Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Publication Year
Articles 1 - 30 of 52
Full-Text Articles in Physical Sciences and Mathematics
Compatibility Of Clique Clustering Algorithm With Dimensionality Reduction, Ug ̆Ur Madran, Duygu Soyog ̆Lu
Compatibility Of Clique Clustering Algorithm With Dimensionality Reduction, Ug ̆Ur Madran, Duygu Soyog ̆Lu
Applied Mathematics & Information Sciences
In our previous work, we introduced a clustering algorithm based on clique formation. Cliques, the obtained clusters, are constructed by choosing the most dense complete subgraphs by using similarity values between instances. The clique algorithm successfully reduces the number of instances in a data set without substantially changing the accuracy rate. In this current work, we focused on reducing the number of features. For this purpose, the effect of the clique clustering algorithm on dimensionality reduction has been analyzed. We propose a novel algorithm for support vector machine classification by combining these two techniques and applying different strategies by differentiating …
Svm-Based Sensitivity Zoning Of Subsidence Disaster Development In The Underground Coal Mining Areas, Xue Yong’An, Zou Youfeng, Zhang Wenzhi, Zhang Mingmei, Liu Guangchun
Svm-Based Sensitivity Zoning Of Subsidence Disaster Development In The Underground Coal Mining Areas, Xue Yong’An, Zou Youfeng, Zhang Wenzhi, Zhang Mingmei, Liu Guangchun
Coal Geology & Exploration
At present, there is no fixed way to predict the sensitive zones of subsidence disaster development in underground coal mining areas, and the prediction result of sensitive zones has a great uncertainty. Herein, the subsidence disaster in Xishan area of Taiyuan City, Shanxi Province was taken as the research object. Totally 4 types of kernel SVM based prediction model for sensitivity zoning of subsidence disaster were constructed with the methods of GIS spatial analysis, statistical analysis and Support Vector Machine (SVM) in combination, taking the subsidence disaster data checked and recorded in 2012 and 2014 as the modeling and verification …
Fatigue Detection Method Based On Facial Features And Head Posture, Rongxiu Lu, Bihao Zhang, Zhenlong Mo
Fatigue Detection Method Based On Facial Features And Head Posture, Rongxiu Lu, Bihao Zhang, Zhenlong Mo
Journal of System Simulation
Abstract: Aiming at the of the single fatigue characteristics, low robustness and inability to customize fatigue thresholds for different drivers of fatigue detection methods, a method based on facial features and head posture is proposed. In face detection and face key point positioning HOG feature operator and regression tree algorithm are used. In head posture estimation, head posture Euler angle is estimated by combining the face key points with the coordinate system transformation. In fatigue feature extraction, a deep residual neural network model is established to extract the eye fatigue features, which the eye, mouth aspect ratio and head posture …
Prediction Of Broken Rotor Bar In Induction Motor Using Spectral Entropy Features And Tlbo Optimized Svm, Sudip Halder, Sunil Bhat, Bimal Dora
Prediction Of Broken Rotor Bar In Induction Motor Using Spectral Entropy Features And Tlbo Optimized Svm, Sudip Halder, Sunil Bhat, Bimal Dora
Turkish Journal of Electrical Engineering and Computer Sciences
The information of the fault frequency characteristics is of great importance for all associated fault diag nostics. This requires a high-resolution spectrum analysis to achieve efficient monitoring of machinery faults, especially while diagnosing rotor bar breakage under light load conditions, because the fault frequencies almost overlap with the fundamental. In this context, rather than looking for frequencies associated with rotor faults, several frequency bands are observed separately in terms of the entropy contained within these bands. First, the motor current signal has been divided into several frequency bands using the continuous wavelet transform (CWT), and the spectral entropy is calculated …
Research On The Coal Thickness Prediction Method Based On Vmd And Svm, Zeng Aiping, Zhang Jiawei, Ren Enming, Liu Tao, Jiang Fei, Liu Xingjin, Su Huairui
