AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A Tsetlin Machine (TM)-based NILM framework is proposed, targeting real-time applications on resource-constrained microcontrollers (MCUs), enabling privacy-preserving edge deployment and achieves an average precision of 90% and recall of 96% for two-appliance classification.
Deep neural networks achieve high accuracy through layered numerical transformations, yet their decisions remain difficult to audit because decision evidence is encoded in hidden activations rather than explicit rules. This paper introduces CRISP, a framework that reconstructs the last-layer activation vector (LLAV) of...
This work investigates the Convolutional Tsetlin Machine for multimodal HAR using only the raw inertial signals of the UCI-HAR dataset, rather than its pre-computed 561-feature representation, to address predictive performance, interpretability and suitability for embedded and mobile platforms.
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