An efficient and highly reliable automatic selection of optimal segmentation algorithm for characterizing particulate matter is presented in this paper. Support vector machines (SVMs) are used as a new self-regulating classifier trained by gray level co-occurrence matrix (GLCM) of the image. This matrix is calculated at various angles and the texture features are evaluated for classifying the images. Results show that the performance of GLCM-based SVMs is drastically improved over the previous histogram-based SVMs. Our proposed GLCM-based approach of training SVM predicts a robust and more accurate segmentation algorithm than the standard histogram technique, as additional information based on the spatial relationship between pixels is incorporated for image classification. Further, the GLCM-based SVM classifiers were more accurate and required less training data when compared to the artificial neural network (ANN) classifiers.
"Liu and Xue (2012) designed a class of kernels by linearly combining the kernels that correspond to each rule via fuzzy entropies for all the fuzzy rules and constructed a new support vector regression based on fuzzy a priori information. Additionally, there are many research studies (Chen, He, & Wang, 2010; Chen, Bo, & Liu, 2011; Chen, Xue, & Ha, 2014; Gordini, 2014; Ha, Wang, & Chen, 2013; Harris, 2013; Kang & Cho, 2014; Li, Tax, & Duin, 2013; Manivannan, Aggarwal, & Devabhaktuni, 2012; Saito, Rezende, & Falcao, 2014; Zhou & Chellappa, 2006) that focus on improving the classical support vector machine and that incorporate a priori knowledge. However, for all the above-described studies, the training set is specified as S ¼ fðx 1 ; y 1 Þ; ðx 2 ; y 2 Þ; . . . "
[Show abstract][Hide abstract] ABSTRACT: In this study, we address the regression problem on set-valued samples that appear in applications. To
solve this problem, we propose a support vector regression approach for set-valued samples that
generalizes the classicale-support vector regression. First, an initial representative point (or an element)
for every set-valued sample is selected, and a weighted distance between the initial representative point
and other points is determined. Second, based on the classification consistency principle, a search
algorithm to determine the best representative point for every set-valued datum is designed. Thus, the
set-valued samples are converted into numeric samples. Finally, a support vector regression that is based
on set-valued data is constructed, and the regression results of the set-valued samples can be approximated using the method used for the numeric samples. Furthermore, the feasibility and efficiency of
the proposed method is demonstrated using experiments with real-world examples concerning wind
speed prediction and the prediction of peak particle velocity.
Expert Systems with Applications 02/2015; 42:2502–2509. · 2.24 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Butterflies can be classified by their outer morphological qualities, genital characteristics that can be obtained using various chemical substances and methods which are carried out manually by preparing genital slides through some certain processes or molecular techniques which is a very expensive method. In this study, a new method which is based on artificial neural networks (ANN) and an image processing technique was used for identification of butterfly species as an alternative to conventional diagnostic methods. Five texture and three color features obtained from 140 butterfly images were used for identification of species. Texture features were obtained by using the average of gray level co-occurrence matrix (GLCM) with different angles and distances. The accuracy of the purposed butterfly classification method has reached 92.85 %. These findings suggested that the texture and color features can be useful for identification of butterfly species.
The Visual Computer 01/2014; 30(1). DOI:10.1007/s00371-013-0782-8 · 0.96 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Increasing demand for fossil fuels due to the luxurious lifestyle, significant growth of population, transportation and the basic industry sectors has caused serious environmental problems. Moreover, a rapid decline in the fossil fuels has led scientists and researchers to look for new alternatives. In this regard, alternative fuels such as biofuels are becoming important increasingly due to environmental and energy concerns. Biofuels are commonly referred to as first generations, which are produced primarily from food crops. However, the use of edible oil to produce biodiesel in many countries is not feasible in view of a big gap in the demand and supply of such oils for dietary consumption. This paper critically reviews the facts and prospects of biofuel utilization especially, three edible biodiesels namely soybean, rapeseed, palm and two non-edible viz. jatropha and cottonseed to reduce engine exhaust gas, noise emission and petro dependency. Based on various biofuel feedstocks, this paper generally found that biodiesel fuels are considered as offering many benefits, including sustainability, reduction of greenhouse gas emissions and many harmful pollutants along with noise emission, regional development, social structure and agriculture, and security of supply.
Renewable and Sustainable Energy Reviews 02/2013; 18:552-567. DOI:10.1016/j.rser.2012.10.036 · 5.90 Impact Factor
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