M. PhridviRaj, C. V. Rao, V. Radhakrishna, Aravind Cheruvu
Association pattern mining is a method of finding interesting relationships or patterns between item sets present in each of the transactions of the transactional databases. Current researchers in this area are focusing on the data mining task of finding frequent patterns among the item sets based on the interestingness measures like the support and confidence which is called as Frequent pattern mining. Till date, in existing frequent pattern mining algorithms, an itemset is said to be frequent if the support of the itemset satisfies the minimum support input. In this paper, the objective of our algorithm is to find interesting patterns among the item sets based on a Gaussian similarity for an input reference threshold which is first of its kind in the research literature. This study is limited to outlining naïve approach of mining frequent itemsets which requires validating every itemset to verify if the itemset is frequent or not.
{"title":"Similarity Association Pattern Mining in Transaction Databases","authors":"M. PhridviRaj, C. V. Rao, V. Radhakrishna, Aravind Cheruvu","doi":"10.1145/3460620.3460752","DOIUrl":"https://doi.org/10.1145/3460620.3460752","url":null,"abstract":"Association pattern mining is a method of finding interesting relationships or patterns between item sets present in each of the transactions of the transactional databases. Current researchers in this area are focusing on the data mining task of finding frequent patterns among the item sets based on the interestingness measures like the support and confidence which is called as Frequent pattern mining. Till date, in existing frequent pattern mining algorithms, an itemset is said to be frequent if the support of the itemset satisfies the minimum support input. In this paper, the objective of our algorithm is to find interesting patterns among the item sets based on a Gaussian similarity for an input reference threshold which is first of its kind in the research literature. This study is limited to outlining naïve approach of mining frequent itemsets which requires validating every itemset to verify if the itemset is frequent or not.","PeriodicalId":36824,"journal":{"name":"Data","volume":"86 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78952003","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}
Key technologies such as a new generation of industrial systems highly depends on artificial intelligence, and electronic physical systems that can digitize the entire supply chain together with data mining, machine learning, and more. At present, uses of artificial intelligence-based solutions are very important to improve the accuracy and efficiency of production processes. Artificial intelligence (AI) is playing a key role in the fourth industrial revolution, and we see significant improvements in different methods of machine learning. Artificial intelligence is widely used by practitioner engineers to solve various problems. This journal provides an international forum for quick articles that describes the practical application of artificial intelligence in all areas of mechanical engineering. Many researchers cited the development of technology in industrial fields to reduce problems in industry. Both the Operations Research (OR) community and Artificial Intelligence (AI) show that these problems are still interesting. While AI focuses linearly on increasing production and mitigating industry difficulties that may be seen as a revolution in the future. AI techniques offer a richer and more flexible presentation of real problems. The article presents the architecture of the industrial laboratory and the challenges associated with the use of artificial intelligence in industrial processes.
{"title":"Hybrid solution of challenges future problems in the new generation of the artificial intelligence industry used operations research industrial processes","authors":"T. Mohammed, Mohammed N. Qasim, O. Bayat","doi":"10.1145/3460620.3460757","DOIUrl":"https://doi.org/10.1145/3460620.3460757","url":null,"abstract":"Key technologies such as a new generation of industrial systems highly depends on artificial intelligence, and electronic physical systems that can digitize the entire supply chain together with data mining, machine learning, and more. At present, uses of artificial intelligence-based solutions are very important to improve the accuracy and efficiency of production processes. Artificial intelligence (AI) is playing a key role in the fourth industrial revolution, and we see significant improvements in different methods of machine learning. Artificial intelligence is widely used by practitioner engineers to solve various problems. This journal provides an international forum for quick articles that describes the practical application of artificial intelligence in all areas of mechanical engineering. Many researchers cited the development of technology in industrial fields to reduce problems in industry. Both the Operations Research (OR) community and Artificial Intelligence (AI) show that these problems are still interesting. While AI focuses linearly on increasing production and mitigating industry difficulties that may be seen as a revolution in the future. AI techniques offer a richer and more flexible presentation of real problems. The article presents the architecture of the industrial laboratory and the challenges associated with the use of artificial intelligence in industrial processes.","PeriodicalId":36824,"journal":{"name":"Data","volume":"179 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74778541","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}
Recommender systems are used to generate meaningful recommendations to users based on their preferences, which will be determined following several approaches. This work targets Arab readers by providing accurate and reliable results that match their needs and desirability. Eventually, it will enhance the reading experience for any Arab readers. The main approach is to filter the recommendations, and this can be achieved either by Content-Based filtering or by Collaborative Filtering. The collaborative filtering techniques presented in this paper compute the similarity matrix between items and users' ratings, and then evaluate the recommendations for users. The techniques cover User-Based and Item-Based Collaborative Filtering, as well as Matrix Factorization through an SVD algorithm. A comparison between these techniques is presented in terms of the fitting and testing time, and accuracy. The KNN-based algorithms showed better performance than the matrix factorization method with respect to fitting and testing time. However, the matrix factorization (SVD) algorithm had the best results in terms of accuracy.
