Mean Absolute Error Criterion Research Articles

Küresel iklim değişikliğiyle mücadelede deniz yüzeyi sıcaklığının ileri beslemeli yapay sinir ağları ile tahminlenmesi: Rize örneği, Türkiye

The increase of the world population, especially in the global competition, together with the increasing use of fossil fuel resources to meet energy needs, leads to more greenhouse gases (more than one CO2, methane etc.) emissions and the global climate crisis. In this process, changes in meteorological events such as temperature, precipitation, and wind, attract attention moreover but when considered as a whole, we know that these negative changes in the ecosystem negatively affect many living groups. Sea Surface Temperature (SST) as measured meteorologically is the most important environmental parameter where these changes are monitored and observed. It draws attention to the fact that changes in SST are not limited to living organisms as habitats, but also catalyze many chain reactions, especially socio-economic impacts. Therefore, much of the work is devoted to forecasting studies to adapt to changing habitats and take the necessary precautions against potential risks. Feed-forward artificial neural networks have been commonly used to address these emerging needs. Artificial neural networks, which are a simple imitation of the human neurological system, have been used as an artificial intelligence method in forecasting problems due to their superior performance and not having the limitations of classical time series. In this study, the forecasting of the time series of monthly mean SST temperature obtained from Rize station between the years 2010 and 2020 is performed by using feed-forward artificial neural networks, and the forecasting performance of the corresponding time series is compared with many forecasting methods with different characteristics. The comparison of the methods used the mean square error and mean absolute percentage error criteria, which are commonly used in the forecasting literature. The analysis results showed that the analysis results obtained with the feed-forward artificial neural networks have the best prediction performance. As a result, it can be stated that the sea surface temperature can be forecasted with a very high accuracy using the feed-forward artificial neural networks.

Arima Model with Box-Cox Transformed Univariate Variable in BSE Sensex

Abstract: Fluctuation of the stock market’s impact on investments of stocks. Sensex prediction plays an important role in the investment of markets. Predicting the stock market is difficult in market scenarios. The present study attempted to predict the stock market due to its complicated features and also compared different Auto-Regressive Integrated Moving Average (ARIMA) models to get the appropriate stock forecasting model using various Box-Cox transformations by using BSE Sensex past daily closing data. The ARIMA Model6 (0 1 0) showed accurate results in calculating the Mean Absolute Percentage Error (MAPE) and Bayesian Information Criterion (BIC) values, which indicates the potential of using the ARIMA model for accurate stock forecasting

Combination of ANNs and heuristic algorithms in modelling and optimizing of Fenton processes for industrial wastewater treatment

This study evaluated the COD removal performances of classical-Fenton and photo-Fenton Processes by different prediction models. To optimize both Fenton processes performed in batch reactors, the effects of H2O2 dose, Fe(II) dose, H2O2/Fe(II) rate, and contact time were determined as the independent variables of the prediction models. Besides response surface methodology, three neural networks were used to more reliably and effectively predict the behaviors of dependent variables at different values of relevant parameters. Multi-Layer Perceptron trained by Levenberg–Marquardt (MLP-LM), Multi-Layer Perceptron and Single Multiplicative Neuron models trained by Particle Swarm Optimization algorithm (MLP-PSO; SMN-PSO) were studied. Models’ prediction performances were evaluated by Root-Mean-Square Error (RMSE) and Mean Absolute Percent Error criteria. Regression analysis was applied to determine the performance of the best model. The results from both criteria indicated that SMN-PSO model produced the best predictive results in almost all cases. Moreover, the key process parameters were determined by applying the genetic algorithm to SMN-PSO model outputs. The optimized conditions achieved the optimum removal with over 99% desirability. The optimum Fe(II) dose was determined as 399.99 mg/L in both Fenton processes. H2O2 dose was found as 726.18 and 894.07 mg/L and removal efficiencies were achieved 86.50 and 87.49% for classical Fenton and photo-Fenton, respectively. As a result, it will be possible to simulate and improve the different Fenton processes and determine the optimum process parameters by the obtained data in the treatment of wastewater with similar characteristics without many experiments, which are difficult and costly.

