Pub Date : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776369
Rohit Shivdas Jayale, S. Desai
The immense pressure and tension has created in the worldwide healthcare systems by disease. Various existing system has defined drug prediction system based on current patient evaluation. In this research we proposed a drug prediction for COVID-19 patient based on protein to protein reactions and availability. In order to evaluate the protein-protein interactions (PPIs) between some of the virus and individual receptors that are also confirmed utilizing biomedical simulations, the framework also defines machine learning models. The classification techniques are consistent with the predictions of separate physical material sequence-based characteristics such as classification of amino acids, distribution of pseudo amino acids and conjoint triads. Finally we will evaluate the system with numerous machine learning algorithm and show the effectiveness of propose systems.
{"title":"Aspect-Level Drug Reviews Sentiment Analysis and COVID-19 Drug prediction using PPI & Deep Learning","authors":"Rohit Shivdas Jayale, S. Desai","doi":"10.1109/CCGE50943.2021.9776369","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776369","url":null,"abstract":"The immense pressure and tension has created in the worldwide healthcare systems by disease. Various existing system has defined drug prediction system based on current patient evaluation. In this research we proposed a drug prediction for COVID-19 patient based on protein to protein reactions and availability. In order to evaluate the protein-protein interactions (PPIs) between some of the virus and individual receptors that are also confirmed utilizing biomedical simulations, the framework also defines machine learning models. The classification techniques are consistent with the predictions of separate physical material sequence-based characteristics such as classification of amino acids, distribution of pseudo amino acids and conjoint triads. Finally we will evaluate the system with numerous machine learning algorithm and show the effectiveness of propose systems.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116679614","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776466
Milind Gayakwad, S. Patil
Information is found in various forms like Misinformation, Dis-information, Impartial Information, legit and complete information. Content is a derived form of the information created by the content writer for conveying the information. Considering the growing volume of content, it is a tough task to decide on useful and irrelevant content. To deal with such a large volume of data processing, storage is necessary. The irrelevant content causes a waste of time and money for the content creator, consumer, and platform provider as well. Search engine Marketing and spammy techniques rank the content and thereby a website. This type of practice is encouraging inorganic methodologies to boost the rank of content. The use of organic methodologies can provide the solution up to a considerable extent. To design the organic models the research carried out earlier in this field is discussed in this paper. Methodologies like Foraging, Collaborative Filtering, Social Commerce, Micro-Video Prediction, Social Commerce.
{"title":"Analysis of Methodologies to Model the Content for Conveying the Correct Information","authors":"Milind Gayakwad, S. Patil","doi":"10.1109/CCGE50943.2021.9776466","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776466","url":null,"abstract":"Information is found in various forms like Misinformation, Dis-information, Impartial Information, legit and complete information. Content is a derived form of the information created by the content writer for conveying the information. Considering the growing volume of content, it is a tough task to decide on useful and irrelevant content. To deal with such a large volume of data processing, storage is necessary. The irrelevant content causes a waste of time and money for the content creator, consumer, and platform provider as well. Search engine Marketing and spammy techniques rank the content and thereby a website. This type of practice is encouraging inorganic methodologies to boost the rank of content. The use of organic methodologies can provide the solution up to a considerable extent. To design the organic models the research carried out earlier in this field is discussed in this paper. Methodologies like Foraging, Collaborative Filtering, Social Commerce, Micro-Video Prediction, Social Commerce.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129808225","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776383
Kamya Johar, B. Ramesh, Ramneek Kalra
With the prediction of billions and trillions connected devices in the upcoming decades, researchers are exploring daily about the ways of making technology as a companion to help consumers/users with better picture of their appliances/products at their homes. With the upcoming fear of controlling and managing appliances as data complexity and usage of same will be increased many folds, there's a need for smarter and green home for everyone in the society. In this paper, authors are focusing to provide a proposed framework for Solar powered home and grid powered home with Edge-based approach to save the electricity consumption. This is reflected by using Machine Learning Regression algorithm over the battery usage and giving required notification through an Android Application to the consumer/home user. The proposed model gives insightful further opportunities to researchers to work on energy-efficient based green home infrastructure.