{"title":"A Book Recommender System Using Collaborative Filtering Method","authors":"Sewar Khalifeh, Amjed Al-mousa","doi":"10.1145/3460620.3460744","DOIUrl":"https://doi.org/10.1145/3460620.3460744","url":null,"abstract":"Recommender systems are used to generate meaningful recommendations to users based on their preferences, which will be determined following several approaches. This work targets Arab readers by providing accurate and reliable results that match their needs and desirability. Eventually, it will enhance the reading experience for any Arab readers. The main approach is to filter the recommendations, and this can be achieved either by Content-Based filtering or by Collaborative Filtering. The collaborative filtering techniques presented in this paper compute the similarity matrix between items and users' ratings, and then evaluate the recommendations for users. The techniques cover User-Based and Item-Based Collaborative Filtering, as well as Matrix Factorization through an SVD algorithm. A comparison between these techniques is presented in terms of the fitting and testing time, and accuracy. The KNN-based algorithms showed better performance than the matrix factorization method with respect to fitting and testing time. However, the matrix factorization (SVD) algorithm had the best results in terms of accuracy.","PeriodicalId":36824,"journal":{"name":"Data","volume":"47 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86582577","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}
Anood Manasrah, Aisha Alkayem, Malik Qasaimeh, Samer Nofal
With technological advances and the use of the Internet everywhere, And the widespread use of machine learning has become important to pay attention to security in all areas of life, especially in the healthcare field, many concerns have arisen regarding the security of patient confidential data in health systems. As it became possible to change patient data, which would lead to a change in data accuracy or to data theft, which would lead to a violation of the safety system in the field of health care. In this paper, a health system was studied in a hospital in Jordan after collecting information on 769 records for pregnant diabetics. The analysis used Python to test the accuracy of this information and improve the performance of the model being created using machine learning algorithms, including decision trees and random forests. Since patient information in any health system has been exposed to many threats and weaknesses, the main goal was to reduce them, and obtain accurate information with good performance and excellent quality, to avoid compromising health rights and data protection for patients.
{"title":"Assessment of Machine Learning Security: The Case of Healthcare Data","authors":"Anood Manasrah, Aisha Alkayem, Malik Qasaimeh, Samer Nofal","doi":"10.1145/3460620.3460738","DOIUrl":"https://doi.org/10.1145/3460620.3460738","url":null,"abstract":"With technological advances and the use of the Internet everywhere, And the widespread use of machine learning has become important to pay attention to security in all areas of life, especially in the healthcare field, many concerns have arisen regarding the security of patient confidential data in health systems. As it became possible to change patient data, which would lead to a change in data accuracy or to data theft, which would lead to a violation of the safety system in the field of health care. In this paper, a health system was studied in a hospital in Jordan after collecting information on 769 records for pregnant diabetics. The analysis used Python to test the accuracy of this information and improve the performance of the model being created using machine learning algorithms, including decision trees and random forests. Since patient information in any health system has been exposed to many threats and weaknesses, the main goal was to reduce them, and obtain accurate information with good performance and excellent quality, to avoid compromising health rights and data protection for patients.","PeriodicalId":36824,"journal":{"name":"Data","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77606553","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}
Distributed Denial of Service (DDoS) attacks in the cloud environment are not as simple as the same attacks which occur in the traditional physical network environment. Not only one single attack is affecting the cloud environment, where as there are multiple sources to affect the environment. DDoS attacks can be detected using the existing machine learning techniques such as neural classifiers. This paper discusses on the survey carried out on DDoS attacks in the cloud environment. Using Machine learning techniques results to detection of higher false positive rates. Some of the widely used methods are ANN, SVM, kNN, J48, Feature rank and Feature selection methods to detect DDoS attacks in the cloud environment. This paper reviews various studies related to detection of network attacks in network and cloud environments.
