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Recent increases in global food demand have made this research and, therefore, the prediction of agricultural commodity prices, almost imperative. The aim of this paper is to build efficient artificial intelligence methods to effectively forecast commodity prices in light of these global events. Using three separate, well-structured models, the commodity prices of eleven major agricultural commodities that have recently caused crises around the world have been predicted. In achieving its objective, this paper proposes a novel forecasting model for agricultural commodity prices using the extreme learning machine technique optimized with the genetic algorithm. In predicting the eleven commodities, the proposed model, the extreme learning machine with the genetic algorithm, outperforms the model formed by the combination of long short-term memory with the genetic algorithm and the autoregressive integrated moving average model. Despite the fluctuations and changes in agricultural commodity prices in 2022, the extreme learning machine with the genetic algorithm model described in this study successfully predicts both qualitative and quantitative behavior in such a large number of commodities and over such a long period of time for the first time. It is expected that these predictions will provide benefits for the effective management, direction and, if necessary, restructuring of agricultural policies by providing food requirements that adapt to the dynamic structure of the countries. [ABSTRACT FROM AUTHOR]

The thermal efficiency for the pelletizing process is intrinsically linked to the diameter and humidity of the iron ore pellets, so that the sensing of the granulometric range in the formation of the pellets becomes essential to the flow of the pelletizing process in the steel industry; this paper presents the assembly of a computer vision system for the detection and segmentation of pellets aiming at the automation of the granulometric measurement, following its formation in the pelletizing disk, using the instance segmentation method to verify whether the particle granulometric distribution (PSD) is adequate for "real-time" applications. The system calculates the normal distribution of the diameter in millimeters, evaluating the normal curve and the standard deviation of the segmented pellets, using a deep neural network based on the You Only Look At CoefficienTs (YOLACT) network, adding speed and precision in the granulometric analysis. In the sample sets, the need for adjustment factors inherent to the pelletizing process became evident. This led to the establishment of the computer vision system, termed the Volumetric Correction Factor (VCF) and Visual Overlay Factor (VOF). The VCF is utilized to estimate the volume of pellets within the pelletizing disk during operation, while the VOF adjusts the millimeter-per-pixel (mpp) ratio. The results of the measurement system proved to be efficient in real-time granulometric measurement. [ABSTRACT FROM AUTHOR]

Bubble point oil formation volume factor (Bob) and solution gas–oil ratio (Rs) are two crucial PVT parameters used for modeling and volumetric calculations in petroleum industry. They are usually determined in laboratory or estimated using empirical correlations. Experimental methods are time-consuming and expensive where empirical correlations have limitations. Artificial intelligence can be sued overcome these limitations to develop more accurate, robust, and quick predictive tools. In this paper, we used three artificial neural network algorithms to develop intelligent models to predict Bob and Rest using 465 experimental data. Application of the Elman neural network (ENN) for this purpose is being reported for the first time. A variety of input parameters were selected based on a sensitivity analysis which include reservoir temperature (T), oil API gravity (°API), bubble point pressure (Pb), gas-specific gravity (γg), and Rs was used to predict the Bob. T, °API, Pb, γg, and Bob was used to predict the Rs. The ENN model was found superior to the other developed smart models and the empirical correlations with coefficient of determination (R2) of 0.993, root mean square error (RMSE) of 0.0093, and average absolute percent relative error (AAPRE) of 0.93% for the Bob and 0.999, 0.016, and 6.72% for the Rs, respectively. The ENN network has fewer adjustable parameters and provides faster training capabilities using fewer neurons and hidden layers compared to other ANN algorithms. The developed smart predictive tools can be safely used instead of laboratory methods and empirical correlations for a much wider ranges of input parameters and with higher accuracy and confidence. [ABSTRACT FROM AUTHOR]

In 2022, the World Health Organization declared an outbreak of monkeypox, a viral zoonotic disease. With time, the number of infections with this disease began to increase in most countries. A human can contract monkeypox by direct contact with an infected human, or even by contact with animals. In this paper, a diagnostic model for early detection of monkeypox infection based on artificial intelligence methods is proposed. The proposed method is based on training the artificial neural network (ANN) with the adaptive artificial bee colony algorithm for the classification problem. In the study, the ABC algorithm was preferred instead of classical training algorithms for ANN because of its effectiveness in numerical optimization problem solutions. The ABC algorithm consists of food and limit parameters and three procedures: employed, onlooker and scout bee. In the algorithm standard, artificial onlooker bees are produced as much as the number of artificially employed bees and an equal number of limit values are assigned for all food sources. In the advanced adaptive design, different numbers of artificial onlooker bees are used in each cycle, and the limit numbers are updated. For effective exploitation, onlooker bees tend toward more successful solutions than the average fitness value of the solutions, and limit numbers are updated according to the fitness values of the solutions for efficient exploration. The performance of the proposed method was investigated on CEC 2019 test suites as examples of numerical optimization problems. Then, the system was trained and tested on a dataset representing the clinical symptoms of monkeypox infection. The dataset consists of 240 suspected cases, 120 of which are infected and 120 typical cases. The proposed model's results were compared with those of ten other machine learning models trained on the same dataset. The deep learning model achieved the best result with an accuracy of 75%. It was followed by the random forest model with an accuracy of 71.1%, while the proposed model came third with an accuracy of 71%. [ABSTRACT FROM AUTHOR]

As a common data-driven method, artificial neural networks have been widely used in electricity spot price forecasting. To improve the accuracy of short-term forecasts, this paper proposes an optimized artificial neural network model for monthly electricity spot prices forecasting. A genetic algorithm is applied to regulate the weights and biases parameters of the artificial neural network structure. This study uses various historical dataset at monthly periods selected from Nordic electricity spot prices. For efficiency comparison, one-step ahead forecast method based on Schwartz-Smith stochastic model and two other prediction models, artificial neural network trained by Levenberg–Marquardt and particle swarm optimization algorithms are also presented. The comparison results show that the prediction model based on the genetic optimization algorithm is more accurate than the other prediction models. The proposed forecasting model can be considered as an alternative technique for the electricity spot price forecasting in the Nordic regions. [ABSTRACT FROM AUTHOR]