Bertalanffy Model Reflects Growth Trajectory in Aseel Chicken

Aseel, a popular breed of native chicken, characterized by its pugnacity, fighting strength and royal gait is being used to create crosses for domestic chicken production. However, information on its growth models is scanty. An experiment was conducted to evaluate different non-linear models and to find out best fitting model in Aseel, being maintained at Central Avian Research Institute, Izatnagar, Bareilly. Data on body weights from 12-weeks of age to 20-weeks of age at biweekly intervals were recorded on a random bred single-hatched flock. Owing to the non-linear characteristic of growth, three non-linear models namely, Gompertz, Bertalanffy and Logistic models were evaluated. Goodness of fit for all the models were checked using coefficient of determination (R2), adjusted coefficient of determination (Adj-R2), mean square error (MSE), mean absolute error (MAE) and Akaike information criterion (AIC). The Bertalanffy model most accurately characterized the growth trend in males, females and pooled sex data. The study revealed that this model may be used to ascertain the average body weights in Aseel chicken under random mating. The investigation has generated baseline data on growth modelling of random bred groups and may be used in similar investigations on other native chicken breeds.

From traditional to modern methods: Comparing and introducing the most powerful model for forecasting the residential natural gas demand

Natural gas demand forecasting is of great importance for politicians and authorities, specifically in the residential sector, as a substantial percentage of residential energy consumption is related to natural gas. In order to find the best forecasting model, comparing different methods in each particular problem is crucial to evaluate the power of various models and find the most accurate model. This article has selected multi-layer perceptron and support vector machine as two neural network models and vector autoregression and multivariate generalized autoregressive conditional heteroskedastic as two econometric models to forecast monthly natural gas demand in the residential sector of Tehran province for 24 months, from the beginning of March 2019 to the end of February 2021. Six variables were used, including weather variables which strongly affect natural gas consumption and improve the accuracy of forecasts. Data for the mentioned variables have been obtained monthly from the National Iranian Gas Company and tutiempo.net from March 2003 till February 2021. The proposed models’ results have been compared using mean square error criterion (MSE), root mean square error criterion (RMSE) and Mean Absolute Percentage Error criterion (MAPE). The research findings confirm that multi-layer perceptron model had the best performance among four proposed models with the lowest measure of error.

Effective artificial neural network-based wind power generation and load demand forecasting for optimum energy management

The variability of power production from renewable energy sources (RESs) presents serious challenges in energy management (EM) and power system stability. Power forecasting plays a crucial role in optimal EM and grid security. Then, accurate power forecasting ensures optimum scheduling and EM. Therefore, this study proposes an artificial neural network- (ANN-) based paradigm to predict wind power (WP) generation and load demand, where the meteorological parameters, including wind speed, temperature, and atmospheric pressure, are fed to the model as inputs. The normalized root mean square error (NRMSE) and normalized mean absolute error (NMAE) criteria are used to evaluate the forecasting technique. The performance of ANN was compared to four machine learning methods: LASSO, decision tree (DT), regression vector machines (RVM), and kernel ridge regression (KRR). The obtained results show that ANN provides high effectiveness and accuracy for WP forecasting. Furthermore, ANN has proven to be an interesting tool in ensuring optimum scheduling and EM.

Forecasting time trends of fatal motor vehicle crashes in Iran using an ensemble learning algorithm

Objective This study aimed to introduce the random forest (RF) method as a valuable tool for short-term crash frequency prediction. Besides, the study compares the forecast efficiency of the RF model with the classical seasonal autoregressive integrated moving average (SARIMA) model in the multivariate time-series analysis of crash counts. Methods To this end, fatal accidents reported by the police and intercity traffic flow extracted from the loop detectors were aggregated in intercity highways at the country’s level monthly from Farvardin 1395 to Mordad 1400. The first 55 data points were used as the training sample, and the remaining ten months were considered the test sample. The Box-Jenkins and random forest machine learning methods were employed for short-term crash frequency prediction. The mean absolute percentage error (MAPE) criterion was utilized to compare the forecast accuracy of the developed models. Results The performance of the random forest model (MAPE = 2.6) with the exogenous variables of traffic flow, crash year, and month outperformed the best SARIMA ( 1 , 0 , 0 ) ( 1 , 0 , 0 ) 12 model (MAPE = 5.7) with traffic flow as the regressor. Conclusions This study suggests that the random forest as an ensemble learning algorithm is a better crash prediction tool compared to the classical Box-Jenkins method, accounting for the non-linear dependencies in crash count time-series. Besides, the results illustrate that the multivariate SARIMA (SARIMAX) model significantly outperforms its univariate counterpart, accounting for the simultaneous impacts of exogenous variables.

