Pub Date : 2020-07-01DOI: 10.1109/databia50434.2020.9190587
E. Yiridoe
The Korea Basic Science Institute (KBSI) is very happy and especially honored to be hosting the 22 International Workshop on ECR ion sources, ECRIS2016, which is the first workshop held in Korea. Due to the effort on the development of 28GHz superconducting ECRIS, we have been decided a host institution of the ECRIS2016, at the IAC of ECRIS2014. Following that the ignition of the first ECR plasma was generated in 2014; recently, we have successfully extracted the various ion beams from KBSI-ECRIS. For further performance improvement of our system, it is now on the overhaul after 2 years operation. For the optimization of the system, some modification of plasma chamber and so on are ongoing that will be provided better performance of the system.
{"title":"Welcome Message from the Chair","authors":"E. Yiridoe","doi":"10.1109/databia50434.2020.9190587","DOIUrl":"https://doi.org/10.1109/databia50434.2020.9190587","url":null,"abstract":"The Korea Basic Science Institute (KBSI) is very happy and especially honored to be hosting the 22 International Workshop on ECR ion sources, ECRIS2016, which is the first workshop held in Korea. Due to the effort on the development of 28GHz superconducting ECRIS, we have been decided a host institution of the ECRIS2016, at the IAC of ECRIS2014. Following that the ignition of the first ECR plasma was generated in 2014; recently, we have successfully extracted the various ion beams from KBSI-ECRIS. For further performance improvement of our system, it is now on the overhaul after 2 years operation. For the optimization of the system, some modification of plasma chamber and so on are ongoing that will be provided better performance of the system.","PeriodicalId":165106,"journal":{"name":"2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128103587","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 : 2020-07-01DOI: 10.1109/DATABIA50434.2020.9190393
Rizal Dwi Prayogo, N. Ikhsan
This study presents evaluation measures for attribute selection effect on classification performance in classifying the 26 uppercase letters in the English alphabet. Attribute selection is an essential method in the classification phase to measure the attribute significance related to the class label since not all attributes are significant for letter recognition. Therefore, insignificant attributes should be reduced by applying dimensionality reduction. The filter-based attribute selection methods using Information Gain, Gain Ratio, Correlation, and Chi-square are proposed. The performances of attribute selection are evaluated by tree-based classifiers using J48, CART, and Random Forest algorithms with the measures of accuracy, precision, recall, F-measure, and processing time. The results indicate that the use of attribute selection methods provides the increase of classification performances for letter recognition. The reduction of insignificant attributes is discussed in terms of the effect on classification accuracy and the processing time. The optimal number of selected attributes is determined for each attribute selection, it provides better classification accuracy with more time-efficient.
{"title":"Attribute Selection Effect on Tree-Based Classifiers for Letter Recognition","authors":"Rizal Dwi Prayogo, N. Ikhsan","doi":"10.1109/DATABIA50434.2020.9190393","DOIUrl":"https://doi.org/10.1109/DATABIA50434.2020.9190393","url":null,"abstract":"This study presents evaluation measures for attribute selection effect on classification performance in classifying the 26 uppercase letters in the English alphabet. Attribute selection is an essential method in the classification phase to measure the attribute significance related to the class label since not all attributes are significant for letter recognition. Therefore, insignificant attributes should be reduced by applying dimensionality reduction. The filter-based attribute selection methods using Information Gain, Gain Ratio, Correlation, and Chi-square are proposed. The performances of attribute selection are evaluated by tree-based classifiers using J48, CART, and Random Forest algorithms with the measures of accuracy, precision, recall, F-measure, and processing time. The results indicate that the use of attribute selection methods provides the increase of classification performances for letter recognition. The reduction of insignificant attributes is discussed in terms of the effect on classification accuracy and the processing time. The optimal number of selected attributes is determined for each attribute selection, it provides better classification accuracy with more time-efficient.","PeriodicalId":165106,"journal":{"name":"2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114211578","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 : 2020-07-01DOI: 10.1109/DATABIA50434.2020.9190613
Agha Haider Raza, Krishnadas Nanath
Thought to be an ever-changing art form, music has been a form of recreational entertainment for ages. The music industry is constantly making efforts for songs to be a hit and earn considerable revenues. It could be an interesting exercise to predict a song making it to top charts from a mathematical perspective. While several studies have looked into factors after a song is released, this research looks at apriori parameters of a song to predict the success of a song. Data sources available from multiple platforms are combined to create a dataset that has technical parameters of a song and sentimental analysis of the lyrics. Four machine learning algorithms (Logistic Regression, Decision Trees, Naïve Bayes and Random Forests) to answer the question-Is there a magical formula for the prediction of hit songs? It was found that there are elements beyond technical data points that could predict a song being hit or not. This paper takes a stand that music prediction is yet not a data science activity.