To tackle the complex problem of providing business intelligence solutions based on business data, bioinspired deep learning has to be considered. This paper focuses on the application of artificial metaplasticity learning in business intelligence systems as an alternative paradigm of achieving a deeper information extraction and learning from arbitrary size data sets. As a case study, artificial metaplasticity multilayer perceptron applied to the automation of credit approval decision based on collected client data is analyzed, showing its potential and improvements over the state-of-the-art techniques. This paper successfully introduces the relevant novelty that the artificial neural network itself estimates the pdf of the input data to be used in the metaplasticity learning, so it is much closer to the biologic reality than previous implementations of artificial metaplasticity. [ABSTRACT FROM AUTHOR]

In recent years, remote data collection and remote monitoring technologies based on artificial neural networks have been increasingly used in various industries. In order to in-depth study whether the data collection system based on artificial neural network theory can analyze the characteristics of different strokes in swimming events, this paper uses simulation comparison method, data integration method, and step-by-step construction method to collect samples, analyze the data collection system, and streamline the algorithm. And integrate and create a data collection system that can analyze the characteristics of swimming styles. After constructing the system, select the image frequency 450 ms once, and set the signal frequency to 2.5 KHZ. Set the waveforms to sawtooth wave and sine wave, respectively; the voltage range of sine wave is − 6 to 8 V, and the sampling frequency is 250. The voltage range of the sawtooth wave is − 8 to 6 V, and the sampling frequency is 45 KHZ. Experiments show that the system basically works normally during the sampling process. To further study the stability of the system, this test is in a swimming pool with a one-story building with a relative humidity of 85%. It is set to send 110 data frames from the coordinator segment to the normal segment every 2.5 s. The acquisition success rate is 88% when there is interference and 96% when there is no interference, which is much higher than that when there is interference. Therefore, a retransmission mechanism must be used when designing the software for common segment points to ensure reliable data transmission. In general, the data acquisition system we designed basically meets the design standards. It is basically realized that starting from the artificial neural network, a data acquisition system that can analyze swimming styles is designed. [ABSTRACT FROM AUTHOR]

Permeability is an important parameter for oil and gas reservoir characterization. Permeability can be traditionally determined by well testing and core analysis. These conventional methods are very expensive and time-consuming. Permeability estimation in heterogeneous carbonate reservoirs is a challenge task to be handled accurately. Many researches tried to relate permeability and reservoir properties using complex mathematical equations which resulted in inaccurate estimation of the formation permeability values. Permeability prediction based on well logs using artificial intelligent techniques was presented by many authors. They used several wire-line logs such as gamma ray, neutron porosity, bulk density, resistivity, sonic, spontaneous potential, hole size, depths, and other logs. The objective of this paper is to develop an artificial neural network (ANN) model that can be used to predict the permeability of heterogeneous reservoir based on three logs only, namely resistivity, bulk density, and neutron porosity. In addition to the ANN model, in this paper and for the first time a mathematical equation from the ANN model will be extracted that can be used for permeability prediction for any data set without the need for the ANN model. Also, in this study and for the first time we introduced a new term which is the mobility index that can be used effectively in the permeability prediction. Mobility index term is derived from the mobile oil saturation that occurred due to the drilling fluid filtrate invasion. The obtained results showed that ANN model gave a comparable results with support vector machine and adaptive neuro-fuzzy inference system model. The developed mathematical equation from ANN model can be used to estimate the permeability for heterogamous carbonate reservoir based only on three parameters: bulk density, neutron porosity, and mobility index. Actual core data points (1223 points) with the three logs were used to train (857 data points, 70% of the data) and test the model for unseen data (366 data points, 30% of the data). The correlation coefficient for training and testing was 0.95, and the root-mean-square error was 0.28. The developed mathematical equation will help the engineers to save time and predict the permeability with a high accuracy using inexpensive technique. Introducing the new parameter, mobility index, in the prediction process greatly improved the permeability prediction from the log data compared to the actual measured data. [ABSTRACT FROM AUTHOR]

Under specific terrain and climatic conditions, it is extremely easy to cause various types of geological hazards, and the occurrence of geological hazards will affect people's production and life, with the consequence that the economic losses are extremely great, and even severely endanger human life. However, the existing geological hazard danger zoning is slightly insufficient in accuracy and operating efficiency, and the effect in practical application needs to be further improved. In view of the above problems, this paper proposes a study on the application of GIS-based artificial neural network models in the geological disaster risk zoning. This article first expounds the related concepts of geological hazards zoning and gives the principles to be followed. Then, the calculation of evaluation factors and risk level probabilities are proposed, and a hierarchical model is constructed using the analytic hierarchy process. The weight of each evaluation factor is multiplied with the information to obtain the weighted information. Calculate the probability based on the topographic features, stratigraphic lithology, and terrain slope. Combine the artificial neural network with BP neural network to predict the results. Through the simulation experiments on the geological data of the low-mountain and hilly areas in Wanli District, Nanchang, the results show that the proportion of high-probability-prone areas and medium-probability-prone areas predicted by this paper is as high as 91.87%, and the evaluation results are consistent with the actual disaster occurrence better. The area under the ROC curve was 87.3%, which also verified the effectiveness of the method in this paper. [ABSTRACT FROM AUTHOR]

This paper deals with modelling ship speed and fuel consumption using artificial neural network (ANN) techniques. These tools allowed us to develop ANN models that can be used for predicting both the fuel consumption and the travel time to the destination for commanded outputs (the ship driveline shaft speed and the propeller pitch) selected by the ship operator. In these cases, due to variable environmental conditions, making decisions regarding setting the proper commanded outputs to is extraordinarily difficult. To support such decisions, we have developed a decision support system. Its main elements are the ANN models enabling ship fuel consumption and speed prediction. To collect data needed for building ANN models, sea trials were conducted. In this paper, the decision support system concept, input and variables of the ship driveline system models, and data acquisition methods are presented. Based on them, we developed appropriate ANN models. Subsequently, we performed a quality assessment of the collected data set, data normalization and division of the data set, selection of an ANN model architecture and assessment of their quality. [ABSTRACT FROM AUTHOR]

