Intelligent network orchestration and management are crucial components of the 6G network. Therefore, machine learning and artificial intelligence play a big part in the 6G paradigm that is being imagined. However, the combination of 6G and AIML utilization may frequently be a double-edged sword because AI has the capacity to either protect or compromise security and privacy. Proactive threat detection, the use of mitigating intelligent techniques, and network automation in future are needed to enable the achievement of independent networks in 6G. As a result, this paper has detailed focus on the ongoing projects based on 6G and factors that make 6G technology necessary. The role of ZT architecture is discussed in detail, use of AIML in 6G, Various application areas and challenges associated in 6G has been mentioned in this paper.
{"title":"Study on Zero-Trust Architecture, Application Areas & Challenges of 6G Technology in Future","authors":"Richa Singh, Gaurav Srivastav, Rekha Kashyap, Satvik Vats","doi":"10.1109/ICDT57929.2023.10150745","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150745","url":null,"abstract":"Intelligent network orchestration and management are crucial components of the 6G network. Therefore, machine learning and artificial intelligence play a big part in the 6G paradigm that is being imagined. However, the combination of 6G and AIML utilization may frequently be a double-edged sword because AI has the capacity to either protect or compromise security and privacy. Proactive threat detection, the use of mitigating intelligent techniques, and network automation in future are needed to enable the achievement of independent networks in 6G. As a result, this paper has detailed focus on the ongoing projects based on 6G and factors that make 6G technology necessary. The role of ZT architecture is discussed in detail, use of AIML in 6G, Various application areas and challenges associated in 6G has been mentioned in this paper.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116290940","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10151106
Monalisha Sinha, Shalini, M. Thejaswini
IoT is an emerging digital technology where every physical object is connected to one another via the internet. IoT devices embedded with GPS sensors are called Geo-IoT systems where spatial data is the most prominent requirement in developing any IoT services and applications. Geo-IoT data can be used in various IoT applications for optimizing routes, tracking assets, real-time traffic notification, auto-driving, precision agriculture, anti-theft prevention, etc. This paper provides a review of the convergence of Geo-IoT with advanced technologies such as artificial intelligence, machine learning, and blockchain technology. This paper mainly discusses the current status and applicability of artificial intelligence and machine learning methods in solving and computing location-based IoT data for developing new advanced Geo-IoT applications, routing protocols, and security issues.
{"title":"Convergence of Geo-IoT with Advanced Technologies","authors":"Monalisha Sinha, Shalini, M. Thejaswini","doi":"10.1109/ICDT57929.2023.10151106","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151106","url":null,"abstract":"IoT is an emerging digital technology where every physical object is connected to one another via the internet. IoT devices embedded with GPS sensors are called Geo-IoT systems where spatial data is the most prominent requirement in developing any IoT services and applications. Geo-IoT data can be used in various IoT applications for optimizing routes, tracking assets, real-time traffic notification, auto-driving, precision agriculture, anti-theft prevention, etc. This paper provides a review of the convergence of Geo-IoT with advanced technologies such as artificial intelligence, machine learning, and blockchain technology. This paper mainly discusses the current status and applicability of artificial intelligence and machine learning methods in solving and computing location-based IoT data for developing new advanced Geo-IoT applications, routing protocols, and security issues.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117254550","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150909
Ramesh Babu P, Tariku Birhanu, K. R. N. K. Kumar, Manjunath Gadiparthi
In general, high-density network services have a large number of users. This is seen as the main problem of that network. As users increase, so does the amount of service provided to them. Thus, have to pay separate attention to serving and serving them. It is imperative to ensure their maximum security if they are the primary user. Thus, security management is much less on high density 5G networks. A security algorithm has been proposed to improve these issues. This algorithm, designed for machine learning, first detects the primary user. Their security is prioritized by calculating their input and output times. It is also designed to detect secondary users and anonymous user. These anonymous users were creating the resource utilization and security vulnerabilities in the network. So, the primary user protection and anonymous user identification getting more priority in the ultra dense cloud networks.
