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Appl. Sci. 2018, 8(9), 1613; https://doi.org/10.3390/app8091613

An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

Department of Computer Engineering, Suleyman Demirel University, Isparta 32260, Turkey
Received: 25 July 2018 / Revised: 1 September 2018 / Accepted: 4 September 2018 / Published: 11 September 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

The prediction of future events based on available time series measurements is a relevant research area specifically for healthcare, such as prognostics and assessments of intervention applications. A measure of brain dynamics, electroencephalogram time series, are routinely analyzed to obtain information about current, as well as future, mental states, and to detect and diagnose diseases or environmental factors. Due to their chaotic nature, electroencephalogram time series require specialized techniques for effective prediction. The objective of this study was to introduce a hybrid system developed by artificial intelligence techniques to deal with electroencephalogram time series. Both artificial neural networks and the ant-lion optimizer, which is a recent intelligent optimization technique, were employed to comprehend the related system and perform some prediction applications over electroencephalogram time series. According to the obtained findings, the system can successfully predict the future states of target time series and it even outperforms some other hybrid artificial neural network-based systems and alternative time series prediction approaches from the literature. View Full-Text
Keywords: artificial neural networks; ant-lion optimizer; time series prediction; electroencephalogram; healthcare; chaotic time series; artificial intelligence artificial neural networks; ant-lion optimizer; time series prediction; electroencephalogram; healthcare; chaotic time series; artificial intelligence
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Kose, U. An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction. Appl. Sci. 2018, 8, 1613.

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