Maximum Likelihood Estimates and a survival function for fuzzy data for the Weibull-Pareto parameters

In this work, we discuss the estimation parameters and the reliability function for Weibull –Pareto distribution by maximum likelihood method. For that we used two iterative approximation techniques called “Newton-Raphson (NR)” and “Fisher’s Scoring (FS) method” when the available data is in the form of fuzzy numbers. The parameters’ and reliability function’s estimates are numerically contrasted using a Monte-Carlo simulation study in terms of their mean square error (MSE), integrated mean square error (IMSE), and mean absolute proportional error (MAPE) criteria. The FS technique outperforms the NR method in simulations, according to the empirical data.

2D-QSAR and Molecular Docking Studies of N-Substituted Rhodanine Derivatives as Potent Cytotoxic Agents

HER2 positive breast cancer is most common malignancy in women globally. Aberrant behavior of HER2 kinase protein is a hallmark of tumorigenesis, and as a result it has been considered as an emerging potential drug target for breast cancer therapy. In the present investigation a series of N-substituted rhodanine derivatives (33 compounds) were subjected to 2D-QSAR studies with the aid of genetic algorithm (GA) method to identify the essential structural features that responsible for cytotoxic activity. Based on the results, the cytotoxic activity of N-substituted rhodanine derivatives can be successfully explained in terms of twodimensional (ATSC7v), and three-dimensional (geomRadius and RDF45i) descriptors. The obtained model was vigorously validated and passed all validation metrics (R2train = 0.913, R2adj = 0.899, Q2LOO = 0.870, R2test = 0.848). Importantly, the model quality was good based on mean absolute error (MAE) criteria and the results were consistent with proposed limits by Golbraikh and Tropsha. Molecular docking study of the active compounds (ligands 4, 16, and 27) revealed the formation of hydrogen and hydrophobic interactions within the active site of HER2 protein. Keywords: HER2 proteins, breast cancer, cytotoxic agents, rhodanine, molecular docking, 2DQSAR.

Mammalian eye gene expression using support vector regression to evaluate a strategy for detecting human eye disease

Background and purpose: Machine learning is a class of modern and strong tools that can solve many important problems that nowadays Humans may be faced with. Support Vector Regression (SVR) is a way to build a regression model which is an incredible member of the machine learning family. SVR has been proven to be an effective tool in real-value function estimation. As a supervised-learning approach, SVR trains using a symmetrical loss function, which equally penalizes high and low misestimates. Recently, high-dimensional datasets are the most challenging problem that may be faced. The main problems in high-dimensional data are the estimation of the coefficients and interpretation. In the high-dimension problems, classical methods are not applicable because of a large number of predictor variables. SVR is an excellent alternative method to analyze such datasets. One of the main advantages of SVR is that its computational complexity does not depend on the dimensionality of the input space. Additionally, it has excellent generalization capability, with high prediction accuracy.
 Methods: SVR is one of the best methods to analyze high-dimensional datasets. It is a really reliable and robust approach to have a good fit with high accuracy. SVR uses the same principles as the support vector machine for classification, with only a few minor differences.
 Results: The techniques for analyzing the high-dimension datasets are really important methods because we frequently face such datasets in medical science and gene expression. It is not easy to analyze the high-dimension datasets because the classic methods cannot be used to estimate and interpret them. Therefore, we have to use alternative methods to analyze them. SVR is one of the best methods that can be applied. In this research, SVR is used in a real high-dimension dataset about the gene expression in eye disease, and then it is compared with well-known methods: LASSO and Sparse least trimmed squared (sparse LTS) methods. Based on the numerical result, SVR and Sparse LTS were better than LASSO, since the real dataset contained outliers (bad observation with big residuals).
 Conclusions: SVR method was the best method to model and predict the high-dimensional mammalian eye dataset, because it was not affected by the outliers' corruptive impact, and it has minimum MSE (mean squares error), MAE (mean absolute error) and RMSE (root mean squared error) fitting criteria in comparison with the classical methods such as LASSO and sparse LTS estimations. Thus, sparse LTS was found to act better than the LASSO method. Moreover, stabilization of the data and freedom from obtaining the regularization parameter by running a complicated algorithmic program, which decreased the computational costs dramatically, were the invaluable advantages of this technique in comparison with the classical methods.

Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self-Compacting Concrete with Class F Fly Ash.