As a supplement to the financing of small- and medium-sized enterprises, bank microfinance companies are nonbank financial institutions, and it has played an active role in maintaining the stability of financial markets. However, in the course of the operation of microfinance companies, due to the lack of careful management and risk control, the problem of risk management has become increasingly prominent. The purpose of this paper is to study the microfinance risk based on polygenic artificial neural network, and it provides theory and practice application for risk management of microcredit enterprises. Taking the risk management of China's microfinance companies as the research object, on the basis of previous studies, this paper analyzes the risks of bank microfinance companies. Secondly, the basic theory of neural network model and its transformation function are introduced, and the learning method of neural network. At the same time, the learning algorithm of neural network and its improved algorithm are mainly introduced. It lays a theoretical foundation for the follow-up empirical research. Then, through the empirical study of data-based risk assessment of microcredit of farmers, the sample data are divided into training samples and test samples for comparison. Then, we use MATLAB software to establish a neural network model for farmers' microcredit risk assessment. Finally, in order to make the neural network model of farmers' credit risk assessment better popularize and apply, and effectively reduce the credit risk of farmers' microcredit. The corresponding policy suggestions are put forward, which proves the validity and applicability of the neural network in the field of farmers' microcredit risk assessment. It provides a good basis for rural credit cooperatives to identify the credit risk of farmers. [ABSTRACT FROM AUTHOR]

Ball mills are extensively used in the size reduction process of different ores and minerals. The fill level inside a ball mill is a crucial parameter which needs to be monitored regularly for optimal operation of the ball mill. In this paper, a vibration monitoring-based method is proposed and tested for estimating the fill level inside a laboratory-scale ball mill. A vibration signal is captured from the base of a laboratory-scale ball mill by using a ± 5 g accelerometer. Features are extracted from the vibration signal by using different transforms such as fast Fourier transform, discrete wavelet transform, wavelet packet decomposition, and empirical mode decomposition. These features are given as input to an artificial neural network which is used to predict the percentage fill level inside the ball mill. In this paper, the predicted fill level obtained by using different features are compared. It is found that the predicted fill level due to features obtained after fast Fourier transform outperforms other transforms. [ABSTRACT FROM AUTHOR]

In order to quantify the time-varying dependent structure between the assets and forecast the portfolio risk accurately, the difference in the preferences for asset risk is taken into consideration in this paper. It is assumed that the new interest rate of asset return sequence is subject to the standard t distribution. A kind of artificial neural network economic forecasting model is put forward. The two-step state transition diagram estimation method for the economic forecasting is deduced, and the forecasting method for the profile risk is obtained. Finally, Shanghai Securities Composite Index and Standard & Poor's 500 Index are selected to verify the feasibility and superiority of the model and method put forward in this paper. At the same time, the model can accurately quantify the time-varying dependent structural characteristics of the two indices after the subprime mortgage crisis. [ABSTRACT FROM AUTHOR]

In the paper, a method of automatic choice of the boundary conditions is presented. This method allows the initial estimation the value of the boundary conditions during the heating operation for the welding process. The solutions described in this paper can successfully support the work of the specialists performing numerical simulations. They also significantly reduce the time required to estimate the boundary conditions parameters consistent with the actual experiment. The accuracy of the solution in the presented method is not dependent on the empirical models of boundary conditions for the heating process. It is also much more universal. Artificial neural networks after the learning process can be used to perform a reverse analysis for the heat treatment process, for example, to determine the parameters of welding process in the production process of steel elements. [ABSTRACT FROM AUTHOR]

Sleep apnea (SA) is a common respiratory disorder, especially among obese people. It is caused by either the relaxation of the upper respiratory tract muscles or the failure of the neural signal to reach the muscles responsible for breathing, both of which interrupt the patient's sleep–wake cycles. The traditional method for diagnosing this disorder, based on polysomnography, is complicated, vexing, expensive, time-consuming, and requires both sleep centers and specialized staff capable of connecting electrodes to the patient's body. This paper proposes an SA prediction system based on merging five soft computing algorithms, specifically, combining the multi-verse optimizer (MVO) with an artificial neural network (ANN) to leverage measurements from heart rate, SpO2, and chest movement sensors. The most substantial novelty of this research is the hybridization of MVO and ANN (MVO-ANN), which improves the ANN performance by selecting the best learning rate and number of neurons in hidden ANN layers. This enables highly accurate prediction of sleep apnea events. This work's experimental results reveal that the MVO-ANN performs better than other algorithms, with mean absolute errors of 0.042, 0.202, and 0.166 for training, testing, and validation of the ANN. In addition, the SA prediction system achieved an accuracy of 98.67%, a sensitivity of 96.71%, and a specificity of 99.24%. These results provide good evidence that the proposed method can reliably predict respiratory events in people suffering from SA. [ABSTRACT FROM AUTHOR]

Recently, high-performance multi-channel microstrip filters are widely demanded by modern multi-service communication systems. Designing these filters with both compact size and low loss is a challenge for the researchers. In this paper and for the first time, we have proposed a novel method based on artificial neural network to design and simulate multichannel microstrip bandpass filters. For this purpose, the frequency, physical dimensions, and substrate parameters, i.e., type and thickness, of the BPF are selected as the inputs and the S-parameters, i.e. S11 and S21, are selected as the outputs of the proposed model. Using an accurate multilayer perceptron neural network trained with back-propagation technique, a high-performance microstrip quad-band bandpass filter (QB-BPF) is designed which has a novel compact structure consisting of meandrous spirals, coupled lines, and patch feeds. The proposed method can be easily used for designing other microstrip devices such as filters, couplers, and diplexers. The designed filter occupies a very small area of 0.0012 λg2, which is the smallest size in comparison with previously published works. It operates at 0.7, 2.2, 3.8, and 5.6 GHz for communication systems. The low insertion loss, high return losses, low group delay, and good frequency selectivity are obtained. To verify the design method and simulation results, the introduced filter is fabricated and measured. The results show an agreement between the simulation and measurement. [ABSTRACT FROM AUTHOR]