Research On The Coal Thickness Prediction Method Based On Vmd And Svm, Zeng Aiping, Zhang Jiawei, Ren Enming, Liu Tao, Jiang Fei, Liu Xingjin, Su Huairui
Coal Geology & Exploration
Changes in coal thickness have an important impact on safe and efficient coal mining. In order to solve the problem of large errors in coal thickness prediction results when the 3D seismic data contains noise, a method in which variable modal decomposition(VMD) and support vector machine(SVM) methods are combined for coal thickness prediction is proposed. Firstly, a coal-thickness wedge model is constructed and seismic forward modeling is performed on it. Based on the condition of thin coal seam thickness, the amplitude attribute and bandwidth attribute have a good positive correlation with the coal thickness, while the instantaneous frequency attribute has …
Research On Intelligent Gait Recognition Method Based On Plantar Pressure Perception, Xueqin Liu, Liu Ning, Su Zhong, Jingxiao Wang, Chaojie Yuan
Research On Intelligent Gait Recognition Method Based On Plantar Pressure Perception, Xueqin Liu, Liu Ning, Su Zhong, Jingxiao Wang, Chaojie Yuan
Journal of System Simulation
Abstract: In view of the complexity and low accuracy of gait recognition in the past, an intelligent gait recognition method based on plantar pressure perception is proposed. The pressure data of the gait of plantar periodic motion is collected and the obtained gait data is classified by the vector machines,the intelligent gait recognition of plantar pressure perception is realized, and the accuracy of gait feature analysis is improved. Through experiment verification, the overall classification accuracy of the classifier is more than 90%, which verifies the rationality of the feature extraction. By evaluating the real state and the results of …
Fault Diagnosis Of Mechanical Equipment Based On Ga-Svr With Missing Data In Small Samples, Jingjing Wei, Qinming Liu, Chunming Ye, Guanlin Li
Fault Diagnosis Of Mechanical Equipment Based On Ga-Svr With Missing Data In Small Samples, Jingjing Wei, Qinming Liu, Chunming Ye, Guanlin Li
Journal of System Simulation
Abstract: In view of the equipment fault diagnosis with small and missing sample data, a method of missing data filling based on support vector regression optimized by genetic algorithm is proposed to improve the accuracy of equipment fault diagnosis. The support vector regression optimized by genetic algorithm was trained by other data values of missing data, and univariate prediction results were obtained. The training set was reconstructed through correlation analysis, so as to obtain the multivariate prediction results. Dynamic weights were established to combine univariate prediction results and multivariate prediction results to fill in the missing data. The …
Safety Evaluation And Risk Level Prediction Of Driving Behavior Considering Multi-Factors Influence, An Yu, Pengpeng Jiao, Zixiu Bai
Safety Evaluation And Risk Level Prediction Of Driving Behavior Considering Multi-Factors Influence, An Yu, Pengpeng Jiao, Zixiu Bai
Journal of System Simulation
Abstract: In order to study the influence of multi-factors of human-vehicle-road on driving behavior and vehicle safety status in road traffic system, a simulated driving comparison test of six scenarios combined by multiple factors is designed. Driving simulator, physiography and eye tracker are used to collect 19 indicators related to driving behavior respectively. The differences of sample data are compared by variance analysis. The nonlinear SVM(support vector machine) is used to classify and predict the sample data. The driving behavior risk level prediction model is established, and the validity of the model is verified by the experimental data. The …
Exploring The Attention Process Differentiation Of Attention Deficit Hyperactivity Disorder (Adhd) Symptomatic Adults Using Artificial Intelligence Onelectroencephalography (Eeg) Signals, Gökhan Güney, Esra Kisacik, Canan Kalaycioğlu, Görkem Saygili
Exploring The Attention Process Differentiation Of Attention Deficit Hyperactivity Disorder (Adhd) Symptomatic Adults Using Artificial Intelligence Onelectroencephalography (Eeg) Signals, Gökhan Güney, Esra Kisacik, Canan Kalaycioğlu, Görkem Saygili
