Pub Date : 2021-11-26DOI: 10.1109/iccica52458.2021.9697278
Neha Shivhare, Shanti Rathod, M. R. Khan
Neurodegenerative diseases, such as dementia, can impact speech, language, and the capability of communication. A recent study to improve the dementia detection accuracy studied the usage of CA (Conversation Analysis) of interviews among patients and neurologists to distinguish among progressive Neurodegenerative Memory Disorders patients & those with (non-progressive) Functional Memory Disorders (FMD). However, manual CA is costly for routine clinical use and difficult to scale. In this work, we present an early dementia detection system using speech recognition and analysis based on NLP technique and acoustic feature processing technique apply on multiple feature extraction and learning using a LSTM (Long Short-Term Memory) and GRU which remarkably captures the temporal features and long-term dependencies from historical data to prove the capabilities of sequence models over a feed-forward neural network in forecasting speech analysis related problems.
{"title":"Automatic Speech Analysis of Conversations for Dementia Detection Using LSTM and GRU","authors":"Neha Shivhare, Shanti Rathod, M. R. Khan","doi":"10.1109/iccica52458.2021.9697278","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697278","url":null,"abstract":"Neurodegenerative diseases, such as dementia, can impact speech, language, and the capability of communication. A recent study to improve the dementia detection accuracy studied the usage of CA (Conversation Analysis) of interviews among patients and neurologists to distinguish among progressive Neurodegenerative Memory Disorders patients & those with (non-progressive) Functional Memory Disorders (FMD). However, manual CA is costly for routine clinical use and difficult to scale. In this work, we present an early dementia detection system using speech recognition and analysis based on NLP technique and acoustic feature processing technique apply on multiple feature extraction and learning using a LSTM (Long Short-Term Memory) and GRU which remarkably captures the temporal features and long-term dependencies from historical data to prove the capabilities of sequence models over a feed-forward neural network in forecasting speech analysis related problems.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123435446","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697322
Anusha Chintam, Rajendra Kumar G, Anitha Rani J, Srilatha Yalamati, C. D
Present days deep neural networks play a crucial role in the prediction and classification of diseases. Without a doubt, DNN has a promising future in the medical area, particularly in clinical imaging. The fame of profound learning approaches is a result of their capacity to deal with a lot of information identified with the patients with reliability, accuracy in a limited ability to focus time. Nonetheless, the specialists might set aside time in breaking down and produce reports. In this work, have proposed a Deep Neural Network-based Parkinson's disease classification (DPDC). Our proposed technique is one such genuine model giving quicker and more precise outcomes for the characterization of Parkinson's sickness patients with magnificent accuracy of 94.87%. Because of the traits of the dataset of the patient, the model can be utilized for the recognizable proof of Parkinsonism's.
{"title":"Deep Neural Network-Based Classification and Diagnosis of Idiopathic Parkinsonism Disease","authors":"Anusha Chintam, Rajendra Kumar G, Anitha Rani J, Srilatha Yalamati, C. D","doi":"10.1109/iccica52458.2021.9697322","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697322","url":null,"abstract":"Present days deep neural networks play a crucial role in the prediction and classification of diseases. Without a doubt, DNN has a promising future in the medical area, particularly in clinical imaging. The fame of profound learning approaches is a result of their capacity to deal with a lot of information identified with the patients with reliability, accuracy in a limited ability to focus time. Nonetheless, the specialists might set aside time in breaking down and produce reports. In this work, have proposed a Deep Neural Network-based Parkinson's disease classification (DPDC). Our proposed technique is one such genuine model giving quicker and more precise outcomes for the characterization of Parkinson's sickness patients with magnificent accuracy of 94.87%. Because of the traits of the dataset of the patient, the model can be utilized for the recognizable proof of Parkinsonism's.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130586058","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697319
A. Raut, S. Khandait
Wireless sensor network (WSN) the unique and utmost encouraging tools for monitoring the real-time applications. It has been utilized in various areas particularly for offering real-time monitoring and control applications which attempts to monitor and record the environmental parameters and takes the appropriate decisions on time in a difficult situation. In recent enlargements Machine Learning (ML) techniques has been used to solve different problems in WSNs to ensure that good decisions can be made in the complex situations in time. Applying ML will help in boosting the efficiency of WSNs, as well as limiting humanoid intervention or re-programming. We have studied previous work for addressing the issues in Quality of Service (QoS) provisioning in WSNs. In addition we done the survey of ML based techniques used to address the issues in WSNs in the recent era.
