Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315766
Pronaya Bhattacharya, Akhilesh Ladha, Ashwani Kumar, A. Verma, Umesh Bodkhe
The current Telecom sector is highly competitive due to increased Mobile Number Portability (MNP) of users. The ease of MNP and plenty of switching options between Telecom providers, leads to rise in attrition, known as the churn behavior in customers. Customer is always in pursuit of better services at cheaper rates from service vendors. Thus, in this competitive Telecom market, the providers face a dual issue to retain loyal customers, as well as attract new potential customers by providing cheap data plans and free calling options. Thus, this unreasonable demand vs. supply rate to satisfy such customers effects the profitability of the company, which is a serious concern. Thus, to mitigate such fluctuations, termed as customer churn (CC) behavior, the paper a novel scheme BaYcP, that addresses the CC problem in two phases. In the first phase, based on customer data-sets, risk profiling score (RPS) is generated based on descision trees, and is compared to a threshold value. Then based on scores higher than threshold, an optimal prediction model is built based on bayesian classifier on appropriate selected features. The model is trained and validated to achieve and accuracy of 97.89% which outperforms other state-of-the art approaches.
{"title":"BaY cP: A novel Bayesian customer Churn prediction scheme for Telecom sector","authors":"Pronaya Bhattacharya, Akhilesh Ladha, Ashwani Kumar, A. Verma, Umesh Bodkhe","doi":"10.1109/PDGC50313.2020.9315766","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315766","url":null,"abstract":"The current Telecom sector is highly competitive due to increased Mobile Number Portability (MNP) of users. The ease of MNP and plenty of switching options between Telecom providers, leads to rise in attrition, known as the churn behavior in customers. Customer is always in pursuit of better services at cheaper rates from service vendors. Thus, in this competitive Telecom market, the providers face a dual issue to retain loyal customers, as well as attract new potential customers by providing cheap data plans and free calling options. Thus, this unreasonable demand vs. supply rate to satisfy such customers effects the profitability of the company, which is a serious concern. Thus, to mitigate such fluctuations, termed as customer churn (CC) behavior, the paper a novel scheme BaYcP, that addresses the CC problem in two phases. In the first phase, based on customer data-sets, risk profiling score (RPS) is generated based on descision trees, and is compared to a threshold value. Then based on scores higher than threshold, an optimal prediction model is built based on bayesian classifier on appropriate selected features. The model is trained and validated to achieve and accuracy of 97.89% which outperforms other state-of-the art approaches.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127662784","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315787
Anuj Chauhan, Vandana Bhatia
Nowadays the most trending and bookmark game is cricket in the whole world in which various types of activities occur like a No-ball, Wide Ball, Boundaries, etc. Here we detect a composite feature combining computer vision Algorithm along with camera view analysis. Many human errors occur in cricket matches because a wide ball or no ball creates very crucial situations and these decisions create very contradictorily during a match. Today technology is playing the most important role in the present world. So we decided that detect the various activities using computer vision techniques that occur during a cricket match like crucial catches, LBW, No ball, wide ball, etc. Here we will discuss activity detection using computer vision. Technology has various dimensions. Today the technology available is not computed the data. The technology has many different applications and magnitudes/aspect at which the software is achieving higher accuracy and greater results when the software is precisely performed. Implementation in any sport is much beneficial. Then Games such as Tennis, Baseball, Rugby, Soccer, Hockey, Cricket, Football, Kabaddi, etc. and single-player games like Chess, Badminton, Shooting, etc. are also being considered well thought out as honor to their countries.