Turkish Journal of Electrical Engineering and Computer Sciences
Attention deficit and hyperactivity disorder (ADHD) onset in childhood and its symptoms can last up till adulthood. Recently, electroencephalography (EEG) has emerged as a tool to investigate the neurophysiological connection of ADHD and the brain. In this study, we investigated the differentiation of attention process of healthy subjects with or without ADHD symptoms under visual continuous performance test (VCPT). In our experiments, artificial neural network (ANN) algorithm achieved 98.4% classification accuracy with 0.98 sensitivity when P2 event related potential (ERP) was used. Additionally, our experimental results showed that fronto-central channels were the most contributing. Overall, we conclude that the attention …
Using Eeg To Detect Driving Fatigue Based On Common Spatial Pattern Andsupport Vector Machine, Li Wang, David Johnson, Yingzi Lin
Using Eeg To Detect Driving Fatigue Based On Common Spatial Pattern Andsupport Vector Machine, Li Wang, David Johnson, Yingzi Lin
Turkish Journal of Electrical Engineering and Computer Sciences
To investigate the correlation between electroencephalogram (EEG) and driving fatigue states, this study used machine learning algorithms to detect driving fatigue based on EEG. 14 channels of EEG data were collected from thirty-four healthy subjects in this research at Northeastern University. Each subject participated in two scenarios (baseline and fatigue scenarios). Subjective ratings of fatigue levels were also obtained from the subjects using the NASA-Task Load Index (TLX). The common spatial pattern (CSP) algorithm was used to extract features from the raw EEG data. The support vector machine (SVM) was used as the classifier in the design of the machine …
Gene Expression Data Classification Using Genetic Algorithm-Basedfeature Selection, Öznur Si̇nem Sönmez, Mustafa Dağteki̇n, Tolga Ensari̇
Gene Expression Data Classification Using Genetic Algorithm-Basedfeature Selection, Öznur Si̇nem Sönmez, Mustafa Dağteki̇n, Tolga Ensari̇
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, hybrid methods are proposed for feature selection and classification of gene expression datasets. In the proposed genetic algorithm/support vector machine (GA-SVM) and genetic algorithm/k nearest neighbor (GA-KNN) hybrid methods, genetic algorithm is improved using Pearson's correlation coefficient, Relief-F, or mutual information. Crossover and selection operations of the genetic algorithm are specialized. Eight different gene expression datasets are used for classification process. The classification performances of the proposed methods are compared with the traditional GA-KNN and GA-SVM wrapper methods and other studies in the literature. Classification results demonstrate that higher accuracy rates are obtained with the proposed methods …
A Novel Pulse Plethysmograph Signal Analysis Method For Identification Of Myocardial Infarction, Dilated Cardiomyopathy, And Hypertension, Muhammad Umar Khan, Sumair Aziz
A Novel Pulse Plethysmograph Signal Analysis Method For Identification Of Myocardial Infarction, Dilated Cardiomyopathy, And Hypertension, Muhammad Umar Khan, Sumair Aziz
Turkish Journal of Electrical Engineering and Computer Sciences
Cardiac diseases (CDs) are one of the leading causes of the growing global mortality rate. Early detectionof CDs is necessary to avoid a high increase in the mortality rate. Machine learning-based computer-aided diagnosisof CDs using various physiological signals has recently been used by researchers. Since pulse plethysmograph (PuPG)signal contains a wealth of information about cardiac pathologies, therefore, this paper presents an expert system designfor the automatic diagnosis of cardiac disorders like hypertension, dilated cardiomyopathy and myocardial infarctionusing a novel fingertip PuPG signal analysis. The proposed system first performs signal denoising of raw PuPG sensordata using discrete wavelet transform (DWT). After …
Fault Diagnosis Of Reverse Osmosis Water Desalination Based On Optimized Support Vector Machine, Zhang Biao, Jianfeng Xing, Zhicheng Ji
