Pub Date : 2021-12-04DOI: 10.1109/BECITHCON54710.2021.9893653
Yogita K. Dubey, Pushkar Wankhede, Tanvi Borkar, Amey Borkar, K. Mitra
Diabetes is one of the most grievous diseases in the world which has no remedy to cure it after a particular stage. Over 422 million people in the world are diagnosed with diabetes and many others are at jeopardy. Thus, timely diagnosis and medication is required to inhibit diabetes and its associated health problems. In this paper a framework is proposed for diabetes diseases prediction and classification using Machine Learning (ML) algorithms. The dataset is collected from Shalinitai Meghe Hospital and Research Centre, Nagpur, NKP Salve Institute of Medical Sciences and Research Centre and Mendeley Data. Four different ML algorithms Logistic Regression, Naive Bayes, Support Vector Machine and Random Forest are applied and evaluated the model with various quantitative measures. The motive of this framework is to diagnose diabetes early and to save money and time of a patient using various machine learning approaches.
{"title":"Diabetes Prediction and Classification using Machine Learning Algorithms","authors":"Yogita K. Dubey, Pushkar Wankhede, Tanvi Borkar, Amey Borkar, K. Mitra","doi":"10.1109/BECITHCON54710.2021.9893653","DOIUrl":"https://doi.org/10.1109/BECITHCON54710.2021.9893653","url":null,"abstract":"Diabetes is one of the most grievous diseases in the world which has no remedy to cure it after a particular stage. Over 422 million people in the world are diagnosed with diabetes and many others are at jeopardy. Thus, timely diagnosis and medication is required to inhibit diabetes and its associated health problems. In this paper a framework is proposed for diabetes diseases prediction and classification using Machine Learning (ML) algorithms. The dataset is collected from Shalinitai Meghe Hospital and Research Centre, Nagpur, NKP Salve Institute of Medical Sciences and Research Centre and Mendeley Data. Four different ML algorithms Logistic Regression, Naive Bayes, Support Vector Machine and Random Forest are applied and evaluated the model with various quantitative measures. The motive of this framework is to diagnose diabetes early and to save money and time of a patient using various machine learning approaches.","PeriodicalId":170083,"journal":{"name":"2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)","volume":"2006 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116935488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/BECITHCON54710.2021.9893606
Md. Abdullah Al Rakib, Mohammad Nasir Uddin, M. H. Imam
Monitoring of patients that is based on the IoT systems is an intelligent health monitoring system that can monitor a patient 24 hours a day, seven days a week. Many people suffer from the chronic disease; however, older folks are at a higher risk of developing chronic disorders. As a result, despite recent advances in health information technology, the usefulness of technology-based chronic illness management for older persons is an important topic of research. This paper provides an overview of IoT-based chronic illness monitoring systems especially for Diabetes patients. Using IoT technology and sensors, patient’s vital signs ate recorded and analyzed to generate decisions on the health condition and these decisions are shared with Doctors and Caregivers. The proposed system shoed the feasibility of the use of Mobile network or Wi-Fi to communicate data which will help the rural people to get health benefit at home without going to distant health centers. The prototype implementation of a low cost IoT based platform is presented here which can improve the health facilities of the developing countries with resource constraints.
