Pub Date : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753773
Sri Adi Pavan Naidu Kavala, R. Pothuraju
Grapes are the most consumed fruit all around the world. For the healthy development of the grapefruit industry, it is calumniatory to control the escalation of grape leaf contamination in the crop. Due to the lack of knowledge in rural areas farmers fail to detect these diseases at the early stages, which results in feeble harvest quality. The proposed system uses the transfer learning VGG models which classify the three most common grape leaf diseases along with the healthy grape leaves. These models achieve a mean accuracy of 98% on the testing data, which indices the feasibility of the neural network approach compared to the manual detection of these diseases.
{"title":"Detection Of Grape Leaf Disease Using Transfer Learning Methods: VGG16 & VGG19","authors":"Sri Adi Pavan Naidu Kavala, R. Pothuraju","doi":"10.1109/ICCMC53470.2022.9753773","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753773","url":null,"abstract":"Grapes are the most consumed fruit all around the world. For the healthy development of the grapefruit industry, it is calumniatory to control the escalation of grape leaf contamination in the crop. Due to the lack of knowledge in rural areas farmers fail to detect these diseases at the early stages, which results in feeble harvest quality. The proposed system uses the transfer learning VGG models which classify the three most common grape leaf diseases along with the healthy grape leaves. These models achieve a mean accuracy of 98% on the testing data, which indices the feasibility of the neural network approach compared to the manual detection of these diseases.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129597460","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753855
P. Umamaheswari, J. Selvakumar
As machine learning becomes more popular and computing power is increasingly needed, hardware-optimized neural networks and other learning models are increasingly needed. In the course of technology's evolution, machine learning and artificial intelligence will also likely be well trained in the near term. The modern fabulous production hardware business model leads unfortunately to security deficiencies throughout the supply chain and to economics. In this article, these safety problems are emphasized through the introduction of Trojan hardware attacks on neural networks to expand the current neural network security taxonomy. This paper proposes the development of a new framework to insert malicious trojans into a classifier application for the neural network. An algorithm using a convolutional neural network is used to evaluate the ability, if this algorithm adds 0.03 percent trojan, it can effectively classify an input gauge as a cluster in any convolution neural network with seven layers. Finally, this work is about the potential defense against hardware Trojan attacks to protect neural networks.
{"title":"Trojan Detection using Convolutional Neural Network","authors":"P. Umamaheswari, J. Selvakumar","doi":"10.1109/ICCMC53470.2022.9753855","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753855","url":null,"abstract":"As machine learning becomes more popular and computing power is increasingly needed, hardware-optimized neural networks and other learning models are increasingly needed. In the course of technology's evolution, machine learning and artificial intelligence will also likely be well trained in the near term. The modern fabulous production hardware business model leads unfortunately to security deficiencies throughout the supply chain and to economics. In this article, these safety problems are emphasized through the introduction of Trojan hardware attacks on neural networks to expand the current neural network security taxonomy. This paper proposes the development of a new framework to insert malicious trojans into a classifier application for the neural network. An algorithm using a convolutional neural network is used to evaluate the ability, if this algorithm adds 0.03 percent trojan, it can effectively classify an input gauge as a cluster in any convolution neural network with seven layers. Finally, this work is about the potential defense against hardware Trojan attacks to protect neural networks.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129149229","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753719
S. Shyam, S. Juliet, K. Ezra
Healthcare sector has been witnessing prodigious developments across the decade. The developments in the sector has been brought about by the amalgamation of several technologies, from IoT to AI. The vast changes in this arena has brought about a change is the quality of life. With the expansion of such systems in a hospital, the tracking and monitoring of medical equipments have become a tedious task. The equipments in the hospital needs to be located in the most efficient manner in terms of cost, accuracy and reliability. Considering the hurdles faced, the authors introduce a system which would trace the medical equipments and monitor them using the technology of Ultra wide band (UWB). TDoA (time difference of arrival) method and 3 D triangulation techniques are implemented along with. The authors have put forth a hardware and software architecture of the system along with a complete component design. The implementation at the first instance is executed inside a hospital room with dimensions of 13.5m X 17.5m and will be later carried forward in the entire hospital premises. The equipments will be further watched upon continually using a proposed novel software application.
