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An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals

International Journal of Automation and computing 2019年第6期16卷 737-747页

作者： Siuly Siuly Varun Bajaj abdulkadir sengur Yanchun Zhang Institute for Sustainable Industries&Liveable Cities Victoria UniversityMelbourne VIC 3011Australia Discipline of Electronics and Communication Engineering PDPM Indian Institute of Information TechnologyDesign and ManufacturingJabalpur 482005India Deptartement of Electrical and Electronics Engineering Faculty of TechnologyFirat UniversityElazig 23119Turkey Cyberspace Institute of Advanced Technology(CIAT) Guangzhou UniversityGuangzhou 510006China

This paper addresses an advanced analysis system for the identification of alcoholic brain states from electroencephalogram(EEG) data in an automatic way. This study introduces an optimum allocation based sampling(OAS) scheme to discover the most favourable representative data points from every single time-window of each EEG signal considering the minimal variability of the observations. Combining all representative samples of each time-window in a set, some statistical features are extracted from every set of each class. The Mann-Whitney U test is used to assess whether each of the features is significant between the two classes(e.g., alcoholic and control). In order to evaluate the effectiveness of the OAS-based features, four well-known machine learning methods(decision table,support vector machine(SVM), k-nearest neighbor(k-NN) and logistic regression) are considered for identification of alcoholic brain state. The experimental results on the UCI KDD(i.e., UCI knowledge discovery in databases) database demonstrate that the OAS based decision table algorithm yields the highest accuracy of 99.58% with a low false alarm rate 0.40%, which is an improvement of up to9.58% over the existing algorithms. A proposed analysis system can be used to detect alcoholism and also to determine the level of alcoholism-related changes in EEG signals.

关键词： Electroencephalogram(EEG) alcoholism optimum allocation technique feature extraction decision table

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Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed to high temperatures

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Frontiers of Structural and Civil Engineering 2020年第6期14卷 1316-1330页

作者： Harun TANYILDIZI abdulkadir sengur Yaman AKBULUT Murat SAHtNa Faculty of Technology Department of Civil EngineeringFirat UniversityElazig 23100Turkey Faculty of Technology Department of Electrical-Electronics EngineeringFir at UniversityElazig 23100Turkey

In this study,the deep learning models for estimating the mechanical properties of concrete containing silica fume subjected to high temperatures were *** fume was used at concentrations of 0%,5%,10%,and 20%.Cube specimens(100 mm×100 mm×100 mm)were prepared for testing the compressive strength and ultrasonic pulse *** were cured at 20℃zb2℃ in a standard cure for 7,28,and 90 *** curing,they were subjected to temperatures of 20℃,200℃,400℃,600℃,and 800℃.Two well-known deep learning approaches,i.e.,stacked autoencoders and long short-term memory(LSTM)networks,were used for forecasting the compressive strength and ultrasonic pulse velocity of concrete containing silica fume subjected to high *** forecasting experiments were carried out using MATLAB deep learning and neural network tools,*** statistical measures were used to validate the prediction performances of both the *** study found that the LSTM network achieved better results than the stacked *** addition,this study found that deep learning,which has a very good prediction ability with little experimental data,was a convenient method for civil engineering.

关键词： concrete high temperature strength properties deep learning stacked auto-encoders LSTM network

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