{"title":"Cloud Based Chronic Disease Monitoring and Management System","authors":"Md. Abdullah Al Rakib, Mohammad Nasir Uddin, M. H. Imam","doi":"10.1109/BECITHCON54710.2021.9893606","DOIUrl":"https://doi.org/10.1109/BECITHCON54710.2021.9893606","url":null,"abstract":"Monitoring of patients that is based on the IoT systems is an intelligent health monitoring system that can monitor a patient 24 hours a day, seven days a week. Many people suffer from the chronic disease; however, older folks are at a higher risk of developing chronic disorders. As a result, despite recent advances in health information technology, the usefulness of technology-based chronic illness management for older persons is an important topic of research. This paper provides an overview of IoT-based chronic illness monitoring systems especially for Diabetes patients. Using IoT technology and sensors, patient’s vital signs ate recorded and analyzed to generate decisions on the health condition and these decisions are shared with Doctors and Caregivers. The proposed system shoed the feasibility of the use of Mobile network or Wi-Fi to communicate data which will help the rural people to get health benefit at home without going to distant health centers. The prototype implementation of a low cost IoT based platform is presented here which can improve the health facilities of the developing countries with resource constraints.","PeriodicalId":170083,"journal":{"name":"2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123823371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/becithcon54710.2021.9893618
{"title":"BECITHCON 2021 Cover Page","authors":"","doi":"10.1109/becithcon54710.2021.9893618","DOIUrl":"https://doi.org/10.1109/becithcon54710.2021.9893618","url":null,"abstract":"","PeriodicalId":170083,"journal":{"name":"2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128450486","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}
Convulsive seizures contribute to a significant portion of the seizure-associated injuries, accidents, and sudden unexpected deaths in Epilepsy (SUDEP). An ambulatory seizure detection system may prevent such accidents and improve the quality of life. Conventional seizure detection methods require specialized approaches such as video or EEG analysis, which are frequently ineffective in non-clinical settings such as during daily activities. In recent years, a couple of wearable accelerometer-based seizure detection systems have been proposed. But the common problem these devices face is low specificity and high False Alarm Rate (FAR). In this work, we proposed an improved way to study and classify accelerometer data using Convolutional Neural Network (CNN) to detect General Tonic Clonic Seizures (GTCS), also known as Convulsive Seizures. Due to the unavailability of a dataset of accelerometer data related to seizure movements, an accelerometer-based wrist-worn data acquisition device was constructed to develop a dataset mimicking seizure-like movement. The accelerometer data were then converted to RGB images for training and testing with three different CNN architectures: DenseNet, ResNet-50, and VGG16, to determine which architecture is best suited for these types of data. Among these three, the DenseNet architecture achieved the highest accuracy of 99.2%, sensitivity of 98.4%, and specificity of 100%. Hence, an algorithm was developed based on the DenseNet model to detect convulsive seizures with a feature to tune according to the patient’s seizure type. The proposed method can be implemented to develop an ambulatory seizure monitoring device to detect seizures before accidents occur.
{"title":"Accelerometer-based Convulsive Seizure Detection using CNN","authors":"Erina Binte Motahar, Farhan Ishtiaque, Md Sharjis Ibne Wadud","doi":"10.1109/BECITHCON54710.2021.9893602","DOIUrl":"https://doi.org/10.1109/BECITHCON54710.2021.9893602","url":null,"abstract":"Convulsive seizures contribute to a significant portion of the seizure-associated injuries, accidents, and sudden unexpected deaths in Epilepsy (SUDEP). An ambulatory seizure detection system may prevent such accidents and improve the quality of life. Conventional seizure detection methods require specialized approaches such as video or EEG analysis, which are frequently ineffective in non-clinical settings such as during daily activities. In recent years, a couple of wearable accelerometer-based seizure detection systems have been proposed. But the common problem these devices face is low specificity and high False Alarm Rate (FAR). In this work, we proposed an improved way to study and classify accelerometer data using Convolutional Neural Network (CNN) to detect General Tonic Clonic Seizures (GTCS), also known as Convulsive Seizures. Due to the unavailability of a dataset of accelerometer data related to seizure movements, an accelerometer-based wrist-worn data acquisition device was constructed to develop a dataset mimicking seizure-like movement. The accelerometer data were then converted to RGB images for training and testing with three different CNN architectures: DenseNet, ResNet-50, and VGG16, to determine which architecture is best suited for these types of data. Among these three, the DenseNet architecture achieved the highest accuracy of 99.2%, sensitivity of 98.4%, and specificity of 100%. Hence, an algorithm was developed based on the DenseNet model to detect convulsive seizures with a feature to tune according to the patient’s seizure type. The proposed method can be implemented to develop an ambulatory seizure monitoring device to detect seizures before accidents occur.","PeriodicalId":170083,"journal":{"name":"2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)","volume":"53 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129485956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/BECITHCON54710.2021.9893703
Shruti Jain, Vinayak Tiku
Diabetic retinopathy refers to damage to the retina, caused by the fine blood vessels of the retina rupturing and bleeding. To re-supply the retina, more blood vessels will form, creating cobwebs of blood vessels on the retinas. These added blood vessels and the scabs (dried blood) on the retinas create black spots in the vision; the patient will perceive black spots/streamers and floaters in their vision. In this paper, a screening system has been designed to detect different severity grades on the online dataset using the Inception V3 model. Computer vision filtering and other filtering techniques are used for the pre-processing of the images. 86.67% accuracy is obtained at 190th and 200th iteration. Cross entropy loss is also evaluated. Cross entropy is one of ancestor probabilistic decision making that minimizes the error but is computationally ineffective.
