiers in Oncology, Volume 13
๐ข Citations: 19
๐
Year: 2023
๐งช๐ Focus: Blood cell image classification using DenseNet and advanced learning techniques
๐ Internet of Things-Based Middleware Against Cyber-Attacks on Smart Homes Using Software-Defined Networking and Deep Learning
๐ Journal/Conference: 2021 2nd International Conference on Computational Methods in Science and Technology
๐ข Citations: 13
๐
Year: 2021
๐ ๐ Focus: Cybersecurity solutions for IoT-enabled smart homes
๐ Multimodal Breast Cancer Hybrid Explainable Computer-Aided Diagnosis Using Medical Mammograms and Ultrasound Images
๐ Journal: Biocybernetics and Biomedical Engineering, Volume 44, Issue 3, Pages 731โ758
๐ข Citations: 9
๐
Year: 2024
๐ง ๐ผ๏ธ Focus: Explainable AI for multimodal cancer diagnosis
๐ A Hybrid Framework of Transformer Encoder and Residual Convolution for Cardiovascular Disease Recognition Using Heart Sounds
๐ Journal: IEEE Access
๐ข Citations: 5
๐
Year: 2024
โค๏ธ๐ง Focus: AI-driven diagnosis of heart conditions from acoustic data