The corrosion problem in the conventional steel reinforcement in concrete structures has diverted the researchers to explore alternative materials. As a substitute to replace the traditional steel bars in reinforced concrete structures, innovative reinforcement such as fiber reinforced polymer (FRP) rebars has been suggested. Consequently, different codes and guidelines have been proposed for forecasting the shear strength of FRP reinforced members using traditional empirical methods and neural networks. The current paper concentrates on the development of a hybrid intelligence model, namely an artificial neural network articulated with a Bayesian optimization algorithm (ANN-BOA) for estimating the shear strength of these types of members without stirrups. Totally, 216 specimens, collected from the literature, were used in this analysis. The input parameters of the model were beam depth, the ratio of shear span and depth, effective reinforcement ratio, and concrete strength. The ANN hyperparameters (viz. neuron numbers in the hidden layer, learning rate) have been tuned automatically to get the best predictions. The estimated shear strengths are compared with the recent design provisions of Japan (JSCE), UK (BISE), Italy (CNR-DT 203), Canada (CHBDC, CSA S806), and the USA (ACI 440.1R). The results were also compared with a similar ANN model. It was observed that the predicted results using the proposed method are better than those of the other methods in terms of some statistical as well as performance measuring parameters with a maximum Pearson correlation coefficient (R) value of 0.97. These values are higher than the other investigated methods. [ABSTRACT FROM AUTHOR]

One of the most significant issues in machine learning is system identification with many applications, e.g., channel estimation (CE) in digital communications. Introducing a new correntropy-based method, this paper deals with the comparison between mean square error (MSE) and information theoretic measures in non-Gaussian noise channel estimation, by analyzing the MSE, minimum error entropy (MEE) and correntropy algorithms in several channel models utilizing neural networks. The first contribution of this paper is introducing a new correntropy-based conjugate gradient (CCG) method and applying it in the CE problem, which this new algorithm converges faster than standard maximum correntropy criterion algorithm. Aiming at this contribution, the better convergence rate is discussed analytically and it is proved that the CCG could converge to the optimal solution quadratically. Next, the performance of an extended MSE algorithm is compared with information theoretic criteria; in addition, a comparison between MEE and correntropy-based algorithm is presented. The Monte Carlo results illustrate that correntropy and MEE outperform MSE algorithm in low-SNR communications especially in the presence of impulsive noise. Then, we apply the trained neural networks in the receiver as an equalizer to obtain the intended performance for different SNR values. [ABSTRACT FROM AUTHOR]

This paper proposes a pursuit system that utilizes the artificial life concept where autonomous mobile agents emulate the social behavior of animals and insects and realize their group behavior. Each agent contains sensors to perceive other agents in several directions, and decides its behavior based on the information obtained by these sensors. In this paper, a neural network is used for behavior decision controlling. The input of the neural network is decided by the existence of other agents, and the distance to the other agents. The output determines the directions in which the agent moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behavior adequately fit the goal and can express group behavior. The validity of the system is verified through simulation. Also in this paper, we have observed the agents’ emergent behavior during simulation. [ABSTRACT FROM AUTHOR]

Predicting the critical buckling loads of functionally graded material (FGM) plates using an analytical method requires solving complex equations with various modes of deformation to determine the minimum loads. The approach is too complex for application in engineering practice. In this paper, a data-driven model using the artificial neural network (ANN) is proposed for the critical buckling load of FGM plates, as an alternative tool for practicing engineers. A database is first developed for randomly selected inputs using an analytical solution based on first-order shear deformation theory for simply supported FGM plates. The database is then divided into a training dataset with 80% of the data and a testing dataset with 20% of the data for developing and validating, respectively, the ANN model. The ANN model developed using six hidden layers with 32 nodes in each layer is found to match the data with a coefficient of determination of 99.95%. Using the ANN model, the stochastic characteristic of the critical buckling load is examined with respect to randomness of the input parameters. The study reveals that along with the dimensional parameters, the critical buckling load is significantly affected by the randomness of the volume fraction ratio and ratio of the modulus of elasticity of the ceramic and the metal. [ABSTRACT FROM AUTHOR]

Failure of roller bearings can cause downtime or a complete shutdown of rotating machines. Therefore, a well-timed detection of bearing defects must be performed. Modern condition monitoring demands simple but effective bearing failure diagnosis by integrating dynamic models with intelligence techniques. This paper presents an integration of Dimensional Analysis (DA) and Artificial Neural Network (ANN) to diagnose the size of the bearing faults. The vibration responses of artificially damaged bearings using Electrode Discharge Machining are collected using Fast Fourier Techniques on a developed rotor-bearing test rig. Two-performance indicators, actual error, and performance of error are used to evaluate the accuracy of models. The simplicity of the DA model and the performance of the ANN model predicting with 5.49% actual error and 97.79 performance of error band enhanced the accuracy of diagnosis compared to the experimental results. Moreover, ANN has shown good performance over experimental results and DA. [ABSTRACT FROM AUTHOR]

Recorded time series of relative humidity (RH) are modeled by using genetic expression programming (GEP) and artificial neural networks (ANNs) models. The data are noisy and contain missing datapoints. RH is modeled as a function of three meteorological variables: temperature, wind speed, and pressure. Various model structures of both of these models are investigated with the aim of testing the robustness of the predicted values in the presence of noise and missing data. Due to the presence of noise, a sophisticated treatment of missing data was not justifiable, and therefore, the strategy adopted was just to carry the datapoints backward, although this may induce bias in the time dimension and contaminate the predicted results. The results of this study indicate that through a careful selection of model structures both GEP and ANN can produce adequately reliable prediction of RH values 1 year into the future. The paper provides evidence that this model structure is feasible when the dependent variables include both the present and past values. [ABSTRACT FROM AUTHOR]

The prediction of gold-stock price indices is considered a great challenge for traders, who try to analyze the historical developments and movements of gold-stock indices. Especially in the short-term prediction, that required a fast accurate decision in order to predict the next price after 5, 10, 15, 30 and 60 min. In this paper, a novel system is proposed to predict gold-stock prices in the short term for gold exchange markets. It is based on the wrapper selection method, artificial neural network and genetic algorithm which consist of three steps. The first step is to determine the number of the effective previous gold-stock indices and selecting the most significant technical parameters. Then, the prediction model construction will be trained by World gold council database. Finally, the next gold-stock price for each case of the short-term periods will be predicted. The results show that the proposed system achieves fast decisions with higher prediction accuracy and nuance rate between the actual and predicted values of the next gold-stock price after 5 min in the short term, which consumed 18 ms for the run time of the price prediction process. [ABSTRACT FROM AUTHOR]