{"title":"A Smart Edge-Based Energy-Efficient Green Home","authors":"Kamya Johar, B. Ramesh, Ramneek Kalra","doi":"10.1109/CCGE50943.2021.9776383","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776383","url":null,"abstract":"With the prediction of billions and trillions connected devices in the upcoming decades, researchers are exploring daily about the ways of making technology as a companion to help consumers/users with better picture of their appliances/products at their homes. With the upcoming fear of controlling and managing appliances as data complexity and usage of same will be increased many folds, there's a need for smarter and green home for everyone in the society. In this paper, authors are focusing to provide a proposed framework for Solar powered home and grid powered home with Edge-based approach to save the electricity consumption. This is reflected by using Machine Learning Regression algorithm over the battery usage and giving required notification through an Android Application to the consumer/home user. The proposed model gives insightful further opportunities to researchers to work on energy-efficient based green home infrastructure.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133076058","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776451
Diwakar Shah, Vidya Rautela, Chirag Sharma, Angelin Florence A
Yoga offers a wide range of asanas, and the angle between body parts plays an important role here. This project carries a non-profit system that strives to develop core muscles using yoga-like poses. While practicing yoga asanas virtually, the proposed technique perfectly detects the human position. To contemplate the dissension of the angle formed with original values, the cosine similarity technique is applied. Multiple dimensions must be addressed since crucial angles are made up of a critical combination of angles. This system detects the difference between the actual and target positions and corrects the user by delivering real-time image output and necessary instructions to correct the identified pose. Human poses estimation is utilized in this research to estimate an individual's Yoga position using computer vision techniques and Open pose (open-source library). In most circumstances, the suggested method retains high accuracy while achieving real-time speed. The proposed model was trained with 90% of data and tested with 10% of same with real-time testing, resulting 94 % of accuracy.
{"title":"Yoga Pose Detection Using Posenet and k-NN","authors":"Diwakar Shah, Vidya Rautela, Chirag Sharma, Angelin Florence A","doi":"10.1109/CCGE50943.2021.9776451","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776451","url":null,"abstract":"Yoga offers a wide range of asanas, and the angle between body parts plays an important role here. This project carries a non-profit system that strives to develop core muscles using yoga-like poses. While practicing yoga asanas virtually, the proposed technique perfectly detects the human position. To contemplate the dissension of the angle formed with original values, the cosine similarity technique is applied. Multiple dimensions must be addressed since crucial angles are made up of a critical combination of angles. This system detects the difference between the actual and target positions and corrects the user by delivering real-time image output and necessary instructions to correct the identified pose. Human poses estimation is utilized in this research to estimate an individual's Yoga position using computer vision techniques and Open pose (open-source library). In most circumstances, the suggested method retains high accuracy while achieving real-time speed. The proposed model was trained with 90% of data and tested with 10% of same with real-time testing, resulting 94 % of accuracy.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127850333","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776394
Devesh Kumar Srivastava, A. Sharma, Deevesh Choudhary
The software development industry has lately become quite intricate, at a global level. As the tools and technologies used keep changing, so does the approach of developing a software. Thus, software effort estimation plays a critical role in doing so. This arises a challenge of accurately estimating the software development effort, and then proceeding with the plan of development. The history shows various algorithmic cost estimation models like Boehm's COCOMO model, Putnam's SLIM, Multiple Regression, Statistical models, and many non-algorithmic soft computing models]. Despite multiple techniques, achieving a higher accuracy of effort estimation has always been challenging. This paper is concerned with a comparison between two algorithmic regression models, one using Multiple Regression, and another model using Random Forest Regression, to predict the estimation of software development effort. It is observed that Random Forest Regression is successfully able to model the complex, by closely matching the effort estimated in the dataset, providing a better accuracy.
