Self-organized direction aware for regularized fuzzy neural networks

Souza, Paulo Vitor de Campos; Nunes, Cristiano; Guimares, Augusto Junio; Rezende, Thiago Silva; Araújo, Vanessa Souza; Arajuo, Vincius Jonathan Silva

doi:10.1007/s12530-019-09278-5

Cited by 17 publications

(9 citation statements)

References 72 publications

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“…e training of deep neural networks with ReLU and a parameterized ReLU (PReLU) has been studied in [21][22][23], both doing research in a theoretical perspective of the performance of those activation functions.…”

Section: Hyperparametersmentioning

confidence: 99%

Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction

Aceves-Fernández

Domínguez-Guevara

Pedraza-Ortega

et al. 2020

Discrete Dynamics in Nature and Society

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Particulate matter with a diameter less than 10 micrometers (PM10) is today an important subject of study, mainly because of its increasing concentration and its impact on environment and public health. is article summarizes the usage of convolutional neural networks (CNNs) to forecast PM10 concentrations based on atmospheric variables. In this particular case-study, the use of deep convolutional neural networks (both 1D and 2D) was explored to probe the feasibility of these techniques in prediction tasks. Furthermore, in this contribution, an ensemble method called Bagging (BEM) is used to improve the accuracy of the prediction model. Lastly, a well-known technique for PM10 forecasting, called multilayer perceptron (MLP) is used as a comparison to show the feasibility, accuracy, and robustness of the proposed model. In this contribution, it was found that the CNNs outperforms MLP, especially when they are executed using ensemble models.Hindawi

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Section: Hyperparametersmentioning

confidence: 99%

Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction

Aceves-Fernández

Domínguez-Guevara

Pedraza-Ortega

et al. 2020

Discrete Dynamics in Nature and Society

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“…In addition to tests with traditional approaches, the results will also be compared with other hybrid models of neural networks and fuzzy systems, where we can highlight an evolving fuzzy neural network model (EFNN) [39], one that works with incremental fuzzification (IFNN) [134] and one that works with self-organizing fuzzification (SFNN) [135]. All models use the extreme learning machine to define the weights of the output layer, have three layers, and are composed of unineurons in the second layer and Gaussian neurons in the first layer.…”

Section: Definitions and Models Used In The Testsmentioning

confidence: 99%

Detection of Anomalies in Large-Scale Cyberattacks Using Fuzzy Neural Networks

Souza

Guimarães

Rezende

et al. 2020

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The fuzzy neural networks are hybrid structures that can act in several contexts of the pattern classification, including the detection of failures and anomalous behaviors. This paper discusses the use of an artificial intelligence model based on the association between fuzzy logic and training of artificial neural networks to recognize anomalies in transactions involved in the context of computer networks and cyberattacks. In addition to verifying the accuracy of the model, fuzzy rules were obtained through knowledge from the massive datasets to form expert systems. The acquired rules allow the creation of intelligent systems in high-level languages with a robust level of identification of anomalies in Internet transactions, and the accuracy of the results of the test confirms that the fuzzy neural networks can act in anomaly detection in high-security attacks in computer networks.

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“…(6) Use f-scores to define the most significant neurons to the problem (L p ). (7) For all K input do (7.1) Calculate the mapping z k (x k ) using logical neurons (8) Estimate the weights of the output layer using Equation 7. (9) Calculate output y using Equation (5).…”

Section: Third Layermentioning

confidence: 99%

“…They can perform efficient training, extract information from the problem data, and maintain high-accuracy results. They effectively resolve problems of various types of science such as pattern classification [4][5][6][7][8], linear regression [9], time series forecasting [10], issues in industry [11], and also resolve problems in the areas of health [12][13][14][15][16][17] and software efforts [18,19]. Even problems in the field of immunotherapy have been the subject of judgment by these models in [20].…”

Section: Introductionmentioning

confidence: 99%

Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

Guimarães

Souza

Araújo

et al. 2019

BDCC

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Human papillomavirus (HPV) infection is related to frequent cases of cervical cancer and genital condyloma in humans. Up to now, numerous methods have come into existence for the prevention and treatment of this disease. In this context, this paper aims to help predict the susceptibility of the patient to forms treatment using both cryotherapy and immunotherapy. These studies facilitate the choice of medications, which can be painful and embarrassing for patients who have warts on intimate parts. However, the use of intelligent models generates efficient results but does not allow a better interpretation of the results. To solve the problem, we present the method of a fuzzy neural network (FNN). A hybrid model capable of solving complex problems and extracting knowledge from the database will pruned through F-score techniques to perform pattern classification in the treatment of warts, and to produce a specialist system based on if/then rules, according to the experience obtained from the database collected through medical research. Finally, binary pattern-classification tests realized in the FNN and compared with other models commonly used for classification tasks capture results of greater accuracy than the current state of the art for this type of problem (84.32% for immunotherapy, and 88.64% for cryotherapy), and extract fuzzy rules from the problem database. It was found that the hybrid approach based on neural networks and fuzzy systems can be an excellent tool to aid the prediction of cryotherapy and immunotherapy treatments.

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Self-organized direction aware for regularized fuzzy neural networks

Cited by 17 publications

References 72 publications

Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction

Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction

Detection of Anomalies in Large-Scale Cyberattacks Using Fuzzy Neural Networks

Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

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