This paper describes recent work on the development of a wireless-based remote monitoring system for household energy consumption and generation in Madeira Island, Portugal. It contains three different main sections: (1) a monitoring system for consumed and produced energy of residencies equipped with photovoltaic (PV) systems, (2) developing a tool to predict the electricity production, (3) and proposing a solution to detect the PV system malfunctions. With the later tool, the user (owner) or the energy management system can monitor its own PV system and make an efficient schedule use of electricity at the consumption side. In addition, currently, the owners of PV systems are notified about a failure in the system only when they receive the bill, whereas using the proposed method conveniently would notify owners prior to bill issue. The artificial neural network was employed as a tool together with the hardware-based monitoring system which allows a daily analysis of the performance of the system. The comparison of the predicted value of the produced electricity with the actual production for each day shows the validity of the method. [ABSTRACT FROM AUTHOR]

Hands are the main environmental manipulator for the human being. After losing a hand, the only alternative for the victim is to use a prosthesis. Despite the progress of science, the modern prosthesis has the same age-old problem of accurate force estimation. Among different kinds of force, clench force is the most important one. Because of this importance, this paper presents a hardware system that has been designed and implemented to estimate the desired clench force using surface Electromyography signals recorded from lower-arm muscles. The implementation includes a two-layer artificial neural network with a surface electromyography integrator. The neural network was trained with the Levenberg–Marquardt back propagation algorithm and was implemented in a field programmable gate array using an off-chip training method. The results from 10 datasets, recorded from five subjects, show that the hardware model is very accurate, with an average mean square error of 0.003. This suggests that the proposed design can mimic the behavior of clench force that a real limb does, and therefore this intelligent system could be a useful tool for any application related to prostheses. [ABSTRACT FROM AUTHOR]

The production of different renewable and non-renewable energies sources can be coordinated efficiently to avoid costly overproduction. For that, it is important to develop models for accurate energy production forecasting. The energy production of wind farms is extremely dependent on the meteorological conditions. In this paper, computational intelligence techniques were used to predict the production of energy in a wind farm. This study is held on publicly accessible climacteric and energy data for a wind farm in Galicia, Spain, with 24 turbines of 9 different models. Data preprocessing was performed in order to delete outliers caused by the maintenance and technical problems. Models of the following types were developed: artificial neural networks, support vector machines and adaptive neuro-fuzzy inference system models. Furthermore, the persistence method was used as a time series forecast baseline model. Overall, the developed computational intelligence models perform better than the baseline model, being adaptive neuro-fuzzy inference system the model with the best results: a ~ 5% performance improvement over the baseline model. [ABSTRACT FROM AUTHOR]

Accurate prediction of non-hydrocarbon (Non-HC) gas components in the gas-oil separators reduces the cost of gas and oil production in petroleum engineering. However, this task is difficult because there is no known relation among the properties of crude oil and the separators. There are studies that attempt to predict hydrocarbons (HCs) components using either Computational Intelligence (CI) techniques or conventional techniques like Equitation-of-State (EOS) and Empirical Correlation (EC). In this paper, we explore the applicability of CI techniques such as Artificial Neural Network, Support Vector Regressions, and Adaptive Neuro-Fuzzy Inference System to predict the Non-HC gas components in gas-oil separator tank. Further, we incorporate Genetic Algorithms (GA) into the Hybrid Computational Intelligence (HCI) models to enhance the accuracy of prediction. GA is used to determine the most favorable values of the tuning parameters in the CI models. The performances of the CI and HCI models are compared with the performance of the conventional techniques like EOS and EC. The experimental results show that accuracy of prediction by CI and HCI models outperform the conventional methods for N and HS gas components. Furthermore, the HCI models perform better than the non-optimized CI models while predicting the Non-HC gas components. [ABSTRACT FROM AUTHOR]

Six algorithms that design secrecy keys are used for digital image encryption. The privacy keys generated by stream cipher generators were tested for their randomness applying five tests of randomness. The images generated by applying each algorithm were tested for their regularity and residual intelligibility. The histograms for images ciphered by stream cipher schemes all have approximately flat histogram less information as compared to one-dimensional ciphering algorithms and threshold generator-based image ciphering scheme. Furthermore, the stream cipher schemes were effectively applied to the colored images of 256-color levels of true-color images (24 bit). In this paper, we try to decrypt automatically using artificial neural network by decryption through multilayer perceptron and radial basis function; networks were tested using the interface by calculating the error rates of decrypted images. [ABSTRACT FROM AUTHOR]

Learning disability (LD) is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned. [ABSTRACT FROM AUTHOR]

Artificial metaplasticity is the machine learning algorithm inspired in the biological metaplasticity of neural synapses. Metaplasticity stands for plasticity of plasticity, and as long as plasticity is related to memory, metaplasticity is related to learning. Implemented in supervised learning assuming input patterns distribution or a related function, it has proved to be very efficient in performance and in training convergence for multidisciplinary applications. Now, for the first time, this kind of artificial metaplasticity is implemented in an unsupervised neural network, achieving also excellent results that are presented in this paper. To compare results, a modified self-organization map is applied to the classification of MIT-BIH cardiac arrhythmias database. [ABSTRACT FROM AUTHOR]

Focal epilepsy is a common neurological disorder of the brain. It is symbolized by recurring seizure activities in particular regions of the brain. Electroencephalogram (EEG) signals contain information about the brain activity that can be used to identify areas affected by seizures. For this, however, neurologists have the challenging task to analyze the EEG signal to imply where the source of epilepsy is located. A novel approach to detect regions of focal epilepsy is proposed in this paper. By using an adaptive mixture of independent component analysis as a decomposition algorithm and information about the position of the measuring electrodes, EEG signals are transformed into two-dimensional brain maps that are further altered by a symmetric-weighted scale-invariant local ternary pattern technique. Features from this altered brain map are used as input in a three-layered artificial neural network for classification between epileptic and artifact brain maps. The brain maps classified as artifact are eliminated, and the epileptic components are given as output for doctors to investigate the regions of the brain affected by focal epilepsy. With the proposed methodology, an accuracy of 99.53% to detect epileptic components in EEG signals is reached. [ABSTRACT FROM AUTHOR]

Road construction projects on the territory of the Republic of Croatia are characterized by the overrun of planned costs. The experience of the contractor on previous road projects is an important element that can help to prevent errors and increase the chances of success in similar future projects. Data on construction costs collected from past projects can be used to estimate costs at different stages of the project life cycle through artificial neural networks. In this paper, artificial neural networks (MLP, GRNN, RBFNN) for estimating road construction costs are modeled. During the modeling, the database of roads constructed on the territory of the Republic of Croatia was used. Comparison of performance of neural networks has shown that the GRNN has obtained the best accuracy with MAPE of 13% and coefficient of determination of 0.9595. The neural network has proven to be a promising approach to use in the initial design phase when there is usually a limited or incomplete set of data for cost analysis, and this method could yield much more accurate results and the estimation error could be reduced. [ABSTRACT FROM AUTHOR]