{"title":"Study of Detection of DDoS attacks in cloud environment Using Regression Analysis","authors":"Arun Nagaraja, U. Boregowda, V. Radhakrishna","doi":"10.1145/3460620.3460750","DOIUrl":"https://doi.org/10.1145/3460620.3460750","url":null,"abstract":"Distributed Denial of Service (DDoS) attacks in the cloud environment are not as simple as the same attacks which occur in the traditional physical network environment. Not only one single attack is affecting the cloud environment, where as there are multiple sources to affect the environment. DDoS attacks can be detected using the existing machine learning techniques such as neural classifiers. This paper discusses on the survey carried out on DDoS attacks in the cloud environment. Using Machine learning techniques results to detection of higher false positive rates. Some of the widely used methods are ANN, SVM, kNN, J48, Feature rank and Feature selection methods to detect DDoS attacks in the cloud environment. This paper reviews various studies related to detection of network attacks in network and cloud environments.","PeriodicalId":36824,"journal":{"name":"Data","volume":"41 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89584289","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}
Wala M. Eltajoury, Abdelsalam M. Maatuk, I. Denna, Ebitisam K. Elberkawi
The use of health information technology has become highly effective in healthcare quality as it enhances personal and public care, broadens diagnostic accuracy, reduces medical costs and errors, and improves the effectiveness of both organizational and clinical processes. This study aims to assess physicians' perceptions of perceived benefits and barriers of electronic prescribing (e-Prescribing) software and their implementation. A self-prepared questionnaire was developed, distributed, and filled by physicians (n = 100) from different departments at Benghazi Medical Center, Libya. The Statistical Package for Social Sciences (SPSS) program was used to analyze the results. The results showed that more than 90% of physicians preferred the e-Prescribing software, with most of them believing that they were able to provide better services to patients by saving time and effort (87%), and checking drug interaction (82%), as well as reducing medical errors (89%). On the other hand, the results indicated that the main barriers are the lack of adequate infrastructure, awareness sessions, and human and material resources. Physicians prefer to use the e-Prescribing software, as it supports decision-makers to design more effective strategies and implementation plans. The study recommended the necessity of holding awareness sessions and training programs for using e-Prescribing software.
