Pub Date : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9544107
N. Gupta, Mridula Batra, A. Khosla
The performance of cloud services depends on the scheduling algorithms that distribute the incoming network traffic among their servers to achieve effectiveness in execution of tasks. These algorithms are assigning the tasks to various computing resources, and these resources are virtual in nature. In cloud, assigning tasks to corresponding resources are NP-hard in nature. The traditional scheduling algorithms like FCFS, SJF, Round Robin etc. will not be suitable to solve NP-hard scheduling problems. Cloud scheduling considers various criteria like resource utilization, cost, makespan and throughput. This paper has implemented the cloud scheduling algorithms such as Max-Min Algorithm, Min-Min Algorithm, Enhanced Max-Min Algorithm and Greedy Algorithm to balance the server load in cloud environment and have analyzed the results of these algorithms to identify the best scheduling algorithm. Results discussed in this paper have shown that, when the numbers of tasks are more, greedy algorithm outperform other scheduling algorithms while for less number of tasks, Enhanced Max-Min algorithm performs extremely well as compared to another task scheduling algorithm.
{"title":"Optimizing Greedy Algorithm to Balance the Server Load in Cloud Simulated Environment","authors":"N. Gupta, Mridula Batra, A. Khosla","doi":"10.1109/ICIRCA51532.2021.9544107","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9544107","url":null,"abstract":"The performance of cloud services depends on the scheduling algorithms that distribute the incoming network traffic among their servers to achieve effectiveness in execution of tasks. These algorithms are assigning the tasks to various computing resources, and these resources are virtual in nature. In cloud, assigning tasks to corresponding resources are NP-hard in nature. The traditional scheduling algorithms like FCFS, SJF, Round Robin etc. will not be suitable to solve NP-hard scheduling problems. Cloud scheduling considers various criteria like resource utilization, cost, makespan and throughput. This paper has implemented the cloud scheduling algorithms such as Max-Min Algorithm, Min-Min Algorithm, Enhanced Max-Min Algorithm and Greedy Algorithm to balance the server load in cloud environment and have analyzed the results of these algorithms to identify the best scheduling algorithm. Results discussed in this paper have shown that, when the numbers of tasks are more, greedy algorithm outperform other scheduling algorithms while for less number of tasks, Enhanced Max-Min algorithm performs extremely well as compared to another task scheduling algorithm.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129328498","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9544845
Saravanan Alagarsamy, K. Selvaraj, V. Govindaraj, A. A. Kumar, S. Harishankar, G. L. Narasimman
In-spite of fast growth of cyber threats, there is need of the methodologies to assist the information systems to deal with the cyber security. In supplement to that, Crime-as-a-service (Caas) model is developed to reinforces the background information occurring in the cybercrime. The motivation of the work is to scrutinize the problems occurring in the cybercrime using the data analysis method for designing the information system. In order to accomplish, First the framework is created for examining the cybercrime activities. Second step, the definition for CaaS need to be created. Third step, classification model is used for classifying the various activities. For the evaluation of the proposed techniques, tested with the dataset collected form the online hacking community is used. Research gap is resolved by developing the effective information system for handling the various problem in cybercrime and also provide the practical perceptions for both the private and public sectors to record the attacks occurring in the cybercrime.
{"title":"Automated Data analytics approach for examining the background economy of Cybercrime","authors":"Saravanan Alagarsamy, K. Selvaraj, V. Govindaraj, A. A. Kumar, S. Harishankar, G. L. Narasimman","doi":"10.1109/ICIRCA51532.2021.9544845","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9544845","url":null,"abstract":"In-spite of fast growth of cyber threats, there is need of the methodologies to assist the information systems to deal with the cyber security. In supplement to that, Crime-as-a-service (Caas) model is developed to reinforces the background information occurring in the cybercrime. The motivation of the work is to scrutinize the problems occurring in the cybercrime using the data analysis method for designing the information system. In order to accomplish, First the framework is created for examining the cybercrime activities. Second step, the definition for CaaS need to be created. Third step, classification model is used for classifying the various activities. For the evaluation of the proposed techniques, tested with the dataset collected form the online hacking community is used. Research gap is resolved by developing the effective information system for handling the various problem in cybercrime and also provide the practical perceptions for both the private and public sectors to record the attacks occurring in the cybercrime.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129623127","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9544695
Yong Li
This article first analyzes the current development status of the multimedia distance education system, so as to summarize the two focal issues that this multimedia distance education system needs to solve: the production of distance multimedia teaching and the development of data resource library. This article analyzes the differences, advantages and disadvantages, and application scenarios of the single-site and multi-site models. Finally, this paper develops a new remote multimedia teaching platform and data resource library based on the multi-site model.