Replacing a specified quantity of cement with Class F fly ash contributes to sustainable development and reducing the greenhouse effect. In order to use Class F fly ash in self-compacting concrete (SCC), a prediction model that will give a satisfactory accuracy value for the compressive strength of such concrete is required. This paper considers a number of machine learning models created on a dataset of 327 experimentally tested samples in order to create an optimal predictive model. The set of input variables for all models consists of seven input variables, among which six are constituent components of SCC, and the seventh model variable represents the age of the sample. Models based on regression trees (RTs), Gaussian process regression (GPR), support vector regression (SVR) and artificial neural networks (ANNs) are considered. The accuracy of individual models and ensemble models are analyzed. The research shows that the model with the highest accuracy is an ensemble of ANNs. This accuracy expressed through the mean absolute error (MAE) and correlation coefficient (R) criteria is 4.37 MPa and 0.96, respectively. This paper also compares the accuracy of individual prediction models and determines their accuracy. Compared to theindividual ANN model, the more transparent multi-gene genetic programming (MGPP) model and the individual regression tree (RT) model have comparable or better prediction accuracy. The accuracy of the MGGP and RT models expressed through the MAE and R criteria is 5.70 MPa and 0.93, and 6.64 MPa and 0.89, respectively.

Efficiency Comparisons of Robust and Non-Robust Estimators for Seemingly Unrelated Regressions Model

This paper studies and reviews several procedures for developing robust regression estimators of the seemingly unrelated regressions (SUR) model, when the variables are affected by outliers. To compare the robust estimators (M-estimation, S-estimation, and MM-estimation) with non-robust (traditional maximum likelihood and feasible generalized least squares) estimators of this model with outliers, the Monte Carlo simulation study has been performed. The simulation factors of our study are the number of equations in the system, the number of observations, the contemporaneous correlation among equations, the number of regression parameters, and the percentages of outliers in the dataset. The simulation results showed that, based on total mean squared error (TMSE), total mean absolute error (TMAE) and relative absolute bias (RAB) criteria, robust estimators give better performance than non-robust estimators; specifically, the MM-estimator is more efficient than other estimators. While when the dataset does not contain outliers, the results showed that the unbiased SUR estimator (feasible generalized least squares estimator) is more efficient than other estimators.

Classical Forecasting of International Tourist Arrivals to Thailand

The objectives of this work are to find the suitable forecasting model and forecasting period of the number of foreign tourists traveling to Thailand. The monthly data is gathered during January 2008 to December 2019 and is divided into two sets. The first set is the data from January 2008 to December 2018 for the modelling by the method of decomposition, Holt–Winter’s exponential smoothing method and the Box–Jenkins. The second is the monthly data in 2019 for comparing the performance of the forecasting models via the criteria of the lowest mean absolute percentage error (MAPE) and the root mean square error (RMSE). The results show that, in term of forecasting, the multiplicative decomposition is the most accurate technique for the short-term (3 months) forecasting period with the lowest MAPE and RMSE of 1.04% and 42,054.29 international tourists, respectively.

A modified one parameter Liu estimator for Conway-Maxwell Poisson response model

The maximum likelihood estimator (MLE) is generally used to estimate the Conway Maxwell Poisson regression model (COMPRM). When the explanatory variables are highly correlated, then the MLE results are not valid. In this study, we proposed a modified one-parameter Liu estimator in the presence of multicollinearity among the regressors for the COMPRM. The theoretical properties of the proposed estimator are derived and compared it with the available biased estimators as well as the MLE based on the matrix mean squared error (MSE) and scalar MSE criteria. To investigate the efficiency of the proposed estimator, a Monte Carlo simulation analysis was performed under various controlled conditions. Finally, two real applications are considered in the superiority of the proposed estimator. The simulation and real applications results show that the proposed estimator outperforms the classical MLE and other biased estimators in terms of the minimum MSE and mean absolute error criterion .

3D Multiple Sound Source Localization by Proposed T-Shaped Circular Distributed Microphone Arrays in Combination with GEVD and Adaptive GCC-PHAT/ML Algorithms.

