A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals.

Driver fatigue is the one of the main reasons of the traffic accidents. The human brain is a complex structure, whose function can be evaluated with electroencephalogram (EEG). Automated driver fatigue detection utilizing EEG decreases the incidence probability of related traffic accidents. Therefore, devising an appropriate feature extraction technique and selecting a competent classification method can be considered as the crucial part of the effective driver fatigue detection. Therefore, in this study, an EEG-based intelligent system was devised for driver fatigue detection. The proposed framework includes a new feature generation network, which is implemented by using texture descriptors, for fatigue detection. The proposed scheme contains pre-processing, feature generation, informative features selection and classification with shallow classifiers phases. In the pre-processing, discrete cosine transform and fast Fourier transform are used together. Moreover, dynamic center based binary pattern and multi threshold ternary pattern are utilized together to create a new feature generation network. To improve the detection performance, we utilized discrete wavelet transform as a pooling method, in which the functional brain network-based feature describing the relationship between fatigue and brain network organization. In the feature selection phase, a hybrid three layered feature selection method is presented, and benchmark classifiers are used in the classification phase to demonstrate the strength of the proposed method. In the experiments, the proposed framework achieved 97.29% classification accuracy for fatigue detection using EEG signals. This result reveals that the proposed framework can be utilized effectively for driver fatigue detection.

A survival classification method for hepatocellular carcinoma patients with chaotic Darcy optimization method based feature selection.

Survey is one of the crucial data retrieval methods in the literature. However, surveys often contain missing data and redundant features. Therefore, missing feature completion and feature selection have been widely used for knowledge extraction from surveys. We have a hypothesis to solve these two problems. To implement our hypothesis, a classification method is presented. Our proposed method consists of missing feature completion with a statistical moment (average) and feature selection using a novel swarm optimization method. Firstly, an average based supervised feature completion method is applied to Hepatocellular Carcinoma survey (HCC). The used HCC survey consists of 49 features. To select meaningful features, a chaotic Darcy optimization based feature selection method is presented and this method selects 31 most discriminative features of the completed HCC dataset. 0.9879 accuracy rate was obtained by using the proposed chaotic Darcy optimization-based HCC survival classification method.

Automated Parkinson's disease recognition based on statistical pooling method using acoustic features.

Parkinson's disease is one of the mostly seen neurological disease. It affects to nervous system and hinders people's vital activities. The majority of Parkinson's patients lose their ability to speak, write and balance. Many machine learning methods have been proposed to automatically diagnose Parkinson's disease using acoustic, hand writing and gaits. In this study, a statistical pooling method is proposed to recognize Parkinson's disease using the vowels. The used Parkinson's disease dataset contains the features of vowels. In the proposed method, the features of dataset are increased by applying statistical pooling method. Then, the most weighted features are selected from increased feature vector by using ReliefF. The classification is applied using the most weighted feature vector obtained. In the proposed method, Support Vector Machine (SVM) and K Nearest Neighbor (KNN) algorithms are used. The success rate was calculated as 91.25% and 91.23% with by using SVM and KNN respectively. The proposed method has two main contributions. The first is to obtain new features from the Parkinson's acoustic dataset using the statistical pooling method. The second one is the selection of the most significant features from the many feature vectors obtained. Thus, successful results were obtained for both KNN and SVM algorithms. The comparatively results clearly show that the proposed method achieved the best success rate among the selected state-of-art methods. Considering the proposed method and the results obtained, it proposed method is successful for Parkinson's disease recognition.