{"title":"Tracking and Monitoring of Medical Equipments using UWB for Smart Healthcare","authors":"S. Shyam, S. Juliet, K. Ezra","doi":"10.1109/ICCMC53470.2022.9753719","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753719","url":null,"abstract":"Healthcare sector has been witnessing prodigious developments across the decade. The developments in the sector has been brought about by the amalgamation of several technologies, from IoT to AI. The vast changes in this arena has brought about a change is the quality of life. With the expansion of such systems in a hospital, the tracking and monitoring of medical equipments have become a tedious task. The equipments in the hospital needs to be located in the most efficient manner in terms of cost, accuracy and reliability. Considering the hurdles faced, the authors introduce a system which would trace the medical equipments and monitor them using the technology of Ultra wide band (UWB). TDoA (time difference of arrival) method and 3 D triangulation techniques are implemented along with. The authors have put forth a hardware and software architecture of the system along with a complete component design. The implementation at the first instance is executed inside a hospital room with dimensions of 13.5m X 17.5m and will be later carried forward in the entire hospital premises. The equipments will be further watched upon continually using a proposed novel software application.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124669264","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9754041
Malliga Subramanian, Adhithiya G J, G. S, Deepti R
As the global epidemic of Covid19 progresses, accurate diagnosis of Covid19 patients becomes important. The biggest problem in diagnosing test-positive people is the lack or lack of test kits due to the rapid spread of Covid19 in the community. As an alternative rapid diagnostic method, an automated detection system is needed to prevent Covid 19 from spreading to humans. This article proposes to use a convolutional neural network (CNN) to detect patients infected with coronavirus using computer tomography (CT) images. In addition, the transfer learning of the deep CNN model VGG16 is investigated to detect infections on CT scans. The pretrained VGG16 classifier is used as a classifier, feature extractor, and fine tuner in three different sets of tests. Image augmentation is used to boost the model's generalization capacity, while Bayesian optimization is used to pick optimum values for hyperparameters. In order to fine-tune the models and reduce training time, transfer learning is being researched. Surprisingly, all of the proposed models scored greater than 93% accuracy, which is on par with or better than previous deep learning models. The results show that optimization improved generalization in all models and highlight the efficacy of the proposed strategies.
{"title":"Screening Covid-19 Infection from Chest CT Images using Deep Learning Models based on Transfer Learning","authors":"Malliga Subramanian, Adhithiya G J, G. S, Deepti R","doi":"10.1109/ICCMC53470.2022.9754041","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9754041","url":null,"abstract":"As the global epidemic of Covid19 progresses, accurate diagnosis of Covid19 patients becomes important. The biggest problem in diagnosing test-positive people is the lack or lack of test kits due to the rapid spread of Covid19 in the community. As an alternative rapid diagnostic method, an automated detection system is needed to prevent Covid 19 from spreading to humans. This article proposes to use a convolutional neural network (CNN) to detect patients infected with coronavirus using computer tomography (CT) images. In addition, the transfer learning of the deep CNN model VGG16 is investigated to detect infections on CT scans. The pretrained VGG16 classifier is used as a classifier, feature extractor, and fine tuner in three different sets of tests. Image augmentation is used to boost the model's generalization capacity, while Bayesian optimization is used to pick optimum values for hyperparameters. In order to fine-tune the models and reduce training time, transfer learning is being researched. Surprisingly, all of the proposed models scored greater than 93% accuracy, which is on par with or better than previous deep learning models. The results show that optimization improved generalization in all models and highlight the efficacy of the proposed strategies.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129682515","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753863
D. Deepa, S. Selvaraj, D. M. Vijaya Lakshmi, Sarneshwar S, V. N, Vikash M
During this COVID-19 pandemic online class platforms are the only solution to transfer the knowledge in the field of education. Even though the physical classes are being practiced slowly in some countries, still academicians are in the need of online classes. In addition to content delivery, teachers are in the need to concern about throughout the class time whether the students are listening and be active in online classes. Due to more bandwidth consumption of the audio and video streaming, students can't be compelled to unmute the audio and video when the teacher delivers the content. So, there is no option for the teachers to observe the student’s activity. With the advancement of technology and enhanced image analysis capacity of deep learning techniques, a system is proposed to compute the student’s activity and can report it to the teachers during the class time itself. Drowsiness detection is tested using CNN based segmentation on our own set of 5000 images collected from 1000 students. The observed result shows 90% accuracy in predicting the drowsiness of the student by observing the face pattern of the student without streaming the video to the teacher’s device.
