Artificial Neural Network Configuration Research Articles

A Technique for Creating and Training an Artificial Neural Network to Detect Network Traffic Anomalies

The article presents a technique for creating and training an artificial neural network to recognize network traffic anomalies using relatively small samples of collected data to generate training data. Various data sources for machine learning and approaches to network traffic analysis are considered. There are data format and the method of generating them from the collected network traffic is described, as well as the steps of the methodology in detail. Using the technique, an artificial neural network was created and trained for the task of recognizing anomalies in the network traffic of the ICMP protocol. The results of testing and comparing various artificial neural network configurations and learning conditions for a given task are presented. The artificial neural network trained according to the method was tested on real network traffic. The presented technique can be applied without requiring changes to detect anomalies of various network protocols and network traffic using a suitable parameterizer and data markup.

Predicting Lifetime of Semiconductor Power Devices Under Power Cycling Stress Using Artificial Neural Network

This paper analyzes the problem of modeling the lifetime in semiconductor power devices subjected to power cycling stress using Artificial Neural Networks (ANNs). The paper discusses the optimal configuration of ANNs for the considered problem, aiming at minimizing the error in the predicted lifetime and at reducing the required number of training data. Moreover, being the device lifetime a stochastic parameter, the suitability of ANNs is verified in the case of variability in the input training data. Power cycling tests are conducted on IGBT devices and experimental number of cycles to failure are adopted for the training process of the ANN.

River system sediment flow modeling using artificial neural networks

Sediment leads to problems with navigation, agricultural productivity, and water pollution. The study of sediment flow in river reaches, which is a non-linear and complex process, is, thus, essential to addressing these issues. The application of artificial neural networks (ANN) to such problems needs to be investigated. For unsteady flow in a river system, river reach storage is an important variable that needs to be considered in data-driven models. However, previous research on sediment modeling did not involve the explicit use of storage variables in such models as is investigated in the current study. In the current study, storage variables have been explicitly (Model 2) used to predict the output state of the system at time ‘t + 1’ from the input state at time ‘t’ using ANNs. Sediment discharge at six gaging stations on the Mississippi River system, USA, has been considered as the state variable. The model has been compared with a model considering implicit variation of the storage parameter in the river system (Model 1). Dynamic ANNs are used for time-series datasets, which are more suitable for incorporating the sequential information within the dataset. Focussed gamma memory neural networks have been used in the current study. The numbers of hidden layers and hidden nodes, activation function, and learning rate have been varied step by step to obtain the optimal ANN configurations. The best selected input–output variables are those used in Model 2 as it performed slightly better than the other model in terms of Nash–Sutcliffe efficiency coefficient (CE) values. Model performance evaluated using normalized root mean square error (NRMSE) and CE shows satisfactory results. NRMSE was < 10% for all the outputs except for the Venedy and Murphysboro locations and CE values for sediment loads were > 0.45 for all locations except Murphysboro indicating acceptable performance by both the models. The models proved highly efficient (CE > 0.80, i.e., very good predictions) for predicting sediment discharge at locations along the main river channel with acceptable accuracy (CE > 0.45) for other locations and the storage change for the river system. These models can be used for real-time forecasting and management of sediment-related problems.

Molecular-based artificial neural networks for selecting deep eutectic solvents for the removal of contaminants from aqueous media

Computational methods for predicting a solvent’s performance in a given application, and for selecting an adequate solvent for that application, are becoming increasingly essential for separation processes. In this context, this article presents a first-of-a-kind machine learning tool based on guided molecular design of solvents to predict the performance of various solvent systems in the solvent extraction process. The tool was demonstrated herein for the extraction of aqueous boron, through the selection of neoteric solvents from a dataset that spans different types of solvents (molecular solvents, deep eutectic solvents (DESs) and ionic liquids). The model was developed by first obtaining the COSMO-RS-based molecular descriptors (σ-profiles) for each solvent system and using them as input parameters to an Artificial Neural Network (ANN), in addition to other operational parameters (e.g., pH, temperature, ion concentration, and A/O ratio), while extraction efficiency as the output. The results showed that the optimal ANN configuration (59–20-15–1) exhibited remarkable predictability for boron extraction with an R2 of 0.988 and 0.977 for the training and testing sets, respectively. The model was used to investigate different solvent systems of which six new DESs were successfully synthesized, experimentally tested, and characterized across different properties such as density, viscosity, and leachability. The combination of Decanol and 2,2,4- trimethyl-1,3-pentanediol exhibited appreciable properties and high experimental extraction efficiency of 97.22%. The experimentally validated model demonstrates the effectiveness of molecular-based descriptors and machine learning for predicting the extraction capabilities of solvents in aqueous media and allows further exploration of new solvent systems based on their extraction performance at different operational conditions. This proof-of-concept approach can be effectively adopted to predict the extraction behavior of different solvent systems towards target contaminants in aqueous environments, thereby supporting both the design of separation processes and solvent screening for future applications.