{"title":"Predicting a Hit Song with Machine Learning: Is there an apriori secret formula?","authors":"Agha Haider Raza, Krishnadas Nanath","doi":"10.1109/DATABIA50434.2020.9190613","DOIUrl":"https://doi.org/10.1109/DATABIA50434.2020.9190613","url":null,"abstract":"Thought to be an ever-changing art form, music has been a form of recreational entertainment for ages. The music industry is constantly making efforts for songs to be a hit and earn considerable revenues. It could be an interesting exercise to predict a song making it to top charts from a mathematical perspective. While several studies have looked into factors after a song is released, this research looks at apriori parameters of a song to predict the success of a song. Data sources available from multiple platforms are combined to create a dataset that has technical parameters of a song and sentimental analysis of the lyrics. Four machine learning algorithms (Logistic Regression, Decision Trees, Naïve Bayes and Random Forests) to answer the question-Is there a magical formula for the prediction of hit songs? It was found that there are elements beyond technical data points that could predict a song being hit or not. This paper takes a stand that music prediction is yet not a data science activity.","PeriodicalId":165106,"journal":{"name":"2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130624877","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 : 2020-07-01DOI: 10.1109/DATABIA50434.2020.9190503
B. Siregar, Azmi Nur Nasution, D. Arisandi
The use of motor vehicles in urban areas is very high. It affects the high levels of air pollution produced. The use of motorized vehicles produces smoke exhaust containing harmful gases such as, Carbon Monoxide (CO), Carbon Dioxide (CO2) and Nitrogen Dioxide (NO2). If the hazardous gas content in the air exceeds the normal limit, it can interfere with human health that inhale it can even cause death. The problem is the lack of information that can be obtained by the community to find out whether the surrounding area has a safe or dangerous air pollution level. Therefore, an application is needed to monitor and analyze the level of air pollution at certain location and inform the results to the user in graphical form through internet network. This monitoring system uses air pollution level analysis based on Indeks Standar Pencemar Udara (ISPU) which is officially used in Indonesia. Based on the tests that have been carried out, the results obtained from the average temperature, CO, CO2 and NO2 gas levels at research site.
