Encoding individual behavioral traits into a low-dimensional latent representation enables the accurate prediction of decision-making patterns across distinct task conditions.
This research paper presents a proactive approach to congestion control in IoT networks using an encoder–decoder LSTM (ED-LSTM) model to predict packet loss ratios ahead of time. By forecasting ...
ABSTRACT: This work presents an innovative Intrusion Detection System (IDS) for Edge-IoT environments, based on an unsupervised architecture combining LSTM networks and Autoencoders. Deployed on ...
Beyond tumor-shed markers: AI driven tumor-educated polymorphonuclear granulocytes monitoring for multi-cancer early detection. Clinical outcomes of a prospective multicenter study evaluating a ...
With the accelerating pace of urbanization, air pollution has emerged as a critical global challenge, where ozone (O 3) concentration dynamics have become a pivotal indicator of atmospheric quality ...
As AI glasses like Ray-Ban Meta gain popularity, wearable AI devices are receiving increased attention. These devices excel at providing voice-based AI assistance and can see what users see, helping ...
This repository contains a PyTorch implementation of a sequence-to-sequence model for football commentary generation from a structured events & stats input. The model utilizes Bi-directional LSTM ...
Abstract: Accurate prediction of blood glucose levels is crucial for automated treatment in diabetic patients. This study proposes a blood glucose prediction model based on an improved attention ...
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