Pub Date : 2021-12-10DOI: 10.1109/SMART52563.2021.9676330
R. Shukla, D. Ather
VANETs (vehicular ad hoc networks) are a rapidly developing technology that allows communication between moving cars and roadside devices without the need for infrastructure. Although MANET has a subtype called VANET, it is distinguished by the inclusion of automobiles as nodes. ITS makes extensive use of self-organizing networks. Because of the VANET’s extremely dynamic topology and frequent disconnection, developing an efficient approach is difficult. Since no one routing protocol is suitable for all VANET applications, it is essential to evaluate the benefits and drawbacks of each. This research project is focused on the VANET and its protocols for routing. This paper discusses the benefits and drawbacks of the DYMO, DSR, AODV and VANET routing protocols.
{"title":"Simulation Based Protocols Comparison for Vehicular Ad-hoc Network Routing","authors":"R. Shukla, D. Ather","doi":"10.1109/SMART52563.2021.9676330","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676330","url":null,"abstract":"VANETs (vehicular ad hoc networks) are a rapidly developing technology that allows communication between moving cars and roadside devices without the need for infrastructure. Although MANET has a subtype called VANET, it is distinguished by the inclusion of automobiles as nodes. ITS makes extensive use of self-organizing networks. Because of the VANET’s extremely dynamic topology and frequent disconnection, developing an efficient approach is difficult. Since no one routing protocol is suitable for all VANET applications, it is essential to evaluate the benefits and drawbacks of each. This research project is focused on the VANET and its protocols for routing. This paper discusses the benefits and drawbacks of the DYMO, DSR, AODV and VANET routing protocols.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126278070","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676235
E. Kaur, Anand Kumar Shukla
Massive information being a brilliant resource of information and understanding from structures to the different end users. However, managing this much quantity of information wishes automation, thus leading to a fashion of statistics processing along with gadget mastering techniques. Inside the ict region, as in various areas of trade and evaluation, structures and equipment have been furnished and advanced in assisting the specialists to deal with the information and study from it routinely. Maximum of the systems returned from large corporations such as Microsoft or Google, or from the apache foundation’s incubators. This evaluation reveals gadget mastering algorithms in big records analytics, and gadget mastering challenges us to make selections where it may be no recognized "right course" for the specified trouble based on the previous training and tallies a number of the headmost used gear to analyze and model massive-statistics.
{"title":"Analysis of Machine Learning Algorithms in Big Data Analytics","authors":"E. Kaur, Anand Kumar Shukla","doi":"10.1109/SMART52563.2021.9676235","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676235","url":null,"abstract":"Massive information being a brilliant resource of information and understanding from structures to the different end users. However, managing this much quantity of information wishes automation, thus leading to a fashion of statistics processing along with gadget mastering techniques. Inside the ict region, as in various areas of trade and evaluation, structures and equipment have been furnished and advanced in assisting the specialists to deal with the information and study from it routinely. Maximum of the systems returned from large corporations such as Microsoft or Google, or from the apache foundation’s incubators. This evaluation reveals gadget mastering algorithms in big records analytics, and gadget mastering challenges us to make selections where it may be no recognized \"right course\" for the specified trouble based on the previous training and tallies a number of the headmost used gear to analyze and model massive-statistics.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116744403","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9675306
Aman Kumar, Hina Hashmi, Shueb Ali Khan, S. Kazim Naqvi
Automated surveillance is always been a matter of curiosity due to its applications and the freedom through implicit monitoring. A smart implicit monitoring needs to be smarter with un-intervening inference-based classification, decision, and alerting processes. In this same sequence, detection and classification of unusual activities is the utmost curiosity among researchers. The entrance of Artificial Intelligence and the various computing ways (like Machine Learning and Deep Learning methods) of achieving it has been proven the most influential and promising computing revolution in the last decade. AI&ML-based object detection, segmentation, and identification has proven its vulnerability towards the achievement of these such goals and making computer vision smarter than ever before. In this paper, we are proposing a framework for an intelligent surveillance system based on AI&ML for video-based live surveillance. The proposed framework will provide a pathway to the intelligent system design for automated monitoring and alerting for unusual events based on detected objects. Basically, it would be a live streaming-based altering system.