{"title":"Physicians' Attitudes towards Electronic Prescribing Software: Perceived Benefits and Barriers","authors":"Wala M. Eltajoury, Abdelsalam M. Maatuk, I. Denna, Ebitisam K. Elberkawi","doi":"10.1145/3460620.3460629","DOIUrl":"https://doi.org/10.1145/3460620.3460629","url":null,"abstract":"The use of health information technology has become highly effective in healthcare quality as it enhances personal and public care, broadens diagnostic accuracy, reduces medical costs and errors, and improves the effectiveness of both organizational and clinical processes. This study aims to assess physicians' perceptions of perceived benefits and barriers of electronic prescribing (e-Prescribing) software and their implementation. A self-prepared questionnaire was developed, distributed, and filled by physicians (n = 100) from different departments at Benghazi Medical Center, Libya. The Statistical Package for Social Sciences (SPSS) program was used to analyze the results. The results showed that more than 90% of physicians preferred the e-Prescribing software, with most of them believing that they were able to provide better services to patients by saving time and effort (87%), and checking drug interaction (82%), as well as reducing medical errors (89%). On the other hand, the results indicated that the main barriers are the lack of adequate infrastructure, awareness sessions, and human and material resources. Physicians prefer to use the e-Prescribing software, as it supports decision-makers to design more effective strategies and implementation plans. The study recommended the necessity of holding awareness sessions and training programs for using e-Prescribing software.","PeriodicalId":36824,"journal":{"name":"Data","volume":"26 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76876303","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}
{"title":"International Conference on Data Science, E-learning and Information Systems 2021","authors":"","doi":"10.1145/3460620","DOIUrl":"https://doi.org/10.1145/3460620","url":null,"abstract":"","PeriodicalId":36824,"journal":{"name":"Data","volume":"191 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76934555","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 : 2020-10-20DOI: 10.1109/ICADEIS49811.2020.9276858
N. Sharef, M. A. Azmi Murad, E. Mansor, Nurul Amelina Nasharuddin, Muhd Khaizer Omar, Normalia Samian, N. Arshad, W. Ismail, F. Shahbodin
Personalised learning enables instructions to be tailored specific to students learning needs, while making sure learning outcomes are attained. Instructors require information that could facilitate them in adapting their pedagogy design so the learning delivery could be optimized. However, existing solutions are limited to descriptive analytic and intervention facilitation is confined to students at risk prediction based on their course engagement frequency. Tools to predict final grade is available but very scarce. Besides, realtime monitoring of reaction to learning events are not available. Therefore, this paper proposes a solution that integrates Internet of Things, learning analytic and chatbot to fill the said gaps. The paper also presents the experience of pilot developments towards the current version of solution.
{"title":"Learning-Analytics based Intelligent Simulator for Personalised Learning","authors":"N. Sharef, M. A. Azmi Murad, E. Mansor, Nurul Amelina Nasharuddin, Muhd Khaizer Omar, Normalia Samian, N. Arshad, W. Ismail, F. Shahbodin","doi":"10.1109/ICADEIS49811.2020.9276858","DOIUrl":"https://doi.org/10.1109/ICADEIS49811.2020.9276858","url":null,"abstract":"Personalised learning enables instructions to be tailored specific to students learning needs, while making sure learning outcomes are attained. Instructors require information that could facilitate them in adapting their pedagogy design so the learning delivery could be optimized. However, existing solutions are limited to descriptive analytic and intervention facilitation is confined to students at risk prediction based on their course engagement frequency. Tools to predict final grade is available but very scarce. Besides, realtime monitoring of reaction to learning events are not available. Therefore, this paper proposes a solution that integrates Internet of Things, learning analytic and chatbot to fill the said gaps. The paper also presents the experience of pilot developments towards the current version of solution.","PeriodicalId":36824,"journal":{"name":"Data","volume":"13 1","pages":"1-6"},"PeriodicalIF":2.6,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80675232","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 : 2020-10-20DOI: 10.1109/ICADEIS49811.2020.9277104
M. I. D. Putra, Budi Irmawati, W. Wedashwara, Dita Pramesti, Siti Oryza Khairunnisa
Internet provides articles that may be categorized to various target readers based on genders, ages, hobbies, etc. To make sure that readers consume a proper article based on their age group, methods and training data were proposed and collected to classify the articles. This paper reported a document classification based on age groups using a binary classification method for Indonesian documents. The document classification used the term frequency and inverse document frequency (TF-IDF) features run on the Multinomial Naïve Bayes Classifier. The dataset was crowdsourced from three different sites: bobo.grid.id, hai.grid.id, and www.detik.com for three age group readers such as elementary school children, teenagers, and adults. The experimental results obtained 0.9406, 0.9341, and 0.9374 of precision, recall, and F-score respectively. This experiment also reported that for the datasets that were not stemmed performed better than those that were stemmed. It shows that the stemming process, which usually be done in the document classification, throws some information in the Indonesian texts. However, because this behavior was not happen on nouns, our future work is to elaborate further on the role of affixations in the lower age group documents.
