Pub Date : 2018-07-01DOI: 10.4018/IJNCR.2018070102
Akanksha Singh, Sanjay Kumar
In this article, the authors propose a computational method for multi criteria decision making problems using dual hesitant fuzzy information. In this study, the authors mention limitation of fuzzy ideals over a semi ring of positive integers and propose fuzzy ideal over a semi ring over subset of rationals. An intuitionistic fuzzy ideal of semi rings is also defined in this article which is used in idealizing aggregated dual hesitant group preference matrixes. The proposed approach appears in the form of simple computational algorithms. The main characteristic of the proposed approach is it considers the relationship between attributes, and so it takes into account relative preferences of attributes to find out the ranking order of attributes while other methods consider various attributes independently. An example of a supplier selection problem is undertaken to understand the implementation of the proposed computational approach based on MCGDM with dual hesitant information and ranking results compared with different methods.
{"title":"Dual Hesitant Fuzzy Set and Intuitionistic Fuzzy Ideal Based Computational Method for MCGDM Problem","authors":"Akanksha Singh, Sanjay Kumar","doi":"10.4018/IJNCR.2018070102","DOIUrl":"https://doi.org/10.4018/IJNCR.2018070102","url":null,"abstract":"In this article, the authors propose a computational method for multi criteria decision making problems using dual hesitant fuzzy information. In this study, the authors mention limitation of fuzzy ideals over a semi ring of positive integers and propose fuzzy ideal over a semi ring over subset of rationals. An intuitionistic fuzzy ideal of semi rings is also defined in this article which is used in idealizing aggregated dual hesitant group preference matrixes. The proposed approach appears in the form of simple computational algorithms. The main characteristic of the proposed approach is it considers the relationship between attributes, and so it takes into account relative preferences of attributes to find out the ranking order of attributes while other methods consider various attributes independently. An example of a supplier selection problem is undertaken to understand the implementation of the proposed computational approach based on MCGDM with dual hesitant information and ranking results compared with different methods.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131139624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-01DOI: 10.4018/IJNCR.2018040101
D. Ashour, D. A. Rayia, N. Dey, A. Ashour, A. Hawas, M. Al-Otaibi
Schistosomiasis is serious liver tissues' parasitic disease that leads to liver fibrosis. Microscopic liver tissue images at different stages can be used for assessment of the fibrosis level. In the current article, the different stages of granuloma were classified after features extraction. Statistical features extraction was used to extract the significant features that characterized each stage. Afterward, different classifiers, namely the Decision Tree, Nearest Neighbor and the Neural Network are employed to carry out the classification process. The results established that the cubic k-NN, cosine k-NN and medium k-NN classifiers achieved superior classification accuracy compared to the other classifiers with 88.3% accuracy value.
{"title":"Schistosomal Hepatic Fibrosis Classification","authors":"D. Ashour, D. A. Rayia, N. Dey, A. Ashour, A. Hawas, M. Al-Otaibi","doi":"10.4018/IJNCR.2018040101","DOIUrl":"https://doi.org/10.4018/IJNCR.2018040101","url":null,"abstract":"Schistosomiasis is serious liver tissues' parasitic disease that leads to liver fibrosis. Microscopic liver tissue images at different stages can be used for assessment of the fibrosis level. In the current article, the different stages of granuloma were classified after features extraction. Statistical features extraction was used to extract the significant features that characterized each stage. Afterward, different classifiers, namely the Decision Tree, Nearest Neighbor and the Neural Network are employed to carry out the classification process. The results established that the cubic k-NN, cosine k-NN and medium k-NN classifiers achieved superior classification accuracy compared to the other classifiers with 88.3% accuracy value.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128307372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-01DOI: 10.4018/IJNCR.2018040103
Priyanka Pandey, Manju Khari, Raghavendra Kumar, Dac-Nhuong Le
India is a land of 122 languages and numerous dialects. Lack of competent lexical resources for Indian languages is a ubiquitous fact, which negatively affects the development of tools for NLP of Indian languages. Recent advancements like the Indo WordNet project has significantly contributed to dealing with the scarcity of lexicons, but the progress and coverage is a matter of dispute. The bottlenecks, cost, time, and skilled lexicographers further slackens the progress. In this article, the authors propose a technique to automate the generation of lexical entries using a machine learning approach which visibly expedites the process of lexicon generation like WordNet. The reluctance to adopt an automated approach is majorly credited to a lack of accuracy, the inability to capture a regional touch of a language, incorrect back-translation, etc. To overcome this issue, the author will use Wikipedia to validate the synsets.
