scholarly journals A Study of Multilayer Perceptron Networks Applied to Classification of Ceramic Insulators Using Ultrasound

2021 ◽  
Vol 11 (4) ◽  
pp. 1592
Author(s):  
Nemesio Fava Sopelsa Neto ◽  
Stéfano Frizzo Stefenon ◽  
Luiz Henrique Meyer ◽  
Rafael Bruns ◽  
Ademir Nied ◽  
...  
Keyword(s):  
Multilayer Perceptron ◽  
Distribution Systems ◽  
Ultrasonic Inspection ◽  
Test Machine ◽  
Electrical Networks ◽  
Electrical Transmission ◽  
Ceramic Insulators ◽  
Distribution Class ◽  
Multilayer Perceptron Networks

Interruptions in the supply of electricity cause numerous losses to consumers, whether residential or industrial and may result in fines being imposed on the regulatory agency’s concessionaire. In Brazil, the electrical transmission and distribution systems cover a large territorial area, and because they are usually outdoors, they are exposed to environmental variations. In this context, periodic inspections are carried out on the electrical networks, and ultrasound equipment is widely used, due to non-destructive analysis characteristics. Ultrasonic inspection allows the identification of defective insulators based on the signal interpreted by an operator. This task fundamentally depends on the operator’s experience in this interpretation. In this way, it is intended to test machine learning applications to interpret ultrasound signals obtained from electrical grid insulators, distribution, class 25 kV. Currently, research in the area uses several models of artificial intelligence for various types of evaluation. This paper studies Multilayer Perceptron networks’ application to the classification of the different conditions of ceramic insulators based on a restricted database of ultrasonic signals recorded in the laboratory.

scholarly journals Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Laura Gagliano ◽  
Elie Bou Assi ◽  
Dang K. Nguyen ◽  
Mohamad Sawan
Keyword(s):  
Neural Networks ◽  
Multilayer Perceptron ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Focal Epilepsy ◽  
Single Layer ◽  
Epileptic Seizures ◽  
Novel Approach ◽  
Multilayer Perceptron Networks

Abstract This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. Two features were first extracted from bilateral intracranial electroencephalography (iEEG) recordings of dogs with naturally occurring focal epilepsy. Single-layer LSTM networks were trained to classify 5-min long feature vectors as preictal or interictal. Classification performances were compared to previous work involving multilayer perceptron networks and higher-order spectral (HOS) features on the same dataset. The proposed LSTM network proved superior to the multilayer perceptron network and achieved an average classification accuracy of 86.29% on held-out data. Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction.

scholarly journals The Application of Artificial Neural Networks in Predicting Children’s Giftedness

2016 ◽  
Vol 19 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Nina Pavlin-Bernardić ◽  
◽  
Silvija Ravić ◽  
Ivan Pavao Matić ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
School Readiness ◽  
Multilayer Perceptron ◽  
Gifted Students ◽  
Test Sample ◽  
Hyperbolic Tangent ◽  
Readiness Assessment ◽  
Artificial Neural

Artificial neural networks have a wide use in the prediction and classification of different variables, but their application in the area of educational psychology is still relatively rare. The aim of this study was to examine the accuracy of artificial neural networks in predicting students’ general giftedness. The participants were 221 fourth grade students from one Croatian elementary school. The input variables for artificial neural networks were teachers’ and peers’ nominations, school grades, earlier school readiness assessment and parents’ education. The output variable was the result on the Standard Progressive Matrices (Raven, 1994), according to which students were classified as gifted or non-gifted. We tested two artificial neural networks’ algorithms: multilayer perceptron and radial basis function. Within each algorithm, a number of different types of activation functions were tested. 80% of the sample was used for training the network and the remaining 20% to test the network. For a criterion according to which students were classified as gifted if their result on the Standard Progressive Matrices was in the 95th centile or above, the best model was obtained by the hyperbolic tangent multilayer perceptron, which had a high accuracy of 100% of correctly classified non-gifted students and 75% correctly classified gifted students in the test sample. When the criterion was the 90th centile or above, the best model was also obtained by the hyperbolic tangent multilayer perceptron, but the accuracy was lower: 94.7% in the classification of non-gifted students and 66.7% in the classification of gifted students. The study has shown artificial neural networks’ potential in this area, which should be further explored. Keywords: gifted students, identification of gifted students, artificial neural networks

scholarly journals Performance evaluation of different classification techniques using different datasets

2019 ◽  
Vol 9 (5) ◽  
pp. 3584
Author(s):  
Abdulkadir Özdemir ◽  
Uğur Yavuz ◽  
Fares Abdulhafidh Dael
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Multilayer Perceptron ◽  
Data Classification ◽  
Major Effect ◽  
Classification Techniques ◽  
The Difference ◽  
Two Parameters ◽  
Accuracy Performance

<span>Nowadays data mining become one of the technologies that paly major effect on business intelligence. However, to be able to use the data mining outcome the user should go through many process such as classified data. Classification of data is processing data and organize them in specific categorize to be use in most effective and efficient use. In data mining one technique is not applicable to be applied to all the datasets. This paper showing the difference result of applying different techniques on the same data. This paper evaluates the performance of different classification techniques using different datasets. In this study four data classification techniques have chosen. They are as follow, BayesNet, NaiveBayes, Multilayer perceptron and J48. The selected data classification techniques performance tested under two parameters, the time taken to build the model of the dataset and the percentage of accuracy to classify the dataset in the correct classification. The experiments are carried out using Weka 3.8 software. The results in the paper demonstrate that the efficiency of Multilayer Perceptron classifier in overall the best accuracy performance to classify the instances, and NaiveBayes classifiers were the worst outcome of accuracy to classifying the instance for each dataset.</span>

scholarly journals Web-Based Simulation Environment for Vehicular Electrical Networks

