Fuzzy Neural Networks based on Fuzzy Logic Neurons Regularized by Resampling Techniques and Regularization Theory for Regression Problems

Souza, Paulo Vitor de Campos; Guimarães, Augusto Júnio; Araújo, Vanessa Souza; Rezende, Thiago Silva; Araújo, Vinícius Jonathan Silva

doi:10.4114/intartif.vol22iss63pp114-133

Cited by 18 publications

(7 citation statements)

References 41 publications

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“…FNNs act on problem-solving with various types of complexity. They can work on simple pattern classification problems by using pruning approaches of their architecture [102] and at the same time, can generate patterns for the evaluation of nonlinear systems [103], time series forecasting [104] and linear and non-linear regression problems [102,105].…”

Section: Fuzzy Neural Network and Their Practical Applicationsmentioning

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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Section: Fuzzy Neural Network and Their Practical Applicationsmentioning

confidence: 99%

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

Souza

Guimarães

Rezende

et al. 2020

Self Cite

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“…A model that is used for fuzzification is the ANFIS model proposed by Jang [40], where its versions can generate membership functions that are equally or differently spaced. Several works such as this use this approach like as de Campos Souza et al [16,17,20,21,35,[68][69][70][71] and Guimaraes et al [34], allowing the data set to be partitioned in a grid format, allowing for inferences and interpretability about the dataset studied.…”

Section: Fuzzification Techniques In Fuzzy Neural Networkmentioning

confidence: 99%

Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network

et al. 2020

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Traditionally, fuzzy neural networks have parametric clustering methods based on equally spaced membership functions to fuzzify inputs of the model. In this sense, it produces an excessive number calculations for the parameters' definition of the network architecture, which may be a problem especially for realtime large-scale tasks. Therefore, this paper proposes a new model that uses a nonparametric technique for the fuzzification process. The proposed model uses an autonomous data density approach in a pruned fuzzy neural network, wich favours the compactness of the model. The performance of the proposed approach is evaluated through the usage of databases related to the Optical Interconnection Network. Finally, binary patterns classification tests for the identification of Temporal Distribution (Asynchronous or Client-Server) were performed and compared with stateof-the-art fuzzy neural-based and traditional machine learning approaches. Results demonstrated that the proposed model is an efficient tool for these challenging classification tasks.

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“…The activation functions of the third layer neuron are of the linear type for FNN and defined by Weka in the initial configuration of the algorithms in the tool. For fuzzy neural networks using Gaussian membership functions, many membership functions (M) were defined as 3 and 5 by the 10-k-fold process (for the interval of M = (2,3,4,5,6,7,8,9) seeking to maximize training accuracy).…”

Section: Test Settingsmentioning

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

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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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Fuzzy Neural Networks based on Fuzzy Logic Neurons Regularized by Resampling Techniques and Regularization Theory for Regression Problems

Cited by 18 publications

References 41 publications

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

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

Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network

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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