{"title":"Machine Learning Algorithms in WSNs and its Applications","authors":"A. Raut, S. Khandait","doi":"10.1109/iccica52458.2021.9697319","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697319","url":null,"abstract":"Wireless sensor network (WSN) the unique and utmost encouraging tools for monitoring the real-time applications. It has been utilized in various areas particularly for offering real-time monitoring and control applications which attempts to monitor and record the environmental parameters and takes the appropriate decisions on time in a difficult situation. In recent enlargements Machine Learning (ML) techniques has been used to solve different problems in WSNs to ensure that good decisions can be made in the complex situations in time. Applying ML will help in boosting the efficiency of WSNs, as well as limiting humanoid intervention or re-programming. We have studied previous work for addressing the issues in Quality of Service (QoS) provisioning in WSNs. In addition we done the survey of ML based techniques used to address the issues in WSNs in the recent era.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123923901","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697266
Shashwat Pandey, Aditya Rohatgi
The process of transforming a physical document to a digital version leaves loose ends in several portions. There is a lack of solutions that offer end-to-end conversion of hard copies entailing images, graphs, tables, and other details into soft copies. To this end, we attempt to develop a computationally efficient algorithm to convert a document into its digital version through LATEX representations of the hard copy. Our research efforts take the problem of using OCR techniques into account for converting an image of a typesetted document into LATEX. This work serves as a proof of concept that equation layouts can be learned and individual character recognition is possible with not so sophisticated OCR techniques. The method we created to break the problem down step by step helped modularize and compartmentalize the tasks so that each can focus on the different types of issues that can occur at different levels of granularity.
{"title":"Using OCR to automate document conversion to LATEX","authors":"Shashwat Pandey, Aditya Rohatgi","doi":"10.1109/iccica52458.2021.9697266","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697266","url":null,"abstract":"The process of transforming a physical document to a digital version leaves loose ends in several portions. There is a lack of solutions that offer end-to-end conversion of hard copies entailing images, graphs, tables, and other details into soft copies. To this end, we attempt to develop a computationally efficient algorithm to convert a document into its digital version through LATEX representations of the hard copy. Our research efforts take the problem of using OCR techniques into account for converting an image of a typesetted document into LATEX. This work serves as a proof of concept that equation layouts can be learned and individual character recognition is possible with not so sophisticated OCR techniques. The method we created to break the problem down step by step helped modularize and compartmentalize the tasks so that each can focus on the different types of issues that can occur at different levels of granularity.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129066843","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697136
Suhashini Chaurasia, S. Sherekar, Vilas Thakare
Social media is the richest source of text generated by the user. So there is a necessity to automate the system to help organizing and classifying the opinions posted on social media sites. Proposed methodology framework using Artificial Recurrent Neural Network (ARNN) with bi-directional long short term memory (LSTM) has been used for the classification of sentiments. Structure for RNN with bidirectional LSTM is depicted. US airline Twitter sentiment dataset has been analysed using bidirectional LSTM model. Text with varying length is taken for the experiment. Graphical representation of the analysis has been depicted in this paper. Confusion matrix shows the result. At the end it is concluded that the sentiments are analysed and classified as positive, negative or neutral.
{"title":"Twitter Sentiment Analysis using Natural Language Processing","authors":"Suhashini Chaurasia, S. Sherekar, Vilas Thakare","doi":"10.1109/iccica52458.2021.9697136","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697136","url":null,"abstract":"Social media is the richest source of text generated by the user. So there is a necessity to automate the system to help organizing and classifying the opinions posted on social media sites. Proposed methodology framework using Artificial Recurrent Neural Network (ARNN) with bi-directional long short term memory (LSTM) has been used for the classification of sentiments. Structure for RNN with bidirectional LSTM is depicted. US airline Twitter sentiment dataset has been analysed using bidirectional LSTM model. Text with varying length is taken for the experiment. Graphical representation of the analysis has been depicted in this paper. Confusion matrix shows the result. At the end it is concluded that the sentiments are analysed and classified as positive, negative or neutral.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114281524","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697173
D. Singh, S. Kediya, R. Mahajan, P. Asthana
The paper has attempted to understand and unveil the non-technical causes of power electric losses. Many studies have covered the reasons for technical losses but here the author has covered the power losses due to manpower employed in MSEB (Maharashtra state electricity Board). The major findings of the study were that employees needs to be made aware about power losses. Employees are uninterested in continuing their inquisitiveness. Furthermore, they are unconcerned in learning new skills. Hence these factors led to negative outcome regarding power loss. They need to be given more training so that they can take effective measures to check the issue.