{"title":"Automatic Generation of Synsets for Wordnet of Hindi Language","authors":"Priyanka Pandey, Manju Khari, Raghavendra Kumar, Dac-Nhuong Le","doi":"10.4018/IJNCR.2018040103","DOIUrl":"https://doi.org/10.4018/IJNCR.2018040103","url":null,"abstract":"India is a land of 122 languages and numerous dialects. Lack of competent lexical resources for Indian languages is a ubiquitous fact, which negatively affects the development of tools for NLP of Indian languages. Recent advancements like the Indo WordNet project has significantly contributed to dealing with the scarcity of lexicons, but the progress and coverage is a matter of dispute. The bottlenecks, cost, time, and skilled lexicographers further slackens the progress. In this article, the authors propose a technique to automate the generation of lexical entries using a machine learning approach which visibly expedites the process of lexicon generation like WordNet. The reluctance to adopt an automated approach is majorly credited to a lack of accuracy, the inability to capture a regional touch of a language, incorrect back-translation, etc. To overcome this issue, the author will use Wikipedia to validate the synsets.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116685722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-01DOI: 10.4018/IJNCR.2018040102
Poornachandra Sandur, C. Naveena, Manjunath Aradhya, B. NagasundaraK.
The quantitative assessment of tumor extent is necessary for surgical planning, as well as monitoring of tumor growth or shrinkage, and radiotherapy planning. For brain tumors, magnetic resonance imaging MRI is used as a standard for diagnosis and prognosis. Manually segmenting brain tumors from 3D MRI volumes is tedious and depends on inter and intra observer variability. In the clinical facilities, a reliable fully automatic brain tumor segmentation method is necessary for the accurate delineation of tumor sub regions. This article presents a 3D U-net Convolutional Neural Network for segmentation of a brain tumor. The proposed method achieves a mean dice score of 0.83, a specificity of 0.80 and a sensitivity of 0.81 for segmenting the whole tumor, and for the tumor core region a mean dice score of 0.76, a specificity of 0.79 and a sensitivity of 0.73. For the enhancing region, the mean dice score is 0.68, a specificity of 0.73 and a sensitivity of 0.77. From the experimental analysis, the proposed U-net model achieved considerably good results compared to the other segmentation models.
{"title":"Segmentation of Brain Tumor Tissues in HGG and LGG MR Images Using 3D U-net Convolutional Neural Network","authors":"Poornachandra Sandur, C. Naveena, Manjunath Aradhya, B. NagasundaraK.","doi":"10.4018/IJNCR.2018040102","DOIUrl":"https://doi.org/10.4018/IJNCR.2018040102","url":null,"abstract":"The quantitative assessment of tumor extent is necessary for surgical planning, as well as monitoring of tumor growth or shrinkage, and radiotherapy planning. For brain tumors, magnetic resonance imaging MRI is used as a standard for diagnosis and prognosis. Manually segmenting brain tumors from 3D MRI volumes is tedious and depends on inter and intra observer variability. In the clinical facilities, a reliable fully automatic brain tumor segmentation method is necessary for the accurate delineation of tumor sub regions. This article presents a 3D U-net Convolutional Neural Network for segmentation of a brain tumor. The proposed method achieves a mean dice score of 0.83, a specificity of 0.80 and a sensitivity of 0.81 for segmenting the whole tumor, and for the tumor core region a mean dice score of 0.76, a specificity of 0.79 and a sensitivity of 0.73. For the enhancing region, the mean dice score is 0.68, a specificity of 0.73 and a sensitivity of 0.77. From the experimental analysis, the proposed U-net model achieved considerably good results compared to the other segmentation models.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123970703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-01DOI: 10.4018/IJNCR.2018040104
Ketan Jha, M. Rani
Researchers and scientists are attracted towards Julia and Mandelbrot sets constantly. They analyzed these sets intensively. Researchers have studied the perturbation in Julia and Mandelbrot sets which is due to different types of noises, but transcendental Julia and Mandelbrot sets remained ignored. The purpose of this article is to study the perturbation in transcendental Julia and Mandelbrot sets. Also, we made an attempt to control the perturbation in transcendental sets by using superior iteration method.