{"title":"Software Development Effort Estimation Using Machine Learning Techniques: Multi-linear Regression versus Random Forest","authors":"Devesh Kumar Srivastava, A. Sharma, Deevesh Choudhary","doi":"10.1109/CCGE50943.2021.9776394","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776394","url":null,"abstract":"The software development industry has lately become quite intricate, at a global level. As the tools and technologies used keep changing, so does the approach of developing a software. Thus, software effort estimation plays a critical role in doing so. This arises a challenge of accurately estimating the software development effort, and then proceeding with the plan of development. The history shows various algorithmic cost estimation models like Boehm's COCOMO model, Putnam's SLIM, Multiple Regression, Statistical models, and many non-algorithmic soft computing models]. Despite multiple techniques, achieving a higher accuracy of effort estimation has always been challenging. This paper is concerned with a comparison between two algorithmic regression models, one using Multiple Regression, and another model using Random Forest Regression, to predict the estimation of software development effort. It is observed that Random Forest Regression is successfully able to model the complex, by closely matching the effort estimated in the dataset, providing a better accuracy.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"9 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131328224","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 development of technology in recent years, a surge in the marketing content and the inexpensive choice of sending text messages for promotional and other advertising purposes has made the practice of SMS (Short Message Service) on cell phones escalate to such a prominent manner that cellphones are constantly overburdened through spam SMS. As a result, important messages like bank or work-related information can get lost among the unimportant spam messages. Moreover, these spam messages are extremely harmful since they can breach our privacy and expose our personal information to hackers and other potentially hazardous sources. This issue can be mitigated by employing the Sentiment Analysis and variety of Machine Learning Algorithms that are appropriate for separating spam from important communication. This paper analyses the methodology of intelligent spam filtering approaches in the SMS paradigm with respect to mobile text message spam. It tests some of the most prominent spam filtering algorithms on publicly available SMS spam datasets to discover which ones perform best in this situation.
{"title":"Predictive Sentimental Analysis of Spam Detection using Machine Learning","authors":"Muskan Agarwal, Richa Goyal, Eshika Verma, Hemlata Goyal, Gulrej Ahmed, Sunita Singhal","doi":"10.1109/CCGE50943.2021.9776352","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776352","url":null,"abstract":"The development of technology in recent years, a surge in the marketing content and the inexpensive choice of sending text messages for promotional and other advertising purposes has made the practice of SMS (Short Message Service) on cell phones escalate to such a prominent manner that cellphones are constantly overburdened through spam SMS. As a result, important messages like bank or work-related information can get lost among the unimportant spam messages. Moreover, these spam messages are extremely harmful since they can breach our privacy and expose our personal information to hackers and other potentially hazardous sources. This issue can be mitigated by employing the Sentiment Analysis and variety of Machine Learning Algorithms that are appropriate for separating spam from important communication. This paper analyses the methodology of intelligent spam filtering approaches in the SMS paradigm with respect to mobile text message spam. It tests some of the most prominent spam filtering algorithms on publicly available SMS spam datasets to discover which ones perform best in this situation.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"55 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131672114","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776408
Purnima S. Pisal, B. D. Jadhav
The rapid increase in population increases the sanitation related issues. The current systems used for waste management have lacunas. This is a challenge that requires innovation and creation of a solution which monitors and manages the garbage collection. This gap can be bridged by creating a solution that gives prior information of the bins which are filled. The schedules of waste collection will help for time management. The route path generated by the software application will optimize the distance fuel. This paper gives a roadmap to local authorities for effective implementation of smart waste management in urban areas. This system will improve the overall eco-system of the city and deliver better services to citizens.
{"title":"Smart System for Garbage Management","authors":"Purnima S. Pisal, B. D. Jadhav","doi":"10.1109/CCGE50943.2021.9776408","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776408","url":null,"abstract":"The rapid increase in population increases the sanitation related issues. The current systems used for waste management have lacunas. This is a challenge that requires innovation and creation of a solution which monitors and manages the garbage collection. This gap can be bridged by creating a solution that gives prior information of the bins which are filled. The schedules of waste collection will help for time management. The route path generated by the software application will optimize the distance fuel. This paper gives a roadmap to local authorities for effective implementation of smart waste management in urban areas. This system will improve the overall eco-system of the city and deliver better services to citizens.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126743583","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776460
Hema Priya V, A. T, P. Priya, S. Saravanan
A Bi-Directional Flyback DC-DC Converter (BDFBC) will operate in two modes of operation under certain conditions: when Solar PV supply is available, the battery will be charged, and power will flow to the load; when Solar PV is unavailable, the battery will be discharged, and power will flow from the battery to the load; The performance of the BDFBC in two modes is analyzed using MAT LAB simulation circuit and 10W laboratory arrangement and presented.
