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This study proposes a Multimodal AI Framework for Decision Intelligence Systems that integrates diverse data sources to enhance prediction accuracy, contextual understanding, and operational efficiency and demonstrates that multimodal AI significantly outperforms traditional unimodal systems.
A secure blockchain-enabled IIoT architecture that integrates industrial sensing, edge computing, distributed ledger technology, cloud analytics, and intelligent decision-making is proposed that employs device authentication, encrypted communication, decentralized consensus, smart contracts, and machine learning-based...
A Multi-Agent Reinforcement Learning (MARL) framework for intelligent resource allocation across transportation, energy, water, healthcare, emergency response, and communication systems is proposed, which enhances resource utilization, reduces energy consumption and response time, improves system reliability, and suppo...
A Multi-Agent Autonomous Control Framework that integrates Multi-Agent Systems (MAS), Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), Graph Neural Networks (GNN), Internet of Things (IoT), Vehicle-to-Everything (V2X) communication, and edge-cloud computing is proposed.
Experimental results demonstrate high model accuracy, reduced privacy leakage, lower communication overhead, faster convergence, enhanced scalability, and strong resilience against security attacks, making the proposed framework suitable for next-generation privacy-preserving smart applications.
Experimental results demonstrate that XORL enhances decision transparency, operator trust, safety awareness, and autonomous task performance while maintaining competitive learning efficiency, supporting the development of trustworthy and human-centric autonomous robotic systems.
This paper proposes the Foundation Model-Based Predictive Analytics Framework for Multi-Domain Decision Intelligence (FMPA-MDI), an integrated architecture that combines heterogeneous data acquisition, multimodal preprocessing, semantic representation learning, transformer-based predictive reasoning, retrieval-augmente...
The proposed hybrid methodology integrates workload prediction, adaptive scheduling, and resource consolidation, demonstrating significant energy savings without compromising system performance is proposed.
This study proposes a Federated Predictive Learning with Privacy-Aware Model Aggregation (FPL-PAMA) framework, suitable for applications including healthcare, IoT, smart manufacturing, transportation, and financial fraud detection, providing a secure and scalable solution for next-generation distributed intelligent sys...
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