Results 51 to 60 of about 1,872,984 (299)
Predictive Value of Composite Inflammatory Markers for Stroke Prognosis: A Prospective Cohort Study
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Novel composite inflammatory markers' role in stroke prognosis is understudied, and the best predictor is unclear, requiring further exploration. Objectives This study aimed to systematically evaluate the associations of 6 novel composite inflammatory markers on stroke prognosis.
Bing Wu +7 more
wiley +1 more source
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
Better and faster hyperparameter optimization with Dask [PDF]
Proceedings of the Python in Science Conference, 2019 Slides about a new hyperparameter optimization algorithm in ...
Scott Sievert +2 more
openaire +3 more sources
Optimizing Deep Learning Models with Improved BWO for TEC Prediction
BiomimeticsThe prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction.
Yi Chen +6 more
doaj +1 more source
dynoGP: Deep Gaussian Processes for Dynamic System Identification
International Journal of Adaptive Control and Signal Processing, EarlyView.This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source
Hyperparameters Optimization for Federated Learning System : Speech Emotion Recognition Case Study
, 2023 Context: Federated Learning (FL) has emerged as a promising, massively distributed way to train a joint deep model across numerous edge devices, ensuring user data privacy by retaining it on the device.
Mohammadi, Mohammadreza, +3 more
core +1 more source
Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Optimizing Hyperparameters in Deep Learning Models Using Bayesian Optimization [PDF]
Hyperparameter optimization is a crucial aspect of deep learning, as the choice of hyperparameters significantly influences model performance.
Kian Hemant, Madan
core +3 more sources
IEEE Access, 2019 Heart failure is considered one of the leading cause of death around the world. The diagnosis of heart failure is a challenging task especially in under-developed and developing countries where there is a paucity of human experts and equipments.
Ashir Javeed +5 more
doaj +1 more source
Artificial Neural Network Hyperparameters Optimization: A Survey [PDF]
, 2022 Machine-learning (ML) methods often utilized in applications like computer vision, recommendation systems, natural language processing (NLP), as well as user behavior analytics.
Kadhim, Zahraa Saddi +2 more
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