Retracted: Software Development, Configuration, Monitoring, and Management of Artificial Neural Networks

Retracted: Software Development, Configuration, Monitoring, and Management of Artificial Neural Networks

Optimal artificial neural network configurations for hourly solar irradiation estimation

&lt;span lang="EN-US"&gt;Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigation of the optimal neurons and layers is investigated. To this end, feedforward neural network, cascade forward neural network and fitting neural network have been applied for this purpose. In this context, we have used different meteorological parameters to estimate the hourly global solar irirradiation in the region of Laghouat, Algeria. The validation process shows that choosing the cascade forward neural network two inputs gives an R2 value equal to 97.24% and an normalized root mean square error (NRMSE) equals to 0.1678 compared to the results of three inputs, which gives an R2 value equaled to 95.54% and an NRMSE equals to 0.2252. The comparison between different existing methods in literature show the goodness of the proposed models.&lt;/span&gt;

Machine learning-enabled thickness estimation of thin coatings on carbon fibre composites using microwaves

Accurate thickness measurement of thin coatings (typically 50–500 μm) on carbon fibre-reinforced polymer composites is a major challenge in the manufacturing and maintenance processes of modern aircraft. Different from the conventional material-dependent technique for prediction, a machine learning-enabled strategy with an artificial neural network configuration is used with no requirement of prior knowledge of the type of coating or substrate under test. In the test, an open microwave cavity resonator sensor is directly placed on a coated composite, and any variation of the coating material, coating thickness and conductivity of the composite alters the resonance frequency. Principal component analysis is employed in the signal pre-processing for the dimensionality reduction of the raw measurement data. In terms of the root-mean-square error, the maximum value for the calibration approach is approximately 15 μm and that for the machine learning-based approach is 12 μm. The sensor system developed enables real-time on-site assessment of coated composite structures and thus offers a new approach for non-destructive evaluation 4.0 with improved efficiency, accuracy and automation.

Quantum-inspired tempering for ground state approximation using artificial neural networks

A large body of work has demonstrated that parameterized artificial neural networks (ANNs) can efficiently describe ground states of numerous interesting quantum many-body Hamiltonians. However, the standard variational algorithms used to update or train the ANN parameters can get trapped in local minima, especially for frustrated systems and even if the representation is sufficiently expressive. We propose a parallel tempering method that facilitates escape from such local minima. This methods involves training multiple ANNs independently, with each simulation governed by a Hamiltonian with a different "driver" strength, in analogy to quantum parallel tempering, and it incorporates an update step into the training that allows for the exchange of neighboring ANN configurations. We study instances from two classes of Hamiltonians to demonstrate the utility of our approach using Restricted Boltzmann Machines as our parameterized ANN. The first instance is based on a permutation-invariant Hamiltonian whose landscape stymies the standard training algorithm by drawing it increasingly to a false local minimum. The second instance is four hydrogen atoms arranged in a rectangle, which is an instance of the second quantized electronic structure Hamiltonian discretized using Gaussian basis functions. We study this problem in a minimal basis set, which exhibits false minima that can trap the standard variational algorithm despite the problem’s small size. We show that augmenting the training with quantum parallel tempering becomes useful to finding good approximations to the ground states of these problem instances.

Artificial Neural Network Optimized Load Forecasting of Smartgrid using MATLAB

The motivation behind the research is the requirement of error-free load prediction for the power industries in India to assist the planners in making important decisions on unit commitments, energy trading, system security reliability, and optimal reserve capacity. The objective is to produce a desktop version of a personal computer-based complete expert system that can be used to forecast the future load of a smart grid. Using MATLAB, we can provide adequate user interfaces in graphical user interfaces. This paper devotes a study of load forecasting in smart grids, a detailed study of the architecture and configuration of Artificial Neural Network (ANN), Mathematical modeling and implementation of ANN using MATLAB, and a detailed study of load forecasting using the backpropagation algorithm.