城市地区机动车辆的使用率很高。它会产生高水平的空气污染。机动车辆的使用产生的废气中含有有害气体，如一氧化碳(CO)，二氧化碳(CO2)和二氧化氮(NO2)。如果空气中的有害气体含量超过正常限度，它会干扰人体健康，吸入它甚至会导致死亡。问题是社区缺乏可以获得的信息来确定周围地区的空气污染水平是安全的还是危险的。因此，需要一个应用程序来监测和分析某一地点的空气污染水平，并通过互联网将结果以图形形式告知用户。该监测系统使用印尼官方使用的Indeks standard Pencemar Udara (ISPU)来分析空气污染水平。根据已进行的试验，得出了研究现场的平均温度、CO、CO2和NO2气体水平的结果。
{"title":"Air Pollution Monitoring System Using Waspmote Gases Sensor Board in Wireless Sensor Network","authors":"B. Siregar, Azmi Nur Nasution, D. Arisandi","doi":"10.1109/DATABIA50434.2020.9190503","DOIUrl":"https://doi.org/10.1109/DATABIA50434.2020.9190503","url":null,"abstract":"The use of motor vehicles in urban areas is very high. It affects the high levels of air pollution produced. The use of motorized vehicles produces smoke exhaust containing harmful gases such as, Carbon Monoxide (CO), Carbon Dioxide (CO2) and Nitrogen Dioxide (NO2). If the hazardous gas content in the air exceeds the normal limit, it can interfere with human health that inhale it can even cause death. The problem is the lack of information that can be obtained by the community to find out whether the surrounding area has a safe or dangerous air pollution level. Therefore, an application is needed to monitor and analyze the level of air pollution at certain location and inform the results to the user in graphical form through internet network. This monitoring system uses air pollution level analysis based on Indeks Standar Pencemar Udara (ISPU) which is officially used in Indonesia. Based on the tests that have been carried out, the results obtained from the average temperature, CO, CO2 and NO2 gas levels at research site.","PeriodicalId":165106,"journal":{"name":"2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114917156","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 : 2020-07-01DOI: 10.1109/DATABIA50434.2020.9190281
Abdullah Faiz Ur Rahman Khilji, Sahinur Rahman Laskar, Partha Pakray, R. A. Kadir, M. S. Lydia, Sivaji Bandyopadhyay
A chatbot is a software application aimed at simulating real-time conversations. This system has been designed to address a plethora of domains where they have proved themselves worthy to complement or in some areas replace human-based information acquisition. Though some domains like travel and food have advanced with the growing consumer demand, the healthcare-based system does require significant advancement to address the issue of medical accessibility. The work aims at providing a suitable dataset as well as proposes a prototype system architecture. The prototype system with the self-created dataset is then analyzed on different parameters by numerous experts.
{"title":"HealFavor: Dataset and A Prototype System for Healthcare ChatBot","authors":"Abdullah Faiz Ur Rahman Khilji, Sahinur Rahman Laskar, Partha Pakray, R. A. Kadir, M. S. Lydia, Sivaji Bandyopadhyay","doi":"10.1109/DATABIA50434.2020.9190281","DOIUrl":"https://doi.org/10.1109/DATABIA50434.2020.9190281","url":null,"abstract":"A chatbot is a software application aimed at simulating real-time conversations. This system has been designed to address a plethora of domains where they have proved themselves worthy to complement or in some areas replace human-based information acquisition. Though some domains like travel and food have advanced with the growing consumer demand, the healthcare-based system does require significant advancement to address the issue of medical accessibility. The work aims at providing a suitable dataset as well as proposes a prototype system architecture. The prototype system with the self-created dataset is then analyzed on different parameters by numerous experts.","PeriodicalId":165106,"journal":{"name":"2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129249768","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 : 2020-07-01DOI: 10.1109/DATABIA50434.2020.9190226
Majid Abdolrazzagh, Shokooh Pour Mahyabadi, Somaye Jalali-Poor, Erna Budhiarti Nababan
Given the rapid growth of data and the reduced implementation quality of data mining and pattern extraction techniques, the use of feature reduction has become an important challenge of data mining and pattern recognition. An important goal of data reduction techniques is to make the minimum effort and achieve the maximum efficiency of data selection for the implementation of data mining process. The two primary objectives of feature selection are to minimize the errors of the patterns identified in the reduced subset and minimize the number of features. The majority of available feature selection algorithms adopts a single-objective approach. This is the first paper focused on clustering used as the identifier of unsupervised hidden patterns. It is also focused on the principal component analysis (PCA) to analyze the values of the features. The goals of the new multi-objective feature selection problem are to minimize the coefficient of PCA, maximize the accuracy of k-medoids clustering, and minimize the number of selected features. Another innovation of this study was to select the best subset of features at the best performance by using the electromagnetism-like mechanism (EM) algorithm. The proposed method was tested on 14 standard UCI datasets. The results indicated the competitive advantage of this algorithm over other algorithms implemented to solve this problem.
