Results 11 to 20 of about 7,354 (203)

Neural Network-Based Study for Rice Leaf Disease Recognition and Classification: A Comparative Analysis Between Feature-Based Model and Direct Imaging Model. [PDF]

Food Sci Nutr
This study proposes a NN‐based framework for automated recognition and classification of rice diseases using leaf imagery. Feature extraction techniques such as texture analysis, GLCM, GLDM, FFT, and DWT extract critical image characteristics, while dimensionality reduction (PCA, KPCA, Sparse AE, Stacked AE) and feature selection (ANOVA F‐measure, Chi ...
Prity FS, Raquib M, Murad SA, Rafi MJ, Bhuiyan MK, Bairagi AK.   +5 more
europepmc   +2 more sources

Process Monitoring Using Kernel PCA and Kernel Density Estimation-Based SSGLR Method for Nonlinear Fault Detection

Applied Sciences, 2022
Fault monitoring is often employed for the secure functioning of industrial systems. To assess performance and enhance product quality, statistical process control (SPC) charts such as Shewhart, CUSUM, and EWMA statistics have historically been utilized.
Faisal Shahzad, Zhensheng Huang, Waqar Hussain Memon   +2 more
doaj   +1 more source

Clinical Profile Identification of Indigenous Infants With Bronchiolitis Through Using Unsupervised Feature Extraction and Clustering. [PDF]

Pediatr Pulmonol
ABSTRACT Objective Infants hospitalized with bronchiolitis may experience persistent symptoms linked to future chronic lung diseases like bronchiectasis. Identifying phenotypes during hospitalization could guide targeted interventions. As traditional clustering requires large datasets, this study explores whether Unsupervised Feature Extraction ...
Niu H, McCallum GB, Chang AB, Khan K, Azam S.   +4 more
europepmc   +2 more sources

Improved KPCA for supernova photometric classification [PDF]

Proceedings of the International Astronomical Union, 2014
AbstractThe problem of supernova photometric identification is still an open issue faced by large photometric surveys. In a previous investigation, we showed how combining Kernel Principal Component Analysis and Nearest Neighbour algorithms enable us to photometrically classify supernovae with a high rate of success. In the present work, we demonstrate
Ishida, E., de Souza, R., Abdalla, F.
openaire   +2 more sources

Research on the Prediction of Green Plum Acidity Based on Improved XGBoost

Sensors, 2021
The acidity of green plum has an important influence on the fruit’s deep processing. Traditional physical and chemical analysis methods for green plum acidity detection are destructive, time-consuming, and unable to achieve online detection. In response,
Yang Liu, Honghong Wang, Yeqi Fei, Ying Liu, Luxiang Shen, Zilong Zhuang, Xiao Zhang   +6 more
doaj   +1 more source

KPCA Based Novelty Detection Method Using Maximum Correntropy Criterion [PDF]

Jisuanji kexue, 2022
Novelty detection is an important research issue in the field of machine learning.Till now,there exist lots of novelty detection approaches.As a commonly used kernel method,kernel principal component analysis(KPCA)has been successfully applied to deal ...
LI Qi-ye, XING Hong-jie
doaj   +1 more source

Scheduling Dimension Reduction of LPV Models -- A Deep Neural Network Approach [PDF]

, 2020
In this paper, the existing Scheduling Dimension Reduction (SDR) methods for Linear Parameter-Varying (LPV) models are reviewed and a Deep Neural Network (DNN) approach is developed that achieves higher model accuracy under scheduling dimension reduction.
casella, chollet, goodfellow, hanin, kingma, luo, shamma, srivastava, tóth, tóth   +9 more
core   +2 more sources

Nonlinear chemical processes fault detection based on adaptive kernel principal component analysis

Systems Science & Control Engineering, 2020
When kernel Principal Component Analysis (KPCA) is applied to fault detection, kernel Principal Components (KPCs) are divided into two spaces according to the size of variance for fault detection, respectively.
Chen Miao, Zhaomin Lv
doaj   +1 more source

Short-Term Load Forecasting of Distributed Energy System Based on Kernel Principal Component Analysis and KELM Optimized by Fireworks Algorithm

Applied Sciences, 2021
Accurate and stable load forecasting has great significance to ensure the safe operation of distributed energy system. For the purpose of improving the accuracy and stability of distributed energy system load forecasting, a forecasting model in view of ...
Yingying Fan, Haichao Wang, Xinyue Zhao, Qiaoran Yang, Yi Liang   +4 more
doaj   +1 more source

Nonlinear process fault detection and identification using kernel PCA and kernel density estimation [PDF]

, 2016
Kernel principal component analysis (KPCA) is an effective and efficient technique for monitoring nonlinear processes. However, associating it with upper control limits (UCLs) based on the Gaussian distribution can deteriorate its performance.
Cao, Yi, Samuel, Raphael
core   +1 more source