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Full-Text Articles in Engineering
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) …
Segmented Character Recognition Using Curvature-Based Global Image Feature, Belaynesh Chekol, Numan Çelebi̇, Tuğrul Taşci
Segmented Character Recognition Using Curvature-Based Global Image Feature, Belaynesh Chekol, Numan Çelebi̇, Tuğrul Taşci
Turkish Journal of Electrical Engineering and Computer Sciences
Character recognition in natural scene images is a fundamental prerequisite for many text-based image analysis tasks. Generally, local image features are employed widely to recognize characters segmented from natural scene images. In this paper, a curvature-based global image feature and description for segmented character recognition is proposed. This feature is entirely dependent on the curvature information of the image pixels. The proposed feature is employed for segmented character recognition using Chars74k dataset and ICDAR 2003 character recognition dataset. From the two datasets, 1068 and 540 images of characters, respectively, are randomly chosen and 573-dimensional feature vector is synthesized per image. …
A Robust Ensemble Feature Selector Based On Rank Aggregation For Developing New Vo\Textsubscript{2}Max Prediction Models Using Support Vector Machines, Fatih Abut, Mehmet Fati̇h Akay, James George
A Robust Ensemble Feature Selector Based On Rank Aggregation For Developing New Vo\Textsubscript{2}Max Prediction Models Using Support Vector Machines, Fatih Abut, Mehmet Fati̇h Akay, James George
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a new ensemble feature selector, called the majority voting feature selector (MVFS), for developing new maximal oxygen uptake (VO2max) prediction models using a support vector machine (SVM). The approach is based on rank aggregation, which meaningfully utilizes the correlation among the relevance ranks of predictor variables given by three state-of-the-art feature selectors: Relief-F, minimum redundancy maximum relevance (mRMR), and maximum likelihood feature selection (MLFS). By applying the SVM combined with MVFS on a self-created dataset containing maximal and submaximal exercise data from 185 college students, several new hybrid (VO2max) prediction models have been created. To compare the …
Application Of Multiscale Fuzzy Entropy Features For Multilevel Subject-Dependent Emotion Recognition, Hamzah Lotfalinezhad, Ali Maleki
Application Of Multiscale Fuzzy Entropy Features For Multilevel Subject-Dependent Emotion Recognition, Hamzah Lotfalinezhad, Ali Maleki
Turkish Journal of Electrical Engineering and Computer Sciences
Emotion recognition can be used in clinical and nonclinical situations. Despite previous works which mostly used time and frequency features of electroencephalogram (EEG) signals in subject-dependent emotion recognition issues, we used multiscale fuzzy entropy as a nonlinear dynamic feature. The EEG signals of the well-known Database for Emotion Analysis Using Physiological signals dataset was used for classification of two and three levels of emotions in arousal and valence space. The compound feature selection with a cost of average accuracy of support vector machine classifier was used to reduce feature dimensions. For subject-dependent systems, the proposed method is superior in comparison …
An Automated Snick Detection And Classification Scheme As A Cricket Decision Review System, Aftab Khan, Syed Qadir Hussain, Muhammad Waleed, Ashfaq Khan, Umair Khan
An Automated Snick Detection And Classification Scheme As A Cricket Decision Review System, Aftab Khan, Syed Qadir Hussain, Muhammad Waleed, Ashfaq Khan, Umair Khan
Turkish Journal of Electrical Engineering and Computer Sciences
Umpire decisions can greatly affect the outcome of a cricket game. When there is doubt about the umpire?s call for a decision, a decision review system (DRS) may be brought into play by a batsman or bowler to validate the decision. Recently, the latest technologies, including Hotspot, Hawk-eye, and Snickometer, have been employed when there is doubt among the on-field umpire, batsman, or bowlers. This research is a step forward in gaging the true class of a snick generated from the contact of the cricket ball with either (i) the bat, (ii) gloves, (iii) pad, or (iv) a combination of …