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6087
Author(s):  
Xavier Dominguez ◽  
Paola Mantilla-Pérez ◽  
Nuria Gimenez ◽  
Islam El-Sayed ◽  
Manuel Alberto Díaz Díaz Millán ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Distribution Systems ◽  
Software Tool ◽  
Automatic Generation ◽  
Electrical Networks ◽  
Web Based ◽  
Electrical Distribution Systems ◽  
Technical Requirements ◽  
Information Interaction

For the validation of vehicular Electrical Distribution Systems (EDS), engineers are currently required to analyze disperse information regarding technical requirements, standards and datasheets. Moreover, an enormous effort takes place to elaborate testing plans that are representative for most EDS possible configurations. These experiments are followed by laborious data analysis. To diminish this workload and the need for physical resources, this work reports a simulation platform that centralizes the tasks for testing different EDS configurations and assists the early detection of inadequacies in the design process. A specific procedure is provided to develop a software tool intended for this aim. Moreover, the described functionalities are exemplified considering as a case study the main wire harness from a commercial vehicle. A web-based architecture has been employed in alignment with the ongoing software development revolution and thus provides flexibility for both, developers and users. Due to its scalability, the proposed software scheme can be extended to other web-based simulation applications. Furthermore, the automatic generation of electrical layouts for EDS is addressed to favor an intuitive understanding of the network. To favor human–information interaction, utilized visual analytics strategies are also discussed. Finally, full simulation workflows are exposed to provide further insights on the deployment of this type of computer platforms.

Melanoma Detection by Meta-Heuristically-Optimized MLP Parameters Using Non-Dermatoscopy Images

2021 ◽  
Vol 12 (4) ◽  
pp. 177-200
Author(s):  
Soumen Mukherjee ◽  
Arunabha Adhikari ◽  
Madhusudan Roy
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Skin Lesion ◽  
Multilayer Perceptron ◽  
Binary Classification ◽  
Ranking Algorithm ◽  
Swarm Optimization ◽  
Melanoma Detection ◽  
Class 1

This paper represents a scheme of melanoma detection using handcrafted feature set with meta-heuristically optimized multilayer perceptron (MLP) parameters. Features including shape, color, and texture are extracted from camera images of skin lesion collected from University of Waterloo database. The features are used in two different ways for binary classification of the data into benign and malignant class. 1) The extracted features are ranked on their relevance using ReleifF ranking algorithm and also converted into PCA components and ranked according to their variance. Best result is obtained with 50 best ranked raw features with accuracy of 87.1%. 2) All 1,888 features are fed to an MLP with two hidden layers, with number of neurons optimized by two different metaheuristic algorithms, namely particle swarm optimization (PSO) and simulated annealing (SA) separately. The latter method is found to be more efficient, and an accuracy of 88.38%, sensitivity of 92.22%, and specificity of 83.07% are achieved by PSO, which is better in comparison with the latest research on this dataset.

Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 35-43
Author(s):  
Pratibha Verma ◽  
Vineet Kumar Awasthi ◽  
Sanat Kumar Sahu
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Multilayer Perceptron ◽  
Artificial Neural ◽  
Artery Disease ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neural network (ANN) method for the classification of the CAD dataset. Therefore, the ten-fold cross-validation (T-FCV) method, P-MLP algorithms, and base classifiers of MLP were employed. The P-MLP algorithm yielded very high accuracy (86.47% in CAD-56 and 98.35% in CAD-59 datasets) and F1-Score (90.36% in CAD-56 and 98.83% in CAD-59 datasets) rates, which have not been reported simultaneously in the MLP.

Energy Management Strategies to Improve Electrical Networks Using Storage Systems

2016 ◽  
pp. 63-75
Author(s):  
Juan Aurelio Montero-Sousa ◽  
Luis Alfonso Fernández-Serantes ◽  
José-Luis Casteleiro-Roca ◽  
Xosé Manuel Vilar-Martínez ◽  
Jose Luis Calvo-Rolle
Keyword(s):  
Energy Management ◽  
Storage Systems ◽  
Management Strategies ◽  
Electrical Networks ◽  
Systems Management ◽  
Electrical System ◽  
Peak Shaving ◽  
Electricity Storage ◽  
General Terms

The successive energy crises, usually linked to the rising prices of oil, bring about new topics of the energy systems management in general terms. Over all, the electrical system is one of these cases. In addition, a greater concern for environmental issues has introduced, to a greater or lesser extent, the generation from renewable sources in the electrical system. In this context, the possibility of developing and using electricity storage systems would manage mismatches between generation and demand at electricity networks, making them more efficiently. In this research, we propose a number of possible strategies based on technical peak shaving and valley filling. The tool is used as energy storage systems in general terms, regardless of the accumulation technique used. The classification of strategies essentially serves two criteria: optimization service and increased profitability.

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