The relationship between data skewness and accuracy of Artificial Neural Network predictive model

This study aims to explore the effect of kurtosis level of the data in the output layer on the accuracy of artificial neural network predictive models. The artificial neural network predictive models are comprised of one node in the output layer and six nodes in the input layer. The number of hidden layer is automatically built by the program. Data are generated using simulation approach. The results show that the kurtosis level of the node in the output layer is significantly affect the accuracy of the artificial neural network predictive model. Platycurtic and leptocurtic data has significantly higher misclassification rates than mesocurtic data. However, the misclassification rates between platycurtic and leptocurtic is not significantly different. Thus, data distribution with kurtosis nearly to zero results in a better ANN predictive model.

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A new predictive model for the minimum strength requirement of steel moment frames using artificial neural network

Soil Dynamics and Earthquake Engineering ◽

10.1016/j.soildyn.2018.09.046 ◽

2019 ◽

Vol 116 ◽

pp. 69-81 ◽

Cited By ~ 5

Author(s):

Amir Ahmad Hedayat ◽

Ehsan Ahmadi Afzadi ◽

Hossein Kalantaripour ◽

Esmaeil Morshedi ◽

Amin Iranpour

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Predictive Model ◽

Moment Frames ◽

Steel Moment Frames ◽

Artificial Neural

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Modelling of Rotary EDM Process Parameters of Inconel 718 Using Artificial Neural Networks

Mechanika ◽

10.5755/j01.mech.26.6.20484 ◽

2020 ◽

Vol 26 (6) ◽

pp. 540-544

Author(s):

Jayaraj JEEVAMALAR ◽

Sundaresan RAMABALAN ◽

Chinnamuthu SENTHILKUMAR

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Process Parameters ◽

Inconel 718 ◽

Machining Process ◽

Artificial Neural ◽

Pulse On Time ◽

Pulse Off Time ◽

Matlab Programming ◽

The Relationship

Modelling is used for correlating the relationship between the input process parameters and the output responses during the machining process. To characterize real-world systems of considerable complexity, an Artificial Neural Network (ANN) model is regularly used to replace the mathematical approximation of the relationship. This paper explains the methodological procedure and the outcome of the ANN modeling process for Electrical Discharge Drilling of Inconel 718 superalloy and hollow tubular copper as tool electrode. The most important process parameters in this work are peak current, pulse on time and pulse off time with machining performances of material removal rate and surface roughness. The experiments were performed by L20 Orthogonal Array. In such conditions, an Artificial Neural Network model is developed using MATLAB programming on the Feed Forward Back Propagation technique was used to predict the responses. The experimental data were separated into three parts to train, test the network and validate the model. The developed model has been confirmed experimentally for training and testing in considering the number of iterations and mean square error convergence criteria. The developed model results are to approximate the responses fairly exactly. The model has the mean correlation coefficient of 0.96558. Results revealed that the proposed model can be used for the prediction of the complex EDM drilling process.

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Solving multiple linear regression problem using artificial neural network

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i1.pp770-775 ◽

2022 ◽

Vol 12 (1) ◽

pp. 770

Author(s):

Mohammad S. Khrisat ◽

Ziad A. Alqadi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Problem ◽

Simple Method ◽

Scientific Experiments ◽

Linear Regression Problem ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

The Relationship ◽

Linear Regressions

<span>Multiple linear regressions are an important tool used to find the relationship between a set of variables used in various scientific experiments. In this article we are going to introduce a simple method of solving a multiple rectilinear regressions (MLR) problem that uses an artificial neural network to find the accurate and expected output from MLR problem. Different artificial neural network (ANN) types with different architecture will be tested, the error between the target outputs and the calculated ANN outputs will be investigated. A recommendation of using a certain type of ANN based on the experimental results will be raised.</span>

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Artificial Neural Network based Dual Layered Predictive Model for Rare Attack Detection

2020 International Conference on Computational Performance Evaluation (ComPE) ◽

10.1109/compe49325.2020.9200134 ◽

2020 ◽

Author(s):

Utpal Shrivastava ◽

Neelam Sharma

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Predictive Model ◽

Attack Detection ◽

Artificial Neural

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A Simple and Sustainable Prediction Method of Liquefaction-Induced Settlement at Pohang Using an Artificial Neural Network

