Results 121 to 130 of about 53,216 (300)
Advanced Intelligent Systems, EarlyView.A hybrid Reinforcement Learning–Explainable AI framework integrates SHAP and LIME explanations directly into a Deep Q‐Network inference loop for real‐time ICU decision support. Trained on 18 142 mechanically ventilated stays from the eICU database, the system attains 93.0% decision accuracy, 20% fewer errors than RL alone, and a 91% clinician trust ...
Jannatul Ferdaus Disha +2 more
wiley +1 more source
Advanced RNN based NARMA predictors
, 2020 An analysis of nonlinear time series prediction schemes, realised though advanced Recurrent Neural Network (RNN) techniques is provided. Due to practical constraints in using common RNNs, such as the problem of vanishing gradient, some other ways to ...
Chambers, JA, Mandic, DP
core +1 more source
Channel and model selection for multi-channel EEG input to neural networks
SICE Journal of Control, Measurement, and System IntegrationStudies employing neural networks to classify emotions from brain waves and other biological signals provide a quantitative perspective on understanding human physiological phenomena.
Kento Harachi +7 more
doaj +1 more source
PREDICTING MEDICINE DEMAND USING DEEP LEARNING TECHNIQUES
Iraqi Journal for Computers and Informatics, 2023 Medication supply and storage are essential components of the medical industry and distribution. Most medications have a predetermined expiration date. When the demand is met in large quantities that exceed the actual need, this leads to the accumulation
Bashaer Abdurahman Mousa +1 more
doaj
Advanced Intelligent Systems, EarlyView.An large language model‐powered multimodal framework is developed for robotic endoscope control. It integrates speech recognition and real‐time instrument tracking, achieving 89.47% command accuracy with ~1s latency for natural human–robot interaction in minimally invasive surgery.
Yisen Huang +7 more
wiley +1 more source
Training t-RNN without concatenation (green bar) led to a degradation in the performance in the action prediction compared to t-RNN trained with concatenation (blue bar) for both empirical datasets (error measured in BCE; black lines indicate s.e.m ...
Eliya Nachmani (17735928) +3 more
core +1 more source
Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy
Advanced Intelligent Systems, EarlyView.This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne +5 more
wiley +1 more source
Graph Neural Network‐Based Reinforcement Learning for Decentralized Multi‐Robot Manipulation
Advanced Intelligent Systems, EarlyView.Robot arms lifting a large object face a trade‐off: centralized controllers explode in parameters, while decentralized ones cannot coordinate. A GNN resolves this—each arm runs its own network but acts on the full team state, achieving centralized‐level coordination with decentralized execution. Trained across team sizes, a single policy scales to four‐
Tong Chen +3 more
wiley +1 more source
Transformers are Multi-State RNNs
Proceedings of the 2024 Conference on Empirical Methods in Natural Language ProcessingTransformers are considered conceptually different from the previous generation of state-of-the-art NLP models - recurrent neural networks (RNNs). In this work, we demonstrate that decoder-only transformers can in fact be conceptualized as unbounded multi-state RNNs - an RNN variant with unlimited hidden state size.
Matanel Oren +4 more
openaire +4 more sources