{"title":"Age Group Based Document Classification in Bahasa Indonesia","authors":"M. I. D. Putra, Budi Irmawati, W. Wedashwara, Dita Pramesti, Siti Oryza Khairunnisa","doi":"10.1109/ICADEIS49811.2020.9277104","DOIUrl":"https://doi.org/10.1109/ICADEIS49811.2020.9277104","url":null,"abstract":"Internet provides articles that may be categorized to various target readers based on genders, ages, hobbies, etc. To make sure that readers consume a proper article based on their age group, methods and training data were proposed and collected to classify the articles. This paper reported a document classification based on age groups using a binary classification method for Indonesian documents. The document classification used the term frequency and inverse document frequency (TF-IDF) features run on the Multinomial Naïve Bayes Classifier. The dataset was crowdsourced from three different sites: bobo.grid.id, hai.grid.id, and www.detik.com for three age group readers such as elementary school children, teenagers, and adults. The experimental results obtained 0.9406, 0.9341, and 0.9374 of precision, recall, and F-score respectively. This experiment also reported that for the datasets that were not stemmed performed better than those that were stemmed. It shows that the stemming process, which usually be done in the document classification, throws some information in the Indonesian texts. However, because this behavior was not happen on nouns, our future work is to elaborate further on the role of affixations in the lower age group documents.","PeriodicalId":36824,"journal":{"name":"Data","volume":"1 1","pages":"1-6"},"PeriodicalIF":2.6,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85477425","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 : 2020-10-20DOI: 10.1109/ICADEIS49811.2020.9276962
I. S. Wijaya, Aditya Perwira Joan Dwitama, I. B. K. Widiartha, Seno Adi Putra
Building crack images classification caused by the earthquake is commonly conducted manually by analyzing walls, beams, columns, and floors based on visual inspection of crack's diameter, depth, and length. Experts in structural engineering who have many experiences in building damage assessment usually handle the mentioned task. In order to speed up and simplify the assessment process a classification system based on pattern recognition is on demand. This paper proposes a crack image classification technique using CNN. This classification technique is proposed to improve the performance of two previous works: the crack classification systems using GLCM features and the SVM classifier and the crack classification systems using Zoning and Moment features and QDA classifier. The experimental results show that the CNN based crack image classification works properly indicated by 99.63% of accuracy, 99.65% of precision, and 99.64% of recall for METU dataset and 93.80% of accuracy, 93.49% of precision, and 93.94% of recall for CDLE dataset. In detail, the CNN based crack image classification provides significantly higher performance than that of the previous works. Furthermore, the proposed system also shows robust performance against large variability of cracks and non-crack images.
{"title":"Classification of Building Cracks Image Using the Convolutional Neural Network Method","authors":"I. S. Wijaya, Aditya Perwira Joan Dwitama, I. B. K. Widiartha, Seno Adi Putra","doi":"10.1109/ICADEIS49811.2020.9276962","DOIUrl":"https://doi.org/10.1109/ICADEIS49811.2020.9276962","url":null,"abstract":"Building crack images classification caused by the earthquake is commonly conducted manually by analyzing walls, beams, columns, and floors based on visual inspection of crack's diameter, depth, and length. Experts in structural engineering who have many experiences in building damage assessment usually handle the mentioned task. In order to speed up and simplify the assessment process a classification system based on pattern recognition is on demand. This paper proposes a crack image classification technique using CNN. This classification technique is proposed to improve the performance of two previous works: the crack classification systems using GLCM features and the SVM classifier and the crack classification systems using Zoning and Moment features and QDA classifier. The experimental results show that the CNN based crack image classification works properly indicated by 99.63% of accuracy, 99.65% of precision, and 99.64% of recall for METU dataset and 93.80% of accuracy, 93.49% of precision, and 93.94% of recall for CDLE dataset. In detail, the CNN based crack image classification provides significantly higher performance than that of the previous works. Furthermore, the proposed system also shows robust performance against large variability of cracks and non-crack images.","PeriodicalId":36824,"journal":{"name":"Data","volume":"61 1","pages":"1-6"},"PeriodicalIF":2.6,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84670856","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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