{"title":"Multi-Objective Feature Selection based on Clustering and Principal Component Analysis by Enhanced Electromagnetic-likes Algorithm","authors":"Majid Abdolrazzagh, Shokooh Pour Mahyabadi, Somaye Jalali-Poor, Erna Budhiarti Nababan","doi":"10.1109/DATABIA50434.2020.9190226","DOIUrl":"https://doi.org/10.1109/DATABIA50434.2020.9190226","url":null,"abstract":"Given the rapid growth of data and the reduced implementation quality of data mining and pattern extraction techniques, the use of feature reduction has become an important challenge of data mining and pattern recognition. An important goal of data reduction techniques is to make the minimum effort and achieve the maximum efficiency of data selection for the implementation of data mining process. The two primary objectives of feature selection are to minimize the errors of the patterns identified in the reduced subset and minimize the number of features. The majority of available feature selection algorithms adopts a single-objective approach. This is the first paper focused on clustering used as the identifier of unsupervised hidden patterns. It is also focused on the principal component analysis (PCA) to analyze the values of the features. The goals of the new multi-objective feature selection problem are to minimize the coefficient of PCA, maximize the accuracy of k-medoids clustering, and minimize the number of selected features. Another innovation of this study was to select the best subset of features at the best performance by using the electromagnetism-like mechanism (EM) algorithm. The proposed method was tested on 14 standard UCI datasets. The results indicated the competitive advantage of this algorithm over other algorithms implemented to solve this problem.","PeriodicalId":165106,"journal":{"name":"2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)","volume":"155 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128694067","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 : 2020-07-01DOI: 10.1109/DATABIA50434.2020.9190316
M. K. Nasution, Opim Salim Sitompul, M. Elveny, Rahmad Syah, Romi Fadillah Rahmat
Extraction is a way to get knowledge from information space, such as social networks. One efficient and concise tool for expressing knowledge related to meaning is to involve the concept of similarity. There are several similarity formulations to approach the same thing from the objects but have different tasks according to their functions. However, the measurement results reveal different meanings, although they remain in a mutually supportive position. Therefore, this paper aims to express the different meanings besides new meanings of the similarity function, which are proven based on the difference between the divisors of the similarity formulation. Different measurements of similarities produce different and new meanings with supporting simulation and clustering to manage big data.
{"title":"A similarity for new meanings","authors":"M. K. Nasution, Opim Salim Sitompul, M. Elveny, Rahmad Syah, Romi Fadillah Rahmat","doi":"10.1109/DATABIA50434.2020.9190316","DOIUrl":"https://doi.org/10.1109/DATABIA50434.2020.9190316","url":null,"abstract":"Extraction is a way to get knowledge from information space, such as social networks. One efficient and concise tool for expressing knowledge related to meaning is to involve the concept of similarity. There are several similarity formulations to approach the same thing from the objects but have different tasks according to their functions. However, the measurement results reveal different meanings, although they remain in a mutually supportive position. Therefore, this paper aims to express the different meanings besides new meanings of the similarity function, which are proven based on the difference between the divisors of the similarity formulation. Different measurements of similarities produce different and new meanings with supporting simulation and clustering to manage big data.","PeriodicalId":165106,"journal":{"name":"2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129667843","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 : 2020-07-01DOI: 10.1109/DATABIA50434.2020.9190626
D. Rachmawati, P. Sihombing, Billy Halim
Traffic congestion is a problem for almost everyone in big cities. Based on the 2017 traffic condition report released by Inrix, a transportation analysis company, on average, Indonesians were wasting time about 51 hours a year stuck in traffic congestion. Therefore, one of the solutions to overcome this traffic jam problem is by creating an application or system which can find routes with the lowest possible level of a traffic jam from the origin location to the destination. Best First Search algorithm works by selecting the best nodes (with the most economical cost) among other generated nodes from the initial node to the goal node. The route generated by the system will be shown on the map, along with the distance, travel time, algorithm running time, and traffic flow condition of the route. The implementation and testing on the system showed that the distance traveled by walking was less than or equal to the distance traveled by driving. On the other hand, using the same travel mode, the route from origin to destination had different distances and travel time than the vice-versa because of the Best First Search algorithm itself. Nevertheless, in some cases, the distance from the origin to the destination may be the same as from destination to origin because both of them are closed together. The average distance, travel time, and algorithm running time generated from the testing were 2.8 km, 20.375 minutes, and 0.182 seconds. However, the routes generated by the system weren't always optimal because the Best First Search algorithm wasn't taking into account the total travel time taken.