{"title":"Bi-Directional Flyback DC-DC Converter For Solar PV - Battery Charger System","authors":"Hema Priya V, A. T, P. Priya, S. Saravanan","doi":"10.1109/CCGE50943.2021.9776460","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776460","url":null,"abstract":"A Bi-Directional Flyback DC-DC Converter (BDFBC) will operate in two modes of operation under certain conditions: when Solar PV supply is available, the battery will be charged, and power will flow to the load; when Solar PV is unavailable, the battery will be discharged, and power will flow from the battery to the load; The performance of the BDFBC in two modes is analyzed using MAT LAB simulation circuit and 10W laboratory arrangement and presented.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126322278","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776368
Lida Bamizadeh, B. Kumar, Ajay Kumar
Ever-growing of software engineering (SE) data requires a structured method to managing it. Software intelligence (SI) helps developers and practitioners for improving decision making process across the development of software process. Software data repositories store large volume of inefficient data which can be used by applying software intelligence to extract actionable and insightful knowledge. This knowledge can be saved in software repositories to use for existing and forthcoming projects. This paper presents creating of new software data repositories and using it to manage knowledge of two types of software engineering data: Software requirement specifications and code smells. Requirement specification is a significant kind of software engineering data. Also, source code is structural part of all software systems and design flaws such as code smells can hamper maintainability. Therefore, the extracted knowledge can be utilized to standardize and improvise Software Requirement Specification and code design for upcoming projects.
{"title":"Software Intelligence through Knowledge Management of Document Repositories","authors":"Lida Bamizadeh, B. Kumar, Ajay Kumar","doi":"10.1109/CCGE50943.2021.9776368","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776368","url":null,"abstract":"Ever-growing of software engineering (SE) data requires a structured method to managing it. Software intelligence (SI) helps developers and practitioners for improving decision making process across the development of software process. Software data repositories store large volume of inefficient data which can be used by applying software intelligence to extract actionable and insightful knowledge. This knowledge can be saved in software repositories to use for existing and forthcoming projects. This paper presents creating of new software data repositories and using it to manage knowledge of two types of software engineering data: Software requirement specifications and code smells. Requirement specification is a significant kind of software engineering data. Also, source code is structural part of all software systems and design flaws such as code smells can hamper maintainability. Therefore, the extracted knowledge can be utilized to standardize and improvise Software Requirement Specification and code design for upcoming projects.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122420463","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776367
K. V. Bhaskar Reddy, S. Sarvanan, S. Vijayakumar, P. Chandrababu
This paper presents a load analysis of hybrid solar wind power generation & comparison using Simulink/MATLAB. The proposed model constitutes of a photovoltaic array, wind turbine with permanent magnet synchronous machine, DC-DC boost converter, CCCV Charger, Lead-acid battery, single-phase MOSFET inverter & Street lights as load. The developed Simulink model of hybrid system performance is analyzed at different input parameters like different irradiation, temperature, & wind speeds. In this proposed system, continuous power supply is observed at the load. The obtained simulation result is validated with the experimental values & the proposed system produces a power that has the potential to meet the demand.
{"title":"Experimental Load Analysis of Hybrid Solar Wind Power Generation System in Comparison with MATLAB/SIMULINK","authors":"K. V. Bhaskar Reddy, S. Sarvanan, S. Vijayakumar, P. Chandrababu","doi":"10.1109/CCGE50943.2021.9776367","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776367","url":null,"abstract":"This paper presents a load analysis of hybrid solar wind power generation & comparison using Simulink/MATLAB. The proposed model constitutes of a photovoltaic array, wind turbine with permanent magnet synchronous machine, DC-DC boost converter, CCCV Charger, Lead-acid battery, single-phase MOSFET inverter & Street lights as load. The developed Simulink model of hybrid system performance is analyzed at different input parameters like different irradiation, temperature, & wind speeds. In this proposed system, continuous power supply is observed at the load. The obtained simulation result is validated with the experimental values & the proposed system produces a power that has the potential to meet the demand.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131101542","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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