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Despite achieving high accuracy on individual datasets, machine learning models for intrusion detection exhibit a significant generalization gap, with performance dropping drastically when tested on unseen network environments.
Predicting heat stress in cows is no longer a guessing game: this digital twin leverages physics and behavior to forecast core body temperature with high accuracy and quantified uncertainty.