{"title":"An Enhanced Machine Learning Security Algorithm for the Anonymous user Detection in Ultra Dense 5G Cloud Networks","authors":"Ramesh Babu P, Tariku Birhanu, K. R. N. K. Kumar, Manjunath Gadiparthi","doi":"10.1109/ICDT57929.2023.10150909","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150909","url":null,"abstract":"In general, high-density network services have a large number of users. This is seen as the main problem of that network. As users increase, so does the amount of service provided to them. Thus, have to pay separate attention to serving and serving them. It is imperative to ensure their maximum security if they are the primary user. Thus, security management is much less on high density 5G networks. A security algorithm has been proposed to improve these issues. This algorithm, designed for machine learning, first detects the primary user. Their security is prioritized by calculating their input and output times. It is also designed to detect secondary users and anonymous user. These anonymous users were creating the resource utilization and security vulnerabilities in the network. So, the primary user protection and anonymous user identification getting more priority in the ultra dense cloud networks.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129104628","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150819
J. Shah, Prashant Johri, Pawan Kumar Singh Nain
In robotic surgery practical applications one of the main bottlenecks is to accurately model tissue and needle interactions, in such modelling generally needle is taken as biocompatible material and tissue a elastic, plastic and viscous material. In this study, we present an adaptive finite element algorithm for simulating the indentation of the needle into tissue which is gelatin like viscoelastic material, the path of the needle takes a unique and non-predetermined route. Apart from the modelling the tissue and needle other aspect of the work requires proper boundary conditions and application of the load which mimic the real-world scenario. A cohesive zone model is employed to describe the fracture process, The distribution of strain energy density in the surrounding tissue is utilized to determine the direction of crack propagation. The simulation results presented in this study are centered on the deep penetration of a bevel-tip needle with a programmable design, which offers steering control by modifying the offset between interlocked needle segments. We primarily discuss the relationship between how size and number of mesh affect the stress in modelling tissue-needle interaction. We have done modelling and simulation in ANSYS software.
{"title":"Modelling and Simulation of Needle-Tissue Interaction in Robotic Surgery","authors":"J. Shah, Prashant Johri, Pawan Kumar Singh Nain","doi":"10.1109/ICDT57929.2023.10150819","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150819","url":null,"abstract":"In robotic surgery practical applications one of the main bottlenecks is to accurately model tissue and needle interactions, in such modelling generally needle is taken as biocompatible material and tissue a elastic, plastic and viscous material. In this study, we present an adaptive finite element algorithm for simulating the indentation of the needle into tissue which is gelatin like viscoelastic material, the path of the needle takes a unique and non-predetermined route. Apart from the modelling the tissue and needle other aspect of the work requires proper boundary conditions and application of the load which mimic the real-world scenario. A cohesive zone model is employed to describe the fracture process, The distribution of strain energy density in the surrounding tissue is utilized to determine the direction of crack propagation. The simulation results presented in this study are centered on the deep penetration of a bevel-tip needle with a programmable design, which offers steering control by modifying the offset between interlocked needle segments. We primarily discuss the relationship between how size and number of mesh affect the stress in modelling tissue-needle interaction. We have done modelling and simulation in ANSYS software.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132209583","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150581
P. Iyappan, Shikha Maheshwari, A. Saranya, M. Jayaprakash
The Internet of Things (IoT) is an open network model that aims to build and link the interactions between the devices and links. Conventional blockchain model aimed at increase the scalability but often it is limited by its capacity and performance. The deep learning algorithms aims to determine the parameters of the blockchain that finds the optimal value required to obtain an increased scalability without any limitations in its performance. In this paper, a deep learning model is integrated with the blockchain to improve the process of communication in a secured way. The deep learning model optimizes the necessary security parameters required to transfer the data in a secured way. The experimental validation shows an increased scalable task allocation than its predecessors.
