Pub Date : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00012
Yalamanchili Salini, J. Harikiran
Social media is a common source of communication for various formal and informal contextual use cases. The conversation in both structured and unstructured forms can be broadly classified as positive/negative. In addition to “sarcasm,” the research about unstructured language has become very interesting due to the fact that very few researchers have offered solutions to problems associated with it. By using deep learning models, some hybrid approaches are used to identify sarcasm sentences. The identification is further refined to mark the content as sarcasm, irony, humour and offensive. This article analyzes and summarizes various works on irony/sarcasm detection in terms of features, approach, architecture and performance. This study analyzed that, the hybrid models superseded the performance of the traditional machine learning approaches for classifying the sarcasm/irony content. Finally, this study has briefed the identified challenges and research directions for building better models for classifying sarcasm/irony content.
{"title":"Sarcasm Detection: A Systematic Review of Methods and Approaches","authors":"Yalamanchili Salini, J. Harikiran","doi":"10.1109/ICSMDI57622.2023.00012","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00012","url":null,"abstract":"Social media is a common source of communication for various formal and informal contextual use cases. The conversation in both structured and unstructured forms can be broadly classified as positive/negative. In addition to “sarcasm,” the research about unstructured language has become very interesting due to the fact that very few researchers have offered solutions to problems associated with it. By using deep learning models, some hybrid approaches are used to identify sarcasm sentences. The identification is further refined to mark the content as sarcasm, irony, humour and offensive. This article analyzes and summarizes various works on irony/sarcasm detection in terms of features, approach, architecture and performance. This study analyzed that, the hybrid models superseded the performance of the traditional machine learning approaches for classifying the sarcasm/irony content. Finally, this study has briefed the identified challenges and research directions for building better models for classifying sarcasm/irony content.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130502312","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00109
Virta Banduji Patil
The goal of designing a dust cleaning robot using embedded systems is to clean the floor automatically using a robot that can work in hazardous environments without the assistance of people, to construct a floor cleaning robot without a driver, and to develop an autonomous robotics system that uses the internet of things. It is typically used when large areas need to be cleaned with few obstructions. Most problems occur on huge floors beyond human capabilities. That means people can get exhausted over large areas of ground. The harmful radiations, chemicals, air pollution, and other factors might cause a man to become ill or perhaps die at places like nuclear facilities or chemical industries. Therefore, this robot can be used there. In this project, numerous features have been included, like a vacuum cleaner, a wiper motor, and a water pump in the centre for wetting the floor followed by wipe down the floor with the vacuum cleaner,
{"title":"Design For Dust Cleaning Robot Using Embedded System","authors":"Virta Banduji Patil","doi":"10.1109/ICSMDI57622.2023.00109","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00109","url":null,"abstract":"The goal of designing a dust cleaning robot using embedded systems is to clean the floor automatically using a robot that can work in hazardous environments without the assistance of people, to construct a floor cleaning robot without a driver, and to develop an autonomous robotics system that uses the internet of things. It is typically used when large areas need to be cleaned with few obstructions. Most problems occur on huge floors beyond human capabilities. That means people can get exhausted over large areas of ground. The harmful radiations, chemicals, air pollution, and other factors might cause a man to become ill or perhaps die at places like nuclear facilities or chemical industries. Therefore, this robot can be used there. In this project, numerous features have been included, like a vacuum cleaner, a wiper motor, and a water pump in the centre for wetting the floor followed by wipe down the floor with the vacuum cleaner,","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133950691","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00088
Garapati. Deva ram ganesh, P. Vidyullatha, Maddipati. Ravi krishna, S.Thanooj Prapulla, A. Pavan Saran, Puppala Ramya
The identification of items on the surface of the earth is widely known to be possible using hyperspectral images. To do classification and identify the various items on the image, the majority of classifiers just take into account spectral information. In this study, a neural network convolutional is used to classify the hyperspectral picture based on spectral and spatial properties (CNN). There are only a few areas in the hyperspectral picture. The multilayer perceptron aids in the categorization of visual characteristics into many classes while CNN builds the upper categorical level of strategic spectral and spatial aspects in each of the patch. The patch size of 13 × 13 is found to be sufficient to attain the best accuracy. Compared to other classifiers, CNN requires greater computing time for training and testing. In comparison to other classifiers, simulation findings indicate that CNN stores the hyperspectral picture with the best classification accuracy.