{"title":"Control of Dynamic Noise in Transcendental Julia and Mandelbrot Sets by Superior Iteration Method","authors":"Ketan Jha, M. Rani","doi":"10.4018/IJNCR.2018040104","DOIUrl":"https://doi.org/10.4018/IJNCR.2018040104","url":null,"abstract":"Researchers and scientists are attracted towards Julia and Mandelbrot sets constantly. They analyzed these sets intensively. Researchers have studied the perturbation in Julia and Mandelbrot sets which is due to different types of noises, but transcendental Julia and Mandelbrot sets remained ignored. The purpose of this article is to study the perturbation in transcendental Julia and Mandelbrot sets. Also, we made an attempt to control the perturbation in transcendental sets by using superior iteration method.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121215502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.4018/IJNCR.2017070102
V. Nadimpalli, R. Wankar, C. R. Rao
In this article, an innovative Genetic Algorithm is proposed to find potential patches enclosing roots of real valued function f:R→R. As roots of f can be real as well as complex, the function is reframed on to complex plane by writing it as f(z). Thus, the problem now is transformed to finding potential patches (rectangles in C) enclosing z such that f(z)=0, which is resolved into two components as real and imaginary parts. The proposed GA generates two random populations of real numbers for the real and imaginary parts in the given regions of interest and no other initial guesses are needed. This is the prominent advantage of the method in contrast to various other methods. Additionally, the proposed ‘Refinement technique' aids in the exhaustive coverage of potential patches enclosing roots and reinforces the selected potential rectangles to be narrow, resulting in significant search space reduction. The method works efficiently even when the roots are closely packed. A set of benchmark functions are presented and the results show the effectiveness and robustness of the new method.
{"title":"Innovative Genetic Algorithmic Approach to Select Potential Patches Enclosing Real and Complex Zeros of Nonlinear Equation","authors":"V. Nadimpalli, R. Wankar, C. R. Rao","doi":"10.4018/IJNCR.2017070102","DOIUrl":"https://doi.org/10.4018/IJNCR.2017070102","url":null,"abstract":"In this article, an innovative Genetic Algorithm is proposed to find potential patches enclosing roots of real valued function f:R→R. As roots of f can be real as well as complex, the function is reframed on to complex plane by writing it as f(z). Thus, the problem now is transformed to finding potential patches (rectangles in C) enclosing z such that f(z)=0, which is resolved into two components as real and imaginary parts. The proposed GA generates two random populations of real numbers for the real and imaginary parts in the given regions of interest and no other initial guesses are needed. This is the prominent advantage of the method in contrast to various other methods. Additionally, the proposed ‘Refinement technique' aids in the exhaustive coverage of potential patches enclosing roots and reinforces the selected potential rectangles to be narrow, resulting in significant search space reduction. The method works efficiently even when the roots are closely packed. A set of benchmark functions are presented and the results show the effectiveness and robustness of the new method.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126763803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.4018/IJNCR.2017070104
Dao Nam Anh, Trinh Minh Duc
This article describes how facial expression detection and adjustment in complex psychological aspects of vision is central to a number of visual and cognitive computing applications. This article presents an algorithm for automatically estimating happiness expression of face images whose demographic aspects like race, gender and eye direction are changeable. The method is also broadening for alteration of level of happiness expression for face images. A schema of the weighted modification is proposed for enhancement of happiness expression. The authors employ a robust face representation which combines the color patch similarity and the self-resemblance of image patches. A large set of face images with appearance of the properties is learned in a statistical model for interpreting the facial expression of happiness. The authors will show the experiments of such a model using face features for learning by SVM and analyze the performance.
{"title":"Modification of Happiness Expression in Face Images","authors":"Dao Nam Anh, Trinh Minh Duc","doi":"10.4018/IJNCR.2017070104","DOIUrl":"https://doi.org/10.4018/IJNCR.2017070104","url":null,"abstract":"This article describes how facial expression detection and adjustment in complex psychological aspects of vision is central to a number of visual and cognitive computing applications. This article presents an algorithm for automatically estimating happiness expression of face images whose demographic aspects like race, gender and eye direction are changeable. The method is also broadening for alteration of level of happiness expression for face images. A schema of the weighted modification is proposed for enhancement of happiness expression. The authors employ a robust face representation which combines the color patch similarity and the self-resemblance of image patches. A large set of face images with appearance of the properties is learned in a statistical model for interpreting the facial expression of happiness. The authors will show the experiments of such a model using face features for learning by SVM and analyze the performance.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131433784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.4018/IJNCR.2017070103
M. Marzouk, M. El-Razek
This article describes how in developing countries, millions of tons of construction and demolition wastes (CDWs) are lost every year due to lack of knowledge of recycling significance and/or procedures. Despite the high value of CDWs, high percentage of this waste is either dumped illegally or disposed in the landfills. Disposal methods should consider saving natural resources and maintaining the environmental conditions through maximizing the value of CDWs. This article aims at choosing the most sustainable disposal alternative using Multi-Criteria Decision Making (MCDM) Process, considering several sustainability measure indicators. The research introduces a list containing the most relevant and significant sustainable indicators that affect the selection of alternative for disposal of CDWs. Then, fuzzy TOPSIS technique is applied considering the significant indicators on each alternative to rank and choose the best alternative for disposal of CDWs.