This paper evaluates the potential of five modeling approaches, namely M5 model tree, random forest, artificial neural networks, support vector machines and Gaussian processes, for the prediction of unconfined compressive strength of stabilized pond ashes with lime and lime sludge. The study not only presents five models for the same set of data but also compares the overall performance of them. Dataset used consists of 255 samples acquired from laboratory experiments. Out of the total, 170 randomly chosen samples were used for training and remaining 85 were used for testing the models. Input dataset consists of eight parameters (uniformity coefficient, coefficient of curvature, maximum dry density, optimum moisture content, lime, lime sludge, curing period and 7-day soaked California bearing ratio), while the output is UCS value at 7, 28, 45, 90 and 180 days of curing. Comparisons of results propose that Gaussian processes modeling strategy works well and the overall performance was substantially nearer to the exact agreement line. As a result of GP model, higher value of CC = 0.997 and lower values of RMSE = 23.016 kPa and MAE = 16.455 were obtained for testing the dataset. Sensitivity analysis suggests that lime, lime sludge, curing period and California bearing ratio are the significant parameters for predicting the unconfined compressive strength of stabilized pond ashes. The results confirmed that GP models are in a position to predict the unconfined compressive strength of stabilized pond ashes with an excessive degree of accuracy; however, GP modeling approach proves that this approach is more economical and less difficult in comparison with tedious laboratory work. [ABSTRACT FROM AUTHOR]

In this paper, the use of artificial neural networks (ANNs) is proposed to manage the local demand of different electric grid elements to smooth their aggregated consumption. The ANNs are based on the load automation of the local electric behavior, following a local strategy but affecting to the global system. In an electrical grid, there is no possibility to share information between the users because anonymity must be warranted. Therefore, a solution to the problem is elaborated with the minimum information possible without the need for communication between the users. A grid environment and behavior of different users is simulated. [ABSTRACT FROM AUTHOR]

In this paper, a projection-based recurrent neural network is proposed to solve convex quadratic bilevel programming problems (CQBPP). The Karush–Kuhn–Tucker optimal conditions (KKT) of the lower level problem are used to obtain identical one-level optimization problem. A projected dynamical system which its equilibrium point coincides with the global optimal solution of the corresponding optimization problem is presented. Compared to existing models, the proposed model has the least number of variables and a simple structure with low complexity. Analytically, it is demonstrated that the state vector of the suggested neural network model is stable in the sense of Lyapunov and globally convergent to an optimal solution of CQBPP in finite time. Some numerical examples, a supply chain model and an application deals with an environmental problem are discussed in order to confirm the efficiency of the theoretical results and the performance of the model. [ABSTRACT FROM AUTHOR]

The automatic detection and cardiac classification are essential tasks for real-time cardiac diseases diagnosis. In this context, this paper describes a field programmable gates array (FPGA) implementation of arrhythmia recognition system, based on artificial neural network. Firstly, we have developed an optimized software-based medical diagnostic approach, capable of defining the best electrocardiogram (ECG) signal classes. The main advantage of this approach is the significant features minimization, compared to the existing researches, which leads to minimize the FPGA prototype size and saving energy consumption. Secondly, to provide a continuous and mobile arrhythmia monitoring system for patients, we have performed a hardware implementation. The FPGA has been referred due to their easy testing and quick implementation. The optimized approach implementation has been designed on the Nexys4 Artix7 evaluation kit using the Xilinx System Generator for DSP. In order to evaluate the performance of our proposal system, the classification performances of proposed FPGA fixed point have been compared to those obtained from the MATLAB floating point. The proposed architecture is validated on FPGA to be a customized mobile ECG classifier for long-term real-time monitoring of patients. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) is a noninvasive technique to measure blood volume changes in blood vessels. Despite the wide usage of PPG signal in medical and non-medical applications, this signal can be affected by the motion artifacts leading to data loss. In this paper, we proposed an algorithm to detect motion artifacts and reconstruct the corrupted parts of the signal using real-time modeling based on multilayer perceptron (MLP), radial basis function (RBF) artificial neural networks (ANNs) and adaptive-neuro fuzzy inference system (ANFIS). The developed algorithm was applied to reconstruct the corrupted parts of PPG signals of 23 healthy 25- to 28-year-old volunteers. In the experimental phase, the left- and right-hand PPG signals of the volunteers were simultaneously obtained. While the left hand of the subjects were fixed, they were asked to shake their hands without any predetermined pattern, to simulate the real-life motion accelerations. To statistically and physiologically evaluate the performance of the proposed models, Pearson correlation coefficient (PCC), intraclass correlation coefficient (ICC), Bland–Altman plot (with the 95% limits of agreement), and time-domain feature analysis tests were adopted. The results indicated that the ANFIS with subtractive clustering algorithm shows the best performance in modeling the lost parts of the right-hand signals with an average PCC and ICC of 0.80 and 0.77, respectively, with the reference signal (left-hand signals) over all the tests. Also, the proposed ANFIS-based algorithm had an ability to retrieve the important time-domain PPG signal features, namely mean of inter-beat intervals (NN), standard deviations of NN (SDNN), root mean square of standard deviations (RMSSD) and standard deviation of standard deviations (SDSD) without any significant difference at p < 0.05 level to those of the reference signal (left-hand signal). [ABSTRACT FROM AUTHOR]

Air-blast overpressure (AOp) is one of the undesirable effects caused by blasting operations in open-pit mines. This side effect of blasting can seriously undermine surrounding residential structures and living quality. To control and mitigate this situation, this study using artificial neural networks to predict AOp implemented at Deo Nai open-pit coal mine, Vietnam. A total of 146 events of blasting were recorded, of which 80% (118 observations) was used for training and 20% (28 observations) was used for testing. A resampling technique, namely tenfold cross-validation, was performed with three repeats to increase the accuracy of the predictive models. In this paper, three different types of neural networks were developed to predict AOp including multilayer perceptron neural network (MLP neural nets), Bayesian regularized neural networks (BRNN) and hybrid neural fuzzy inference system (HYFIS). Each type was tested with ten model configurations to discover the best performing ones based on comparing standard metrics, including root-mean-square error (RMSE), coefficient of determination (R2), and a simple ranking method. Eight parameters were considered for these models, including charge per delay, burden, spacing, length of stemming, powder factor, air humidity, and monitoring distance. The results indicated that MLP neural nets model with RMSE = 2.319, R2 = 0.961 on testing datasets and a total ranking of 12 yielded the most accurate prediction over BRNN and HYFIS models. [ABSTRACT FROM AUTHOR]