{"title":"Machine Vision based Object Detection using Deep Learning Techniques","authors":"Garapati. Deva ram ganesh, P. Vidyullatha, Maddipati. Ravi krishna, S.Thanooj Prapulla, A. Pavan Saran, Puppala Ramya","doi":"10.1109/ICSMDI57622.2023.00088","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00088","url":null,"abstract":"The identification of items on the surface of the earth is widely known to be possible using hyperspectral images. To do classification and identify the various items on the image, the majority of classifiers just take into account spectral information. In this study, a neural network convolutional is used to classify the hyperspectral picture based on spectral and spatial properties (CNN). There are only a few areas in the hyperspectral picture. The multilayer perceptron aids in the categorization of visual characteristics into many classes while CNN builds the upper categorical level of strategic spectral and spatial aspects in each of the patch. The patch size of 13 × 13 is found to be sufficient to attain the best accuracy. Compared to other classifiers, CNN requires greater computing time for training and testing. In comparison to other classifiers, simulation findings indicate that CNN stores the hyperspectral picture with the best classification accuracy.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133863233","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00014
Abir Mishra, Akshat Sahay, M. Pandey, S. Routaray
Every day, at least 2.5 quintillion bytes of data are generated worldwide. This results in information explosion. Excessive information about a subject makes it difficult to focus on the most important concepts and findings. As a result, it becomes challenging for data analysts to determine which data is correct and which data is unnecessary for a given task. Natural Language Processing (NLP) based text summarization is an effective solution to this problem. Text summarization helps to reduce the size of a data or text while retaining the information. At the same time, it is highly difficult to manually summarize lengthy text documents. The primary goal of the proposed text summarization model is to highlight and present consumers with the most pertinent information from the provided text data. Using text summarization and NLTK, this study attempts to propose a text sentiment analysis on news material.
{"title":"News text Analysis using Text Summarization and Sentiment Analysis based on NLP","authors":"Abir Mishra, Akshat Sahay, M. Pandey, S. Routaray","doi":"10.1109/ICSMDI57622.2023.00014","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00014","url":null,"abstract":"Every day, at least 2.5 quintillion bytes of data are generated worldwide. This results in information explosion. Excessive information about a subject makes it difficult to focus on the most important concepts and findings. As a result, it becomes challenging for data analysts to determine which data is correct and which data is unnecessary for a given task. Natural Language Processing (NLP) based text summarization is an effective solution to this problem. Text summarization helps to reduce the size of a data or text while retaining the information. At the same time, it is highly difficult to manually summarize lengthy text documents. The primary goal of the proposed text summarization model is to highlight and present consumers with the most pertinent information from the provided text data. Using text summarization and NLTK, this study attempts to propose a text sentiment analysis on news material.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123169669","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 : 2023-03-01DOI: 10.1109/icsmdi57622.2023.00001
{"title":"Proceedings 2023 3rd International Conference on Smart Data Intelligence","authors":"","doi":"10.1109/icsmdi57622.2023.00001","DOIUrl":"https://doi.org/10.1109/icsmdi57622.2023.00001","url":null,"abstract":"","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133772220","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}
Chilli is among the most commonly used spices globally and is an integral part of many cuisines. Many countries like Mexico, India, China, and Korea are known for growing and consuming chillies. Amongst all, India is the largest producer of chillies worldwide. When cultivated on a large scale, these crops are highly susceptible to fungal, pests, weeds, bacterial, viral and pathogen attacks that substantially hinder production. Among these plant attacks, the most common is Chilli anthracnose, caused by the Colletotrichum fungus, which affects the leaves and the fruit of the chilli plant, causing a devastating loss to the farmers. Our paper proposes a solution based on Deep Neural Network (DNN) using transfer learning to classify disease-affected Anthracnose chillies from Healthy chillies. This study has developed a dataset by collecting the chilli samples from the University of Agricultural Sciences, Dharwad and chilli farms in Kusugal, outskirts of Hubli. The dataset consists of 4 classes with two types of chilli; red and green. Each coloured chilli has two stages; the healthy stage and the Anthracnose diseased stage. Here, different pre-trained DNN architectures and transfer learning methods are used to train the model on our dataset. Finally, the results are compared based on accuracy and model size for all architectures trained on the proposed dataset. And choose the architecture with the smallest model size and high accuracy for embedding in an edge device.
