Conference Proceedings

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Browsing Conference Proceedings by Subject "Adaptation models"

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    AI-Powered collaborative robotics for next-gen industrial automation
    (IEEE, 2026-02-20) Ramya, M; Subburam, S.; Patel, Pushplata; Seetaram, J.; Singh, K. Ranjith; Ali, Guma; Salami, Zayd Ajzan
    The runaway development of Industry 4.0 has brought very interconnected, intelligent, and automated production systems in place, but conventional robotics based on rules cannot react to dynamic production conditions, unpredictable working processes, and real-time disruptions, which tend to result in lower efficiency, time delays, and bottlenecks in the work process. Among common challenges faced by factories are inefficiencies in task allocation, the absence of collaborative coordination among robots, poor predictive maintenance, and vulnerabilities in data exchange during interactions. To address those issues, this research proposes AI-Powered Multi-Agent Collaborative Robotic System (AI-MACROS), a highly sophisticated industrial automation system integrating swarm intelligence, reinforcement learning, digital twin simulation, and blockchain-based security. AI-MACROS allows teams of robotic agents to work together, learn dynamically based on environmental interactions, optimize task distribution and deal with unexpected events in the real world. Through digital twins, it provides virtual replicas of robots and work processes, with which to monitor the system in real time, preemptive maintenance and simulate scenarios without disrupting the existing processes. Also, blockchain protocols guarantee integrity and safety of communication and data across the industrial network. Compared to a typical rulebased robotics, one may observe that AI-MACROS is executed by the high performance score (i.e. higher efficiency, accuracy, recall, and F1-scores), and the coordination and energy efficiency is additionally simplified with the help of particle swarm optimization, genetic algorithms and deep reinforcement learning. The proposed solution addresses the disadvantages of the traditional automation, making the operations more resilient in addition to improving the security and scale, not mentioning that it is deemed as not only a potential game changer but also as a next-generation smart factory solution. In a clever synergy, predictive analytics and safe decentralized information processing, AI-MACROS creates high-performance prototype of adaptable, fruitful and credible automation of production under realistic production scenarios.
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    AZTEM: A Self-evolving zero-trust model for adaptive cloud security using AI-driven threat mitigation and quantum-resilient encryption
    (IEEE, 2026-02-20) Khemraj, Ikhar Avinash; Buckshumiyan, A.; Balassem, Zayd; Shekar, A.; Arunkumar, B.; Ali, Guma; Jabborov, Nurboy
    The new digital ecosystems are being based on cloud environments but they are highly vulnerable to the shifts in cyber threat, which can take advantage of the stationary trust models, loose access rules and encryption functions that hangs in the air with the advent of quantum computing. The traditional models of perimeter based security fail to operate in highly dynamic cloud environments with insider based mauling, cross-lateral mauling and the zero day mauling bypassing the traditional security models. In this work, A Self-evolving Zero-Trust Model of Adaptive Cloud security with AI-based Threat Mitigation and Quantumresilient Encryption is introduced and is capable of addressing these emerging problems. AZTEM eliminates the implicit trust, validating and permitting all user, device, and microservice relations through the utilization of a contextsensitive trust engine that is reinforced by reinforcement learning algorithms. To counter the rapid adaptations of attack vectors, the system relies on the deep-learning-based anomaly detection methods such as LSTM-based sequence classifiers on cloud workload telemetry, network flow logs, and publicly accessible datasets (UNSW-NB15 and CICIDS2018). An adaptive policy controller is the basis of dynamic orchestration of mitigation strategies and uses AI-based response mechanisms to quarantine, reroute, or restrict malicious sessions in real time. Besides, the model includes quantum-resilient encryption that extends the protection of both data-at-rest and data-in-transit by using lattice cryptography to secure quantum decryption threats where confidentiality is ensured at the long-term scale. An initial deployment of AWS on Kubernetes clusters demonstrated that threat detection accuracy and false positives decreased by 27 percent and 34 percent respectively over a baseline zero-trust deployment, and performance overhead is less than 8 percent. Its application in adversarial context was assessed through measures such as precision, recall, F1-score and confidence intervals and proved it to be qualified. The architecture is not only making the cloud more resilient to the present, and the infrastructures of the quantum age impervious to the threats of the quantum age, but it is also providing businesses, governments and hard-to-secure areas in need of cloud security without a trade-off an ethical and scaleable and deployment-ready path.