Neuromodel of an Eddy Current Brake for Load Emulation

The eddy current brake (ECB) is an electromechanical energy conversion device that can be used as a load emulator to load a motor according to the intended load scenario. However, conducting an analysis in the time domain is difficult due to its complex behavior involving mechanical, electrical, and magnetic phenomena. The challenges with the time domain analysis of the ECB require new modeling approaches that provide reliability, robustness, and controllability over a wide speed interval. If the ECB can be modeled with high accuracy, it can be controlled like a load emulator that can simulate nonlinear industrial loads. This paper describes a neuromodeling approach taken to develop an ECB. The nonlinear characteristic of the brake system was modeled with a high performance by using an artificial neural network (ANN), which is a potent nonlinear system identification tool. Several characteristics of a designed and optimized brake system undergoing various excitation currents in whole speed regions are described and verified experimentally. Eventually, an electromechanical brake system is proposed that aims to provide the required linear or nonlinear load model dynamics throughout an emulation process in line with the obtained neuromodel. To identify the most suitable ANN architecture for the problem, various ANN configurations, ranging from 1 neuron to 20 neurons in the hidden layer, as well as a statistical approach that differs from the existing literature, are presented. Additionally, the suggested model’s scalability is discussed.

STEM TAPERING OF Eucalyptus spp. USING DIFFERENT CONFIGURATIONS OF ARTIFICIAL NEURAL NETWORKS

The objective of this work was to test and evaluate different configurations of artificial neural networks (ANNs) for modeling tree stem taper in Eucalyptus spp. in strands in the microregion of Pirapora, Minas Gerais. The data used came from 8,410 Eucalyptus spp. at different speeds. The quantitative variables measured were: age, total height, diameter at the height of 1.30 m (dbh), diameter and height in different positions on the stem. The only qualitative variable measured was the clone. Four scenarios were evaluated: scenario 1 with Ht, dbh, hi, A and Clone inputs; scenario 2 with Ht, dbh, hi and Clone; scenario 3 with Ht, dbh, hi and A; and scenario 4 with Ht, dbh and hi. We tested different ANNs topologies of the Multilayer Perceptron type. The ANNs 102 (neurons in the hidden layer = 18; function = Exponential; algorithm = Rprop), 91 (neurons in the hidden layer = 19; function = Exponential; algorithm = Rprop), 13 (neurons in the hidden layer = 7; Function = Exponential; Algorithm = SCG) and 27 (neurons in the hidden layer = 6; function = Exponential; algorithm = Rprop) presented the best measures of statistical accuracy in training to predict the bottleneck in scenarios 1, 2, 3 and 4, respectively. The ANN 103 (neurons in the hidden layer = 19; function = Exponential; algorithm = Rprop) from scenario 1 presented good statistical results in the validation. Thus, the ANNs were efficient in predicting the diameter along the Eucalyptus spp stem.

Modeling of Solar Photocatalytic Degradation of Rhodamine B Dye by TiO2 Nanoparticles Using an Artificial Neural Network

AbstractAn artificial neural network (ANN) model was developed for the photocatalytic degradation of rhodamine B by TiO2 nanoparticles synthesized by the electrochemical method, under direct sunlight irradiation. The four inputs are: irradiation time, initial dye concentration, pH of the initial solution, and catalyst loading. The ANN model with 20 hidden neurons and with R2 of 0.999 shows minimum values of the performance metrics of 0.169, 2.020, 1.531, and 0.215 for the mean square error, the root mean square error, the mean absolute error, and the mean absolute percentage error, respectively. The optimized ANN configuration of 4‐20‐1 shows a good fit with the experimental data. The result shows that the sunlight irradiation time has the main impact on rhodamine B degradation at ∼55 %.

Predicting intubation risk among COVID-19 hospitalized patients using artificial neural networks.

Accurately predicting the intubation risk in COVID-19 patients at the admission time is critical to optimal use of limited hospital resources, providing customized and evidence-based treatments, and improving the quality of delivered medical care services. This study aimed to design a statistical algorithm to select the best features influencing intubation prediction in coronavirus disease 2019 (COVID-19) hospitalized patients. Then, using selected features, multiple artificial neural network (ANN) configurations were developed to predict intubation risk. In this retrospective single-center study, a dataset containing 482 COVID-19 patients who were hospitalized between February 9, 2020 and July 20, 2021 was used. First, the Phi correlation coefficient method was performed for selecting the most important features affecting COVID-19 patients' intubation. Then, the different configurations of ANN were developed. Finally, the performance of ANN configurations was assessed using several evaluation metrics, and the best structure was determined for predicting intubation requirements among hospitalized COVID-19 patients. The ANN models were developed based on 18 validated features. The results indicated that the best performance belongs to the 18-20-1 ANN configuration with positive predictive value (PPV) = 0.907, negative predictive value (NPV) = 0.941, sensitivity = 0.898, specificity = 0.951, and area under curve (AUC) = 0.906. The results demonstrate the effectiveness of the ANN models for timely and reliable prediction of intubation risk in COVID-19 hospitalized patients. Our models can inform clinicians and those involved in policymaking and decision making for prioritizing restricted mechanical ventilation and other related resources for critically COVID-19 patients.