Fault Diagnosis Of Reverse Osmosis Water Desalination Based On Optimized Support Vector Machine, Zhang Biao, Jianfeng Xing, Zhicheng Ji
Journal of System Simulation
Abstract: According to the reverse osmosis membrane fault problems in reverse osmosis water desalination system, a fault diagnosis method based on support vector machine (SVM) was introduced for fault diagnoses. To solve the problem of parameter optimization in SVM, an improved chaos particle swarm algorithm was proposed. The introduction of Chaos theory to particle swarm optimization algorithm may not only enhance the diversity of the population and particle global search ability, but also improve the convergence speed and accuracy of the particle swarm algorithm. The optimized SVM model was applied to the fault diagnosis of reverse osmosis water desalination system. …
Study On Hand Gesture Recognition And Portfolio Optimization Model Based On Svm, Zhiwei Cai, Shuyan Wu, Junfeng Song
Study On Hand Gesture Recognition And Portfolio Optimization Model Based On Svm, Zhiwei Cai, Shuyan Wu, Junfeng Song
Journal of System Simulation
Abstract: Hand gesture recognition was researched. The idea of extracting related features was proposed by using SVM algorithm in machine learning domain, and combination optimization method was used, which consists of ANN, HMM and DTW, to do hand gesture recognition. The experimental results show that portfolio optimization model based gesture recognition method has high accuracy and is very effective.
Texture Classification Based On Multi-Scale Wavelet, Liao Ning, Lisha Xu, Xiaoshan Qian
Texture Classification Based On Multi-Scale Wavelet, Liao Ning, Lisha Xu, Xiaoshan Qian
Journal of System Simulation
Abstract: Texture analysis is quite sensitive to rotations. An efficient approach, called Invariant Contourlet-Fourier Descriptor, was proposed to achieve rotation invariance in texture analysis by extracting a set of Shannon entropy in contourlet domain. Discrete Fourier Transform analysis was applied to entropy vectors of each scale to form rotation invariant feature vectors, the dimensionality of which was reduced further due to the symmetry of DFT magnitude spectrum. Two classifiers, including the well-known Euclidean distance and Support Vector Machine, were studied to measure the distance between the known and unknown features. Experimental results on 1500 texture images show that …
Simulation And Application Of Dkipso-Svc Combined Model For Credit Risk Assessment, Zhenhai Wan, Tieying Liu, Zhang Yang, Jishuang Li
Simulation And Application Of Dkipso-Svc Combined Model For Credit Risk Assessment, Zhenhai Wan, Tieying Liu, Zhang Yang, Jishuang Li
Journal of System Simulation
Abstract: In order to improve the problem of inefficient parameter selection of the GDS-SVC model and DIPSO-SVC model, and utilize the generalization ability and robustness of support vector classification (SVC), the reduction factor of location updating was introduced based on the dynamic improvement Particle Swarm Optimization (DIPSO), and then the DKIPSO-SVC of parameters selecting in SVC was established based on DKIPSO. The method was applied to credit scoring of commercial banks. The simulation results demonstrate that the robustness of the DKIPSO-SVC model is better than DIPSO-SVC. But beyond that, the accuracy of DKIPSO-SVC model achieves 96.6049%, higher than that of …
Study Of Adaptive Dynamic Search Pso Based Svm Parameter Optimization, Chunneng Gao, Zhang Biao, Zhicheng Ji
Study Of Adaptive Dynamic Search Pso Based Svm Parameter Optimization, Chunneng Gao, Zhang Biao, Zhicheng Ji
Journal of System Simulation
Abstract: According to critical control points (CCPs) selection problem in wheat processing HACCP (hazard analysis and critical control point), an automatic identification method based on SVM model was introduced. In order to improve the model’s recognition stability and accuracy, an adaptive dynamic search particle swarm optimization (ADS-PSO) for the optimization of kernel function parameters in SVM was proposed. ADS-PSO introduced an evolutionary factor and threshold (ET) to estimate the evolutionary state and adjusted the search strategy adaptively. Besides, an inertia parameter for the velocity was defined in ADS-PSO. The simulation results show that the improved SVM model can identify …