{"title":"Implementation of Best First Search Algorithm in Determining Best Route Based on Traffic Jam Level in Medan City","authors":"D. Rachmawati, P. Sihombing, Billy Halim","doi":"10.1109/DATABIA50434.2020.9190626","DOIUrl":"https://doi.org/10.1109/DATABIA50434.2020.9190626","url":null,"abstract":"Traffic congestion is a problem for almost everyone in big cities. Based on the 2017 traffic condition report released by Inrix, a transportation analysis company, on average, Indonesians were wasting time about 51 hours a year stuck in traffic congestion. Therefore, one of the solutions to overcome this traffic jam problem is by creating an application or system which can find routes with the lowest possible level of a traffic jam from the origin location to the destination. Best First Search algorithm works by selecting the best nodes (with the most economical cost) among other generated nodes from the initial node to the goal node. The route generated by the system will be shown on the map, along with the distance, travel time, algorithm running time, and traffic flow condition of the route. The implementation and testing on the system showed that the distance traveled by walking was less than or equal to the distance traveled by driving. On the other hand, using the same travel mode, the route from origin to destination had different distances and travel time than the vice-versa because of the Best First Search algorithm itself. Nevertheless, in some cases, the distance from the origin to the destination may be the same as from destination to origin because both of them are closed together. The average distance, travel time, and algorithm running time generated from the testing were 2.8 km, 20.375 minutes, and 0.182 seconds. However, the routes generated by the system weren't always optimal because the Best First Search algorithm wasn't taking into account the total travel time taken.","PeriodicalId":165106,"journal":{"name":"2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133525289","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 : 2020-07-01DOI: 10.1109/DATABIA50434.2020.9190301
S. Saad, Krishnadas Nanath
Since 1983, the start of the mobile industry has led to some great inventions. Due to the rapid increase in technology, the world of mobile applications has grown stupendously. While some applications achieve great success both from rating and financial perspective, several applications do not perform well in the application store. This research attempts to develop a model for the prediction of app ratings based on several data points collected from multiple sources. The research is restricted to Android apps, and the ratings are predicted using Linear Regression and Logistic Regression. The study brings in a new perspective of review sentiment and analyzes the impact of various parameters on the successful ratings of mobile applications.
{"title":"Investing in Applications Based on Predictive Modeling","authors":"S. Saad, Krishnadas Nanath","doi":"10.1109/DATABIA50434.2020.9190301","DOIUrl":"https://doi.org/10.1109/DATABIA50434.2020.9190301","url":null,"abstract":"Since 1983, the start of the mobile industry has led to some great inventions. Due to the rapid increase in technology, the world of mobile applications has grown stupendously. While some applications achieve great success both from rating and financial perspective, several applications do not perform well in the application store. This research attempts to develop a model for the prediction of app ratings based on several data points collected from multiple sources. The research is restricted to Android apps, and the ratings are predicted using Linear Regression and Logistic Regression. The study brings in a new perspective of review sentiment and analyzes the impact of various parameters on the successful ratings of mobile applications.","PeriodicalId":165106,"journal":{"name":"2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125656184","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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