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These findings challenge the prevailing view of deep learning dominance in time-series forecasting and establish new accuracy benchmarks for electricity load prediction.
MEPO-SLM is presented, a framework that reformulates prompt engineering for SLMs as a four-objective Pareto problem over task inaccuracy, and Phi-3-mini and Gemma-2B on English TriviaQA and Arabic medical QA, and TinyLlama-1.1B on TriviaQA only are evaluated.
A Digital Twin (DT)-enabled IDS framework that combines deep learning with real-time process simulation is proposed that consistently outperforms the evaluated representative baseline IDS methods under identical experimental conditions.
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