Results 141 to 150 of about 59,339 (255)

Machine Learning Model for Predicting Postoperative Pain in Cases of Irreversible Pulpitis

International Endodontic Journal, EarlyView.
ABSTRACT Aim Postoperative pain is a frequent clinical concern following endodontic treatment. This study aimed to develop and validate supervised machine learning models to predict the occurrence of postoperative pain in cases of irreversible pulpitis.
Pedro Felipe de Jesus Freitas, Ingrid Luiza Mendonça Cunha, Thalita de Paris Matos, Flares Baratto‐Filho, Bianca Marques de Mattos de Araujo, Manoel Damião Sousa‐Neto, Leandro de Moura Martins, Fernando José Herkrath, Emílio Carlos Sponchiado‐Júnior, Cristiano Miranda de Araujo   +9 more
wiley   +1 more source

Forecasting Job Burnout among University Faculty Members of Yazd Payame Noor University Using Artificial Neural Network Technique

طب کار, 2019
Background: Faculty members are one of the main factors in the higher education system, that high level of occupational stress caused by educational, research, and executive duties makes them exposed to burnout.
Hamideh Shekari
doaj  

Application of RBF neural network based on improved Harris eagle algorithm optimization for free-form surface machining error prediction

International Journal of Metrology and Quality Engineering
With the aim of addressing the problem of low efficiency in free-form surface machining error inspection, an improved Harris hawk optimization-radial basis function (IHHO-RBF) neural network prediction model is proposed in this work.
Chen Yueping, Liu Yapeng
doaj   +1 more source

The Application of RBF Neural Network Model Based on Deep Learning for Flower Pattern Design in Art Teaching. [PDF]

Comput Intell Neurosci, 2022
Xiao L, Luo Y.
europepmc   +1 more source

A Note on Local Polynomial Regression for Time Series in Banach Spaces

Journal of Time Series Analysis, EarlyView.
ABSTRACT This work extends local polynomial regression to Banach space‐valued time series for estimating smoothly varying means and their derivatives in non‐stationary data. The asymptotic properties of both the standard and bias‐reduced Jackknife estimators are analyzed under mild moment conditions, establishing their convergence rates.
Florian Heinrichs
wiley   +1 more source

A Systematic Review on Applications of Artificial Intelligence for Obesity Prevention

Obesity Reviews, EarlyView.
ABSTRACT This systematic review examines the applications of artificial intelligence (AI) in preventing obesity, addressing a critical public health issue that affects a substantial portion of the population. With obesity rates rising alarmingly, particularly in the United States, this review synthesizes findings from 46 studies published between 2008 ...
Atefehsadat Haghighathoseini, Shuo‐yu Lin, Ge Song, Ruopeng An, Hong Xue   +4 more
wiley   +1 more source

Prediction of Short-Term Stock Price Trend Based on Multiview RBF Neural Network. [PDF]

Comput Intell Neurosci, 2021
Lv B, Jiang Y.
europepmc   +1 more source

Machine Learning‐Driven Construction of High‐Yielding Cucumber Plant Architectures in Greenhouse Environments

Plant Biotechnology Journal, EarlyView.
Schematic summary of the machine learning‐driven analysis for high‐yield cucumber architecture. This study employs machine learning methods to analyze key shoot and root traits, building a predictive model for yield. The analysis identifies an optimal plant architecture: a compact and sturdy shoot structure, combined with a narrow yet larger‐diameter ...
Cuifang Zhu, Hongjun Yu, Caili Zhao, Hongyang Wu, Xiaoyang Wan, Tao Lu, Yang Li, Weijie Jiang, Qiang Li   +8 more
wiley   +1 more source

RBF Neural Network Sliding Mode Control for Passification of Nonlinear Time-Varying Delay Systems with Application to Offshore Cranes. [PDF]

Sensors (Basel), 2022
Jiang B, Liu D, Karimi HR, Li B.
europepmc   +1 more source

A Survey for Deep Reinforcement Learning Based Network Intrusion Detection

Applied AI Letters, Volume 7, Issue 2, June 2026.
This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang, Alberto Acuto, Yihang Zhou, Dominik Wojtczak   +3 more
wiley   +1 more source