Neural Network‐Based Study for Rice Leaf Disease Recognition and Classification: A Comparative Analysis Between Feature‐Based Model and Direct Imaging Model [PDF]
Food Science &Nutrition, Volume 13, Issue 12, December 2025.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 ...
Farida Siddiqi Prity +5 more
wiley +2 more sources
Clinical Profile Identification of Indigenous Infants With Bronchiolitis Through Using Unsupervised Feature Extraction and Clustering [PDF]
Pediatric Pulmonology, Volume 61, Issue 1, January 2026.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 ...
Hongqi Niu +4 more
wiley +2 more sources
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 +9 more
core +2 more sources
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
Training Echo State Networks with Regularization through Dimensionality Reduction [PDF]
, 2016 In this paper we introduce a new framework to train an Echo State Network to predict real valued time-series. The method consists in projecting the output of the internal layer of the network on a space with lower dimensionality, before training the ...
Bianchi, Filippo Maria +2 more
core +2 more sources
Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach [PDF]
, 2015 Reformulating computer vision problems over Riemannian manifolds has demonstrated superior performance in various computer vision applications. This is because visual data often forms a special structure lying on a lower dimensional space embedded in a ...
Alavi, Azadeh +3 more
core +2 more sources
Optimized kernel minimum noise fraction transformation for hyperspectral image classification [PDF]
, 2017 This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear ...
Gao, Lianru +4 more
core +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
Kernel principal component analysis (KPCA) for the de-noising of communication signals [PDF]
, 2002 This paper is concerned with the problem of de-noising for non-linear signals. Principal Component Analysis (PCA) cannot be applied to non-linear signals however it is known that using kernel functions, a non-linear signal can be transformed into a ...
Koutsogiannis, G., Soraghan, J.J.
core +2 more sources
Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval [PDF]
, 2014 We present a simple but powerful reinterpretation of kernelized locality-sensitive hashing (KLSH), a general and popular method developed in the vision community for performing approximate nearest-neighbor searches in an arbitrary reproducing kernel ...
Jiang, Ke, Kulis, Brian, Que, Qichao
core +1 more source