Energy-and-deadline-aware optimal resource management and task consolidation using a hybrid Adaptive Neuro-Fuzzy Inference System with binary Chaotic Jaya optimization for cloud-ba...

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MCDM-Fed: an adaptive federated learning approach for enhanced distributed intelligence

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Towards dynamic pricing for computation offloading in mobile edge computing: a federated learning approach

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Topology control for multi-layer LEO networks: towards a reliable space edge-cloud architecture

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Transformative impact of edge computing with 6G network using cloud computing

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Seizing the future: a robust epileptic seizure recognition model-based cloud computing platform and advanced deep learning model

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Enhancing energy forecasting accuracy through human-inspired optimization: a novel iHowOA-BiLSTM framework for IoT applications

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ERecommend: a search scheme for encrypted storage supporting association analysis and active recommendation

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Distributed carbon emission prediction in edge environment considering spatial-temporal context factors

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A framework for the predictive monitoring and data quality assurance in the cloud continuum

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Optimizing competitive team synergy through compatibility-aware graph search in distributed platforms

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AI-powered dynamic queue optimization in bursty multi traffic environment

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CloudSeqAnim: cloud-based sequential behavior modeling for large-scale animation content services

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Privacy-preserving cloud-based dermatological image processing for medical applications: a review

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Supporting Lean Software Enterprises with Agile Development Methods-Identifying the Relationship between Lean and Agile

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A Parallel Version of COCR Method for Solving Complex Symmetric Linear Systems

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The Evolution of Environment for Collaborative Mobile Cloud-Proposals Advanced Mobile Cloud Service Model

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Few-shot urban strategic group discovery with LLM annotation and hypergraph learning

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GenOM: ontology matching with description generation and large language models

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Cluster-SCP: Similarity and Contrastive Learning to Enhance Pseudo Labels for Fine Tuning under Few Labels

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