Epilepsy is a brain abnormality that leads its patients to suffer from seizures, which conditions their behavior and lifestyle. Neurologists use an electroencephalogram (EEG) to diagnose this disease. This test illustrates the signaling behavior of a person's brain, allowing, among other things, the diagnosis of epilepsy. From a visual analysis of these signals, neurologists identify patterns such as peaks or valleys, looking for any indication of brain disorder that leads to the diagnosis of epilepsy in a purely qualitative way. However, by applying a test based on Fourier signal analysis through rapid transformation in the frequency domain, patterns can be quantitatively identified to differentiate patients diagnosed with the disease and others who are not. In this article, an analysis of the EEG signal is performed to extract characteristics in patients already classified as epileptic and non-epileptic, which will be used in the training of models based on classification techniques such as logistic regression, artificial neural networks, support vector machines, and convolutional neural networks. Based on the results obtained with each technique, an analysis is performed to decide which of these behaves better.
In this study traditional classification techniques were implemented that had as data frequency data in the channels with distinctive information of EEG examinations, this was done through a feature extraction obtained with Fourier analysis considering frequency bands. The techniques used for classification were implemented in Python and through a comparison of metrics and performance, it was concluded that the best classification technique to characterize epileptic patients are artificial neural networks with an accuracy of 86%.
This paper proposes a methodology for the design of fuzzy inference systems based on Boolean relations. The approach using Boolean sets presents limited performance due to the abrupt transitions that occur during its functioning, therefore, fuzzy sets can be used aiming the improvement of the performance. In this approach, firstly, the design of a Boolean controller is performed, which is later extended into fuzzy under design guidelines proposed in this paper. The methodology uses Kleene algebra via truth tables for the fuzzy system design, allowing the simplification of the equations that implement the fuzzy system.
The objective of this article is to review how Artificial Intelligence (AI) tools have helped the process of formulating agricultural public policies in the world. For this, a search process was carried out in the main scientific repositories finding different publications. The findings have shown that, first, the most commonly used AI tools are agent-based models, cellular automata, and genetic algorithms. Secondly, they have been utilized to determine land and water use, and agricultural production. In the end, the large usefulness that AI tools have in the process of formulating agricultural public policies is concluded.
This document presents some considerations and procedures to design a compact fuzzy system based on Boolean relations. In the design process, a Boolean codification of two elements is extended to a Kleene’s of three elements to perform simplifications for obtaining a compact fuzzy system. The design methodology employed a set of considerations producing equivalent expressions when using Boole and Kleene algebras establishing cases where simplification can be carried out, thus obtaining compact forms. In addition, the development of two compact fuzzy systems based on Boolean relations is shown, presenting its application for the identification of a nonlinear plant and the control of a hydraulic system where it can be seen that compact structures describes satisfactory performance for both identification and control when using algorithms for optimizing the parameters of the compact fuzzy systems. Finally, the applications where compact fuzzy systems are based on Boolean relationships are discussed allowing the observation of other scenarios where these structures can be used.
Bio-inspired algorithms are a suitable alternative for solving multi-objective optimization problems. Among different proposals, a widely used approach is based on the Pareto front. In this document, a proposal is made for the analysis of the optimal front for multi-objective optimization problems using clustering techniques. With this approach, an alternative is sought for further use and improvement of multi-objective optimization algorithms considering solutions and clusters found. To carry out the clustering, the methods k-means and fuzzy c-means are employed, in such a way that there are two alternatives to generate the possible clusters. Regarding the results, it is observed that both clustering algorithms perform an adequate separation of the optimal Pareto continuous fronts; for discontinuous fronts, k-means and fuzzy c-means obtain results that complement each other (there is no superior algorithm). In terms of processing time, k-means presents less execution time than fuzzy c-means.
Systems of distributed generation have shown to be a remarkable alternative to a rational use of energy. Nevertheless, the proper functioning of them still manifests a range of challenges, including both the adequate energy dispatch depending on the variability of consumption and the interaction between generators. This paper describes the implementation of an adaptive neurofuzzy system for voltage control, regarding the changes observed in the consumption within the distribution system. The proposed design employs two neurofuzzy systems, one for the plant dynamics identification and the other for control purposes. This focus optimizes the controller using the model achieved through the identification of the plant, whose changes are produced by charge variation; consequently, this process is adaptively performed. The results show the performance of the adaptive neurofuzzy system via statistical analysis.
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