Fault Diagnosis Method Of Pmsm Based On Adaptive Dynamic Cat Swarm Optimization Of Svm, Wang Yan, Wang Xin, Zhicheng Ji, Dahu Yan
Fault Diagnosis Method Of Pmsm Based On Adaptive Dynamic Cat Swarm Optimization Of Svm, Wang Yan, Wang Xin, Zhicheng Ji, Dahu Yan
Journal of System Simulation
Abstract: In order to solve the problems of common inter-turn short circuit faults of permanent magnet synchronous motor (PMSM), a corresponding motor fault model based on the existing basis of PMSM is established. The eigenvector is extracted by energy spectrum analysis. The penalty factor and RBF-kernel parameter of SVM are optimized by adaptive dynamic cat swarm optimization (ADACSO) algorithm. The optimized SVM is adopted to motor fault diagnosis. The eigenvector obtained by energy spectrum analysis is taken as sample data to conduct simulation experiment. The experiment results indicate that, compared with other optimization algorithms, using ADACSO to optimize …
Radar Emitter Signal Identification Based On Slide+Svm, Yingkun Huang, Weidong Jin
Radar Emitter Signal Identification Based On Slide+Svm, Yingkun Huang, Weidong Jin
Journal of System Simulation
Abstract: For the deficiency of traditional techniques of emitter signal feature extraction which heavily rely on experience, a model of radar emitting signal identification based on feature self-learning was proposed. This model consists of following 2 parts. Firstly, transform radar signal into frequency domain, then reduce signal dimension by using improved Piecewise Aggregate Approximation (PAA) method. Secondly, create the model of multi-layer Liner Denoiser (LIDE) to feature learning by using unsupervised training method. The validity of model was verified by simulating 5 different kinds of emitting signal with the outcome that excellent identification accuracy could be achieved at …
Forecasting Of Short-Term Power Load Of Secrpso-Svm Based On Data-Driven, Hairong Sun, Bixia Xie, Tian Yao, Zhuoqun Li
Forecasting Of Short-Term Power Load Of Secrpso-Svm Based On Data-Driven, Hairong Sun, Bixia Xie, Tian Yao, Zhuoqun Li
Journal of System Simulation
Abstract: For the parameter selection of support vector machine in modeling, a particle swarm optimization algorithm based on second-order oscillation and repulsion factor was proposed to optimize the parameter of SVM. The algorithm employed the nonlinear decreasing weight to balance the global and local search ability. Second-order oscillation factor could maintain the population diversity. The repulsion factor was introduced to make the swarm even distribution in search space, which could avoid local optimum. For the complex characteristics of nonlinearity, time-varying and multifactorial of electric power load, a support vector machine forecasting model based on data was proposed, and the influence …
An Automated Eye Disease Recognition System From Visual Content Of Facial Imagesusing Machine Learning Techniques, Ashrafi Akram, Rameswar Debnath
An Automated Eye Disease Recognition System From Visual Content Of Facial Imagesusing Machine Learning Techniques, Ashrafi Akram, Rameswar Debnath
Turkish Journal of Electrical Engineering and Computer Sciences
Many eye diseases like cataracts, trachoma, or corneal ulcer can cause vision problems. Progression of these eye diseases can only be prevented if they are recognized accurately at the early stage. Visually observable symptoms differ a lot among these eye diseases. However, a wide variety of symptoms is necessary to be analyzed for the accurate detection of eye diseases. In this paper, we propose a novel approach to provide an automated eye disease recognition system using visually observable symptoms applying digital image processing techniques and machine learning techniques such as deep convolution neural network (DCNN) and support vector machine (SVM). …
Modeling Compaction Parameters Using Support Vector And Decision Treeregression Algorithms, Abdurrahman Özbeyaz, Mehmet Söylemez
Modeling Compaction Parameters Using Support Vector And Decision Treeregression Algorithms, Abdurrahman Özbeyaz, Mehmet Söylemez
Turkish Journal of Electrical Engineering and Computer Sciences
