Guest Editor(s)
Dr. Anwar Ghani
Email: anwar.ghani@nu.edu.kz
Affiliation: Department of Computer Science, School of Engineering & Digital Sciences, Nazarbayev University, Astana, Kazakhstan
Homepage:
Research Interests: information security, cybersecurity, cryptography, authenticated encryption, automated security tool, wireless sensor networks, internet of things, edge computing, machine learning
Dr. Qazi Waqas Khan
Email: waqasqazi920@gmail.com
Affiliation: Department of Computer Engineering, Jeju National University, Jeju, Republic of Korea
Homepage:
Research Interests: applied machine learning, multi-task and multi, modal deep learning, decentralized machine learning optimization, scalable & efficient federated learning systems, privacy-preserving AI, secure and trustworthy distributed intelligence, computer vision, edge AI and on,device intelligence
Dr. HARI MOHAN RAI
Email: hari.rai@nu.edu.kz
Affiliation: Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan
Homepage:
Research Interests: digital logic design,antenna design, digital electronics, digital signal processing, internet of things (loT), robotics, electronics circuits, artificial intelligence, machine learning, deep learning, cyber security, security for mobile technology
Summary
The rapid growth of intelligent automation, cyber-physical systems, medical and industrial imaging, IoT, autonomous platforms, and smart digital ecosystems has created a strong demand for machine learning systems that are not only accurate but also secure, explainable, privacy-preserving, and reliable in real-world environments. This Special Issue aims to bring together high-quality research on trustworthy machine learning, intelligent imaging, cybersecurity, and futuristic AI techniques for next-generation digital systems.
The Special Issue welcomes original research and review articles addressing advanced machine learning, deep learning, computer vision, multimodal imaging, secure AI, explainable AI, adversarial robustness, privacy-preserving learning, edge intelligence, and autonomous decision-making. Particular attention will be given to methods that integrate imaging intelligence with secure and trustworthy computational frameworks for applications in healthcare, IoT, robotics, industrial automation, smart cities, digital twins, cyber-physical systems, and intelligent surveillance.
This collection will provide a platform for researchers to present innovative models, architectures, algorithms, and applications that support the development of reliable, transparent, and future-ready intelligent systems.
Topics of interest include, but are not limited to:
· Trustworthy machine learning and explainable AI
· Secure deep learning for intelligent automation
· Cryptographic protocols design and analysis
· Computer vision and imaging intelligence
· Medical, industrial, satellite, and IoT-based imaging systems
· Multimodal learning and vision-language models
· Foundation models and generative AI for imaging and security
· Privacy-preserving AI, federated learning, and secure data sharing
· Adversarial attacks and defenses in machine learning systems
· AI-driven cybersecurity and malware detection
· Edge AI, TinyML, and resource-efficient intelligent systems
· Digital twins for secure and intelligent decision-making
· Blockchain-enabled trust management in AI systems
· Quantum-inspired and future-ready AI security techniques
· Robust AI for cyber-physical systems and autonomous platforms
· Human-centered, ethical, and responsible AI systems
Keywords
trustworthy machine learning, secure AI, computer vision, imaging intelligence,deep learning, explainable AI, cybersecurity, privacy-preserving learning, federated learning, edge intelligence, digital twins, generative AI, foundation models, adversarial robustness, autonomous systems.
Login
Register
Submit a Paper
Propose a Special lssue