Lei Shi, Yaqian Qin, Juanjuan Zhang, Yan Wang, H. Qiao, Haiping Si
Agricultural production and operation produce a large amount of data, which hides valuable knowledge. Data mining technology can effectively explore the connection between various factors from the massive agricultural data. Classification prediction is one of the most valuable agricultural data mining techniques. This paper presents a new algorithm consisting of machine learning algorithms, feature ranking method and instance filter, which aims to enhance the capability of the random forest algorithm and better solve the problem of agricultural multi-class classification. The performance of the new algorithm was tested by using four standard agricultural multi-class datasets, and the experimental results showed that the newly proposed method performed well on all datasets. Among them, substantial rise in classification accuracy is observed for Eucalyptus dataset. Applying random forest algorithm on Eucalyptus dataset results in classification accuracy as 53.4% and after applying the new algorithm (rough set) the classification accuracy significantly increases to 83.7%.
{"title":"Multi-Class Classification of Agricultural Data Based on Random Forest and Feature Selection","authors":"Lei Shi, Yaqian Qin, Juanjuan Zhang, Yan Wang, H. Qiao, Haiping Si","doi":"10.4018/jitr.298618","DOIUrl":"https://doi.org/10.4018/jitr.298618","url":null,"abstract":"Agricultural production and operation produce a large amount of data, which hides valuable knowledge. Data mining technology can effectively explore the connection between various factors from the massive agricultural data. Classification prediction is one of the most valuable agricultural data mining techniques. This paper presents a new algorithm consisting of machine learning algorithms, feature ranking method and instance filter, which aims to enhance the capability of the random forest algorithm and better solve the problem of agricultural multi-class classification. The performance of the new algorithm was tested by using four standard agricultural multi-class datasets, and the experimental results showed that the newly proposed method performed well on all datasets. Among them, substantial rise in classification accuracy is observed for Eucalyptus dataset. Applying random forest algorithm on Eucalyptus dataset results in classification accuracy as 53.4% and after applying the new algorithm (rough set) the classification accuracy significantly increases to 83.7%.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117334215","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}
The emergence of the new economic model of Internet credit industry brings convenience to people's lives, and it also impacts the business model of traditional commercial banking to a great extent. How to better improve the operation mode, correctly assess and avoid the risks of Internet finance, and create a healthy, orderly, safe and sustainable development environment of Internet finance industry is an important research topic in this industry under the current situation. This paper studies the application of innovative risk early warning model based on big data technology in Internet credit financial risk assessment, aiming at maximizing the utilization efficiency of internal and external data, building a timely, accurate and effective early warning system with independent characteristics, and creating a sharp weapon for intelligent risk early warning. In order to promote the healthy and benign development of China's Internet finance industry.
{"title":"Application of Innovative Risk Early Warning Model Based on Big Data Technology in Internet Credit Financial Risk","authors":"Bingqiu Zhang","doi":"10.4018/jitr.299920","DOIUrl":"https://doi.org/10.4018/jitr.299920","url":null,"abstract":"The emergence of the new economic model of Internet credit industry brings convenience to people's lives, and it also impacts the business model of traditional commercial banking to a great extent. How to better improve the operation mode, correctly assess and avoid the risks of Internet finance, and create a healthy, orderly, safe and sustainable development environment of Internet finance industry is an important research topic in this industry under the current situation. This paper studies the application of innovative risk early warning model based on big data technology in Internet credit financial risk assessment, aiming at maximizing the utilization efficiency of internal and external data, building a timely, accurate and effective early warning system with independent characteristics, and creating a sharp weapon for intelligent risk early warning. In order to promote the healthy and benign development of China's Internet finance industry.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124241352","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}
This research was conducted to perform an in-depth analysis of the coupling metrics of 10 Open Source Software (OSS) projects obtained from the Comets dataset. More precisely, we analyze the dataset of object-oriented OSS projects (having 17 code related metrics such as coupling, complexity, and size metrics) to (1) examine the relationships among the coupling and other metrics (size, complexity), (2) analyze the pattern in the growth of software metrics, and (3) propose a model for prediction of coupling. To generalize the model of coupling prediction, we have applied different machine learning algorithms and validated their performance on similar datasets. The results indicated that the Random forests algorithm outperforms all other models. The relation analysis specifies the existence of strong positive relationships between the coupling, size, and complexity metrics while the pattern analysis pinpointed the increasing growth trend for coupling. The obtained outcomes will help the developers, project managers, and stakeholders in better understating the state of software health