{"title":"Diagnostic System for Detection of Diabetic Retinopathy Severity Diseases","authors":"Shruti Jain, Vinayak Tiku","doi":"10.1109/BECITHCON54710.2021.9893703","DOIUrl":"https://doi.org/10.1109/BECITHCON54710.2021.9893703","url":null,"abstract":"Diabetic retinopathy refers to damage to the retina, caused by the fine blood vessels of the retina rupturing and bleeding. To re-supply the retina, more blood vessels will form, creating cobwebs of blood vessels on the retinas. These added blood vessels and the scabs (dried blood) on the retinas create black spots in the vision; the patient will perceive black spots/streamers and floaters in their vision. In this paper, a screening system has been designed to detect different severity grades on the online dataset using the Inception V3 model. Computer vision filtering and other filtering techniques are used for the pre-processing of the images. 86.67% accuracy is obtained at 190th and 200th iteration. Cross entropy loss is also evaluated. Cross entropy is one of ancestor probabilistic decision making that minimizes the error but is computationally ineffective.","PeriodicalId":170083,"journal":{"name":"2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125277122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/BECITHCON54710.2021.9893716
A. Sarkar, K. K. Halder
This paper addresses the problem of speckle noise in medical ultrasound imaging. This noise drastically affects the image quality as it is random in nature. For this reason, a new weighted-average filter-based image restoration technique is developed to suppress speckle noise from ultrasound images. The proposed filter structure is based on geometric Euclidean distances of the pixels in an image. The potency of the proposed filter is tested by applying it on a gray-level image and then real ultrasound images using several image quality metrics. A performance comparison with other traditional methods indicates the better performance of the proposed filter.
{"title":"Speckle Noise Reduction Using a New Weighted-Average Filter Based on Euclidean Distance","authors":"A. Sarkar, K. K. Halder","doi":"10.1109/BECITHCON54710.2021.9893716","DOIUrl":"https://doi.org/10.1109/BECITHCON54710.2021.9893716","url":null,"abstract":"This paper addresses the problem of speckle noise in medical ultrasound imaging. This noise drastically affects the image quality as it is random in nature. For this reason, a new weighted-average filter-based image restoration technique is developed to suppress speckle noise from ultrasound images. The proposed filter structure is based on geometric Euclidean distances of the pixels in an image. The potency of the proposed filter is tested by applying it on a gray-level image and then real ultrasound images using several image quality metrics. A performance comparison with other traditional methods indicates the better performance of the proposed filter.","PeriodicalId":170083,"journal":{"name":"2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124632782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/BECITHCON54710.2021.9893674
Md. Siam Ansary
Text summarization helps us to obtain the most significant content from any document saving time and resources. Many researches of automatic summarization have been done with documents of general domain. In recent years, artificial intelligence and machine learning are being more and more integrated with medical field. As the field of medical requires efficiency more than any other field of science, proper summarization of medical documents is important. Some works and studies have been done in this topic but they have many limitations and restrictions. In this paper, we have presented a hybrid approach for extractive summarization of medical documents. In the combinational method, we have filtered neutral content of a document through sentiment analysis and with interconnection and content of sentences and presence of keyphrases, summarization has been done. After evaluation, the introduced method has shown promise with good scores.
{"title":"A Hybrid Approach for Extractive Summarization of Medical Documents","authors":"Md. Siam Ansary","doi":"10.1109/BECITHCON54710.2021.9893674","DOIUrl":"https://doi.org/10.1109/BECITHCON54710.2021.9893674","url":null,"abstract":"Text summarization helps us to obtain the most significant content from any document saving time and resources. Many researches of automatic summarization have been done with documents of general domain. In recent years, artificial intelligence and machine learning are being more and more integrated with medical field. As the field of medical requires efficiency more than any other field of science, proper summarization of medical documents is important. Some works and studies have been done in this topic but they have many limitations and restrictions. In this paper, we have presented a hybrid approach for extractive summarization of medical documents. In the combinational method, we have filtered neutral content of a document through sentiment analysis and with interconnection and content of sentences and presence of keyphrases, summarization has been done. After evaluation, the introduced method has shown promise with good scores.","PeriodicalId":170083,"journal":{"name":"2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131087954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/BECITHCON54710.2021.9893638
Md. Moklesur Rahman, Md Aktaruzzaman
The development of brain-computer interface (BCI) applications has recently piqued the interest of researchers because it can help physically handicapped people to communicate with their brain electroencephalogram (EEG) signal. The automatic classification of mental workload tasks from multi-channel EEG signal analysis is critical for BCI applications. In this paper, we propose an end-to-end deep learning (DL) model for the classification of the mental arithmetic task (MAT) from multi-channel EEG signals. As an end-to-end DL model, a residual-based temporal attention network (RTA-Net) is developed to achieve optimal performance for MAT classification. We have mainly considered two MAT: before mental arithmetic calculation and during mental arithmetic calculation. The RTA-Net model is validated on a freely available MAT-based EEG dataset. The results show that our proposed model yield the best performance with classification accuracy: 99.32%, F1-score: 99.20%, and Cohen’s Kappa: 98.15%, which defeat the performance of all existing methods for MAT classification. For real-world applications, our automated MAT system is ready to be tested with additional datasets.