{"title":"Cricket Activity Detection Using Computer Vision","authors":"Anuj Chauhan, Vandana Bhatia","doi":"10.1109/PDGC50313.2020.9315787","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315787","url":null,"abstract":"Nowadays the most trending and bookmark game is cricket in the whole world in which various types of activities occur like a No-ball, Wide Ball, Boundaries, etc. Here we detect a composite feature combining computer vision Algorithm along with camera view analysis. Many human errors occur in cricket matches because a wide ball or no ball creates very crucial situations and these decisions create very contradictorily during a match. Today technology is playing the most important role in the present world. So we decided that detect the various activities using computer vision techniques that occur during a cricket match like crucial catches, LBW, No ball, wide ball, etc. Here we will discuss activity detection using computer vision. Technology has various dimensions. Today the technology available is not computed the data. The technology has many different applications and magnitudes/aspect at which the software is achieving higher accuracy and greater results when the software is precisely performed. Implementation in any sport is much beneficial. Then Games such as Tennis, Baseball, Rugby, Soccer, Hockey, Cricket, Football, Kabaddi, etc. and single-player games like Chess, Badminton, Shooting, etc. are also being considered well thought out as honor to their countries.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128104540","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315849
Nikhil Saini, Jeet Rabari, Mamta C. Padole, Vaibhav Solanki
Load balancing is the process of improving the performance of the system by sharing of workload among the processors. The workload of a machine means the total processing time it requires to execute all the tasks assigned to it. Load balancing is one of the important factors to heighten the working performance of the cloud service provider. The benefits of distributing the workload include increased resource utilization ratio which further leads to enhancing the overall performance thereby achieving maximum client satisfaction. In this paper, we are demonstrating the use of the singleton model for load balancing.
{"title":"Load Balancing in Heterogeneous Distributed Systems Using Singleton Model","authors":"Nikhil Saini, Jeet Rabari, Mamta C. Padole, Vaibhav Solanki","doi":"10.1109/PDGC50313.2020.9315849","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315849","url":null,"abstract":"Load balancing is the process of improving the performance of the system by sharing of workload among the processors. The workload of a machine means the total processing time it requires to execute all the tasks assigned to it. Load balancing is one of the important factors to heighten the working performance of the cloud service provider. The benefits of distributing the workload include increased resource utilization ratio which further leads to enhancing the overall performance thereby achieving maximum client satisfaction. In this paper, we are demonstrating the use of the singleton model for load balancing.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115322754","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315801
Ramya Balabhadrapathruni, Suman De
In recent times, one of the most used scenarios in many industry domains is enhancing the bids or tenders made by suppliers. In this paper, we will be analyzing one such use case for studying the effects of mixed feature selection to optimize the Learning model. The use case is to target and build a predictive clustering model in such a way that the scheduler receives the suggestions based on the most optimal options. There are few feature selection, enhancement, and scaling methodologies which this paper aims to explore with real-time data. Based on the analysis, the most important feature derived would be used to predict the optimal suggestion. The results will then be compared to understand the shortfalls and strong points of this new approach based on the accuracy of prediction. A clustering model will not just help reduce the hours of manual effort put into selecting the right source but will also provide an authentic and optimal option for a scheduler's consideration.
{"title":"A Study on Analysing the impact of Feature Selection on Predictive Machine Learning Algorithms","authors":"Ramya Balabhadrapathruni, Suman De","doi":"10.1109/PDGC50313.2020.9315801","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315801","url":null,"abstract":"In recent times, one of the most used scenarios in many industry domains is enhancing the bids or tenders made by suppliers. In this paper, we will be analyzing one such use case for studying the effects of mixed feature selection to optimize the Learning model. The use case is to target and build a predictive clustering model in such a way that the scheduler receives the suggestions based on the most optimal options. There are few feature selection, enhancement, and scaling methodologies which this paper aims to explore with real-time data. Based on the analysis, the most important feature derived would be used to predict the optimal suggestion. The results will then be compared to understand the shortfalls and strong points of this new approach based on the accuracy of prediction. A clustering model will not just help reduce the hours of manual effort put into selecting the right source but will also provide an authentic and optimal option for a scheduler's consideration.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115724153","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315807
R. Mittal, Varun Malik, Vikram Singh, Jaiteg Singh, Amandeep Kaur
Web mining is an important approach to retrieve and analyse the information from web server log data. In the internet-driven information age, a lot of data is present on the web in many ways and analysing such data using the web mining methods cam result in some novel insights. Such data can be extracted from the server log files and can be preprocessed to be used for various web mining functionalities. In this paper authors used the data from web server log files, preprocessed it and then applied various classification algorithms such as Naïve bayes,KNN,decision tree,random forest and analysed the results. The best approach was then chosen to further improve the performance of the classifier by integrating it with genetic algorithm. In this context, a hybrid approach, namely RFGA was used integrating Random forest and genetic algorithm on the dataset and the results of different machine learning classifiers were compared with RFGA in terms of the predictive accuracy.