{"title":"In-Depth Analysis and Prediction of Coupling Metrics of Open Source Software Projects","authors":"Munish Saini, Raghuvar Arora, S. O. Adebayo","doi":"10.4018/jitr.301267","DOIUrl":"https://doi.org/10.4018/jitr.301267","url":null,"abstract":"This research was conducted to perform an in-depth analysis of the coupling metrics of 10 Open Source Software (OSS) projects obtained from the Comets dataset. More precisely, we analyze the dataset of object-oriented OSS projects (having 17 code related metrics such as coupling, complexity, and size metrics) to (1) examine the relationships among the coupling and other metrics (size, complexity), (2) analyze the pattern in the growth of software metrics, and (3) propose a model for prediction of coupling. To generalize the model of coupling prediction, we have applied different machine learning algorithms and validated their performance on similar datasets. The results indicated that the Random forests algorithm outperforms all other models. The relation analysis specifies the existence of strong positive relationships between the coupling, size, and complexity metrics while the pattern analysis pinpointed the increasing growth trend for coupling. The obtained outcomes will help the developers, project managers, and stakeholders in better understating the state of software health","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124561782","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}
This study utilized an extended model of the Unified Theory of Acceptance and Use of Technology (UTAUT2) to explore the factors influencing the future adoption of accounting information systems (AIS) by Qatari students. A research model was proposed to predict future adoption, partially moderated by voluntary status of using the system. A sample of 237 students was used to probe their perceptions regarding the use of such systems in their future careers. Students were enrolled in an accounting information systems course in Qatar University. Results indicated that perceived facilitating conditions, performance expectancy and enjoyment were significant predictors of AIS. The other factors failed to be significant predictors. The estimated R2 was 48.4%. The moderation effect of voluntariness was also significant in influencing the relationship between enjoyment and future adoption. The moderator yielded a negative beta, which means that it faded the relationship under consideration. Conclusions and future recommendations are reported at the end of paper.
{"title":"When Users Enjoy Using the System: The Case of AIS","authors":"E. Abu-Shanab, I. B. Salah","doi":"10.4018/jitr.299952","DOIUrl":"https://doi.org/10.4018/jitr.299952","url":null,"abstract":"This study utilized an extended model of the Unified Theory of Acceptance and Use of Technology (UTAUT2) to explore the factors influencing the future adoption of accounting information systems (AIS) by Qatari students. A research model was proposed to predict future adoption, partially moderated by voluntary status of using the system. A sample of 237 students was used to probe their perceptions regarding the use of such systems in their future careers. Students were enrolled in an accounting information systems course in Qatar University. Results indicated that perceived facilitating conditions, performance expectancy and enjoyment were significant predictors of AIS. The other factors failed to be significant predictors. The estimated R2 was 48.4%. The moderation effect of voluntariness was also significant in influencing the relationship between enjoyment and future adoption. The moderator yielded a negative beta, which means that it faded the relationship under consideration. Conclusions and future recommendations are reported at the end of paper.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116503893","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}
Abdou-Aziz Sobabe, Tahirou Djara, Blaise Blochaou, A. Vianou
This manuscript presents the design of a new approach of human skin color authentication. Skin color is one of the most popular soft biometric modalities. Since a soft biometric modality alone cannot reliably authenticate an individual, this new system is designed to combine skin color results with other pure biometric modalities to increase recognition performance. In the classification process, we first perform facial skin detection by segmentation using the thresholding method in the HSV color space. Then, the K-means algorithm of the clustering method is used to determine the dominant colors on the skin pixels in the RGB model. Variations according to the R, G and B components are recorded in a reference model to enable an individual’s identity to be predicted on the basis of 30 clusters. Experimental results are promising and give a false acceptance rate (FAR) of 29.47% and a false rejection rate (FRR) of 70.53%.
{"title":"Soft Biometrics Authentication: A Cluster-Based Skin Color Classification System","authors":"Abdou-Aziz Sobabe, Tahirou Djara, Blaise Blochaou, A. Vianou","doi":"10.4018/jitr.298620","DOIUrl":"https://doi.org/10.4018/jitr.298620","url":null,"abstract":"This manuscript presents the design of a new approach of human skin color authentication. Skin color is one of the most popular soft biometric modalities. Since a soft biometric modality alone cannot reliably authenticate an individual, this new system is designed to combine skin color results with other pure biometric modalities to increase recognition performance. In the classification process, we first perform facial skin detection by segmentation using the thresholding method in the HSV color space. Then, the K-means algorithm of the clustering method is used to determine the dominant colors on the skin pixels in the RGB model. Variations according to the R, G and B components are recorded in a reference model to enable an individual’s identity to be predicted on the basis of 30 clusters. Experimental results are promising and give a false acceptance rate (FAR) of 29.47% and a false rejection rate (FRR) of 70.53%.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126521502","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}
Beall’s list heavily used as a base for selection of predatory journals by large no. of research studies was ceased from internet in 2017. Thus, status of journal declared as predatory in list is debatable. To verify quality of journals in terms of accuracy and standard of peer review, a sample of Medical Science journals from Beall list and indexed in reputed indexing/abstracting databases was taken. sample of journals was put to quality and credibility check by submitting a deliberately flawed research article. deliberate errors exceed an acceptable norm in submitted research paper. It is astonishing to see that majority of journals (61.96%) accept flawed article on such a sensitive issue, i.e., COVID-19 without peer review and desired revisions. Instant mails reporting paper's acceptance, preceded by multiple emails requesting for submission for Article processing fee, were received frequently. It is found that such publishing ventures are a scare story that only wants to generate as much revenue as possible.