{"title":"Selecting Demolition Waste Materials Disposal Alternatives Using Fuzzy TOPSIS Technique","authors":"M. Marzouk, M. El-Razek","doi":"10.4018/IJNCR.2017070103","DOIUrl":"https://doi.org/10.4018/IJNCR.2017070103","url":null,"abstract":"This article describes how in developing countries, millions of tons of construction and demolition wastes (CDWs) are lost every year due to lack of knowledge of recycling significance and/or procedures. Despite the high value of CDWs, high percentage of this waste is either dumped illegally or disposed in the landfills. Disposal methods should consider saving natural resources and maintaining the environmental conditions through maximizing the value of CDWs. This article aims at choosing the most sustainable disposal alternative using Multi-Criteria Decision Making (MCDM) Process, considering several sustainability measure indicators. The research introduces a list containing the most relevant and significant sustainable indicators that affect the selection of alternative for disposal of CDWs. Then, fuzzy TOPSIS technique is applied considering the significant indicators on each alternative to rank and choose the best alternative for disposal of CDWs.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122038981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.4018/IJNCR.2017070101
Suparna DasGupta, Soumyabrata Saha, S. Das
This article describes how as day-to-day Android users are increasing, the Internet has become the type of environment preferred by attackers to inject malicious packages. This is content with the intention of gathering critical information, spying on user details, credentials, call logs, contact details, and tracking user location. Regrettably it is very hard to detect malware even with antivirus software/packages. In addition, this type of attack is increasing day by day. In this article the authors have chosen a Supervised Learning Classification Tree-based algorithm to detect malware on the data set. Comparison amongst all the classifiers on the basis of accuracy and execution time are used to build the classifier model which has the highest executed detections.
{"title":"Malware Detection in Android Using Data Mining","authors":"Suparna DasGupta, Soumyabrata Saha, S. Das","doi":"10.4018/IJNCR.2017070101","DOIUrl":"https://doi.org/10.4018/IJNCR.2017070101","url":null,"abstract":"This article describes how as day-to-day Android users are increasing, the Internet has become the type of environment preferred by attackers to inject malicious packages. This is content with the intention of gathering critical information, spying on user details, credentials, call logs, contact details, and tracking user location. Regrettably it is very hard to detect malware even with antivirus software/packages. In addition, this type of attack is increasing day by day. In this article the authors have chosen a Supervised Learning Classification Tree-based algorithm to detect malware on the data set. Comparison amongst all the classifiers on the basis of accuracy and execution time are used to build the classifier model which has the highest executed detections.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122671946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-01DOI: 10.4018/IJNCR.2015100103
K. G. Dhal, Sanjoy Das
This study is organized into two parts. The first part introduces two image enhancement methods with the ability to preserve the original brightness of the image. These two methods are: optimal ranged brightness preserved contrast stretching ORBPCS method and weighted thresholded histogram equalization WTHE method. The efficiency of these two methods crucially depends on the method's associated parameters. To find the optimal values of the parameters Artificial Bee Colony ABC algorithm and a novel objective function have been employed in this study. The second part of this study mainly concentrates on the efficiency increment of ABC algorithm and to develop the proper objective functions to preserve the original brightness of the image. Some new mechanisms like population diversity measurement technique, use of chaotic sequence etc. are also introduced to enhance the efficiency of traditional ABC algorithm. The objective functions have been developed by using co-occurrence matrix and peak-signal to noise ratio PSNR.
{"title":"Diversity Conserved Chaotic Artificial Bee Colony Algorithm based Brightness Preserved Histogram Equalization and Contrast Stretching Method","authors":"K. G. Dhal, Sanjoy Das","doi":"10.4018/IJNCR.2015100103","DOIUrl":"https://doi.org/10.4018/IJNCR.2015100103","url":null,"abstract":"This study is organized into two parts. The first part introduces two image enhancement methods with the ability to preserve the original brightness of the image. These two methods are: optimal ranged brightness preserved contrast stretching ORBPCS method and weighted thresholded histogram equalization WTHE method. The efficiency of these two methods crucially depends on the method's associated parameters. To find the optimal values of the parameters Artificial Bee Colony ABC algorithm and a novel objective function have been employed in this study. The second part of this study mainly concentrates on the efficiency increment of ABC algorithm and to develop the proper objective functions to preserve the original brightness of the image. Some new mechanisms like population diversity measurement technique, use of chaotic sequence etc. are also introduced to enhance the efficiency of traditional ABC algorithm. The objective functions have been developed by using co-occurrence matrix and peak-signal to noise ratio PSNR.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114610789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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