{"title":"Integrating Genetic Algorithm with Random Forest for Improving the Classification Performance of Web Log Data","authors":"R. Mittal, Varun Malik, Vikram Singh, Jaiteg Singh, Amandeep Kaur","doi":"10.1109/PDGC50313.2020.9315807","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315807","url":null,"abstract":"Web mining is an important approach to retrieve and analyse the information from web server log data. In the internet-driven information age, a lot of data is present on the web in many ways and analysing such data using the web mining methods cam result in some novel insights. Such data can be extracted from the server log files and can be preprocessed to be used for various web mining functionalities. In this paper authors used the data from web server log files, preprocessed it and then applied various classification algorithms such as Naïve bayes,KNN,decision tree,random forest and analysed the results. The best approach was then chosen to further improve the performance of the classifier by integrating it with genetic algorithm. In this context, a hybrid approach, namely RFGA was used integrating Random forest and genetic algorithm on the dataset and the results of different machine learning classifiers were compared with RFGA in terms of the predictive accuracy.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"47 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123479816","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315761
Ishpreet Kaur, Jasleen Kaur
Customer Churning is also known as customer attrition. Nowadays, there are almost 1.5 million customers that are churning in a year that is rising every year. The Banking industry faces challenges to hold clients. The clients may shift over to different banks due to fluctuating reasons, for example, better financial services at lower charges, bank branch location, low-interest rates, etc. Thus, prediction models are utilized to predict clients who are probably going to churn in the future. Because serving long-term customers is less costly as compared to losing a client that leads to a loss in profit for the bank. Also, old customers create higher benefits and provide new referrals. In this paper, different models of machine learning such as Logistic regression (LR), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), etc. are applied to the bank dataset to predict the probability of customer who is going to churn. The comparison in terms of performance like accuracy, recall, etc. is presented.
{"title":"Customer Churn Analysis and Prediction in Banking Industry using Machine Learning","authors":"Ishpreet Kaur, Jasleen Kaur","doi":"10.1109/PDGC50313.2020.9315761","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315761","url":null,"abstract":"Customer Churning is also known as customer attrition. Nowadays, there are almost 1.5 million customers that are churning in a year that is rising every year. The Banking industry faces challenges to hold clients. The clients may shift over to different banks due to fluctuating reasons, for example, better financial services at lower charges, bank branch location, low-interest rates, etc. Thus, prediction models are utilized to predict clients who are probably going to churn in the future. Because serving long-term customers is less costly as compared to losing a client that leads to a loss in profit for the bank. Also, old customers create higher benefits and provide new referrals. In this paper, different models of machine learning such as Logistic regression (LR), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), etc. are applied to the bank dataset to predict the probability of customer who is going to churn. The comparison in terms of performance like accuracy, recall, etc. is presented.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"356 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123106638","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 : 2020-11-06DOI: 10.1109/tale.2016.7851755
S. Siengchin
I am glad to learn that the Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) in Virtual Mode is being organized by the Department of Computer Science & Engineering and Information Technology at Jaypee University of Information Technology, Waknaghat, Himachal Pradesh from 6th to 8th November, 2020.
{"title":"Message","authors":"S. Siengchin","doi":"10.1109/tale.2016.7851755","DOIUrl":"https://doi.org/10.1109/tale.2016.7851755","url":null,"abstract":"I am glad to learn that the Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) in Virtual Mode is being organized by the Department of Computer Science & Engineering and Information Technology at Jaypee University of Information Technology, Waknaghat, Himachal Pradesh from 6th to 8th November, 2020.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"90 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113954854","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315748
Rakhee, Archana Singh, Mamta Mittal
A timely and accurate prediction of solar radiation results in proper plant growth, seed germination and stages of flowering and fruiting. Neural Network is becoming popular in designing predictive models. However, issues like importance of variables and long training process has limited its accuracy. The objective of this study is to explore the performance of predictive model by integrating neural network with traditional step-wise discriminant analysis forming a hybrid model. The inclusion of selected features from discriminant analysis to the neural network will improve the accuracy of the designed predicted model. The paper also examines that the hybrid approach outperforms the neural network by selecting different architecture of neural network.
