Awwab Mohammad

About

Dr. Awwab is an Assistant Professor specializing in Robotics and Artificial Intelligence, with a strong focus on advancing research and pedagogy in cutting-edge domains of machine learning and intelligent systems. His academic and research interests span Generative Artificial Intelligence, Explainable AI, Cybersecurity, and Privacy-Aware Machine Learning, with an emphasis on developing robust, interpretable, and ethically aligned AI solutions.

He has contributed to the scholarly community through multiple peer-reviewed publications, including IEEE conference papers and Scopus-indexed journal articles, with ongoing research in high-impact Q1 and Q2 journals. His current research pipeline includes work on privacy-aware frameworks for cyber-attack detection and optimization-enabled quantum neural networks, reflecting his commitment to addressing contemporary challenges at the intersection of AI and security.

Dr. Awwab is actively engaged in innovative teaching practices, integrating blended learning methodologies, micro-certifications, and industry-relevant tools into his curriculum. He is particularly interested in fostering interdisciplinary learning environments and mentoring students in research-driven and application-oriented projects.

With a vision to bridge academia and industry, he continues to explore collaborative research opportunities, grant-funded projects, and global academic partnerships, aiming to contribute meaningfully to the evolving landscape of intelligent and responsible AI systems.

Employment

• Manav Rachna University Assistant Professor
  2024 - Present

Education

• Jamia Hamdard PhD (Computer Science and Engineering)
  2017 - 2024
• Jamia Hamdard M.Tech (Information Security and Cyber Forensics)
  2015 - 2017
• Jamia Hamdard B.Tech (Information Technology)
  2011 - 2015

Projects & Funding

Projects & funding information is unavailable.

Publications (14)

• DEHOA-optimized deep intrusion detection with quantum neural network extensions for IoT network security
  Array 2026 DOI: 10.1016/j.array.2026.101085
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• Trust-aware clinical decision support in IoMT using hesitant fuzzy sets and deep learning-based medical image analysis
  Scientific Reports 2026 DOI: 10.1038/s41598-026-63609-6
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• Deep Learning-Based Cancer Diagnosis Using a Bluefin Trevally-Optimized Convolutional Neural Network
  Engineering Technology and Applied Science Research 2026 DOI: 10.48084/etasr.17713
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• Giant Trevally Optimization Driven CNN Transformer Framework for Intrusion-aware Secure Cloud Storage
  International Journal of Intelligent Engineering and Systems 2026 DOI: 10.22266/ijies2026.0630.39
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• NetPhish-Mix: A Multi-Modal Phishing Detection Method Utilizing URL Graphs and Page Screenshot Vision Transformer
  Engineering Technology and Applied Science Research 2026 DOI: 10.48084/etasr.15759
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• Context-Embedding in Natural Language for Keener Chatbot Responses
  Proceedings of IEEE International Conference on Emerging Engineering Technologies and Applications IC Eeta 2025 2025 DOI: 10.1109/IC-EETA66496.2025.11548050
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• Enhancing Cloud Data Security against Quantum Threats Using E91 QRD and Lattice-Based Encryption
  Proceedings of IEEE International Conference on Emerging Engineering Technologies and Applications IC Eeta 2025 2025 DOI: 10.1109/IC-EETA66496.2025.11548388
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• Tri-model classifiers for EEG based mental task classification: hybrid optimization assisted framework
  BMC Bioinformatics 2023 DOI: 10.1186/s12859-023-05544-1
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• Deep Neural Network for EEG Signal-Based Subject-Independent Imaginary Mental Task Classification
  Diagnostics 2023 DOI: 10.3390/diagnostics13040640
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• Deep Learning Models in EEG Signals: Comparative Analysis
  Micro-Electronics and Telecommunication Engineering 2023 DOI: 10.1007/978-981-19-9512-5_39
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