物联网(Internet of Things, IoT)是一个开放的网络模型，旨在建立和连接设备和链路之间的交互。传统的区块链模型旨在提高可扩展性，但往往受到其容量和性能的限制。深度学习算法旨在确定区块链的参数，这些参数可以找到获得更高可扩展性所需的最佳值，而不会对其性能产生任何限制。本文将深度学习模型与区块链相结合，以安全的方式改进通信过程。深度学习模型优化了以安全方式传输数据所需的必要安全参数。实验验证表明，该方法比以前的方法具有更高的可扩展性任务分配。
{"title":"Deep Learning Model on Blockchain for Secured Mobile Communication","authors":"P. Iyappan, Shikha Maheshwari, A. Saranya, M. Jayaprakash","doi":"10.1109/ICDT57929.2023.10150581","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150581","url":null,"abstract":"The Internet of Things (IoT) is an open network model that aims to build and link the interactions between the devices and links. Conventional blockchain model aimed at increase the scalability but often it is limited by its capacity and performance. The deep learning algorithms aims to determine the parameters of the blockchain that finds the optimal value required to obtain an increased scalability without any limitations in its performance. In this paper, a deep learning model is integrated with the blockchain to improve the process of communication in a secured way. The deep learning model optimizes the necessary security parameters required to transfer the data in a secured way. The experimental validation shows an increased scalable task allocation than its predecessors.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121311557","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}
Today’s fast-paced world requires everyone to remain updated about new opportunities arising in their field of interest. It is especially crucial for engineering students who are eager to be placed in top IT companies. This research paper presents an android recruitment assistance application that directly targets the students of an engineering institution in search of a technical job or an internship. This job aggregator provides a user-friendly environment that assists students in all placement-related activities. The application is created using Android Studio and can run on version 6 and above. The design of the application has been implemented using Kotlin instead of Java.
{"title":"Job and Internship Assistance Application","authors":"Disha Tyagi, Daniyal Kazim, Soumen Bhadra, Avantika Gupta, Praveen Kumar, Abhishek Sharma, Himanshu Chaudhary","doi":"10.1109/ICDT57929.2023.10150490","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150490","url":null,"abstract":"Today’s fast-paced world requires everyone to remain updated about new opportunities arising in their field of interest. It is especially crucial for engineering students who are eager to be placed in top IT companies. This research paper presents an android recruitment assistance application that directly targets the students of an engineering institution in search of a technical job or an internship. This job aggregator provides a user-friendly environment that assists students in all placement-related activities. The application is created using Android Studio and can run on version 6 and above. The design of the application has been implemented using Kotlin instead of Java.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121103279","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}
Emotions may be expressed in many different ways, making automatic affect recognition challenging. Several industries may benefit from this technology, including audiovisual search and human- machine interface. Recently, neural networks have been developed to assess emotional states with unprecedented accuracy. We provide an approach to emotion identification that makes use of both visual and aural signals. It’s crucial to isolate relevant features in order to accurately represent the nuanced emotions conveyed in a wide range of speech patterns. We do this by using a Convolutional Neural Network (CNN) to parse the audio track for feature extraction and a 50-layer deep ResNet to process the visual track. Machine learning algorithms, in addition to needing to extract the characteristics, should also be robust against outliers and reflective of their surroundings. To solve this problem, LSTM networks are used. We train the system from the ground up, using the RECOLA datasets from the AVEC 2016 emotion recognition research challenge, and we demonstrate that our method is superior to prior approaches that relied on manually constructed aural and visual cues for identifying genuine emotional states. It has been demonstrated that the visual modality predicts valence more accurately than arousal. The best results for the valence dimension from the RECOLA dataset are shown in Table III below.
{"title":"Deep Neural Networks for Comprehensive Multimodal Emotion Recognition","authors":"Ashutosh Tiwari, Satyam Kumar, Tushar Mehrotra, Rajneesh Kumar Singh","doi":"10.1109/ICDT57929.2023.10150945","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150945","url":null,"abstract":"Emotions may be expressed in many different ways, making automatic affect recognition challenging. Several industries may benefit from this technology, including audiovisual search and human- machine interface. Recently, neural networks have been developed to assess emotional states with unprecedented accuracy. We provide an approach to emotion identification that makes use of both visual and aural signals. It’s crucial to isolate relevant features in order to accurately represent the nuanced emotions conveyed in a wide range of speech patterns. We do this by using a Convolutional Neural Network (CNN) to parse the audio track for feature extraction and a 50-layer deep ResNet to process the visual track. Machine learning algorithms, in addition to needing to extract the characteristics, should also be robust against outliers and reflective of their surroundings. To solve this problem, LSTM networks are used. We train the system from the ground up, using the RECOLA datasets from the AVEC 2016 emotion recognition research challenge, and we demonstrate that our method is superior to prior approaches that relied on manually constructed aural and visual cues for identifying genuine emotional states. It has been demonstrated that the visual modality predicts valence more accurately than arousal. The best results for the valence dimension from the RECOLA dataset are shown in Table III below.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116768004","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150947
Namrata Mohan Bagal, Madhuri Dinesh Gabhane, C. Mahamuni
The taxi service industry has been growing recently, and in the coming years, a strong increase is predicted. So many companies have developed to respond to this increased demand for cab rides. To maintain transparency and avoid unfair practices, the main goal is to predict travel costs before booking a taxi reservation. Our system is made to enable users to calculate the cost of a taxi trip by using a variety of dynamic factors, including the weather, the availability of cabs, cab size, and the distance between two sites. Here our system uses many algorithms to predict the fare amount but in all of them, the DNN algorithm works better than other algorithms.