{"title":"SSE: A Smart Framework for Live Video Streaming based Alerting System","authors":"Aman Kumar, Hina Hashmi, Shueb Ali Khan, S. Kazim Naqvi","doi":"10.1109/SMART52563.2021.9675306","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9675306","url":null,"abstract":"Automated surveillance is always been a matter of curiosity due to its applications and the freedom through implicit monitoring. A smart implicit monitoring needs to be smarter with un-intervening inference-based classification, decision, and alerting processes. In this same sequence, detection and classification of unusual activities is the utmost curiosity among researchers. The entrance of Artificial Intelligence and the various computing ways (like Machine Learning and Deep Learning methods) of achieving it has been proven the most influential and promising computing revolution in the last decade. AI&ML-based object detection, segmentation, and identification has proven its vulnerability towards the achievement of these such goals and making computer vision smarter than ever before. In this paper, we are proposing a framework for an intelligent surveillance system based on AI&ML for video-based live surveillance. The proposed framework will provide a pathway to the intelligent system design for automated monitoring and alerting for unusual events based on detected objects. Basically, it would be a live streaming-based altering system.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115178070","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676286
Ashutosh Shankhdhar, N. Agrawal, Ayushi Srivastava
Covid-19 is a compelling infection occur due to freshly discovered virus in Covid family in December 2019. It is an irresistible sickness that fundamentally influences lungs territory of human body and have comparable side effects as an ordinary flue has which makes it difficult to perceive. It has a quick spread across the globe, which has conveyed dangerous difficulties since the time it began. As nations hope to extend testing, such test arrangements should not exclusively be technically sound, yet ought to likewise be achievable and helpful for the user. [2] Recently, X rays and CT scans have indicated remarkable highlights that delineate the seriousness of Covid in lungs. Since radiographs, for example, Xrays and CT scans are practical and generally accessible at general wellbeing offices, emergency clinic trauma centers and even at rustic facilities, they could be utilized for quick recognition of conceivable COVID-19-prompted lung contaminations. Advanced AI in sending a profound learning based clinical field is staying amazing to deal with a gigantic information with precise and quick outcomes in clinical image to analyze sicknesses all the more precisely and efficiently with additional help in the distant regions. In this paper, we are using deep learning to analyze Covid-19 by CT-scans x-ray pictures. [7],[8] The chest x-beam is performed to check the spread of contamination. It separates features from pictures and it is expected that there is no clamor in picture and every pixel contributes in feature building of a picture. This strategy gives favored results over various methodologies.
{"title":"COVID-19 Detection System using Chest X-rays or CT Scans","authors":"Ashutosh Shankhdhar, N. Agrawal, Ayushi Srivastava","doi":"10.1109/SMART52563.2021.9676286","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676286","url":null,"abstract":"Covid-19 is a compelling infection occur due to freshly discovered virus in Covid family in December 2019. It is an irresistible sickness that fundamentally influences lungs territory of human body and have comparable side effects as an ordinary flue has which makes it difficult to perceive. It has a quick spread across the globe, which has conveyed dangerous difficulties since the time it began. As nations hope to extend testing, such test arrangements should not exclusively be technically sound, yet ought to likewise be achievable and helpful for the user. [2] Recently, X rays and CT scans have indicated remarkable highlights that delineate the seriousness of Covid in lungs. Since radiographs, for example, Xrays and CT scans are practical and generally accessible at general wellbeing offices, emergency clinic trauma centers and even at rustic facilities, they could be utilized for quick recognition of conceivable COVID-19-prompted lung contaminations. Advanced AI in sending a profound learning based clinical field is staying amazing to deal with a gigantic information with precise and quick outcomes in clinical image to analyze sicknesses all the more precisely and efficiently with additional help in the distant regions. In this paper, we are using deep learning to analyze Covid-19 by CT-scans x-ray pictures. [7],[8] The chest x-beam is performed to check the spread of contamination. It separates features from pictures and it is expected that there is no clamor in picture and every pixel contributes in feature building of a picture. This strategy gives favored results over various methodologies.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131601791","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676201
Gaurav Kumar Rajput, Shakti Kundu, Ashok Kumar
The rapid growth of internet users combined with the increasing dominance of online review sites and social media platforms, have given rise to the importance of sentiment analysis, also known as opinion mining, seeks to determine what other people believe and comment. Almost every enthusiastic or person who loves social platforms likely to articulate their ideas in the shape of comments on various social media platforms, and this is viewed as the main resource of sentiment analysis. These comments not only communicate people’s feelings, but also provide insight into their moods. Because the text on these media is unstructured, we must first preprocess it, employing six different preprocessing approaches, before extracting features from the pre-processed data. Some of the examples of feature extraction techniques are TF-IDF, word embedding, Bag of Words and word count, noun count are feature based natural language processing. Apart from the work that has already been done in text analytics, feature extraction in sentiment analysis is presently a hot topic of research. The impact of existing methodologies and approaches for feature extraction in sentiment analysis on the performance of various sentiment classification algorithms is discussed in this review study.
