Results 81 to 90 of about 16,838,211 (334)
Machine Learning-Driven Mathematical Models for Energy-Efficient Fisheries Operations [PDF]
BIO Web of ConferencesEnergy consumption is a major challenge in fisheries, affecting operating costs and environmental sustainability. This study develops machine learning (ML)-driven mathematical models to predict and optimize energy use by integrating operational variables,
Lenggo Ginta Turnad +9 more
doaj +1 more source
Revisiting Batch Normalization for Training Low-Latency Deep Spiking Neural Networks From Scratch
Frontiers in Neuroscience, 2021 Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield huge energy efficiency benefits on neuromorphic hardware.
Youngeun Kim, Priyadarshini Panda
doaj +1 more source
High performance and energy efficient inference for deep learning on ARM processors
CoRR, 2021 13 pages, 7 ...
Adrián Castelló 0001 +4 more
openaire +3 more sources
Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif +17 more
wiley +1 more source
Arthritis Care &Research, EarlyView.Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos +2 more
wiley +1 more source
Sample-Efficient Methods for Real-World Deep Reinforcement Learning
, 2022 Reinforcement learning (RL) is a general framework for learning and evaluating intelligent behaviors in any domain. Deep reinforcement learning combines RL with deep learning to learn expressive nonlinear functions that can interpret rich sensory signals
Boney, Rinu +7 more
core +1 more source
A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions
Advanced Engineering Materials, EarlyView.This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice +2 more
wiley +1 more source
Deep Reinforcement Learning for Energy-Efficient Data Dissemination Through UAV Networks
IEEE Open Journal of the Communications SocietyThe rise of the Internet of Things (IoT), marked by unprecedented growth in connected devices, has created an insatiable demand for supplementary computational and communication resources.
Abubakar S. Ali +5 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.Geometry‐driven thermal behavior in wire‐arc additive manufacturing (WAAM) influences microstructural evolution during nonequilibrium solidification of a chemically complex Fe–Cr–Nb–W–Mo–C nanocomposite system. By comparing different deposits configurations, distinct entropy–cooling rate correlations, segregation, and carbide evolution are revealed ...
Blanca Palacios +5 more
wiley +1 more source
Device-Aware Optimization of Energy-Efficient Deep Learning for Edge Deployment
IEEE AccessDeep neural networks deliver strong accuracy but their energy and latency costs hinder sustainable deployment on resource constrained edge devices. We propose an energy aware framework that couples three complementary efficiency mechanisms: structured ...
Guiling Xiang
doaj +1 more source