{"title":"Development of a Long-distance Multimedia Teaching Platform and Data Resource Library Based on the Multi-site Model","authors":"Yong Li","doi":"10.1109/ICIRCA51532.2021.9544695","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9544695","url":null,"abstract":"This article first analyzes the current development status of the multimedia distance education system, so as to summarize the two focal issues that this multimedia distance education system needs to solve: the production of distance multimedia teaching and the development of data resource library. This article analyzes the differences, advantages and disadvantages, and application scenarios of the single-site and multi-site models. Finally, this paper develops a new remote multimedia teaching platform and data resource library based on the multi-site model.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130024417","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9545072
Zichun Yan
This research paper conducts the research on the simulation of regional economic development potential forecast model based on remote sensing information mining. With the help of relevant advanced technology, the logistics system can improve its operational efficiency and enhance its logistics supply capacity. At the same time, to a certain extent, the potential logistics demand in the region will be further stimulated by growth. For the efficient analysis, the remote sensing models and the image processing frameworks are combined for the optimal selection. The prediction model is verified through the test. Compared with the other approaches, the proposed is efficient.
{"title":"Simulation of Regional Economic Development Potential Forecast Model Based on Remote Sensing Information Mining","authors":"Zichun Yan","doi":"10.1109/ICIRCA51532.2021.9545072","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9545072","url":null,"abstract":"This research paper conducts the research on the simulation of regional economic development potential forecast model based on remote sensing information mining. With the help of relevant advanced technology, the logistics system can improve its operational efficiency and enhance its logistics supply capacity. At the same time, to a certain extent, the potential logistics demand in the region will be further stimulated by growth. For the efficient analysis, the remote sensing models and the image processing frameworks are combined for the optimal selection. The prediction model is verified through the test. Compared with the other approaches, the proposed is efficient.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128932696","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9544999
P. Juneja, S. Sunori, Abhinav Sharma, Anshu Sharma, Gurpreet Singh, Ishaan Bhasin, Vinayak Sharma
Control engineering is an area which deals with the designing and optimization of closed loop control systems for various domestic, defense, and industrial processes. The prime objective is to monitor and regulate, one or multiple parameters associated with the considered process, to maintain an optimum value. The feedback path senses these output parameters and drives back the controller to manipulate the input parameters of the process, and finally the output parameters settle to the desired levels. Headbox is one of the vital and primary process equipment in the paper machine process which delivers stock at a specified velocity by sustaining the pressure inside the box. In this paper, for headbox consistency process model, IMC controller is designed, using MATLAB, for multiple values of time constant (closed loop), and compared for its set point tracking performance. The optimal value of open loop time constant is decided based on the analysis. Also, comparison is performed, in performance, with different values of delay times, keeping intact the optimal value of closed loop time constant. Further, this work deals with robustness testing of designed IMC controller. The control system is said to be robust if it handles the uncertainties contained by the process model very well. The robust control techniques have proved to be very successful for processes where the process dynamics or disturbances are unknown. Robust control techniques have been reported to be very effective until the model mismatch errors go beyond some acceptable limits. In this work, the robustness of the IMC controller is tested by observing its behavior for the slightly perturbed process model.
