Results 71 to 80 of about 117,260 (178)

A Bayesian Neural Network based on Dropout Regulation

, 2020
Bayesian Neural Networks (BNN) have recently emerged in the Deep Learning world for dealing with uncertainty estimation in classification tasks, and are used in many application domains such as astrophysics, autonomous driving...BNN assume a prior over the weights of a neural network instead of point estimates, enabling in this way the estimation of ...
Theobald, Claire, Pennerath, Frédéric, Conan-Guez, Brieuc, Couceiro, Miguel, Napoli, Amedeo   +4 more
openaire   +3 more sources

Estimation of residual stress in dissimilar metals welding using deep fuzzy neural networks with rule-dropout

Nuclear Engineering and Technology
Welding processes are used to connect several components in nuclear power plants. These welding processes can induce residual stress in welding joints, which has been identified as a significant factor in primary water stress corrosion cracking ...
Ji Hun Park, Man Gyun Na
doaj   +1 more source

Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout

AI
Background/Objectives: Convolutional Neural Networks (CNNs), while effective in tasks such as image classification and language processing, often experience overfitting and inefficient training due to static, structure-agnostic regularization techniques ...
Mehdi Ghayoumi
doaj   +1 more source

A Soft Sensor for Flow Estimation and Uncertainty Analysis Based on Artificial Intelligence: A Case Study of Water Supply Systems

Automation
The fourth industrial revolution has transformed the industry, with information technology playing a crucial role in this shift. The increasing digitization of industrial systems demands efficient sensing and control methods, giving rise to soft sensors ...
Gabryel M. Raposo de Alencar, Fernanda M. Lima Fernandes, Rafael Moura Duarte, Petrônio Ferreira de Melo, Altamar Alencar Cardoso, Heber Pimentel Gomes, Juan M. Mauricio Villanueva   +6 more
doaj   +1 more source

A model for predicting dropout of higher education students

Data Science and Management
Higher education institutions are becoming increasingly concerned with the retention of their students. This work is motivated by the interest in predicting and reducing student dropout, and consequently in reducing the financial losses of said ...
Anaíle Mendes Rabelo, Luis Enrique Zárate   +1 more
doaj   +1 more source

Comparative Analysis of Physics-Guided Bayesian Neural Networks for Uncertainty Quantification in Dynamic Systems

Forecasting
Uncertainty quantification (UQ) is critical for modeling complex dynamic systems, ensuring robustness and interpretability. This study extends Physics-Guided Bayesian Neural Networks (PG-BNNs) to enhance model robustness by integrating physical laws into
Xinyue Xu, Julian Wang
doaj   +1 more source

Improved Dropout for Shallow and Deep Learning

, 2016
Dropout has been witnessed with great success in training deep neural networks by independently zeroing out the outputs of neurons at random. It has also received a surge of interest for shallow learning, e.g., logistic regression.
Gong, Boqing, Li, Zhe, Yang, Tianbao
core  

Regularized Urdu Speech Recognition with Semi-Supervised Deep Learning

Applied Sciences, 2019
Automatic Speech Recognition, (ASR) has achieved the best results for English, with end-to-end neural network based supervised models. These supervised models need huge amounts of labeled speech data for good generalization, which can be quite a ...
Mohammad Ali Humayun, Ibrahim A. Hameed, Syed Muslim Shah, Sohaib Hassan Khan, Irfan Zafar, Saad Bin Ahmed, Junaid Shuja   +6 more
doaj   +1 more source

CSD: Channel Selection Dropout for Regularization of Convolutional Neural Networks

IEEE Access
In this study, we present a novel approach, Channel Selection Dropout (CSD), designed to regularize deep convolutional neural network (CNN) architectures. Unlike standard Dropout, which randomly deactivates neurons in fully connected layers, CSD works on
Imrus Salehin, Dae-Ki Kang
doaj   +1 more source

Multimodal machine learning framework for predicting and enhancing higher education using reinforcement learning and graph neural networks

Automatika
There has been a growing emphasis on intelligent educational systems to foster student success, with universities working to aid timely interventions and personalized learning.
Chen Jie, Huang Min, Chen Bin
doaj   +1 more source