Muhammad Wajid | Computer Vision | Best Researcher Award

Mr. Muhammad Wajid | Computer Vision | Best Researcher Award

Lecturer at Sir syed Case Institute of Technology Islamabad, Pakistan

🌟 Muhammad Wajid is a passionate computer scientist specializing in deep learning 🤖, natural language processing 📖, and computer vision 👁️‍🗨️. As a Lecturer at Sir Syed CASE Institute of Technology, he teaches and mentors in cutting-edge fields. With a strong academic foundation from COMSATS University and Gomal University 🎓, he has earned recognition for his impactful research, including publications in prestigious journals 📜. Muhammad’s expertise spans Python, TensorFlow, PyTorch, and Scikit-Learn, making him a skilled innovator in AI and machine learning 🚀.

Publication Profile : 

Google Scholar

Education 🎓

Muhammad Wajid earned his Master of Science in Computer Science from COMSATS University Islamabad (2020–2023) with a CGPA of 3.43/4.00. His thesis focused on using deep learning and preprocessing techniques to detect communities in graph-structured data. Prior to this, he completed his Bachelor of Computer Science at Gomal University (2015–2020), graduating with a stellar CGPA of 3.79/4.00. His undergraduate thesis involved the classification and identification of iris flowers using machine learning. Muhammad also holds a certification in machine learning from Stanford and freelancing from the Virtual University of Pakistan, further showcasing his commitment to technical growth.

Professional Experience💼

Muhammad is a Lecturer at the Sir Syed CASE Institute of Technology in Islamabad, Pakistan (Feb 2024–Present). He teaches advanced courses in Deep Learning, Natural Language Processing (NLP), Programming Fundamentals, and Web Technology. He also supervises final-year projects in cutting-edge domains like computer vision, deep learning, and NLP. Previously, he served as a Research Assistant at COMSATS University Islamabad (Feb 2019–Jul 2021), contributing to impactful research under expert supervision. His efforts have led to publications and advancements in deep learning and NLP.

Research Interests 🔬

Muhammad’s research interests lie at the intersection of deep learning, natural language processing, computer vision, and medical imaging. He is particularly passionate about applying AI techniques to tackle real-world challenges, including tumor segmentation, financial forecasting, and community detection in graph-structured data.

Publications Top Notes 📚

  • Wajid, Muhammad, Iqbal, Ahmed, Malik, Isra, Syed Jawad Hussain, and Yasir Jan.
    “A semi-supervised approach for breast tumor segmentation using sparse transformer attention UNet.”
    Pattern Recognition Letters, 2024.
    [Impact Factor: 3.9]
    DOI: 10.1016/j.patrec.2024.11.008
  • Wajid, Muhammad, and Ahmad Kamran Malik.
    “A Stacked GRU-based Recurrent Deep Learning Approach for Bitcoin Price Prediction to Maximize Profit.”
    Complex & Intelligent Systems, 2024.
    [Impact Factor: 5.0]
    [Under Review]

 

 

 

Hayelom Gebrye | Computer Vision | Best Researcher Award

Mr. Hayelom Gebrye | Computer Vision | Best Researcher Award

Ph.D. student of UESTC, China, Ethiopia

Hayelom Muleta Gebrye is a dedicated researcher and educator specializing in computer science and information technology. He is currently pursuing his Ph.D. in Computer Science and Technology at the University of Electronic Science and Technology of China (UESTC), where his research focuses on machine learning, deep learning, computer vision, and network security, particularly in the context of IoT and cybersecurity. He holds an MSc in Information Technology from Aksum University and a BSc in Information Technology from Hawassa University.

Publication Profile : 

Scopus

 

🎓 Educational Background :

Hayelom Muleta Gebrye is currently pursuing a Ph.D. in Computer Science and Technology at the University of Electronic Science and Technology of China (UESTC), which he commenced in September 2019. He holds a Master’s degree in Information Technology from Aksum University (2015-2017) and a Bachelor’s degree in Information Technology from Hawassa University (2008-2012). Additionally, he completed his Secondary School Certificate at Tadagiwa Ethiopia in 2008.

💼 Professional Experience :

Hayelom has a robust teaching and training background in computer science and technology. He served as a lecturer at several institutions, including Harambee University, where he taught courses such as Fundamentals of Programming I, Web Programming, and Research Methods in Computer Science (Sep 2022 – Apr 2023), and Adama Science and Technology University, focusing on Data Structures and Algorithms (Jul 2022 – Jun 2023). He has also provided training on digital technology and effective social media management through the local NGO LIVE ADDIS. In addition to his academic roles, he has practical experience as a Data Manager for TZG General Development Research, leading data collection efforts and ensuring data quality standards. Previously, he worked in various capacities at Raya University, including Quality Assurance Coordinator and Assistant Registrar, where he was instrumental in enhancing educational quality and managing student records. His earlier role as the Head of IT at Jigjiga University involved overseeing the IT department, lecturing on advanced programming and system simulation, and mentoring students.

📚 Research Interests : 

Hayelom’s research interests encompass machine learning, deep learning, computer vision, network security, and intrusion detection systems. He has contributed to several research projects, including studies on traffic data extraction for attack detection in IoT networks and deep reinforcement learning for resource allocation in blockchain-based computing. His published work includes articles in prestigious journals, demonstrating his commitment to advancing knowledge in the fields of cybersecurity and machine learning.

📝 Publication Top Notes :

  1. Gebrye, H., Wang, Y., & Li, F. (2023). Traffic data extraction and labeling for machine learning-based attack detection in IoT networks. International Journal of Machine Learning & Cybernetics, 14, 2317–2332. https://doi.org/10.1007/s13042-022-01765-7. (Impact Factor: 5.6)
  2. Mohammed, A., Nahom, H., Tewodros, A., Habtamu, Y., & Gebrye, H. (2020). Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Blockchain-Based Multi-UAV-Enabled Mobile Edge Computing. In 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) (pp. 295-299). Chengdu, China. doi: 10.1109/ICCWAMTIP51612.2020.9317445.
  3. Gebrye, H., Wang, Y., & Li, F. (In Progress). Computer Vision based DDoS Attack Detection for resource-limited devices. Computers and Electrical Engineering. (Status: 1st Revision)
  4. Gebrye, H., Wang, Y., & Li, F. (Under Review). Undersampling and IGFS-GIWRF Boosting Machine Learning Models for IoT Botnet Detection using Unbalanced Dataset. International Journal of Machine Learning and Cybernetics. (Status: Under Review)