Multi-layer Perceptron Configuration Research Articles

Machine Learning Algorithms for Predictive Maintenance in Industrial Environments: A Comparative Study

In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases.
 
 While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications

Security Challenges of Vehicular Cloud Computing

In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases. While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications.

Machine Learning Algorithms for Predictive Maintenance in Industrial Environments: A Comparative Study

In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases. While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications.

Security Challenges of Vehicular Cloud Computing

In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases.
 
 While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications.

Enhancing multilayer perceptron neural network using archive-based harris hawks optimizer to predict gold prices

The success of the Multi-Layer Perceptron Neural Network (MLP) relies on carefully configuring its weights and biases to promising values. The gradient descent technique is usually used to select the optimal MLP configuration. Such a technique can stuck in local optima and its convergence towards promising regions in the search space is slow. Harris Hawks optimizer (HHO) is a recently proposed metaheuristic technique that mimics the harris’ hawks behavior to chase and catch prey. In this paper, the algorithm search process is enhanced by introducing an external archive that saves the best solutions to be used in the next iterations. Gold price prediction is essential for future investment planning and decision-making in mining projects. The gold price is predicted using a multilayer perceptron (MLP) neural network (NN) that has the optimal control parameters obtained using the improved version of Harris Hawks optimizer (HHO) with an external archive which is called (AHHO-NN). The input features with a complicated relationship can influence gold price prediction models such as inflation rate, precious metals prices, and other features. In this sense, the literature suggests a specific reduced number of features for the MLP-NN model to yield high-quality prediction results. In addition to the prediction model, two feature reduction methods are used to select the proper list of the input features for the MLP-NN model to forecast gold price namely, Pearson’s correlation and a newly proposed categorized correlation. Surprisingly, new features not mentioned in the literature are discovered and some are discarded. The time series dataset used has been extracted from several sources and pre-processed to fit the proposed model. Furthermore, the prediction results have been evaluated using several measures such as prediction accuracy, Mean square error (MSE), Root mean square error (RMSE), Mean absolute error (MAE), and average absolute relative deviation (AARD). The performance of the proposed AHHO-NN model is compared against four swarm intelligence algorithms (i.e., HHO-NN, JAYA-NN, MFO-NN, PSO-NN) as well as four classical machine learning techniques (i.e., linear regression (LR), MLP Regressor (MLP), RANSAC Regressor (RANSAC), and TheilSen Regressor (TR)). Interestingly, the proposed AHHO-NN is able to comparatively produce high-quality prediction results for gold prices with relatively high accuracy. This proves the viability of the proposed AHHO-NN model as well as the proper selection of the primitive input features.

A generic model towards maximizing the performance of ship propulsion system using artificial neural network

In this study, optimization based on artificial neural network (ANN) was established to predict the optimal performance parameters of the system, leading to an optimized propulsion system. Optimization related works on propulsion system investigated by researchers are based on specific fuel consumption, emission characteristics, trim and draft optimization using conventional methods amongst others. This creates a gap for a research window in this direction which this work tries to fill by optimizing overall ship propulsion system efficiency using ANN. The models computational development for this research were actualized using ANN tool box in MATLAB. Attention was given to performance parameter that influences the overall performance of the propulsion system. Hence, the overall ship efficiency, η_sp and energy efficiency design index (EEDI) constitute the parameters of interest to be optimized. A comprehensive ANN program code was developed and implemented to create, configure, train and optimize these parameters of interest. Various ANN-based models of a two layered multilayer perceptron (MLP) structure with different configurations were trained and investigated. Results analysis from MATLAB simulation yields η_sp of 0.507398386 and EEDI of 3.490301699 g of CO2/tm. Results show that the MLP configuration of 14-20-7 gives an optimal model for the ANN. The resulting model could predict the optimized performance output of the system with high degree of accuracy with a minimum mean square error (MSE) at 206 epochs. This point gives the lowest MSE performances value of 3.2923e-10 and regression plot between 0.99999 and 1. The percentage parametric optimization of the propulsion system parameters in ANN gives a 2.4% improvement of η_sp and 3.5% improvement of EEDI. These indicate that the use of ANN for parametric optimization of propulsion system is satisfactory

Detection of Corrosion-Indicating Oxidation Product Colors in Steel Bridges under Varying Illuminations, Shadows, and Wetting Conditions