{"title":"Prediction of Solar Radiation using Hybrid Discriminant-Neural Network","authors":"Rakhee, Archana Singh, Mamta Mittal","doi":"10.1109/PDGC50313.2020.9315748","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315748","url":null,"abstract":"A timely and accurate prediction of solar radiation results in proper plant growth, seed germination and stages of flowering and fruiting. Neural Network is becoming popular in designing predictive models. However, issues like importance of variables and long training process has limited its accuracy. The objective of this study is to explore the performance of predictive model by integrating neural network with traditional step-wise discriminant analysis forming a hybrid model. The inclusion of selected features from discriminant analysis to the neural network will improve the accuracy of the designed predicted model. The paper also examines that the hybrid approach outperforms the neural network by selecting different architecture of neural network.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122851200","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315796
N. Pradhan, Vijaypal Singh Dhaka
In the medical field, day by day a new technology is introduced to reduce the efforts of doctors as well as patients. Before the actual treatment, patients' needs satisfaction to diagnose a defect in the body part. The current techniques available to detect the correct fractured/damaged bone part of a human is either a Computerized Tomography scan or Magnetic Resonance Imaging scan. The mentioned techniques are either unavailable in rural areas or are costly compare to the X-ray technique. This issue attracts the attention to design a technique that converts a 2-Dimensional (2-D) images into its equivalent 3- Dimensional (3-D) images. For this purpose, the authors used the Generative Adversarial Network to implement a technique that takes an X-ray image as input and gives its equivalent 0° to 360° images.
{"title":"A Deep Learning Technique for Multi-view Prediction of Bone","authors":"N. Pradhan, Vijaypal Singh Dhaka","doi":"10.1109/PDGC50313.2020.9315796","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315796","url":null,"abstract":"In the medical field, day by day a new technology is introduced to reduce the efforts of doctors as well as patients. Before the actual treatment, patients' needs satisfaction to diagnose a defect in the body part. The current techniques available to detect the correct fractured/damaged bone part of a human is either a Computerized Tomography scan or Magnetic Resonance Imaging scan. The mentioned techniques are either unavailable in rural areas or are costly compare to the X-ray technique. This issue attracts the attention to design a technique that converts a 2-Dimensional (2-D) images into its equivalent 3- Dimensional (3-D) images. For this purpose, the authors used the Generative Adversarial Network to implement a technique that takes an X-ray image as input and gives its equivalent 0° to 360° images.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129865642","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315751
M. Khanna, Harmaninder Jit Singh Sidhu, R. Bansal
Industry4.0 was originated in the Germany who defines major technological changes in manufacturing and laid down certain protocols for worldwide competitiveness of German industry. As the new era of ‘smart’ factory is about to begin, in which computers are connected with robotics remotely and use machine learning programs that can control the automatic machines with ease. In this paper, the basic inspiration of industry4.0 will be shared. The analysis of the effectiveness of Government of India's ‘Make in India’ initiative on manufacturing industry is assceesd. In the end, India's competitiveness in automotive industry and India readiness as preferred investment destination by all major automobiles giants will be discussed. And further some of the Government of India's initiative to boost up Auto Sector is also discussed.
{"title":"Industry 4.0: A Study of India's Readiness as Preferred Investment Destination in Automotive and Auto Component Industry","authors":"M. Khanna, Harmaninder Jit Singh Sidhu, R. Bansal","doi":"10.1109/PDGC50313.2020.9315751","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315751","url":null,"abstract":"Industry4.0 was originated in the Germany who defines major technological changes in manufacturing and laid down certain protocols for worldwide competitiveness of German industry. As the new era of ‘smart’ factory is about to begin, in which computers are connected with robotics remotely and use machine learning programs that can control the automatic machines with ease. In this paper, the basic inspiration of industry4.0 will be shared. The analysis of the effectiveness of Government of India's ‘Make in India’ initiative on manufacturing industry is assceesd. In the end, India's competitiveness in automotive industry and India readiness as preferred investment destination by all major automobiles giants will be discussed. And further some of the Government of India's initiative to boost up Auto Sector is also discussed.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129683753","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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