{"title":"Identification of Anthracnose in Chillies using Deep Learning on Embedded Platforms","authors":"Sneha Varur, Akshath Mugad, Arya Kinagi, Akhil Shanbhag, Karthik Hiremath, Uday Kulkarni","doi":"10.1109/ICSMDI57622.2023.00068","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00068","url":null,"abstract":"Chilli is among the most commonly used spices globally and is an integral part of many cuisines. Many countries like Mexico, India, China, and Korea are known for growing and consuming chillies. Amongst all, India is the largest producer of chillies worldwide. When cultivated on a large scale, these crops are highly susceptible to fungal, pests, weeds, bacterial, viral and pathogen attacks that substantially hinder production. Among these plant attacks, the most common is Chilli anthracnose, caused by the Colletotrichum fungus, which affects the leaves and the fruit of the chilli plant, causing a devastating loss to the farmers. Our paper proposes a solution based on Deep Neural Network (DNN) using transfer learning to classify disease-affected Anthracnose chillies from Healthy chillies. This study has developed a dataset by collecting the chilli samples from the University of Agricultural Sciences, Dharwad and chilli farms in Kusugal, outskirts of Hubli. The dataset consists of 4 classes with two types of chilli; red and green. Each coloured chilli has two stages; the healthy stage and the Anthracnose diseased stage. Here, different pre-trained DNN architectures and transfer learning methods are used to train the model on our dataset. Finally, the results are compared based on accuracy and model size for all architectures trained on the proposed dataset. And choose the architecture with the smallest model size and high accuracy for embedding in an edge device.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132788071","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}
Currently millions of individuals worldwide are suffering from ocular diseases. Diagnosis of ocular diseases by conventional methods is challenging, labor-intensive and prone to mistakes. Unfortunately, delayed diagnosis and treatment frequently results in blindness. Therefore, an automatic ocular illness detection method is the need of the hour. Fundus images are widely used for identifying ocular diseases. However, there is a chance that the patient may be suffering from multiple ocular diseases. In such cases the ophthalmologist cannot effectively identify the disease from the fundus images. To aid the ophthalmologist, this work aims to develop a revolutionary multi-class classification model for diagnosing ocular diseases from fundus images. The model's performance is assessed with DenseNet, Inception ResNet, EfficientNetB4, and EfficientNetB6, in terms of losses, accuracy, and precision.
{"title":"Application of Deep CNN Networks in Ocular Disease Detection","authors":"Khaia Mohinuddin Shaik, C. Anupama, Supraja Paluru, Sarath Chandra Pedada, Balaram Krishna Attuluri","doi":"10.1109/ICSMDI57622.2023.00072","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00072","url":null,"abstract":"Currently millions of individuals worldwide are suffering from ocular diseases. Diagnosis of ocular diseases by conventional methods is challenging, labor-intensive and prone to mistakes. Unfortunately, delayed diagnosis and treatment frequently results in blindness. Therefore, an automatic ocular illness detection method is the need of the hour. Fundus images are widely used for identifying ocular diseases. However, there is a chance that the patient may be suffering from multiple ocular diseases. In such cases the ophthalmologist cannot effectively identify the disease from the fundus images. To aid the ophthalmologist, this work aims to develop a revolutionary multi-class classification model for diagnosing ocular diseases from fundus images. The model's performance is assessed with DenseNet, Inception ResNet, EfficientNetB4, and EfficientNetB6, in terms of losses, accuracy, and precision.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132710615","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00104
A. Gopi, Nedunuri Madhu Venkata Sai Daswanth, S. S. Aravinth, P. Rambabu
Internet of Things (IoT) allows both physical and virtual objects to communicate with each other over a network. The services provided with IoT helps in easing the day-to-day activities. IoT has numerous advantages like scalability and ease of access but the drawback of the IoT system is that a centralized cloud is required for data storage and ensure the security and privacy of the users. In order to eliminate the drawback of IoT, recent studies have highlighted the integration of IoT systems with the distributed ledger technologies like Blockchain. The Blockchain technology would provide military grade security to the data. This article presents a comprehensive literature review for the Internet of Things (IoT) and Blockchain protocols.
