Determining the Efficient Structure of Feed-Forward Neural Network to Classify Breast Cancer Dataset

Ahmed, Kawsar; Noureldien, A.

doi:10.14569/ijacsa.2014.051212

Cited by 8 publications

(6 citation statements)

References 10 publications

(15 reference statements)

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“…ANNs simulate the human brain; they have been used to model various problems in the economic, social, medical, and engineering sciences. ANNs are data-driven self-adaptive methods capable of arbitrary adjustment to model the system without any explicit specification of functional form for the underlying model, and, consequently, they can map any function with arbitrary accuracy [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

et al. 2021

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The study’s primary purpose was to explore the abrasive water jet (AWJ) cut machinability of stainless steel X5CrNi18-10 (1.4301). The study analyzed the effects of such process parameters as the traverse speed (TS), the depth of cut (DC), and the abrasive mass flow rate (AR) on the surface roughness (Ra) concerning the thickness of the workpiece. Three different thicknesses were cut under different conditions; the Ra was measured at the top, in the middle, and the bottom of the cut. Experimental results were used in the developed feed-forward artificial neural network (ANN) to predict the Ra. The ANN’s model was validated using k-fold cross-validation. A lowest test root mean squared error (RMSE) of 0.2084 was achieved. The results of the predicted Ra by the ANN model and the results of the experimental data were compared. Additionally, as TS and DC were recognized, analysis of variance at a 95% confidence level was used to determine the most significant factors. Consequently, the ANN input parameters were modified, resulting in improved prediction; results show that the proposed model could be a useful tool for optimizing AWJ cut process parameters for predicting Ra. Its main advantage is the reduced time needed for experimentation.

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Section: Methodsmentioning

confidence: 99%

Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

et al. 2021

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“…They are made of constitutive units called neurons, which are interconnected to each other with connecting links, where each link has a weight that is multiplied by the signal transmitted in the network [46]. The advantage of ANNs is that neural networks are data-driven self-adaptive methods, so that they can adjust themselves to the data without any explicit specification of functional form for the underlying model, and they can approximate any function with arbitrary accuracy [47].…”

Section: Methodsmentioning

confidence: 99%

Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks

Berus

Klančnik

Brezočnik

et al. 2018

Sensors

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In recent years, neural networks have become very popular in all kinds of prediction problems. In this paper, multiple feed-forward artificial neural networks (ANNs) with various configurations are used in the prediction of Parkinson’s disease (PD) of tested individuals, based on extracted features from 26 different voice samples per individual. Results are validated via the leave-one-subject-out (LOSO) scheme. Few feature selection procedures based on Pearson’s correlation coefficient, Kendall’s correlation coefficient, principal component analysis, and self-organizing maps, have been used for boosting the performance of algorithms and for data reduction. The best test accuracy result has been achieved with Kendall’s correlation coefficient-based feature selection, and the most relevant voice samples are recognized. Multiple ANNs have proven to be the best classification technique for diagnosis of PD without usage of the feature selection procedure (on raw data). Finally, a neural network is fine-tuned, and a test accuracy of 86.47% was achieved.

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“…Neural networks are tools that can solve classification and prediction problems [11]. Neural networks are self-adaptive methods based on methods that fit data without any explicit specification of functional form for an underlying model and can also approximate any function with a certain precision.…”

Section: B Artificial Neural Networkmentioning

confidence: 99%

Regression Model and Neural Network Applied to the Public Spending Execution

Morales¹,

Huanca²

2020

IJACSA

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Artificial Neural Networks are connectionist systems formed by numerous process units called neurons connected to each other, which adapt their structure through learning techniques to solve problems of function approximation and pattern classification. They process information that is supplied to them, either to obtain relationships between them and the objective function that is intended to be approximated, or by classifying these data into different categories. Regression analysis aims to determine the type of functional relationship that exists between a dependent variable and one or more independent variables. The purpose of the research is to use regression methods (multiple regression) and artificial neural networks (multilayer perceptron) to determine the influence of spending execution on the regional government's public budget. 95% of the variability of the budget of Moquegua region has been determined and explained by the three sectors (primary, secondary and tertiary) and 5% is determined by other factors outside the regional government budget. The determination coefficients R 2 = 95.9% in the regression model and R 2 = 95.3% in the neural network (multilayer perceptron). It has been demonstrated that Artificial neural networks and regression models have obtained very similar results, achieving good and good-fit models.

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Determining the Efficient Structure of Feed-Forward Neural Network to Classify Breast Cancer Dataset

Cited by 8 publications

References 10 publications

Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks

Regression Model and Neural Network Applied to the Public Spending Execution

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