Symbiotic organisms search (SOS) is a new metaheuristic optimization algorithm proposed by Cheng and Prayogo (Comput Struct 139:98–112, 2014). In this paper, SOS has been applied to determine the functional forms of different time series which are used to predict the time series. There are some previous attempts by researchers where genetic algorithm has been used to find the functional form of a time series. Here, we explore this new algorithm in time series analysis. SOS mimics the symbiotic relationships among organisms in the ecosystem. Improvement in SOS in parasitism phase has been proposed here. Also, several types of time series have been tested to compare the performance of the original SOS with its improved version and with already well-established artificial neural network (ANN) in the field of time series forecasting. [ABSTRACT FROM AUTHOR]

Atmospheric Ar:H2 plasma is an eco-friendly option for the reduction of metal oxides. For better reduction performance and safety concern, the hydrogen gas injected into the reactor should be monitored. A hydrogen flow rate estimation system is presented in this paper by using an artificial neural network (ANN) model fed with features of optical emission spectra of the plasma. ANN models are studied with two different sets of input, i.e. for the first case the inputs to the model are the three features of Hα line such as the peak intensity count, full-width half maximum and area under Hα line, while for the second case, the peak intensity count of a group of emission lines like Hα, Ar I, O I, K I, Na D lines are considered as the inputs. ANN model is developed for estimating four different sets of hydrogen flow rates 5, 8, 10 and 12 litres per minute (lpm) when the argon flow rate is constant at 10 lpm. For both the input features, the model performances are compared, and it is shown that improved estimation accuracy is observed from the second case, i.e. from peak intensity count of a group of emission lines instead of only hydrogen emission line. [ABSTRACT FROM AUTHOR]

The use of artificial neural networks (ANN) in fault detection analysis is widespread. This paper aims to provide an overview on its application in the field of fault identification and diagnosis (FID), as well as the guiding elements behind their successful implementations in engineering-related applications. In most of the reviewed studies, the ANN architecture of choice for FID problem-solving is the multilayer perceptron (MLP). This is likely due to its simplicity, flexibility, and established usage. Its use managed to find footing in a variety of fields in engineering very early on, even before the technology was as polished as it is today. Recurrent neural networks, while having overall stronger potential for solving dynamic problems, are only suggested for use after a simpler implementation in MLP was attempted. Across various ANN applications in FID, it is observed that preprocessing of the inputs is extremely important in obtaining the proper features for use in training the network, particularly when signal analysis is involved. Normalization is practically a standard for ANN use, and likely many other decision-based learning methods due to its ease of use and high impact on speed of convergence. A simple demonstration of ANN's ease of use in solving a unique FID problem was also shown. [ABSTRACT FROM AUTHOR]

A vital issue for customer correlation management and consumer conservation in the telecommunications business is increased customer churn. The data mining approaches can aid in the prediction of churn behavior of consumers. This article aims to propose a system to predict customer churn through hybrid probabilistic possibilistic fuzzy C-means clustering (PPFCM) along with artificial neural network (PPFCM-ANN). This paper comprises of two modules: (1) proposing clustering component on the basis of PPFCM and (2) churn prediction component on the basis of ANN. The input dataset is gathered into clusters, with the help of probabilistic possibilistic fuzzy C-means clustering algorithm in the clustering module. The obtained clustered information is used in the artificial neural network, and this hybrid construction is further used in the churn prediction module. During the testing process, the clustered test data select the most accurate ANN classifier which corresponds to the closest cluster of the test data, according to minimum distance or similarity measures. Finally, to predict the churn customer the output score value is utilized. Three sets of experiments are carried out: the primary set of experiments comprises PPFCM clustering algorithm, the secondary set assesses the classification result, and the third set authenticates the proposed hybrid model presentation. The proposed hybrid PPFCM-ANN model provides maximum accuracy when compared to any single model. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the method of solving partial differential equations (PDEs) using artificial neural network (ANN) structures, which have been actively applied in artificial intelligence models. The ANN model for solving PDEs offers the advantage of providing explicit and continuous solutions. However, the ANN model for solving PDEs cannot construct a conventionally solvable linear system with known matrix solvers; thus, computational speed could be a significant concern. We study the implementation of the multigrid method, developing a general concept for a coarse-grid correction method to be integrated into the ANN-PDE architecture, with the goal of enhancing computational efficiency. By developing a reduced form of the multigrid method for ANN, we demonstrate that it can be interpreted as an equivalent representation of the classic multigrid expansion. We validated the applicability of the proposed method through rigorous experiments, which included analyzing loss decay and the number of iterations along with improvements in terms of accuracy, speed, and complexity. We accomplished this by employing the gradient descent method and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method to update the gradients while solving the given ANN systems of PDEs. [ABSTRACT FROM AUTHOR]

In this paper, a novel observer structure for nonlinear fractional-order systems is presented to estimate the states of fractional-order nonlinear chaotic system with unknown dynamical model. A new fractional error back-propagation learning algorithm is derived to adapt weights of the artificial neural network, by taking advantage of the Lyapunov stability strategy of fractional-order systems which is called Miattag–Leffler stability. The main contribution is the extension of neural observer for fractional dynamics in a way that satisfies Miattag–Leffler conditions. Observer design procedure guarantees the convergence of observer error to the neighborhood of zero. Furthermore, the robustness of the proposed estimator against uncertainties and external disturbances are the main benefits of the proposed method. Simulation results show the effectiveness and capabilities of the proposed observer. [ABSTRACT FROM AUTHOR]