Artificial neural network with the Levenberg-Marquardtalgorithm for numerical solution of two-dimension Poisson’sequation

This study introduces an Artificial Neural Network (ANN) framework to address the two-dimensional Poisson’sequation within a rectangular domain. It places a focus on the training process of a neural network with threelayers, incorporating hidden neurons. The feedforward ANN is trained using MATLAB, which calculates weights forall neurons within the network structure. These acquired weights are subsequently applied in the trained networkmodel to make predictions for the desired output of a specific partial differential equation. The architecture of theANN consists of three layers: one input layer, one hidden layer, and one output layer. In this study, we specificallyemploy an ANN configuration with 50 hidden neurons. The training process is executed using MATLAB, utilizing theLevenberg–Marquardt algorithm (LMA) for optimization. Furthermore, the study encompasses the development ofsurface and contour plots that illustrate the computational solution of the partial differential equation. Additionally,error functions are graphed to assess the effectiveness of the ANN model.

Renewable energy solutions based on artificial intelligence for farms in the state of Minas Gerais, Brazil: Analysis and proposition

Rural areas have great renewable energy potential. With an introduction to sustainable development goals, the smart farm concept presents a novel idea for providing energy in rural areas using artificial intelligence and renewable energy management. We proposed the following topics in this research: (i) methodologies for sizing generation systems by integer linear programming, (ii) use of analytic hierarchy process (AHP) to select the alternative source by financial, environmental, social, and physical criteria, and (iii) training of an artificial neural network (ANN) for process optimization based on the agroeconomic profile of farms. We applied the proposed generic methodology to farms in São Francisco do Glória, Brazil. The biomass, solar, and wind systems were indicated by AHP for implementation in 62.16, 15.32, and 22.52% of farms, respectively. The best configuration of ANN presented a maximum precision of 81.80 ± 3.36%. If the systems were to be implemented, 1.975 GWh yr−1 would be generated, 435.23 tonnes of CO2 would no longer be emitted per year, and a CO2 credit of 366.62 tonnes yr−1 would be injected into the national electric system. Public policies are necessary for this scenario to become a reality in Brazil, such as research incentives and market development.

Machine Learning Approach for Rapid Estimation of Five-Day Biochemical Oxygen Demand in Wastewater

Improperly managed wastewater effluent poses environmental and public health risks. BOD evaluation is complicated by wastewater treatment. Using key parameters to estimate BOD in wastewater can improve wastewater management and environmental monitoring. This study proposes a BOD determination method based on the Artificial Neural Networks (ANN) model to combine Chemical Oxygen Demand (COD), Suspended Solids (SS), Total Nitrogen (T-N), Ammonia Nitrogen (NH4-N), and Total Phosphorous (T-P) concentrations in wastewater. Twelve different transfer functions are investigated, including the common Hyperbolic Tangent Sigmoid (HTS), Log-sigmoid (LS), and Linear (Li) functions. This research evaluated 576,000 ANN models while considering the variable random number generator due to the ten alternative ANN configuration parameters. This study proposes a new approach to assessing water resources and wastewater facility performance. It also demonstrates ANN’s environmental and educational applications. Based on their RMSE index over the testing datasets and their configuration parameters, twenty ANN architectures are ranked. A BOD prediction equation written in Excel makes testing and applying in real-world applications easier. The developed and proposed ANN-LM 5-8-1 model depicting almost ideal performance metrics proved to be a reliable and helpful tool for scientists, researchers, engineers, and practitioners in water system monitoring and the design phase of wastewater treatment plants.

Developing an Intelligent Tool for Breast Cancer Prognosis Using Artificial Neural Network

Today, there is ample scientific evidence that Breast Cancer (BC) is a global health challenge given its prevalence and invasive nature. Therefore, early detection of BC can help minimize the devastating effects of the disease. This study aimed to design a Clinical Decision Support System (CDSS) based on the best Artificial Neural Network (ANN) configuration to identify patients quickly. Using a single-center registry, we retrospectively reviewed the records of 3380 suspected BC cases. The independence test of Chi-Square at P&lt;0.01 was utilized to select the most important criteria. Then the different ANN configuration was implemented in the Matlab R2013 environment and compared using some evaluation criteria. Finally, the best ANN configuration was obtained. After implementing feature selection, 20 variables were determined as the most relevant factors. The experimental results indicate that the best performance was obtained by the 20-25-1 configuration with PPV=90.9%, NPV=99.7%, Sensitivity=98.9%, Specificity=97.9%, Accuracy=98.1%, and AUC=0.958. The proposed software can identify cases of BC from healthy individuals with optimal diagnostic accuracy. Additionally, it might be integrated as a practical and helpful tool in natural clinical settings for easy and effective disease screening.

