S Muhammad Ahmed Hassan Shah | Computer Vision | Best Researcher Award

Published on by Innovation Technologist

Mr. S Muhammad Ahmed Hassan Shah | Computer Vision | Best Researcher Award

Research Scholar at Central South University, Pakistan

Syed Muhammad Ahmed Hassan Shah is a multidisciplinary AI engineer and researcher specializing in 3D computer vision, digital twins, and AI applications in both civil infrastructure and healthcare. He holds a BS in Computer Science from COMSATS University Islamabad and is currently pursuing an MPhil in Civil Engineering at Central South University, China. His experience includes roles as an AI engineer in railway infrastructure monitoring and medical imaging diagnostics, with a focus on smart transportation systems, predictive maintenance, and deep learning. He is a senior member of the American Society of Civil Engineers (ASCE) and has led and participated in multiple AI-focused workshops and summits.

Publication Profile 

Google Scholar

Educational Background 🎓

  • Master of Philosophy (MPhil), Civil Engineering – Smart Infrastructure Management
    Central South University (CSU), Changsha, China
    Duration: Sep 2024 – Present
    Focus Areas: AI in Transportation, Infrastructure Monitoring, Digital Twins
    CSU Website

  • Bachelor of Science (BSCS), Computer Science
    COMSATS University Islamabad (Attock Campus), Pakistan
    Duration: Jul 2018 – Jul 2022
    COMSATS Website

Professional Experience 💼

AI Engineer (Research Assistant)

Center for Railway Infrastructure Smart Monitoring and Management, CSU
Duration: Sep 2024 – Present | Location: Changsha, China

  • Application of AI, IoT, and 3D computer vision to monitor high-speed rail infrastructure (tracks, tunnels, fasteners).

AI Engineer (Developer)

NeuroCare.AI
Duration: Jun 2024 – Sep 2024 | Location: Remote (USA-based)

  • Developed AI solutions for medical imaging, particularly in breast cancer and brain hemorrhage detection.

Computer Vision Expert (Research Assistant)

NCAI-MIDL Lab, COMSATS University Islamabad
Duration: Oct 2022 – Jul 2023 | Location: Islamabad, Pakistan

  • Focused on MRI, CT, X-ray, Ultrasound imaging using deep learning for automated diagnosis tools.

Research Interests 🔬

  • Artificial Intelligence & Deep Learning

  • 3D Computer Vision & Point Cloud Processing

  • Digital Twins & Predictive Maintenance for Infrastructure

  • Multimodal Learning and Fusion

  • Natural Language Processing (NLP) & Generative AI

  • Medical Imaging Diagnostics & Explainable AI

  • Smart Transportation Systems & Structural Health Monitoring

Awards and Honors🏆✨

  • 🏆 Senior Member, American Society of Civil Engineers (ASCE)
    Awarded: Oct 2024
    Also affiliated with Transportation and Development Institute (T&DI) of ASCE
    View Certificate Folder

  • 🧠 Trainer – Mastering AI in 5 Days Workshop
    Conducted: Feb 2023, COMSATS Islamabad (HEC & MIDL-supported)
    Hands-on workshop training on real-world AI applications.
    Workshop Certificate

  • 🧬 AI Healthcare Summit – FAST NUCES Islamabad
    Participated: Jan 2023
    Collaborated with SLOSH AI-SOLUTIONS and MIDL Lab.
    Summit Certificate

Conclusion🌟

Ahmed Hassan is a multidisciplinary AI engineer and researcher actively engaged in bridging the domains of civil infrastructure and intelligent computing. With strong foundations in computer science and advanced research in civil engineering, he continues to push the boundaries of how AI, 3D vision, and digital twins can solve real-world problems. His contributions span both transportation infrastructure and healthcare diagnostics, making him a versatile innovator dedicated to smart, sustainable, and tech-driven development.

Publications 📚

  1. 🧠 Classifying and localizing abnormalities in brain MRI using channel attention based semi-Bayesian ensemble voting mechanism and convolutional auto-encoder
    ✍️ SMAH Shah, A Ullah, J Iqbal, S Bourouis, SS Ullah, S Hussain, MQ Khan, …
    📘 IEEE Access, Vol. 11, pp. 75528–75545, 2023.
    🔗 Cited by: 23

  2. 🗣️ Arabic sentiment analysis and sarcasm detection using probabilistic projections-based variational switch transformer
    ✍️ SMAH Shah, SFH Shah, A Ullah, A Rizwan, G Atteia, M Alabdulhafith
    📘 IEEE Access, Vol. 11, pp. 67865–67881, 2023.
    🔗 Cited by: 22

  3. 🩺 CADFU for dermatologists: A novel chronic wounds & ulcers diagnosis system with DHuNeT (Dual-Phase Hyperactive UNet) and YOLOv8 algorithm
    ✍️ SMAH Shah, A Rizwan, G Atteia, M Alabdulhafith
    📘 Healthcare, Vol. 11(21), Art. 2840, 2023.
    🔗 Cited by: 11

  4. 🧠 Computer-aided diagnosis of Alzheimer’s disease and neurocognitive disorders with multimodal Bi-Vision Transformer (BiViT)
    ✍️ SMAH Shah, MQ Khan, A Rizwan, SU Jan, NA Samee, MM Jamjoom
    📘 Pattern Analysis and Applications, Vol. 27(3), Art. 76, 2024.
    🔗 Cited by: 10

  5. 🧱 Multimodal Fusion Network for Crack Segmentation with Modified U-Net and Transfer Learning–Based MobileNetV2
    ✍️ S Qiu, Q Zaheer, H Ehsan, SMAH Shah, C Ai, J Wang, AA Zheng
    📘 Journal of Infrastructure Systems, Vol. 30(4), Art. 04024029, 2024.
    🔗 Cited by: 4

  6. 🔬 A Hybrid Neuro-Fuzzy Approach for Heterogeneous Patch Encoding in ViTs Using Contrastive Embeddings and Deep Knowledge Dispersion
    ✍️ SMAH Shah, MQ Khan, YY Ghadi, SU Jan, O Mzoughi, M Hamdi
    📘 IEEE Access, Vol. 11, pp. 83171–83186, 2023.
    🔗 Cited by: 3

  7. ⚖️ JusticeAI: A Large Language Models Inspired Collaborative & Cross-Domain Multimodal System for Automatic Judicial Rulings in Smart Courts
    ✍️ NA Samee, M Alabdulhafith, SMAH Shah, A Rizwan
    📘 IEEE Access, 2024.
    🔗 Cited by: 2

  8. 🔐 A transparent captchas verification system for cloud-based smart and secure applications
    ✍️ SFH Shah, SMAH Shah, SS Ullah, N Yingta
    📘 IEEE ISC2 Conference, 2023, pp. 1–5.
    🔗 Cited by: 2

  9. 🚦 Traffic Forecasting & Route Optimization in Smart Environment Using Graph Representation Learning
    ✍️ SMAH Shah, SFH Shah, S Hussain, KS Turrakheil
    📘 IEEE ISC2 Conference, 2023, pp. 1–5.
    🔗 Cited by: 2

  10. 🧱 Intelligent Multitasking Framework for Boundary-Preserving Semantic Segmentation, Width Estimation, and Propagation Modeling of Concrete Cracks
    ✍️ Q Zaheer, S Qiu, SMAH Shah, C Ai, J Wang
    📘 Journal of Infrastructure Systems, Vol. 31(3), Art. 04025009, 2025.

 

 

 

 

Muhammad Wajid | Computer Vision | Best Researcher Award

Published on by Innovation Technologist

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

Published on by Innovation Technologist

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)