Multiple simultaneous sound source localization (SSL) is one of the most important applications in the speech signal processing. The one-step algorithms with the advantage of low computational complexity (and low accuracy), and the two-step methods with high accuracy (and high computational complexity) are proposed for multiple SSL. In this article, a combination of one-step-based method based on the generalized eigenvalue decomposition (GEVD), and a two-step-based method based on the adaptive generalized cross-correlation (GCC) by using the phase transform/maximum likelihood (PHAT/ML) filters along with a novel T-shaped circular distributed microphone array (TCDMA) is proposed for 3D multiple simultaneous SSL. In addition, the low computational complexity advantage of the GCC algorithm is considered in combination with the high accuracy of the GEVD method by using the distributed microphone array to eliminate spatial aliasing and thus obtain more appropriate information. The proposed T-shaped circular distributed microphone array-based adaptive GEVD and GCC-PHAT/ML algorithms (TCDMA-AGGPM) is compared with hierarchical grid refinement (HiGRID), temporal extension of multiple response model of sparse Bayesian learning with spherical harmonic (SH) extension (SH-TMSBL), sound field morphological component analysis (SF-MCA), and time-frequency mixture weight Bayesian nonparametric acoustical holography beamforming (TF-MW-BNP-AHB) methods based on the mean absolute estimation error (MAEE) criteria in noisy and reverberant environments on simulated and real data. The superiority of the proposed method is presented by showing the high accuracy and low computational complexity for 3D multiple simultaneous SSL.

Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia.

With many countries experiencing a resurgence in COVID-19 cases, it is important to forecast disease trends to enable effective planning and implementation of control measures. This study aims to develop Seasonal Autoregressive Integrated Moving Average (SARIMA) models using 593 data points and smoothened case and covariate time-series data to generate a 28-day forecast of COVID-19 case trends during the third wave in Malaysia. SARIMA models were developed using COVID-19 case data sourced from the Ministry of Health Malaysia’s official website. Model training and validation was conducted from 22 January 2020 to 5 September 2021 using daily COVID-19 case data. The SARIMA model with the lowest root mean square error (RMSE), mean absolute percentage error (MAE) and Bayesian information criterion (BIC) was selected to generate forecasts from 6 September to 3 October 2021. The best SARIMA model with a RMSE = 73.374, MAE = 39.716 and BIC = 8.656 showed a downward trend of COVID-19 cases during the forecast period, wherein the observed daily cases were within the forecast range. The majority (89%) of the difference between the forecasted and observed values was well within a deviation range of 25%. Based on this work, we conclude that SARIMA models developed in this paper using 593 data points and smoothened data and sensitive covariates can generate accurate forecast of COVID-19 case trends.

Deep Learning–Based Forecasting of COVID-19 in India

Abstract During the past two decades, the world has confronted many pandemic disease outbreaks. Ebola, severe acute respiratory syndrome, Middle East respiratory syndrome, and, recently, coronavirus disease (COVID-19) have had a massive global impact in terms of stress on local and global human health, economic destruction, and, above all, damage to usual human life. Analyzing past similar infections will help in drawing inferences such as maintaining social distancing, herd immunity, and vaccinating massively to go forward beyond this pandemic. The development of a forecasting model of COVID-19 infectious disease spreading rate plays a vital role in the future preparation of hospital facilities, such as setting up isolated wards, oxygen cylinders, and ventilators, etc., for future patients by the government. Also, the forecasting technique and model is in immediate need for us to understand and face the effect of this and future pandemics. The main objective of this work is to develop an intelligent model based on deep learning for forecasting or estimating COVID-19 future spreading rate in terms of confirmed, recovered, and deceased cases of 85 days in 4 states in India and India overall. Deep learning neural networks, a kind of machine learning technique, are a powerful tool to predict the future because of their nature of discovering complex nonlinear dependencies. A deep learning long short-term memory (LSTM) network, which is explicitly designed for learning long-term dependencies, is utilized in this work. Hence, one can predict 1 day ahead to any number of (up to 400) days ahead by using this model. To evaluate the performance of the deep learning forecasting model and to endorse its forecasting accuracy, the criteria of mean absolute error, mean square error, root mean square error, mean absolute percentage error, and Ro are used. The results of the proposed deep learning–based LSTM model are validated by statistical analysis and graphical analysis. Moreover, the proposed model exhibited superior forecasting accuracy.