{"title":"Study of non technical factors responsible for power losses at MSEB","authors":"D. Singh, S. Kediya, R. Mahajan, P. Asthana","doi":"10.1109/iccica52458.2021.9697173","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697173","url":null,"abstract":"The paper has attempted to understand and unveil the non-technical causes of power electric losses. Many studies have covered the reasons for technical losses but here the author has covered the power losses due to manpower employed in MSEB (Maharashtra state electricity Board). The major findings of the study were that employees needs to be made aware about power losses. Employees are uninterested in continuing their inquisitiveness. Furthermore, they are unconcerned in learning new skills. Hence these factors led to negative outcome regarding power loss. They need to be given more training so that they can take effective measures to check the issue.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125328154","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697270
Swapna Choudhary, S. Dorle
Vehicular ad hoc networks (VANETs) are used in intelligent transportation systems to provide safety and security with a reduction in traffic jams using vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) unit communications. During the communication, nodes are always under various security threads. In order to reduce the possibility of these attacks and to normalize traffic flow in the network, a software-defined network (SDN) is used. SDN will improve centralized visibility as all the underlying open flow switches are connected to the controller, which will reduce the routing load in the network. SDN doesn’t provide a high level of security to the network, hence protocols like encryption, hashing, etc. are applied to the VANET. In the paper, SDN based blockchain algorithm is applied, which coordinates network traffic and improves the overall security of the network. Security analysis of the proposed algorithm demonstrates that blockchain with encrypted SDN removes more than 90% of the network attacks as compared to its non- blockchain SDN.
{"title":"SDN based Blockchain Architecture for Security Performance of VANETs","authors":"Swapna Choudhary, S. Dorle","doi":"10.1109/iccica52458.2021.9697270","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697270","url":null,"abstract":"Vehicular ad hoc networks (VANETs) are used in intelligent transportation systems to provide safety and security with a reduction in traffic jams using vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) unit communications. During the communication, nodes are always under various security threads. In order to reduce the possibility of these attacks and to normalize traffic flow in the network, a software-defined network (SDN) is used. SDN will improve centralized visibility as all the underlying open flow switches are connected to the controller, which will reduce the routing load in the network. SDN doesn’t provide a high level of security to the network, hence protocols like encryption, hashing, etc. are applied to the VANET. In the paper, SDN based blockchain algorithm is applied, which coordinates network traffic and improves the overall security of the network. Security analysis of the proposed algorithm demonstrates that blockchain with encrypted SDN removes more than 90% of the network attacks as compared to its non- blockchain SDN.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131593874","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697282
Pramod Jadhav, A. Shelke, C. Sonar
The cognitive intelligence is vital for human adaptation and subsistence. It encompasses wisdom and mental ability in regards to learning, evaluation and solving the problems. In almost all sectors, companies are facing acute competition. Employing cognitive intelligence, industries are adopting enormous operational excellence measures to thrive their success. Hence cognitive leadership is important driving force that influences the organizational success through the human capital. This research endeavors to study such cognitive leadership attempts in anticipating the vulnerability, defining and applying various strategies in creation of innovation nurturing environment. An influence of cognitive leadership in influencing the risk mitigation and non-technical innovation strategies is analyzed while examining their impact on the business success within a theoretical lens of socio-cognitive space and capabilities-based view (CBV) of strategic management frameworks using partial least squares (PLS) method of structural equations modelling (SEM).