Shortening the periods of compaction tests can be possible by analyzing the data obtained from previous laboratory tests with regression methods. The regression analysis applied to current data reduces the cost of experiments, saves time, and gives estimated outputs. In this study, the MLS-SVR, KB-SVR, and DTR algorithms were employed for the first time for the estimation of soil compaction parameters. The performances of these regression algorithms in estimating maximum dry unit weight (MDD) and optimum water content (OMC) were compared. Furthermore, the soil properties (fine-grained soil, sand, gravel, specific gravity, liquid limit, and plastic limit) were employed as inputs …
Combined Morphology And Svm-Based Fault Feature Extraction Technique Fordetection And Classification Of Transmission Line Faults, Revati Godse, Dr. Sunil Bhat
Combined Morphology And Svm-Based Fault Feature Extraction Technique Fordetection And Classification Of Transmission Line Faults, Revati Godse, Dr. Sunil Bhat
Turkish Journal of Electrical Engineering and Computer Sciences
A transmission line is the main commodity of power transmission network through which power is transmitted to the utility. These lines are often swayed by accidental breakdowns owing to different random origins. Hence, researchers try to detect and track down these failures at the earliest to avoid financial prejudice. This paper offers a new realtime mathematical morphology based approach for fault feature extraction. The morphological open-close-median filter is exploited to wrest unique fault features which are then fed as an input to support vector machine to detect and classify the short circuit faults. The acquired graphical and numerical results of …
Image Feature Extraction And Online Grading Method For Weight And Shape Of Strawberry, Zhang Qing, Xiangjun Zou, Guichao Lin, Yanhui Sun
Image Feature Extraction And Online Grading Method For Weight And Shape Of Strawberry, Zhang Qing, Xiangjun Zou, Guichao Lin, Yanhui Sun
Journal of System Simulation
Abstract: To deal with the classification problems of strawberry in production, a machine vision based strawberry weight and shape grading method was proposed. The strawberry image was segmented by thresholding to extract the fruit. The area and perimeter parameters of the fruit were then calculated and used to build the strawberry weight grading model through regression analysis. Elliptic Fourier descriptor was used to extract the shape features of the fruit, and these shape features were applied to train a support vector machine (SVM) which represented the strawberry shape grading model. 200 samples of strawberries were selected to test both …
Evaluation Of Green Smart Cities In China Based On Entropy Weight - Cloud Model, Chen Li, Haixia Zhang
Evaluation Of Green Smart Cities In China Based On Entropy Weight - Cloud Model, Chen Li, Haixia Zhang
Journal of System Simulation
Abstract: Based on the research on green smart city at home and abroad; and aiming at the shortcomings and deficiencies of traditional evaluation methods, this paper proposes an evaluation method of combining entropy and cloud model based on the cloud model which can realize the conversion of qualitative concept and quantitative value. This method synthetically considers the subjective and objective factors; carries on the correlation analysis to the index; determines the set of evaluation indicators; uses the X-conditional cloud generator in cloud model to obtain the different levels of membership matrix corresponding to each evaluation object; and carries on the …
Image Classification Based On Sparse Autoencoder And Support Vector Machine, Liu Fang, Lixia Lu, Hongjuan Wang, Wang Xin
Image Classification Based On Sparse Autoencoder And Support Vector Machine, Liu Fang, Lixia Lu, Hongjuan Wang, Wang Xin
Journal of System Simulation
Abstract: A new algorithm of image classification based on the sparse autoencoder and the support vector machine was proposed in view of the drawbacks that the single layer sparse autoencoder for feature learning is easy to lose the deep abstract feature and the features lack the robustness. The deep sparse autoencoder is constructed to learn each image layer and the feature of each layer is automatically extracted. The each feature weights and the reorganized set of feature are obtained according to the feature weighting method. By combining the strong global search ability of genetic algorithm and the excellent performance of …