Confining damaged concrete columns using fibre-reinforced concrete (FRP) has proven to be effective in restoring strength and ductility. However, extensive experimental tests are generally required to fully understand the behaviour of such columns. This paper proposes the artificial neural networks (ANNs) models to simulate the FRP-repaired concrete subjected to pre-damaged loading. The models were developed based on two databases which contained the experimental results of 102 and 68 specimens for restored strength and strain, respectively. The proposed models agreed well with testing data with a general correlation factor of more than 97%. Subsequently, simplified equations in designing the restored strength and strain of FRP-repaired columns were proposed based on the trained ANN models. The proposed equations are simple but reasonably accurate and could be used directly in the design of such columns. The accuracy of the proposed equations is due to the incorporation of most affecting factors such as pre-damaged level, concrete compressive strength, confining pressure and ultimate confined concrete strength. [ABSTRACT FROM AUTHOR]

Simulation of creep curves using data obtained from a limited number of short-time creep tests is helpful for predicting the long-time creep life of materials by extrapolation techniques. The present paper demonstrates the application of artificial neural network (ANN) for the prediction of creep curves for HP40Nb micro-alloyed steel. The network consists of stress, temperature and time as the input parameters and the creep strain as the output parameter. The data used are taken from accelerated creep tests carried out at constant temperatures in the range 650-1050 °C and constant stresses 47-120 MPa. The network was trained using a three-layer feed-forward back-propagation network, having a 15-neuron hidden layer, using the Levenberg-Marquardt optimization algorithm. After successful network training, the model was subjected to several tests to demonstrate consistent prediction capability. 98% of the creep strain data could be predicted within an error of ±10% deviation from the experimental values. An additional experiment carried out to check the prediction capability of the model confirms very good prediction capability, with a correlation coefficient of 0.994, by ANN modeling. [ABSTRACT FROM AUTHOR]

This paper proposes a machining performance prediction approach on multiple performances of wire electrical discharge machining (WEDM) on Inconel 718. Artificial neural network (ANN) is emphasized to predict the machining performances. Many efforts have been made to model the performances of WEDM using ANN. However, to obtain the best ANN model, generally the network parameters are not consistent and so far, the selection has been made in a random manner and resulted in an excessive experimental trial. Taguchi design orthogonal array L256 is implemented in the process of network parameter selection to search for the potential machining model. This approach, prescribed as OrthoANN, is simplified to avoid as much as unnecessary experimentations. Material removal rate, surface roughness (Ra), cutting speed (Vc) and sparking gap (Sg) are the machining performances considered in this study. Five machining parameters considered; pulse on time, pulse off time, peak current, servo voltage and flushing pressure. Cascade forward back-propagation neural network (CFNN) is found to be the best network type of the selected data set. One hidden layer 5-14-4 CFNN showed the most precise and generalized network architecture with very good prediction accuracy. An average of 5.16% error is generated which seems to be in superior concurrence with the actual experimental results. Confirmation test is carried out to verify the machining performances suggested by this approach. [ABSTRACT FROM AUTHOR]

Application of back-propagation (BP) artificial neural network (ANN) as an accurate, practical and quick tool in indirect estimation of uniaxial compressive strength (UCS) of rocks has recently been highlighted in the literature. This is mainly due to difficulty in direct determination of UCS in laboratory as preparing the core samples for this test is troublesome and time-consuming. However, ANN technique has some limitations such as getting trapped in local minima. These limitations can be minimized by combining the ANNs with robust optimization algorithms like particle swarm optimization (PSO). This paper gives insight into development of a hybrid PSO-BP predictive model of UCS. For this reason, dataset comprising the results of 228 laboratory tests including dry density, moisture content, P wave velocity, point load index test, slake durability index and UCS was prepared. These tests were conducted on 38 sandstone samples which were taken from two excavation sites in Malaysia. Findings showed that PSO-BP model performs well in predicting UCS. Nevertheless, to compare the prediction performance of the PSO-BP model, the UCS is predicted using ANN-based PSO and BP models. The correlation coefficient, R, values equal to 0.988 and 0.999 for training and testing datasets, respectively, suggest that the PSO-BP model outperforms the other predictive models. [ABSTRACT FROM AUTHOR]

In this paper, two artificial intelligent systems, the artificial neural network (ANN) and particle swarm optimization (PSO), were combined to form a hybrid PSO-ANN model that was used to improve estimates of glucose and xylose yields from the microwave-acid pretreatment and enzymatic hydrolysis of lignocellulosic biomass based on pretreatment parameters. ANN is a powerful tool capable of determining the relationship between the desired input and output data while PSO was used as a robust population-based search algorithm to optimize the performance of the ANN model. Specifically, it was used to determine the optimum number of neurons in the hidden layer and the best value of the learning rate of the ANN model. The optimization method includes minimizing the fitness function mean absolute error that was found to be 0.0176. The PSO algorithm suggested an optimum number of neurons in the hidden layer as 15 and a learning rate of 0.761 these consequently used to construct the ANN model. After constructing the hybrid PSO-ANN model, the performance of the intelligent system was examined by determining the regression coefficient (R2) for estimating the experimental values of glucose and xylose and compared to the results from a response surface methodology (RSM) model. The results of R2 of the hybrid PSO-ANN model for glucose and xylose were 0.9939 and 0.9479, respectively, while the RSM model results for the same sugars were 0.8901 and 0.8439. This analysis reveals that the hybrid PSO-ANN model offers a higher degree of accuracy in comparison with the more commonly used RSM model. [ABSTRACT FROM AUTHOR]

The functional design of submerged breakwaters is still developing, particularly with respect to modelling of the nearshore wave field behind the structure. This paper describes a method for predicting the wave transmission coefficients behind submerged breakwaters using machine learning algorithms. An artificial neural network using the radial-basis function approach has been designed and trained using laboratory experimental data expressed in terms of non-dimensional parameters. A wave transmission coefficient calculator is presented, based on the proposed radial-basis function model. Predictions obtained by the radial-basis function model were verified by experimental measurements for a two dimensional breakwater. Comparisons reveal good agreement with the experimental results and encouraging performance from the proposed model. Applying the proposed neural network model for predictions, guidance is given to appropriately calculate wave transmission coefficient behind two dimensional submerged breakwaters. It is concluded that the proposed predictive model offers potential as a design tool to predict wave transmission coefficients behind submerged breakwaters. A step-by-step procedure for practical applications is outlined in a user-friendly form with the intention of providing a simplified tool for preliminary design purposes. Results demonstrate the model’s potential to be extended to three dimensional, rough, permeable structures. [ABSTRACT FROM AUTHOR]