{"title":"Web Application to Track Student Attentiveness during Online Class using CNN and Eye Aspect Ratio","authors":"D. Deepa, S. Selvaraj, D. M. Vijaya Lakshmi, Sarneshwar S, V. N, Vikash M","doi":"10.1109/ICCMC53470.2022.9753863","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753863","url":null,"abstract":"During this COVID-19 pandemic online class platforms are the only solution to transfer the knowledge in the field of education. Even though the physical classes are being practiced slowly in some countries, still academicians are in the need of online classes. In addition to content delivery, teachers are in the need to concern about throughout the class time whether the students are listening and be active in online classes. Due to more bandwidth consumption of the audio and video streaming, students can't be compelled to unmute the audio and video when the teacher delivers the content. So, there is no option for the teachers to observe the student’s activity. With the advancement of technology and enhanced image analysis capacity of deep learning techniques, a system is proposed to compute the student’s activity and can report it to the teachers during the class time itself. Drowsiness detection is tested using CNN based segmentation on our own set of 5000 images collected from 1000 students. The observed result shows 90% accuracy in predicting the drowsiness of the student by observing the face pattern of the student without streaming the video to the teacher’s device.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127009063","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753868
Genyuan Chen, Qizhen Jia, Yaofeng Yue, Shuai Wang
Based on MRI image feature extraction, this paper analyzes the intelligent analysis of swainsonine in the imaging manifestation of Oxytropis sclerophylla poisoning. In this study, a single plant of Oxytropis sphaerocarpa was used as explants to isolate and culture endophytic fungi in vitro. It is studied by microbiology and molecular biology methods, and then MRI image features are used for feature extraction and classification, combined with biological imaging performance for intelligent analysis. Amplify and determine the sequence of the fungus 5.8SrDNA/ITS region and analyze it. According to the results of microbiology and molecular biology, the endophytic fungi groups are identified.
{"title":"Intelligent Analysis of Swainsonine in the Imaging Manifestation of Oxytropis Poisoning Based on MRI Image Feature Extraction and Analysis","authors":"Genyuan Chen, Qizhen Jia, Yaofeng Yue, Shuai Wang","doi":"10.1109/ICCMC53470.2022.9753868","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753868","url":null,"abstract":"Based on MRI image feature extraction, this paper analyzes the intelligent analysis of swainsonine in the imaging manifestation of Oxytropis sclerophylla poisoning. In this study, a single plant of Oxytropis sphaerocarpa was used as explants to isolate and culture endophytic fungi in vitro. It is studied by microbiology and molecular biology methods, and then MRI image features are used for feature extraction and classification, combined with biological imaging performance for intelligent analysis. Amplify and determine the sequence of the fungus 5.8SrDNA/ITS region and analyze it. According to the results of microbiology and molecular biology, the endophytic fungi groups are identified.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130665756","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9754079
K. Kalaivani, C. Kanimozhiselvi, V. Rajasekar
The task of evaluating the polarity of an opinionated text as positive or negative is known as sentiment classification. Companies nowadays are interested in learning how their consumers feel about their products by studying their views on review pages, blogs, tweets, discussion boards and web portals. Politicians and governments are also interested in sentiment classification for defining campaign plans and policies. The aim of this work is to use Part of Speech (POS) based knowledge in a machine learning approach to decide whether an opinionated document is positive or negative. In order to have a more effective feature space and to reduce the sparsity of the feature vector, generalization of bigrams is done by backing-off the first word or the second word to their respective POS cluster. Experiments conducted show that the use of combined POS features of unigrams and generalized bigrams outperform other features in terms of accuracy using Multinomial Naive Bayes (MNB) classifier.