{"title":"Implementation of IoT Security System by Incorporating Block Chain Technology","authors":"A. Gopi, Nedunuri Madhu Venkata Sai Daswanth, S. S. Aravinth, P. Rambabu","doi":"10.1109/ICSMDI57622.2023.00104","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00104","url":null,"abstract":"Internet of Things (IoT) allows both physical and virtual objects to communicate with each other over a network. The services provided with IoT helps in easing the day-to-day activities. IoT has numerous advantages like scalability and ease of access but the drawback of the IoT system is that a centralized cloud is required for data storage and ensure the security and privacy of the users. In order to eliminate the drawback of IoT, recent studies have highlighted the integration of IoT systems with the distributed ledger technologies like Blockchain. The Blockchain technology would provide military grade security to the data. This article presents a comprehensive literature review for the Internet of Things (IoT) and Blockchain protocols.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"249 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122162037","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00031
B. Veerasamy, A.Sai Kumar Reddy, Animgi Chandu, K.Siva Sankar Reddy, K. Venkata Naga Gopi Manikanta
The market for assistive technology for the blind and visually impaired is plagued by high prices and a lack of usefulness. These people face numerous challenges in their daily lives. This study has developed and deployed a low-cost smart assistive cane based on computer vision, sensors, and a local cloud cooperation system, specifically for people with visual impairments. Obstacle detection, fall detection, and visitor light detection features have also been developed and included to make moving easier for those with visual impairments. As part of a part-cloud cooperation strategy to improve the user experience, a photo captioning tool and an object recognition function with high-speed processing power were also developed. It shows the characteristics of low energy consumption, potent real-time performance, flexibility to a few circumstances, and convenience, which can protect the safety of visually impaired individuals while travelling and may enable them to more effectively perceive and interpret their environment.
{"title":"Edge Cloud Collaboration Intelligent Assistive Cane for Visually Impaired People","authors":"B. Veerasamy, A.Sai Kumar Reddy, Animgi Chandu, K.Siva Sankar Reddy, K. Venkata Naga Gopi Manikanta","doi":"10.1109/ICSMDI57622.2023.00031","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00031","url":null,"abstract":"The market for assistive technology for the blind and visually impaired is plagued by high prices and a lack of usefulness. These people face numerous challenges in their daily lives. This study has developed and deployed a low-cost smart assistive cane based on computer vision, sensors, and a local cloud cooperation system, specifically for people with visual impairments. Obstacle detection, fall detection, and visitor light detection features have also been developed and included to make moving easier for those with visual impairments. As part of a part-cloud cooperation strategy to improve the user experience, a photo captioning tool and an object recognition function with high-speed processing power were also developed. It shows the characteristics of low energy consumption, potent real-time performance, flexibility to a few circumstances, and convenience, which can protect the safety of visually impaired individuals while travelling and may enable them to more effectively perceive and interpret their environment.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129079887","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00047
Donthireddy Vijaya Lakshmi, Mohammed Yaseen, K. Akhil, Jogi Naga Shankar Manikanta, T. Shankar, Basant Sah
Information integrity and secure accessibility with the various mathematical methods are essential in the cloud environment for the creation of a protected system, which compels to do a comprehensive study and presents the techniques of safeguarding systems. Many applications need a secure zone of execution for providing essential services by avoiding security flaws. This paper highlights protection techniques such as stream cipher, block cipher with the hashing function, and another strategy employed globally to ensure maximum privacy by lowering risks and threats.
{"title":"Approaches of Security in Cloud Computing","authors":"Donthireddy Vijaya Lakshmi, Mohammed Yaseen, K. Akhil, Jogi Naga Shankar Manikanta, T. Shankar, Basant Sah","doi":"10.1109/ICSMDI57622.2023.00047","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00047","url":null,"abstract":"Information integrity and secure accessibility with the various mathematical methods are essential in the cloud environment for the creation of a protected system, which compels to do a comprehensive study and presents the techniques of safeguarding systems. Many applications need a secure zone of execution for providing essential services by avoiding security flaws. This paper highlights protection techniques such as stream cipher, block cipher with the hashing function, and another strategy employed globally to ensure maximum privacy by lowering risks and threats.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132290485","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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