{"title":"Effect of Leadership and Innovations on Business Performance: A Structural Equation Modelling Analysis","authors":"Pramod Jadhav, A. Shelke, C. Sonar","doi":"10.1109/iccica52458.2021.9697282","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697282","url":null,"abstract":"The cognitive intelligence is vital for human adaptation and subsistence. It encompasses wisdom and mental ability in regards to learning, evaluation and solving the problems. In almost all sectors, companies are facing acute competition. Employing cognitive intelligence, industries are adopting enormous operational excellence measures to thrive their success. Hence cognitive leadership is important driving force that influences the organizational success through the human capital. This research endeavors to study such cognitive leadership attempts in anticipating the vulnerability, defining and applying various strategies in creation of innovation nurturing environment. An influence of cognitive leadership in influencing the risk mitigation and non-technical innovation strategies is analyzed while examining their impact on the business success within a theoretical lens of socio-cognitive space and capabilities-based view (CBV) of strategic management frameworks using partial least squares (PLS) method of structural equations modelling (SEM).","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"340 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131836123","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697291
C. Sekhar, K. Pavani, M. Rao
Daily, large amounts of data are generated. Unauthorized users should be kept away from the data. Issues and problems arose one after the other as a result of the continuous development of network security. To avoid these malicious attacks, deep learning and machine learning methodologies are frequently used. Machine learning is a branch of the computer field that studies computational algorithms to convert empirical data into usable models. This field originated from the communities of traditional statics and intelligent retrieval. Machine learning includes deep learning as a subset. A system that can be trained to recognise objects using raw input has referred to as a deep learning system. In this study, we are applying DL techniques such as CNN, DNN, LSTM and RNN on NSL-KDD dataset. In this paper, we conduct a comparative analysis of multiple algorithms to determine which model is best for network security based on the network conditions and environment.
{"title":"Comparative analysis on Intrusion Detection system through ML and DL Techniques: Survey","authors":"C. Sekhar, K. Pavani, M. Rao","doi":"10.1109/iccica52458.2021.9697291","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697291","url":null,"abstract":"Daily, large amounts of data are generated. Unauthorized users should be kept away from the data. Issues and problems arose one after the other as a result of the continuous development of network security. To avoid these malicious attacks, deep learning and machine learning methodologies are frequently used. Machine learning is a branch of the computer field that studies computational algorithms to convert empirical data into usable models. This field originated from the communities of traditional statics and intelligent retrieval. Machine learning includes deep learning as a subset. A system that can be trained to recognise objects using raw input has referred to as a deep learning system. In this study, we are applying DL techniques such as CNN, DNN, LSTM and RNN on NSL-KDD dataset. In this paper, we conduct a comparative analysis of multiple algorithms to determine which model is best for network security based on the network conditions and environment.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116609091","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697122
Shatrughan Dubey, Yogadhar Pandey
This paper proposed a new model which isi based oni the classification methods such asi support vector machine neurali network andi optimization methods which isi bi-logically inspired method for the improving the classifier results in the terms ofisome performance parameters such as accuracy, precision, recall etc., here we measure the all performance parameters for the various dataset such as heart patients, liver patients andi cancer patients and improve the rate of classification or resultsi with compare than other existing techniques. The alli patient’s dataset whichi is taken fromitheiuci machine learning repository whichi providei the authentic dataset for the research work and thei simulation software isimatlab. Ini thisi paper our experimental results shows thati theibetter detectioniratei of classification for performance parameters thani other existingi techniques.
{"title":"Machine Learning Based Automated Approach To Detect Brain Disease Anomalies","authors":"Shatrughan Dubey, Yogadhar Pandey","doi":"10.1109/iccica52458.2021.9697122","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697122","url":null,"abstract":"This paper proposed a new model which isi based oni the classification methods such asi support vector machine neurali network andi optimization methods which isi bi-logically inspired method for the improving the classifier results in the terms ofisome performance parameters such as accuracy, precision, recall etc., here we measure the all performance parameters for the various dataset such as heart patients, liver patients andi cancer patients and improve the rate of classification or resultsi with compare than other existing techniques. The alli patient’s dataset whichi is taken fromitheiuci machine learning repository whichi providei the authentic dataset for the research work and thei simulation software isimatlab. Ini thisi paper our experimental results shows thati theibetter detectioniratei of classification for performance parameters thani other existingi techniques.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131663763","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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