Determination Of Distance Between Dc Traction Power Centers In A 1500-V Dc Subway Line With Artificial Intelligence Methods, Mehmet Taci̇ddi̇n Akçay, İlhan Kocaarslan
Determination Of Distance Between Dc Traction Power Centers In A 1500-V Dc Subway Line With Artificial Intelligence Methods, Mehmet Taci̇ddi̇n Akçay, İlhan Kocaarslan
Turkish Journal of Electrical Engineering and Computer Sciences
The electrification system in rail systems is designed with regard to the operating data and design parameters. While the electrification system is formed, the minimum voltage rating that the traction force requires during the operation needs to be provided. The highest value of the voltage drop occurring on the line is determined by the distance between power centers. This value needs to be kept within certain limits for the continuity of operation. In this study, the determination of the distance between DC traction power centers for a 1500-V DC-fed rail system is done by means of the adaptive neuro-fuzzy inference …
A Multiseed-Based Svm Classification Technique For Training Sample Reduction, Imran Sharif, Debasis Chaudhuri
A Multiseed-Based Svm Classification Technique For Training Sample Reduction, Imran Sharif, Debasis Chaudhuri
Turkish Journal of Electrical Engineering and Computer Sciences
A support vector machine (SVM) is not a popular method for a very large dataset classification because the training and testing time for such data are computationally expensive. Many researchers try to reduce the training time of SVMs by applying sample reduction methods. Many methods reduced the training samples by using a clustering technique. To reduce its high computational complexity, several data reduction methods were proposed in previous studies. However, such methods are not effective to extract informative patterns. This paper demonstrates a new supervised classification method, multiseed-based SVM (MSB-SVM), which is particularly intended to deal with very large datasets …
Optimal Set Of Eeg Features In Infant Sleep Stage Classification, Maja Cic, Mario Milicevic, Igor Mazic
Optimal Set Of Eeg Features In Infant Sleep Stage Classification, Maja Cic, Mario Milicevic, Igor Mazic
Turkish Journal of Electrical Engineering and Computer Sciences
This paper evaluates six classification algorithms to assess the importance of individual EEG rhythms in the context of automatic classification of infant sleep. EEG features were obtained by Fourier transform and by a novel technique based on the empirical mode decomposition and generalized zero crossing method. Of six evaluated classification algorithms, the best classification results were obtained with the support vector machine for the combination of all presented features from four EEG channels. Three methods of attribute ranking were assessed: relief, principal component analysis, and wrapper-based optimized attribute weights. The outcomes revealed that the optimal selection of features requires one …
Speech Emotion Recognition Using Semi-Nmf Feature Optimization, Surekha Reddy Bandela, T Kishore Kumar
Speech Emotion Recognition Using Semi-Nmf Feature Optimization, Surekha Reddy Bandela, T Kishore Kumar
Turkish Journal of Electrical Engineering and Computer Sciences
In recent times, much research is progressing forward in the field of speech emotion recognition (SER). Many SER systems have been developed by combining different speech features to improve their performances. As a result, the complexity of the classifier increases to train this huge feature set. Additionally, some of the features could be irrelevant in emotion detection and this leads to a decrease in the emotion recognition accuracy. To overcome this drawback, feature optimization can be performed on the feature sets to obtain the most desirable emotional feature set before classifying the features. In this paper, semi-nonnegative matrix factorization (semi-NMF) …