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This work proposes an unsupervised, phrase-based framework for event detection from Persian social media streams, in which semantically meaningful phrases serve as the fundamental units of event representation, and shows superior ability to identify novel and previously unseen phrases.
Deep learning models hold considerable potential for predicting MCI-to-AD conversion, yet substantial barriers remain to their translation into routine clinical practice, including lack of diverse datasets, overfitting, and poor model interpretability.
This paper presents a lightweight IoT security decision framework that combines entropy-guided feature selection with an adaptive ensemble-based intrusion detection strategy, establishing intrusion detection as an efficient and deployable security decision layer for real-world IoT environments.
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