Computing single step operators of logic programming in radial basis function neural networks

Hamadneh, Nawaf N.; Sathasivam, Saratha; Choon, Ong Hong

doi:10.1063/1.4887632

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…We use a number of accuracy criteria including the mean absolute percentage error (MAPE), the mean absolute error (MAE), the mean error (ME), and the root mean squared error (RMSE) [38,39] as follows:…”

Section: Performance Measuresmentioning

confidence: 99%

Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions

Alenezy

Ismail

Wadi

et al. 2021

Journal of Mathematics

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This study aims to model and enhance the forecasting accuracy of Saudi Arabia stock exchange (Tadawul) data patterns using the daily stock price indices data with 2026 observations from October 2011 to December 2019. This study employs a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions, namely, Haar, Daubechies (Db), Least Square (LA-8), Best localization (BL14), and Coiflet (C6) in conjunction with adaptive network-based fuzzy inference system (ANFIS). We have selected oil price (Loil) and repo rate (Repo) as input values according to correlation, the Engle and Granger Causality test, and multiple regressions. The input variables in this study have been collected from Saudi Authority for Statistics and Saudi Central Bank. The output variable is obtained from Tadawul. The performance of the proposed model (MODWT-LA8-ANFIS) is evaluated in terms of mean error (ME), root mean square error (RMSE), and mean absolute percentage error (MAPE). Also, we have compared the MODWT-LA8-ANFIS model with traditional models, which are autoregressive integrated moving average (ARIMA) model and ANFIS model. The obtained results show that the performance of MODWT-LA8-ANFIS is better than that of the traditional models. Therefore, the proposed forecasting model is capable of decomposing in the stock markets.

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Section: Performance Measuresmentioning

confidence: 99%

Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions

Alenezy

Ismail

Wadi

et al. 2021

Journal of Mathematics

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“…Particles (parameters) in PSO determine their new location by following the current optimum particle in the problem space. It has been found to be effective while applied to various optimization problems including artificial neural networks [31], mechanical engineering design optimization problems [32][33][34], and chaotic systems [35][36][37]. Moreover, it is easy for it to achieve high accuracy with fast converging speed [38,39].…”

Section: Particle Swarm Optimization Algorithm Pso Firstmentioning

confidence: 99%

“…Moreover, it is easy for it to achieve high accuracy with fast converging speed [38,39]. It has been widely used in many real-life optimization problems of different domains [31,33,34]. In this study, we have used the PSO algorithm to determine the optimal parameter values of our RBFNN model in order to find the minimum value of mean squared error (MSE).…”

Section: Particle Swarm Optimization Algorithm Pso Firstmentioning

confidence: 99%

Application of Radial Basis Function Neural Network Coupling Particle Swarm Optimization Algorithm to Classification of Saudi Arabia Stock Returns

Rashedi

Ismail

Hamadneh

et al. 2021

Journal of Mathematics

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Artificial intelligence (AI) based business process optimization has a significant impact on a country’s economic development. We argue that the use of artificial neural networks in business processes will help optimize these processes ensuring the necessary level in the functioning and compliance with the foundations of sustainable development. In this paper, we proposed a mathematical model using AI to detect outliers in the daily return of Saudi stock market (Tadawul). An outlier is defined as a data point that deviates too much from the rest of the observations in a data sample. Based on the Engle and Granger Causality test, we selected inflation rate, repo rate, and oil prices as input variables. In order to build the mathematical model, we first used the Tukey method to detect outliers in the stock return data from Tadawul that are collected during the period from October 2011 to December 2019. In this way, we categorized the stock return data into two classes, namely, outliers and nonoutliers. These data are further used to train artificial neural network in conjunction with particle swarm optimization algorithm. In order to assess the performance of the proposed model, we employed the mean squared error function. Our proposed model is signified by the mean squared error value of 0.05. The proposed model is capable of detecting outlier values directly from the inflation rate, repo rate, and oil prices. The proposed model can be helpful in developing and applying intelligent optimization techniques to solve problems in business processes.

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“…The supervised machine learning algorithms generally perform superior compared to unsupervised machine learning algorithms [123]. However, the major drawback associated with the supervised machine learning algorithms is the requirement of labelled data, which is not readily available.…”

Section: Limitations Of Available Solutions and Future Recommendationsmentioning

confidence: 99%

Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review

et al. 2020

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Electricity theft and fraud in energy consumption are two of the major issues for power distribution companies (PDCs) for many years. PDCs around the world are trying different methodologies for detecting electricity theft. The traditional methods for non-technical losses (NTLs) detection such as onsite inspection and reward and penalty policy have lost their place in the modern era because of their ineffective and time-consuming mechanism. With the advancement in the field of Artificial Intelligence (AI), newer and efficient NTL detection methods have been proposed by different researchers working in the field of data mining and AI. The AI-based NTL detection methods are superior to the conventional methods in terms of accuracy, efficiency, time-consumption, precision, and labor required. The importance of such AI-based NTL detection methods can be judged by looking at the growing trend toward the increasing number of research articles on this important development. However, the authors felt the lack of a comprehensive study that can provide a one-stop source of information on these AI-based NTL methods and hence became the motivation for carrying out this comprehensive review on this significant field of science. This article systematically reviews and classifies the methods explored for NTL detection in recent literature, along with their benefits and limitations. For accomplishing the mentioned objective, the opted research articles for the review are classified based on algorithms used, features extracted, and metrics used for evaluation. Furthermore, a summary of different types of algorithms used for NTL detection is provided along with their applications in the studied field of research. Lastly, a comparison among the major NTL categories, i.e., data-based, network-based, and hybrid methods, is provided on the basis of their performance, expenses, and response time. It is expected that this comprehensive study will provide a one-stop source of information for all the new researchers and the experts working in the mentioned area of research.

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Computing single step operators of logic programming in radial basis function neural networks

Cited by 4 publications

References 9 publications

Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions

Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions

Application of Radial Basis Function Neural Network Coupling Particle Swarm Optimization Algorithm to Classification of Saudi Arabia Stock Returns

Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review

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