{"title":"The Sensitivity of Research on COVID-19: An Analysis of the Response of Peer Review Systems of Predatory Journals","authors":"Rosy Jan, Sumeer Gul","doi":"10.4018/jitr.299389","DOIUrl":"https://doi.org/10.4018/jitr.299389","url":null,"abstract":"Beall’s list heavily used as a base for selection of predatory journals by large no. of research studies was ceased from internet in 2017. Thus, status of journal declared as predatory in list is debatable. To verify quality of journals in terms of accuracy and standard of peer review, a sample of Medical Science journals from Beall list and indexed in reputed indexing/abstracting databases was taken. sample of journals was put to quality and credibility check by submitting a deliberately flawed research article. deliberate errors exceed an acceptable norm in submitted research paper. It is astonishing to see that majority of journals (61.96%) accept flawed article on such a sensitive issue, i.e., COVID-19 without peer review and desired revisions. Instant mails reporting paper's acceptance, preceded by multiple emails requesting for submission for Article processing fee, were received frequently. It is found that such publishing ventures are a scare story that only wants to generate as much revenue as possible.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131591873","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}
Cloud computing has risen as a new computing paradigm providing computing, resources for networking and storage as a service across the network. Data replication is a phenomenon which brings the available and reliable data (e.g., maybe the databases) nearer to the consumers (e.g., cloud applications) to overcome the bottleneck and is becoming a suitable solution. In this paper, we study the performance characteristics of a replicated database in cloud computing data centres which improves QoS by reducing communication delays. We formulate a theoretical queueing model of the replicated system by considering the arrival process as Poisson distribution for both types of client request, such as read and write applications. We solve the proposed model with the help of the recursive method, and the relevant performance matrices are derived. The evaluated results from both the mathematical model and extensive simulations help to study the unveil performance and guide the cloud providers for modelling future data replication solutions.
{"title":"Performance Enhancement of Cloud Datacenters Through Replicated Database Server","authors":"S. Patra, V. Goswami","doi":"10.4018/jitr.299948","DOIUrl":"https://doi.org/10.4018/jitr.299948","url":null,"abstract":"Cloud computing has risen as a new computing paradigm providing computing, resources for networking and storage as a service across the network. Data replication is a phenomenon which brings the available and reliable data (e.g., maybe the databases) nearer to the consumers (e.g., cloud applications) to overcome the bottleneck and is becoming a suitable solution. In this paper, we study the performance characteristics of a replicated database in cloud computing data centres which improves QoS by reducing communication delays. We formulate a theoretical queueing model of the replicated system by considering the arrival process as Poisson distribution for both types of client request, such as read and write applications. We solve the proposed model with the help of the recursive method, and the relevant performance matrices are derived. The evaluated results from both the mathematical model and extensive simulations help to study the unveil performance and guide the cloud providers for modelling future data replication solutions.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123291820","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}
Detection of abnormal crowd behavior is one of the important tasks in real-time video surveillance systems for public safety in public places such as subway, shopping malls, sport complexes and various other public gatherings. Due to high density crowded scenes, the detection of crowd behavior becomes a tedious task. Hence, crowd behavior analysis becomes a hot topic of research and requires an approach with higher rate of detection. In this work, the focus is on the crowd management and present an end-to-end model for crowd behavior analysis. A feature extraction-based model using contrast, entropy, homogeneity, and uniformity features to determine the threshold on normal and abnormal activity has been proposed in this paper. The crowd behavior analysis is measured in terms of receiver operating characteristic curve (ROC) & area under curve (AUC) for UMN dataset for the proposed model and compared with other crowd analysis methods in literature to prove its worthiness. YouTube video sequences also used for anomaly detection.