{"title":"The Impact of Feature Extraction on Multi-Source Sentiment Analysis","authors":"Gaurav Kumar Rajput, Shakti Kundu, Ashok Kumar","doi":"10.1109/SMART52563.2021.9676201","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676201","url":null,"abstract":"The rapid growth of internet users combined with the increasing dominance of online review sites and social media platforms, have given rise to the importance of sentiment analysis, also known as opinion mining, seeks to determine what other people believe and comment. Almost every enthusiastic or person who loves social platforms likely to articulate their ideas in the shape of comments on various social media platforms, and this is viewed as the main resource of sentiment analysis. These comments not only communicate people’s feelings, but also provide insight into their moods. Because the text on these media is unstructured, we must first preprocess it, employing six different preprocessing approaches, before extracting features from the pre-processed data. Some of the examples of feature extraction techniques are TF-IDF, word embedding, Bag of Words and word count, noun count are feature based natural language processing. Apart from the work that has already been done in text analytics, feature extraction in sentiment analysis is presently a hot topic of research. The impact of existing methodologies and approaches for feature extraction in sentiment analysis on the performance of various sentiment classification algorithms is discussed in this review study.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115137507","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 : 2021-12-10DOI: 10.1109/smart52563.2021.9676326
{"title":"Robotics, Control, Instrumentation and Automation","authors":"","doi":"10.1109/smart52563.2021.9676326","DOIUrl":"https://doi.org/10.1109/smart52563.2021.9676326","url":null,"abstract":"","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128016730","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676314
A. Sowjanya, K. Swaroop, Sandeep Kumar, Arpit Jain
Soil classification is the disintegration of soil sets to specific gatherings having like attributes and comparable behaviors. Practically many nations do product trading, in which those nations sending out higher horticulture products are especially rely upon the soil qualities. In this manner, soil quality recognition and classification are a lot of significant. Recognition of the soil kind assists with keeping away from horticultural product amount misfortune. This paper introduces a fully connected network (FCN), deep learning model-based recognition of the soil kinds. Soil classification incorporates steps like image acquisition, feature extraction, and classification. The proposed method produces an average accuracy of 97.2% with an average mean of 65.27 and average energy of 0.0298. The proposed model classifies peat, sandy Clay, Silty Sand, and Human clay soil types effectively. Keywords: Classification; Fully Connected Network; Deep Learning, Soil Detection, Soil Classification.
{"title":"Neural Network-based Soil Detection and Classification","authors":"A. Sowjanya, K. Swaroop, Sandeep Kumar, Arpit Jain","doi":"10.1109/SMART52563.2021.9676314","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676314","url":null,"abstract":"Soil classification is the disintegration of soil sets to specific gatherings having like attributes and comparable behaviors. Practically many nations do product trading, in which those nations sending out higher horticulture products are especially rely upon the soil qualities. In this manner, soil quality recognition and classification are a lot of significant. Recognition of the soil kind assists with keeping away from horticultural product amount misfortune. This paper introduces a fully connected network (FCN), deep learning model-based recognition of the soil kinds. Soil classification incorporates steps like image acquisition, feature extraction, and classification. The proposed method produces an average accuracy of 97.2% with an average mean of 65.27 and average energy of 0.0298. The proposed model classifies peat, sandy Clay, Silty Sand, and Human clay soil types effectively. Keywords: Classification; Fully Connected Network; Deep Learning, Soil Detection, Soil Classification.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132633865","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676316
Sheetal Agarwal, Srishty Jain, Amit Kumar
Docker is a free and open-source container engine created by Docker Inc and distributed under the Apache 2.0 licence in 2013. Containers have a unique place in computing history because of their role in infrastructure virtualization. Containers execute user space on top of the operating system kernel, unlike traditional hypervisor virtualization, which runs one or more independent computers virtually on physical hardware via an intermediate layer. Containers allow a user’s work environment to be divided into several instances. Docker containers are created using application images saved and maintained in Docker hub. The Containers/Apps view shows all of your containers and applications in real time. It lets you to communicate with containers and applications directly from your machine, as well as manage the lifetime of your applications. This paper focus on a user-friendly interface for inspecting, interacting with, and managing Docker objects, such as containers and Docker Compose-based applications.