Prediction of the exergy performance of a hybrid photovoltaic/thermal-thermal wheel system using an optimal artificial neural network

In the present study, the aim is to estimate the annual exergy yield of a hybrid system consisting of a photovoltaic/thermal (PV/T) system and a thermal wheel. By using the hybrid system, the outside air can be cooled or heated and also, part of the electricity required by a building can be provided. The input parameters of the predictive model are the dimensions of the channel of PV/T system, the air mass flow rate and the thermal wheel length and diameter. For modeling the exergy of the studied hybrid system, a systematic method is proposed to find the optimal configuration of Artificial Neural Network (ANN). For this purpose, a novel method is proposed to discover the optimal architecture and training algorithm of the ANN by the Genetic Algorithm (GA) among 20,160 possible combinations. A dataset with 2000 input-target pairs is investigated using this method. The results indicate that a Feed-Forward (FF) network containing 19 and 10 “tansig” neurons in the first and second hidden layers that is trained by Bayesian regularization backpropagation (trainbr) can reach the best performance is the optimal ANN configuration. Based on the results, 74% of the investigated pairs have an error smaller than 0.05 %.

The effect of soiling on the performance of solar mirror materials: Experimentation and modeling

The main purpose of the current work is to examine and evaluate the impact of outdoor soiling on the performance of a sample concentrating solar power reflector. The exposure period is from April 2019 to October 2019, and during this period the mirror was left without cleaning. Achieved results from the experiment showed that the soiling index increased by almost 63 % with a maximum accumulation of dust on the mirror after approximately 20 weeks of exposure, then, it decreased due to rain event. Modeling the performance of the exposed mirror is also investigated in this paper. Thus, correlations between essential weather parameters (ambient temperature, wind speed, wind direction, relative humidity, rainfall, and dust optical depth) and soiling index are determined to select the most appropriate attributes for predicting daily soiling. The prediction is performed using Multilayer Perceptron Neural Networks approach with the adjustment of hidden layers, hidden neurons, and epoch iterations. Hence, three artificial neural networks configurations are trained, validated, and compared to other methods. The modeling study presented satisfying results, and the adapted structure includes two hidden layers and 20 hidden nodes for each layer. The best achieved performance showed a correlation coefficient of 1 and 0 % for the different calculated statistical metric errors.

Artificial Neural Network Model to Predict Production Rate of Electrical Submersible Pump Wells

Summary Production data are essential for designing and operating electrical submersible pump (ESP) systems. This study aims to develop artificial neural network (ANN) models to predict flow rates of ESP artificially lifted wells. The ANN models were developed using 31,652 data points randomly split into 80% (25,744 data points) for training and 20% (5,625 data points) for testing. Each data set included measurements for wellhead parameters, fluid properties, ESP downhole sensor measurements, and variable speed drive (VSD) sensors parameters. The models consisted of four separate neural networks to predict oil, water, gas, and liquid flow rates. Sensitivity analyses were performed to determine the optimum number of input parameters that can be used in the model. The best performance was achieved with ANN models of 16 input parameters that are readily available in ESP wells. The results of the best ANN configuration indicate that the mean absolute percent error (MAPE) between the predicted flow rates and the actual measurements for the testing data points of the oil, water, gas, and liquid networks is 3.7, 5.2, 6.4, and 4.1%, respectively. In addition, the correlation coefficients (R2) are 0.991, 0.992, 0.983, and 0.979 for the estimated oil, water, gas, and liquid flow rates for the testing data points, respectively. The performance of the ANN models was compared against performance of published physics-based models and the results were comparable. Unlike the physics-based methods, the ANN models have the advantage that they do not require periodic calibration. The ANN models were used to predict the flow rate curves of an oilwell in the Western Desert of Egypt. The results were compared to the actual separator test data. It was clear that the model results matched the actual test data. The ANN model is useful for predicting individual well production rates within wide variety of pumping conditions and completion configurations. This should allow for continuous monitoring, optimization, and performance analysis of ESP wells as well as quicker response to operational issues. In comparison to traditional separators and multiphase flowmeters (MPFMs), the use of the developed ANN models is simple, quick, and inexpensive.