Sustainability ◽

10.3390/su12104001 ◽

2020 ◽

Vol 12 (10) ◽

pp. 4001

Author(s):

Sung-Sik Park ◽

Peter D. Ogunjinmi ◽

Seung-Wook Woo ◽

Dong-Eun Lee

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Predictive Model ◽

Model Equation ◽

Soil Layer ◽

Unit Weight ◽

Ann Model ◽

Matrix Formulation ◽

Input Parameters ◽

Artificial Neural

Conventionally, liquefaction-induced settlements have been predicted through numerical or analytical methods. In this study, a machine learning approach for predicting the liquefaction-induced settlement at Pohang was investigated. In particular, we examined the potential of an artificial neural network (ANN) algorithm to predict the earthquake-induced settlement at Pohang on the basis of standard penetration test (SPT) data. The performance of two ANN models for settlement prediction was studied and compared in terms of the R2 correlation. Model 1 (input parameters: unit weight, corrected SPT blow count, and cyclic stress ratio (CSR)) showed higher prediction accuracy than model 2 (input parameters: depth of the soil layer, corrected SPT blow count, and the CSR), and the difference in the R2 correlation between the models was about 0.12. Subsequently, an optimal ANN model was used to develop a simple predictive model equation, which was implemented using a matrix formulation. Finally, the liquefaction-induced settlement chart based on the predictive model equation was proposed, and the applicability of the chart was verified by comparing it with the interferometric synthetic aperture radar (InSAR) image.

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Corrigendum to “A generalized predictive model for TiO2–Catalyzed photo-degradation rate constants of water contaminants through artificial neural network” [Environ. Res., 187, (2020), 109697]

Environmental Research ◽

10.1016/j.envres.2020.109850 ◽

2020 ◽

Vol 188 ◽

pp. 109850

Author(s):

Zhuoying Jiang ◽

Jiajie Hu ◽

Xijin Zhang ◽

Yihang Zhao ◽

Xudong Fan ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Predictive Model ◽

Rate Constants ◽

Degradation Rate ◽

Water Contaminants ◽

Photo Degradation ◽

Artificial Neural

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The relationship between market sentiment and equity premium: an artificial neural network analysis

International Journal of Electronic Finance ◽

10.1504/ijef.2008.017542 ◽

2008 ◽

Vol 2 (2) ◽

pp. 227

Author(s):

Nik R. Hassan ◽

Shee Q. Wong

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Analysis ◽

Equity Premium ◽

Neural Network Analysis ◽

Market Sentiment ◽

Artificial Neural Network Analysis ◽

Artificial Neural ◽

The Relationship

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Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death

Scientific Reports ◽

10.1038/s41598-020-77546-5 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Chen-Chih Chung ◽

Lung Chan ◽

Oluwaseun Adebayo Bamodu ◽

Chien-Tai Hong ◽

Hung-Wen Chiu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Intracerebral Hemorrhage ◽

Predictive Model ◽

Sensitivity And Specificity ◽

Characteristic Curve ◽

Intravenous Tissue Plasminogen Activator ◽

Symptomatic Intracerebral Hemorrhage ◽

Ann Models ◽

Artificial Neural

AbstractDespite the salient benefits of the intravenous tissue plasminogen activator (tPA), symptomatic intracerebral hemorrhage (sICH) remains a frequent complication and constitutes a major concern when treating acute ischemic stroke (AIS). This study explored the use of artificial neural network (ANN)-based models to predict sICH and 3-month mortality for patients with AIS receiving tPA. We developed ANN models based on evaluation of the predictive value of pre-treatment parameters associated with sICH and mortality in a cohort of 331 patients between 2009 and 2018. The ANN models were generated using eight clinical inputs and two outputs. The generalizability of the model was validated using fivefold cross-validation. The performance of each model was assessed according to the accuracy, precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). After adequate training, the ANN predictive model AUC for sICH was 0.941, with accuracy, sensitivity, and specificity of 91.0%, 85.7%, and 92.5%, respectively. The predictive model AUC for 3-month mortality was 0.976, with accuracy, sensitivity, and specificity of 95.2%, 94.4%, and 95.5%, respectively. The generated ANN-based models exhibited high predictive performance and reliability for predicting sICH and 3-month mortality after thrombolysis; thus, its clinical application to assist decision-making when administering tPA is envisaged.

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