{"title":"Crowd Abnormality Detection Using Optical Flow and GLCM-Based Texture Features","authors":"R. Lalit, R. Purwar","doi":"10.4018/jitr.2022010110","DOIUrl":"https://doi.org/10.4018/jitr.2022010110","url":null,"abstract":"Detection of abnormal crowd behavior is one of the important tasks in real-time video surveillance systems for public safety in public places such as subway, shopping malls, sport complexes and various other public gatherings. Due to high density crowded scenes, the detection of crowd behavior becomes a tedious task. Hence, crowd behavior analysis becomes a hot topic of research and requires an approach with higher rate of detection. In this work, the focus is on the crowd management and present an end-to-end model for crowd behavior analysis. A feature extraction-based model using contrast, entropy, homogeneity, and uniformity features to determine the threshold on normal and abnormal activity has been proposed in this paper. The crowd behavior analysis is measured in terms of receiver operating characteristic curve (ROC) & area under curve (AUC) for UMN dataset for the proposed model and compared with other crowd analysis methods in literature to prove its worthiness. YouTube video sequences also used for anomaly detection.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122448056","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}
B. Paul, Shubham Agnihotri, B. Kavya, Prachi Tripathi, C. Babu
Traditional agriculture is facing numerous serious issues such as climate variation, population rise, water scarcity, soil degradation, and food security and many more. Though, Aquaponics is a promising solution, research on building an economically feasible smart Aquaponics system is still a challenge. In this paper, a sustainable smart Aquaponics system using Internet of Things (IOT) and Data Analytics is proposed. The acquired data from sensors such as Ph sensor, and temperature sensor, is analyzed using machine learning techniques to interpret the health of the system. Further, the proposed system includes automated fish feeder which is controlled by Raspberry Pi to automate and reduce the maintenance issues. The android application helps the user to remotely control and monitor the health of the system and also track the critical system parameters. Further the system is driven by the solar power to make it sustainable. A comprehensive survey on the key aspects of Aquaponics including comparison of the proposed model with the traditional aquaponics model is also presented.
{"title":"Sustainable Smart Aquaponics Farming Using IoT and Data Analytics","authors":"B. Paul, Shubham Agnihotri, B. Kavya, Prachi Tripathi, C. Babu","doi":"10.4018/jitr.299914","DOIUrl":"https://doi.org/10.4018/jitr.299914","url":null,"abstract":"Traditional agriculture is facing numerous serious issues such as climate variation, population rise, water scarcity, soil degradation, and food security and many more. Though, Aquaponics is a promising solution, research on building an economically feasible smart Aquaponics system is still a challenge. In this paper, a sustainable smart Aquaponics system using Internet of Things (IOT) and Data Analytics is proposed. The acquired data from sensors such as Ph sensor, and temperature sensor, is analyzed using machine learning techniques to interpret the health of the system. Further, the proposed system includes automated fish feeder which is controlled by Raspberry Pi to automate and reduce the maintenance issues. The android application helps the user to remotely control and monitor the health of the system and also track the critical system parameters. Further the system is driven by the solar power to make it sustainable. A comprehensive survey on the key aspects of Aquaponics including comparison of the proposed model with the traditional aquaponics model is also presented.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123825548","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}
G. Siddesh, S. R. M. Sekhar, Srinidhi Hiriyannaiah, G. SrinivasaK.
The stock market volume and price are an active area of research for the past many years. Behind every dollar of investment, the customer will be hoping for profit in one or the other way. There is a positive correlation between investor sentiment and stock volume. Predicting the stock market is the most difficult task due to the dynamic fluctuation of volume and price. The traditional analysis methods carried out leads to satisfactory results. In this paper, the proposed system uses real-time data from Twitter to detect the user opinion about the product along with the stock volume for prediction. The stock volume data and the Twitter data are collected first and then the classification of the polarity is carried out using the SentiWordnet dictionary. The algorithm for the prediction of the stock prices uses Long-short term memory, a neural network as the prices are sequential evolving in nature. The results of the proposed system are correlated between the stock market and Twitter data to obtain better insights that are positive.
{"title":"Forecasting Stock Market Volume Price Using Sentimental and Technical Analysis","authors":"G. Siddesh, S. R. M. Sekhar, Srinidhi Hiriyannaiah, G. SrinivasaK.","doi":"10.4018/jitr.299383","DOIUrl":"https://doi.org/10.4018/jitr.299383","url":null,"abstract":"The stock market volume and price are an active area of research for the past many years. Behind every dollar of investment, the customer will be hoping for profit in one or the other way. There is a positive correlation between investor sentiment and stock volume. Predicting the stock market is the most difficult task due to the dynamic fluctuation of volume and price. The traditional analysis methods carried out leads to satisfactory results. In this paper, the proposed system uses real-time data from Twitter to detect the user opinion about the product along with the stock volume for prediction. The stock volume data and the Twitter data are collected first and then the classification of the polarity is carried out using the SentiWordnet dictionary. The algorithm for the prediction of the stock prices uses Long-short term memory, a neural network as the prices are sequential evolving in nature. The results of the proposed system are correlated between the stock market and Twitter data to obtain better insights that are positive.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127419554","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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