{"title":"Enhancing the Performance of POS based Features using Generalization for Sentiment Classification","authors":"K. Kalaivani, C. Kanimozhiselvi, V. Rajasekar","doi":"10.1109/ICCMC53470.2022.9754079","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9754079","url":null,"abstract":"The task of evaluating the polarity of an opinionated text as positive or negative is known as sentiment classification. Companies nowadays are interested in learning how their consumers feel about their products by studying their views on review pages, blogs, tweets, discussion boards and web portals. Politicians and governments are also interested in sentiment classification for defining campaign plans and policies. The aim of this work is to use Part of Speech (POS) based knowledge in a machine learning approach to decide whether an opinionated document is positive or negative. In order to have a more effective feature space and to reduce the sparsity of the feature vector, generalization of bigrams is done by backing-off the first word or the second word to their respective POS cluster. Experiments conducted show that the use of combined POS features of unigrams and generalized bigrams outperform other features in terms of accuracy using Multinomial Naive Bayes (MNB) classifier.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"R-33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130677191","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9754141
Weichen Lu
The application of digital media technology in the process of modern art design has a great influence on its classification method, language expression, thinking space and communication form, and new developments have been made in art design. Use digital media technology to carry out modern art design and display it more intuitively. Based on this, starting from the application significance of digital information technology in public art design, the characteristic data mining is explored in order to explore the urban public art in the digital context The innovative development of design can be used for reference.
{"title":"Analysis on the Application of Information Technology in Comprehensive Art Design in the Digital Scenario Considering Feature Mining Algorithms","authors":"Weichen Lu","doi":"10.1109/ICCMC53470.2022.9754141","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9754141","url":null,"abstract":"The application of digital media technology in the process of modern art design has a great influence on its classification method, language expression, thinking space and communication form, and new developments have been made in art design. Use digital media technology to carry out modern art design and display it more intuitively. Based on this, starting from the application significance of digital information technology in public art design, the characteristic data mining is explored in order to explore the urban public art in the digital context The innovative development of design can be used for reference.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"565 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123319651","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753777
Divakar R, S. P, S. G., Primya T
Nowadays, large quantities of data may not be able to handle by the traditional big data analytics. Organizations / Companies started to realize the seriousness of data flying to generate the right decision and backing their strategies. The internet, digital production and social network are constantly increasing. The term "Big Data" represents companies digital data and to the entities that are characterized by large volume, velocity and the variety. This article defines the concepts, importance, technologies and applications of Big Data analytics.
{"title":"Prescriptive and Predictive Analysis of Intelligible Big Data","authors":"Divakar R, S. P, S. G., Primya T","doi":"10.1109/ICCMC53470.2022.9753777","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753777","url":null,"abstract":"Nowadays, large quantities of data may not be able to handle by the traditional big data analytics. Organizations / Companies started to realize the seriousness of data flying to generate the right decision and backing their strategies. The internet, digital production and social network are constantly increasing. The term \"Big Data\" represents companies digital data and to the entities that are characterized by large volume, velocity and the variety. This article defines the concepts, importance, technologies and applications of Big Data analytics.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116349507","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753903
Haijiang Wu
This article focuses on analyzing the application characteristics of electric power big data, determining the advantages that electric power big data provides to the development of enterprises, and expounding the power information security protection technology and management measures under the background of big data. Focus on the protection of power information security, and fundamentally control the information security control issues of power enterprises. Then analyzed the types of big data structure and effective measurement modeling, and finally combined with the application status of big data concepts in the construction of electric power information networks, and proposed optimization strategies, aiming to promote the effectiveness of big data concepts in power information network management activities. Applying the creation conditions, the results show that the measurement model is improved by 7.8%
{"title":"Effective Metrics Modeling of Big Data Technology in Electric Power Information Security","authors":"Haijiang Wu","doi":"10.1109/ICCMC53470.2022.9753903","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753903","url":null,"abstract":"This article focuses on analyzing the application characteristics of electric power big data, determining the advantages that electric power big data provides to the development of enterprises, and expounding the power information security protection technology and management measures under the background of big data. Focus on the protection of power information security, and fundamentally control the information security control issues of power enterprises. Then analyzed the types of big data structure and effective measurement modeling, and finally combined with the application status of big data concepts in the construction of electric power information networks, and proposed optimization strategies, aiming to promote the effectiveness of big data concepts in power information network management activities. Applying the creation conditions, the results show that the measurement model is improved by 7.8%","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126302221","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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