{"title":"Rideshare Transportation Fare Prediction using Deep Neural Networks","authors":"Namrata Mohan Bagal, Madhuri Dinesh Gabhane, C. Mahamuni","doi":"10.1109/ICDT57929.2023.10150947","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150947","url":null,"abstract":"The taxi service industry has been growing recently, and in the coming years, a strong increase is predicted. So many companies have developed to respond to this increased demand for cab rides. To maintain transparency and avoid unfair practices, the main goal is to predict travel costs before booking a taxi reservation. Our system is made to enable users to calculate the cost of a taxi trip by using a variety of dynamic factors, including the weather, the availability of cabs, cab size, and the distance between two sites. Here our system uses many algorithms to predict the fare amount but in all of them, the DNN algorithm works better than other algorithms.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122534140","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 constant use of the Internet has led to an increased vulnerability to malware attacks through malicious websites. The goal of this research is to create a machine-learning algorithm that will detect whether URLs contain susceptible activities such as viruses, phishing, malware, worms, etc. or are secure. Malicious URLs are compromised URLs that are employed in drive-by downloads and online attacks. Phishing and social engineering are common types of attacks that use malicious URLs. The fact that one-third of all websites have the potential to be harmful shows how widespread bad URLs are in online crime. This work deals with three machine learning models, such as random forest, light GBM, and XG Boost, to analyse our data and give the best one as per the results and analysis.
{"title":"URL Based Malicious Activity Detection Using Machine Learning","authors":"Tagba Zoukarneini Difaizi, Ouedraogo Pengd-Wende Leonel Camille, Tadiwanashe Caleb Benhura, Ganesh Gupta","doi":"10.1109/ICDT57929.2023.10150899","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150899","url":null,"abstract":"The constant use of the Internet has led to an increased vulnerability to malware attacks through malicious websites. The goal of this research is to create a machine-learning algorithm that will detect whether URLs contain susceptible activities such as viruses, phishing, malware, worms, etc. or are secure. Malicious URLs are compromised URLs that are employed in drive-by downloads and online attacks. Phishing and social engineering are common types of attacks that use malicious URLs. The fact that one-third of all websites have the potential to be harmful shows how widespread bad URLs are in online crime. This work deals with three machine learning models, such as random forest, light GBM, and XG Boost, to analyse our data and give the best one as per the results and analysis.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121644129","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150782
Rupali Aggarwal, P. Sarangi, A. Sahoo
In the era where most of our transactions whether it is for shopping, electricity bills, insurance payments, school and college fees are paid using plastic money through wireless and various online modes. Increase in both online transactions and ecommerce platforms has given rise to many online frauds these days and also security threats. To detect these fraudulent activities, we created a machine learning model. In this research we modeled a dataset using Machine Learning Algorithms. It is proposed to predict fraudulent transactions made by users. It is a real-life example of a binary Classification problem. This research emphasizes on analyzing and pre-processing the dataset and implementing various python libraries, and used concepts like Exploratory Data Analysis, Data Modeling, Feature Extraction etc. and implemented a fraud detection process using the four algorithms.
{"title":"Credit Card Fraud Detection: Analyzing the Performance of Four Machine Learning Models","authors":"Rupali Aggarwal, P. Sarangi, A. Sahoo","doi":"10.1109/ICDT57929.2023.10150782","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150782","url":null,"abstract":"In the era where most of our transactions whether it is for shopping, electricity bills, insurance payments, school and college fees are paid using plastic money through wireless and various online modes. Increase in both online transactions and ecommerce platforms has given rise to many online frauds these days and also security threats. To detect these fraudulent activities, we created a machine learning model. In this research we modeled a dataset using Machine Learning Algorithms. It is proposed to predict fraudulent transactions made by users. It is a real-life example of a binary Classification problem. This research emphasizes on analyzing and pre-processing the dataset and implementing various python libraries, and used concepts like Exploratory Data Analysis, Data Modeling, Feature Extraction etc. and implemented a fraud detection process using the four algorithms.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132568851","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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