{"title":"Headbox Control System Design and Analysis under Model Mismatch","authors":"P. Juneja, S. Sunori, Abhinav Sharma, Anshu Sharma, Gurpreet Singh, Ishaan Bhasin, Vinayak Sharma","doi":"10.1109/ICIRCA51532.2021.9544999","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9544999","url":null,"abstract":"Control engineering is an area which deals with the designing and optimization of closed loop control systems for various domestic, defense, and industrial processes. The prime objective is to monitor and regulate, one or multiple parameters associated with the considered process, to maintain an optimum value. The feedback path senses these output parameters and drives back the controller to manipulate the input parameters of the process, and finally the output parameters settle to the desired levels. Headbox is one of the vital and primary process equipment in the paper machine process which delivers stock at a specified velocity by sustaining the pressure inside the box. In this paper, for headbox consistency process model, IMC controller is designed, using MATLAB, for multiple values of time constant (closed loop), and compared for its set point tracking performance. The optimal value of open loop time constant is decided based on the analysis. Also, comparison is performed, in performance, with different values of delay times, keeping intact the optimal value of closed loop time constant. Further, this work deals with robustness testing of designed IMC controller. The control system is said to be robust if it handles the uncertainties contained by the process model very well. The robust control techniques have proved to be very successful for processes where the process dynamics or disturbances are unknown. Robust control techniques have been reported to be very effective until the model mismatch errors go beyond some acceptable limits. In this work, the robustness of the IMC controller is tested by observing its behavior for the slightly perturbed process model.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128869047","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9544841
B. Venkatesh, H. N. Vishwas
Sarcasm means saying the opposite of what you mean in order to make fun of someone and a type of humour that responds to a situation. Sarcasm reorganisation approach is quite beneficial to enhancing automated sentiment analysis data from microblogging and social media sites. The term “sentiment analysis” relates to the study of internet users reported feelings and viewpoints in a particular group, as well as their identification and aggregation. One of the most complicated problems in sentiment analysis is detecting sarcasm. It's a tough task to classify sarcastic sentence forms. This work uses two hybrid machine learning approaches, namely Stacked Generalization and Boosting ensemble methods with Support Vector Machine (SVM), Random Forest (RF) and KNN as base classifiers and Logistic Regression (LR) as Meta classifiers to detect real-time sarcasm on Twitter.
{"title":"Real Time Sarcasm Detection on Twitter using Ensemble Methods","authors":"B. Venkatesh, H. N. Vishwas","doi":"10.1109/ICIRCA51532.2021.9544841","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9544841","url":null,"abstract":"Sarcasm means saying the opposite of what you mean in order to make fun of someone and a type of humour that responds to a situation. Sarcasm reorganisation approach is quite beneficial to enhancing automated sentiment analysis data from microblogging and social media sites. The term “sentiment analysis” relates to the study of internet users reported feelings and viewpoints in a particular group, as well as their identification and aggregation. One of the most complicated problems in sentiment analysis is detecting sarcasm. It's a tough task to classify sarcastic sentence forms. This work uses two hybrid machine learning approaches, namely Stacked Generalization and Boosting ensemble methods with Support Vector Machine (SVM), Random Forest (RF) and KNN as base classifiers and Logistic Regression (LR) as Meta classifiers to detect real-time sarcasm on Twitter.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125575044","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9544614
Yangmin Qin
In the field of architecture at this stage, if you want to solve the main problems of component processing and construction, the application of BIM Technology is a more feasible solution. The application of BIM Technology can not only solve the problems of component processing and construction, but also effectively reduce the cost waste of the construction industry in the process of development. At the same time, BIM Technology can comprehensively optimize the relevant model design after the completion of parametric construction, which has a great role in promoting the construction quality of construction engineering. This paper applies the data mining model to construct the efficient combined cmputer system for the systematic implementations.
{"title":"BIM Combined Intelligent Computer System in Precast Concrete Structure Design with Data Mining Integration","authors":"Yangmin Qin","doi":"10.1109/ICIRCA51532.2021.9544614","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9544614","url":null,"abstract":"In the field of architecture at this stage, if you want to solve the main problems of component processing and construction, the application of BIM Technology is a more feasible solution. The application of BIM Technology can not only solve the problems of component processing and construction, but also effectively reduce the cost waste of the construction industry in the process of development. At the same time, BIM Technology can comprehensively optimize the relevant model design after the completion of parametric construction, which has a great role in promoting the construction quality of construction engineering. This paper applies the data mining model to construct the efficient combined cmputer system for the systematic implementations.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126356513","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9544593
S. K. Reddy, T. Krishnaveni, G. Nikitha, E. Vijaykanth
Diabetes also known as chronic illness, in which people have high levels of sugar (or) glucose for a long period of time in blood. The general symptoms of diabetes include increase in thirst, hunger, weight loss, frequent urination. Diabetic people will have a risk of acquiring diseases like heart disease, nerve damage etc.‐‐. The risk factor and seriousness of diabetes can be reduced if early prediction is possible. Machine learning plays a major role in medical industry. The occurrence of diabetes can be predicted by applying different classification methods (Random forest and K-NN algorithms). This paper utilizes pima Indian diabetes dataset, which is downloaded from Kaggle.