Early detection of corrosion in steel bridges is essential for strategizing the mitigation of further corrosion damage. Although various image-based approaches are available in the literature for corrosion detection, most of these approaches are tested on images acquired under uniform natural daylight illuminations i.e., inherent variations in the ambient lighting conditions are ignored. Owing to the fact that varying natural daylight illuminations, shadows, water wetting, and oil wetting are unavoidable in real-world scenarios, it is important to devise a robust technique for corrosion identification. In the current study, four different color spaces namely ‘RGB’, ‘rgb’, ‘HSV’ and ‘CIE La*b*’ along with a multi-layer perceptron (MLP) is configured and trained for detecting corrosion under above-mentioned real-world illumination scenarios. Training (5000 instances) and validation (2064 instances) datasets for this purpose are generated from the images of corroded steel plates acquired in the laboratory under varying illuminations and shadows, respectively. Each combination of color space and an MLP configuration is individually assessed and the best suitable combination that yields the highest ‘Recall’ value is determined. An MLP configuration with a single hidden layer consisting of 4 neurons (1st Hidden Layer (HL)(4N)) in conjunction with ‘rgb’ color space is found to yield the highest ‘Accuracy’ and ‘Recall’ (up to 91% and 82% respectively). The efficacy of the trained MLP to detect corrosion is then demonstrated on the test image database consisting of both lab-generated partially corroded steel plate images and field-generated images of a bridge located in Moorhead (Minnesota). Lab-generated images used for testing are acquired under varying illuminations, shadows, water wetting, and oil wetting conditions. Based on the validation studies, ‘rgb’ color space and an MLP configuration consisting of single hidden layer with 4 neurons (1st HL(4N)) trained on lab-generated corroded plate images identified corrosion in the steel bridge under ambient lighting conditions.

Detection of liner surface defects in solid rocket motors using multilayer perceptron neural networks

Debonding problems along the propellant/liner/insulation interface are a critical point to the integrity and one of the major causes of structural failures of solid rocket motors. Current solutions are typically restricted to methods for assessing the integrity of the rocket motors structure and visually inspecting their components. In this context, this paper presents an improved algorithm to detect liner surface defects that may compromise the bonding between the solid propellant and the insulation. The use of Local Binary Patterns (LBP) provides a structural and statistical approach to texture analysis of liner sample images. Along with color information extraction, these two methods allow the representation of image pixels by feature vectors that are further processed by a Multilayer Perceptron (MLP) neural network classifier. The MLP neural network analyzes liner sample images and classifies each pixel into one of three classes: non-defect, foreign object, and defect. Several tests were executed varying different parameters to find the optimal MLP configuration, and as a result, the best classification accuracy of 99.08%, 90.66%, and 99.48% was achieved for the corresponding classes. Moreover, the defect size estimate showed that the MLP classifier correctly identified defects less than 1 mm long, with a relatively small number of training examples. Positive results indicate that the algorithm can identify liner surface defects with a performance similar to human inspectors and has the potential to assist or even automate the liner inspection process of solid rocket motors.

Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation

In this paper a genetic algorithm (GA) approach to design of multi-layer perceptron (MLP) for combined cycle power plant power output estimation is presented. Dataset used in this research is a part of publicly available UCI Machine Learning Repository and it consists of 9568 data points (power plant operating regimes) that is divided on training dataset that consists of 7500 data points and testing dataset containing 2068 data points. Presented research was performed with aim of increasing regression performances of MLP in comparison to ones available in the literature by utilizing heuristic algorithm. The GA described in this paper is performed by using mutation and crossover procedures. These procedures are utilized for design of 20 different chromosomes in 50 different generations. MLP configurations that are designed with GA implementation are validated by using Bland - Altman (B-A) analysis. By utilizing GA, MLP with five hidden layers of 80,25,65,75 and 80 nodes, respectively, is designed. For aforementioned MLP, k - fold cross-validation is performed in order to examine its generalization performances. The Root Mean Square Error ( R M S E ) value achieved with aforementioned MLP is 4.305 , that is significantly lower in comparison with MLP presented in available literature, but still higher than several complex algorithms such as KStar and tree based algorithms.

