Artificial Neural Networks Combined with the Principal Component Analysis for Non-Fluent Speech Recognition

Świetlicka, Izabela; Kuniszyk-Jóźkowiak, Wiesława; Świetlicki, Michał

doi:10.3390/s22010321

Cited by 16 publications

(6 citation statements)

References 53 publications

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“…Principal component analysis (PCA) can be considered as an appropriate numerically scheme for diminishing the dimensions of a measurements set in linear form even though holding information [6,7]. Figure 1 shows PCA neural networks consist of M input node and single output layer containing N neurons.…”

Section: Pca Neural Networkmentioning

confidence: 99%

An Integrated Kernel PCA Neural Network and EGM for Number of Sources Estimation in Wireless Communication

Mohammed Hussein Miry

2023

IJRITCC

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The present work argues estimating number of sources in communication system using an integrated model of Principal Component Analysis (PCA) neural network and kernel method to produce Eigenvalue Grads Method (EGM). The essential advantage of this new suggested model is that, PCA neural is used to determine the covariance matrix instead of the traditional computation process which is time consuming. Simulation outcomes of this adopted model demonstrate wonderful responses through effectiveness, fast converge speed for (PCA) neural network, as well as achieving correct number of sources.

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Section: Pca Neural Networkmentioning

confidence: 99%

An Integrated Kernel PCA Neural Network and EGM for Number of Sources Estimation in Wireless Communication

Mohammed Hussein Miry

2023

IJRITCC

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“…The BFGS (Broyden-Fletcher-Goldfarb-Shanno) algorithm was used in the learning process. The BFGS algotithm is a quasi-Newton optimization method, which provides good convergence and is the most recommended technique for training neural networks [82][83][84].…”

Section: Selecting Neural Network's Type and Carrying Out The Process...mentioning

confidence: 99%

The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus

et al. 2022

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The aim of this paper is to develop neural models enabling the determination of biomechanical parameters for giant miscanthus stems. The static three-point bending test is used to determine the bending strength parameters of the miscanthus stem. In this study, we assume the modulus of elasticity bending and maximum stress in bending as the dependent variables. As independent variables (inputs of the neural network) we assume water content, internode number, maximum bending force value and dimensions characterizing the cross-section of miscanthus stem: maximum and minimum stem diameter and stem wall thickness. The four developed neural models, enabling the determination of the value of the modulus of elasticity in bending and the maximum stress in bending, demonstrate sufficient and even very high accuracy. The neural networks have an average relative error of 2.18%, 2.21%, 3.24% and 0.18% for all data subsets, respectively. The results of the sensitivity analysis confirmed that all input variables are important for the accuracy of the developed neural models—correct semantic models.

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“…In this respect, to improve the ANN performance of implemented models, some researchers, such as those in [23][24][25][26], have combined ANN models with a principal component analysis (PCA) to create new features based on the original variables in order to reduce the dimensions, resulting in a favorable prediction performance of the ANN in their respective applications.…”

Section: Introductionmentioning

confidence: 99%

Combination of Artificial Neural Networks and Principal Component Analysis for the Simultaneous Quantification of Dyes in Multi-Component Aqueous Mixtures

Estrada-Moreno,

Rendon-Lara,

Jiménez-Núñez

2024

Applied Sciences

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Dyes are organic compounds capable of transmitting their color to materials, which is why they are widely used, for example, in textile fibers, leather, paper, plastic, and the food industry. In the dying process, measuring the dye’s content is extremely important to evaluate the process efficiency and minimize the dye’s discharge in wastewater, but most of the time, dyes are present in multi-component mixtures; hence, quantification by spectrophotometric methods presents a great challenge because the signal obtained in the measurement overlaps the components in the mixture. In order to overcome this issue, the use of the high-performance liquid chromatography (HPLC) method is recommended; however, it has the disadvantage of being an expensive technique, complex, and requiring excessive sample preparation. In recent years, some direct spectrophotometric methods based on multivariate regression algorithms for the quantification of dyes in bicomponent mixtures have been reported. This study presents a new framework that uses a combined ANN and principal component analysis (PCA) model for the determination of the concentration of three dyes in aqueous mixtures: Tartrazine (TZ), Amaranth Red (AR), and Blue 1 CFC (B1) dyes. The PCA–ANN model was trained and validated with ternary mixture samples of TZ, AR, and B1, and with known different compositions, spectra absorbance samples were measured in a UV-Vis spectrophotometer at wavelengths between 350–700 nm with intervals of 1 nm. The PCA–ANN model showed a mean absolute prediction error and correlation coefficient (r2) of less than 1% and greater than 0.99, respectively. The results demonstrate that the PCA–ANN model is a quick and highly accurate alternative in the simultaneous determination of dyes in ternary aqueous mixtures.

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Artificial Neural Networks Combined with the Principal Component Analysis for Non-Fluent Speech Recognition

Cited by 16 publications

References 53 publications

An Integrated Kernel PCA Neural Network and EGM for Number of Sources Estimation in Wireless Communication

An Integrated Kernel PCA Neural Network and EGM for Number of Sources Estimation in Wireless Communication

The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus

Combination of Artificial Neural Networks and Principal Component Analysis for the Simultaneous Quantification of Dyes in Multi-Component Aqueous Mixtures

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