The increasing integration of renewable energy sources (RES) and electric vehicles (EVs) has transformed modern power systems into complex cyber-physical networks. While these advancements enhance sustainability and flexibility, they introduce vulnerabilities to cyber-attacks such as false data injection and denial-of-service attacks. Load Frequency Control (LFC), a critical mechanism for maintaining system stability, is particularly susceptible due to its reliance on communication networks. This paper proposes an AI-based framework for real-time cyber-attack detection and mitigation in LFC systems. The proposed approach integrates a deep learning-based anomaly detection model with an adaptive control strategy to ensure resilient frequency regulation. Simulation results demonstrate improved detection accuracy (96.8%) and reduced frequency deviation compared to conventional methods. The framework effectively balances robustness and computational efficiency, making it suitable for next-generation smart grids.
V. I, A. T, S. K· 2026 7th International Confe...· 0 citations
This study presents a graph-based explainable artificial intelligence framework for social network analysis, integrating graph neural models with SHAP (Shapley Additive Explanations) and other post-hoc explanation techniques. The framework is evaluated on the Stanford Network Analysis Project (SNAP) Facebook Dataset consisting of 15,000 nodes and 45,000 edges. Results from experiments show that the suggested model enhances classification performance by 7–12% compared to baseline methods while providing interpretable feature-level insights. The findings highlight the potential of combining graph learning with explainability to support transparent decision-making in network analysis tasks.
S. K, Sagar Dhanraj Pande, Girish H et al.· 2026 7th International Confe...· 0 citations
Autonomous agents—systems that make independent decisions without human input—are foundational to modern robotics and enable intelligent responses in dynamic situations. In this work, a hybrid agent-based system that integrates software agents, or programs that represent users with multiple decision-making modules. The design integrates perception (gathering and interpreting sensory data), planning (scheduling a sequence of actions), and reinforcement learning, in which agents use feedback from their surroundings to improve their actions through try and error. Mathematical modelling and experimental evaluation reveal efficiency gains over rule-based systems that rely only on predefined instructions. Consequently, the framework ensures adaptability, scalability, and robustness in uncertain, unpredictable environments. Results show a 17% higher success rate and reduced execution time.
S. K, Sheetal Kusal, Usha Desai· 2026 6th International Confe...· 0 citations
The rapid expansion of cloud computing and large-scale data centers has significantly increased energy consumption and carbon emissions, creating critical sustainability concerns for modern computing infrastructures. This paper proposes the Adaptive Carbon-Aware Virtualized Energy-efficient Scheduling (ACAVES) framework to improve resource utilization and reduce environmental impact in cloud environments. The framework combines workload monitoring, task classification, virtual machine consolidation, carbon-aware scheduling, and energy optimization within an integrated architecture. An adaptive scheduling mechanism allocates workloads according to utilization patterns, energy requirements, and carbon emission estimates. Experimental evaluation was performed using heterogeneous workloads containing 10,000 tasks executed over 50 physical servers and 200 virtual machines. Results demonstrate that the proposed ACAVES framework reduced energy consumption from 520 kWh to 385 kWh and carbon emissions from 310 kgCO2 to 215 kgCO2. Additionally, server utilization improved from 68% to 87%, while average task completion time decreased from 820 ms to 670 ms, confirming the effectiveness and scalability of the proposed sustainable scheduling framework.
S. K, Kishore Bitra, Usha Desai· 2026 International Conferenc...· 0 citations
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