Thermal performance prediction of wickless heat pipe with Al2O3/water nanofluid using artificial neural network

A wickless heat pipe (WHP) comprises of an evacuated-close tube filled with an appropriate amount of working fluid. In this study, the effect of Al2O3/water nanofluid as the working media on thermal performance of WHP investigated and compared with pure water by designing an optimized Artificial Neural Network (ANN). ANN trained with the collected test data obtained from experimental setup and validated. Multilayer Perceptron configuration (MLP) adopted for the ANN. The MLP architecture consists of four input nodes representing the parameters; input power, volume concentration of nanofluid, filling ratio and mass rate in condenser section, and a single output node representing the thermal efficiency of WHP. According to sensitivity analysis results, volume concentration is the most significant parameter which affects the WHP performance. Also, since the ANN test output data are sufficiently close to experimental one, it can be inferred that the ANN model can be applied to accurately model WHP thermal performance.

AVALIAÇÃO DO USO DE REGRESSÃO E REDE NEURAL ARTIFICIAL PARA MODELAGEM DO AFILAMENTO DO FUSTE DE EUCALIPTO EM SISTEMA SILVIPASTORIL

The aim of this study was to evaluate the efficiency of using an artificial neural network (ANN) to estimate the stem taper of eucalypt trees in a silvopastoral system composed with two spatial arrangements. The data used were collected out of 35 sample-trees scaled in a silvipastoral system with spatial arrangements of 12 m x 4 m and 12 m x 2 m. The taper model proposed by Garay was fitted for each spatial arrangement. Also, the ANN with Multilayer Perceptron configuration, using the spatial arrangement as a categorical variable, was trained. The others input variables for the ANN were the diameter at breast height 1.30 m height, total height, height of each section and the corresponding diameters. The accuracy of the methods was evaluated using the Root Mean-square Error, the correlation between observed and estimated diameters, dispersion of percentage errors and the mean absolut deviaton. The ANN achieved a similar performance compared to the two functions of tapering, proving to be an appropriate methodology for small eucalyptus plantations in silvopastoral system, where there may be restrictions for logging.

Classification of seismic signals at Villarrica volcano (Chile) using neural networks and genetic algorithms

Each volcano has its own unique seismic activity. The aim of this work is to construct a system able to classify seismic signals for the Villarrica volcano, one of the most active volcanoes in South America. Since seismic signals are the result of particular processes inside the volcano's structure, they can be used to forecast volcanic activity. This paper describes the different kinds of seismic signals recorded at the Villarrica volcano and their significance. Three kind of signals were considered as most representative of this volcano's activity: the long-period, the tremor, and the energetic tremor signals. A classifier is implemented to read the seismic registers at 30-second intervals, extract the most relevant features of each interval, and classify them into one of the three kinds of signals considered as most representative of this particular volcano. To do so, 1033 different kinds of 30-s signals were extracted and classified by a human expert. A feature extraction process was applied to obtain the main characteristics of each of them. This process was developed using criteria which have been shown by others to effectively classify seismic signals, based on the experience of a human expert. The classifier was implemented with a Multi-Layer Perceptron (MLP) artificial neural network whose architecture and training process were optimized by means of a genetic algorithm. This technique searched for the most adequate MLP configuration to improve the classification performance, optimizing the number of hidden neurons, the transfer functions of the neurons, and the training algorithm. The optimization process also performed a feature selection to reduce the number of signal features, optimizing the number of network inputs. The results show that the optimized classifier reaches more than 93% exactitude. identifying the signals of each kind. The amplitude of the signals is the most important feature for its classification, followed by its frequency content. The described methodology can be used to classify more seismic signals to improve the study of the activity of this volcano or to extend the study to other active volcanoes of the region.

Automatic tool state identification in a metal turning operation using MLP neural networks and multivariate process parameters

This paper describes results of the application of feed-forward Multi-Layer Perceptron (MLP) neural networks for cutting tool state identification in a metal turning operation. Test cuts were conducted using P25 carbide inserts with and without wear (i.e. nominally sharp) on EN24 alloy steel. The acquired data were used to train, cross-validate and test the generalisation capabilities of two MLP configurations. Both networks had exactly the same input and output nodes but differing number of nodes in a single middle layer. Training was achieved via back-propagation of error enhanced by the addition of a momentum term and adaptive learning rate. Different error goal targets during training of the MLP were used, and the validation results of the model investigation analysed and presented. Obtained results for successful classification of the tool state with respect to only two classes (worn or sharp) were between 83 and 96%.