{"title":"GUI Docker Implementation: Run Common Graphics User Applications Inside Docker Container","authors":"Sheetal Agarwal, Srishty Jain, Amit Kumar","doi":"10.1109/SMART52563.2021.9676316","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676316","url":null,"abstract":"Docker is a free and open-source container engine created by Docker Inc and distributed under the Apache 2.0 licence in 2013. Containers have a unique place in computing history because of their role in infrastructure virtualization. Containers execute user space on top of the operating system kernel, unlike traditional hypervisor virtualization, which runs one or more independent computers virtually on physical hardware via an intermediate layer. Containers allow a user’s work environment to be divided into several instances. Docker containers are created using application images saved and maintained in Docker hub. The Containers/Apps view shows all of your containers and applications in real time. It lets you to communicate with containers and applications directly from your machine, as well as manage the lifetime of your applications. This paper focus on a user-friendly interface for inspecting, interacting with, and managing Docker objects, such as containers and Docker Compose-based applications.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114739461","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676248
Sunny Arora, Shailender Kumar, Pardeep Kumar
Deep learning has added conveniences for the diagnosis and prediction of various diseases making an influence in healthcare facilities. Diabetes mellitus is a dominant health issue faced by many around the globe. The number of people with this disease went up from one hundred eight million to six hundred in 1980, to four sixty million in 2019. Predicting trends of blood glucose prediction using deep learning methods make the management of the disease much easier. In this work, we are predicting future trends of the disease using training data. We have used the publically available dataset Ohio T1DM dataset in this work. In this paper, we have implemented LSTM to predict future trends. Root mean square error is used as the performance evaluation measure for this work.
{"title":"Implementation of LSTM for Prediction of Diabetes using CGM","authors":"Sunny Arora, Shailender Kumar, Pardeep Kumar","doi":"10.1109/SMART52563.2021.9676248","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676248","url":null,"abstract":"Deep learning has added conveniences for the diagnosis and prediction of various diseases making an influence in healthcare facilities. Diabetes mellitus is a dominant health issue faced by many around the globe. The number of people with this disease went up from one hundred eight million to six hundred in 1980, to four sixty million in 2019. Predicting trends of blood glucose prediction using deep learning methods make the management of the disease much easier. In this work, we are predicting future trends of the disease using training data. We have used the publically available dataset Ohio T1DM dataset in this work. In this paper, we have implemented LSTM to predict future trends. Root mean square error is used as the performance evaluation measure for this work.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122954884","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9675302
V. Mishra, Vivek Kumar, Neeraj Kumar Pandey
Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNNs)have the ability to find the accurate result in images and text respectively. The best classification results are still awaited due to the high cost of computation and high memory requirements of CNN and RNN. Our work suggests a framework that improves the quality of data at various layers by providing feedback to suggested system. The proposed framework leads to an error free processing system.
{"title":"IBD: A Feedback Framework with Deep-learning for IoT-generated Big Data Processing","authors":"V. Mishra, Vivek Kumar, Neeraj Kumar Pandey","doi":"10.1109/SMART52563.2021.9675302","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9675302","url":null,"abstract":"Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNNs)have the ability to find the accurate result in images and text respectively. The best classification results are still awaited due to the high cost of computation and high memory requirements of CNN and RNN. Our work suggests a framework that improves the quality of data at various layers by providing feedback to suggested system. The proposed framework leads to an error free processing system.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122780808","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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