{"title":"Diabetes Prediction Using Different Machine Learning Algorithms","authors":"S. K. Reddy, T. Krishnaveni, G. Nikitha, E. Vijaykanth","doi":"10.1109/ICIRCA51532.2021.9544593","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9544593","url":null,"abstract":"Diabetes also known as chronic illness, in which people have high levels of sugar (or) glucose for a long period of time in blood. The general symptoms of diabetes include increase in thirst, hunger, weight loss, frequent urination. Diabetic people will have a risk of acquiring diseases like heart disease, nerve damage etc.‐‐. The risk factor and seriousness of diabetes can be reduced if early prediction is possible. Machine learning plays a major role in medical industry. The occurrence of diabetes can be predicted by applying different classification methods (Random forest and K-NN algorithms). This paper utilizes pima Indian diabetes dataset, which is downloaded from Kaggle.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125736886","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9544886
Ganga Rama Koteswara Rao, P. V. Sgar, Siva Naga Prasad Mannem, C. Prasad, Naresh Cherukuri, Vamsidhar Talasila
The goal of this paper is to successfully learn hi-fives for human-robot interaction. The proposed research work has used the Imitation Learning approach by incorporating Bayesian Interaction Primitives [1]. Through expert-guided demonstrations, the robot has been trained to learn relationships between human and robot trajectories. The research study has demonstrated that, the robot is able to complete the interaction with a human and successfully issue a hi-five. Also, the Bayesian Interaction Primitives are implemented to teach a Baxter Robot to give hi-five through imitation learning. Additionally, the trajectories are compared with human biomechanics data.
{"title":"Imitation Learning with Baxter Robot using Hi-Fives","authors":"Ganga Rama Koteswara Rao, P. V. Sgar, Siva Naga Prasad Mannem, C. Prasad, Naresh Cherukuri, Vamsidhar Talasila","doi":"10.1109/ICIRCA51532.2021.9544886","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9544886","url":null,"abstract":"The goal of this paper is to successfully learn hi-fives for human-robot interaction. The proposed research work has used the Imitation Learning approach by incorporating Bayesian Interaction Primitives [1]. Through expert-guided demonstrations, the robot has been trained to learn relationships between human and robot trajectories. The research study has demonstrated that, the robot is able to complete the interaction with a human and successfully issue a hi-five. Also, the Bayesian Interaction Primitives are implemented to teach a Baxter Robot to give hi-five through imitation learning. Additionally, the trajectories are compared with human biomechanics data.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126803281","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 : 2021-09-02DOI: 10.1109/ICIRCA51532.2021.9544865
T. P, Baranidharan. B
Technological support to agriculture will enhance its productivity. Deep learning is known for its high accuracy level in whichever domain it is implemented and sometimes even it surpasses human performance. Deep learning is making a huge difference in the current agricultural landscape. It is being widely used for improving irrigation facilities, pest - disease detection at the earlier stage and crop yield estimation. Deep learning-based image processing shows better improved results than the traditional image processing techniques. This research paper gives an overview of the applications of deep learning methods used in precision agriculture particularly in irrigation, pest and diseases control, and yield estimation.
{"title":"An Analysis on Application of Deep Learning Techniques for Precision Agriculture","authors":"T. P, Baranidharan. B","doi":"10.1109/ICIRCA51532.2021.9544865","DOIUrl":"https://doi.org/10.1109/ICIRCA51532.2021.9544865","url":null,"abstract":"Technological support to agriculture will enhance its productivity. Deep learning is known for its high accuracy level in whichever domain it is implemented and sometimes even it surpasses human performance. Deep learning is making a huge difference in the current agricultural landscape. It is being widely used for improving irrigation facilities, pest - disease detection at the earlier stage and crop yield estimation. Deep learning-based image processing shows better improved results than the traditional image processing techniques. This research paper gives an overview of the applications of deep learning methods used in precision agriculture particularly in irrigation, pest and diseases control, and yield estimation.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114298014","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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