Solar photovoltaic power prediction using different machine learning methods

The main aim of the present study is to explore the relationship between numerous input parameters and the solar photovoltaic (PV) power using machine learning (ML) models. Two different ML approaches such as support vector machine (SVM) and Gaussian process regression (GPR) were considered and compared. The basic input parameters including solar PV panel temperature, ambient temperature, solar flux, time of the day and relative humidity were considered for predicting the solar PV power. The results showed that among the proposed ML approaches, Matern 5/2 GPR algorithm provided the optimal performance; whereas cubic SVM had the worst performance. Furthermore, the predicted output results are in good agreement with the experimental values, indicating that the proposed ML approaches are appropriate for use in predicting the power of different solar PV panel. Additionally, to showcase the effectiveness and the accuracy of SVM and GPR models in forecasting solar PV power, the results of these models are compared using root mean squared error (RMSE) and mean absolute error (MAE) criteria.

QSAR Analysis of Quinazolinyl-arylurea Derivatives as Potential Anti- Cancer Agents: GA-MLR Chemometric Approach

Background: Cancer is the most common malignancy in men and women globally. The tyrosine kinases and serine/threonine kinases are essential to cell mediators for extra & intracellular signal transduction processes and play a key role in cell proliferation, differentiation, migration, metabolism, and programmed cell deaths. In this context, kinases are considered as a potential drug target for cancer therapy. Methods: In the present study, a two-dimensional (2D) quantitative structure-activity relationship (2D-QSAR) was performed to analyze anticancer activities of 28 quinazolinyl-arylurea (QZA) derivatives based on the liver (BEL-7402), stomach (MGC-803), and colon (HCC-827) cancer cell lines using multiple linear regression (MLR) analysis. It was accomplished using 2D-QSAR analysis on the available IC50 data of 28 molecules based on theoretical molecular descriptors to develop predictive models that correlate structural features of QZA derivatives to their anticancer activities. A suitable set of molecular descriptors, such as constitutional, topological, geometrical, electrostatic, and quantum-chemical descriptors were calculated to represent the structural features of compounds. The genetic algorithm (GA) method was used to identify the important molecular descriptors to build the QSAR models and used to predict the anti-cancer activities. Results and Discussion: The obtained 2D-QSAR models were vigorously validated using various statistical metrics using leave-one-out (LOO) and external test set prediction approaches. The best predictive models by MLR gave highly significant square of correlation coefficient (R2 train) values of 0.799, 0.815, and 0.779 for the training set, and the correlation coefficients (R2 test) were obtained 0.885, 0.929, and 0.774 for the test set for the liver, stomach, and colon cancer cell lines. The models also demonstrated good predictive power confirmed by the high value of cross-validated correlation coefficient Q2 value of 0.663, 0.717, and 0.671 for three different cancer cell lines. Importantly, the model's quality was judged as well based on mean absolute error (MAE) criteria, and the results were consistent with proposed limits by Golbraikh and Tropsha. Conclusion: The QSAR results of the study indicated that the proposed models were robust and free from chance correlation. This study indicated that maxHBint7, SpMax8_Bhm, and ETA_Beta_ns_d have positively contributed descriptors for anti-cancer activity in the liver, stomach, and colon cancer cell lines and a detailed mechanistic interpretation of each model revealed important structural features that were responsible for favorable or unfavorable for anticancer activity. The predictive ability of the proposed models was good and may be useful for developing more potent quinazolinyl-arylurea compounds as anti-cancer agents.

Comparison Bennett's Inequality and Regression in Determining the Optimum Sample Size for Estimating the Net Reclassification Index (NRI) Using Simulation

Researchers have increased interest in recent years in determining the optimum sample size to obtain sufficient accuracy and estimation and to obtain high-precision parameters in order to evaluate a large number of tests in the field of diagnosis at the same time. In this research, two methods are used to determine the optimum sample size to estimate the parameters of high-dimensional data. These methods are the Bennett inequality method and the regression method. The nonlinear logistic regression model is estimated by the size of each sampling method in high-dimensional data using artificial intelligence, which is the method of artificial neural network (ANN) as it gives a high-precision estimate commensurate with the data type and type of medical study. The probabilistic values obtained from the artificial neural network are used to calculate the net reclassification index (NRI). A program is written for this purpose using the statistical programming language (R), where the mean maximum absolute error criterion (MME) of the net reclassification network index (NRI) was used to compare the methods of specifying the sample size and the presence of the number of different default parameters in light of the value of a specific error margin (ε). To verify the performance of the methods using the comparison criteria above, the most important conclusions are that the Bennett inequality method is the best in determining the optimum sample size according to the number of default parameters and the error margin value