{"title":"An End-to-End Deep Learning Model for Mental Arithmetic Task Classification from Multi-Channel EEG","authors":"Md. Moklesur Rahman, Md Aktaruzzaman","doi":"10.1109/BECITHCON54710.2021.9893638","DOIUrl":"https://doi.org/10.1109/BECITHCON54710.2021.9893638","url":null,"abstract":"The development of brain-computer interface (BCI) applications has recently piqued the interest of researchers because it can help physically handicapped people to communicate with their brain electroencephalogram (EEG) signal. The automatic classification of mental workload tasks from multi-channel EEG signal analysis is critical for BCI applications. In this paper, we propose an end-to-end deep learning (DL) model for the classification of the mental arithmetic task (MAT) from multi-channel EEG signals. As an end-to-end DL model, a residual-based temporal attention network (RTA-Net) is developed to achieve optimal performance for MAT classification. We have mainly considered two MAT: before mental arithmetic calculation and during mental arithmetic calculation. The RTA-Net model is validated on a freely available MAT-based EEG dataset. The results show that our proposed model yield the best performance with classification accuracy: 99.32%, F1-score: 99.20%, and Cohen’s Kappa: 98.15%, which defeat the performance of all existing methods for MAT classification. For real-world applications, our automated MAT system is ready to be tested with additional datasets.","PeriodicalId":170083,"journal":{"name":"2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126149317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/BECITHCON54710.2021.9893646
Ravi Kumar, K. Srinivas, Anudeep Peddi, P. Vardhini
Attention as a key aspect of brain activity is one of the most usable area of brain study. It has a significant impact on the brain activities such as learning process and critical activities like driving vehicles. As real-time bidirectional linkages between living brains and actuators, brain-computer interfaces (BCIs) have showed considerable promise. The area of BCIs has been accelerated by artificial intelligence (AI), which can improve the analysis and decoding of brain activity. This paper deals with how attention of a person is detected using Electroencephalogram (EEG) and Brain Computer Interface (BCI).
{"title":"Artificial Intelligence based Human Attention Detection through Brain Computer Interface for Health Care Monitoring","authors":"Ravi Kumar, K. Srinivas, Anudeep Peddi, P. Vardhini","doi":"10.1109/BECITHCON54710.2021.9893646","DOIUrl":"https://doi.org/10.1109/BECITHCON54710.2021.9893646","url":null,"abstract":"Attention as a key aspect of brain activity is one of the most usable area of brain study. It has a significant impact on the brain activities such as learning process and critical activities like driving vehicles. As real-time bidirectional linkages between living brains and actuators, brain-computer interfaces (BCIs) have showed considerable promise. The area of BCIs has been accelerated by artificial intelligence (AI), which can improve the analysis and decoding of brain activity. This paper deals with how attention of a person is detected using Electroencephalogram (EEG) and Brain Computer Interface (BCI).","PeriodicalId":170083,"journal":{"name":"2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115214902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1109/BECITHCON54710.2021.9893594
Rachit Desai, Am Carolyn McGregor
Anemia is the leading cause of transfusion in premature infants. Anemia can be caused by an excess loss of blood through laboratory testing and phlebotomy. Heart Rate Variability (HRV) has been known to be an indicator of distress within the body and research has been conducted showing association between HRV and transfusion. This paper presents the current state of knowledge regarding transfusion in the premature population and literature assessing what association of HRV and transfusion is known
{"title":"Transfusion in Neonatal Care in relation to Heart Rate Variability (HRV) and Anemia: A Literature Review","authors":"Rachit Desai, Am Carolyn McGregor","doi":"10.1109/BECITHCON54710.2021.9893594","DOIUrl":"https://doi.org/10.1109/BECITHCON54710.2021.9893594","url":null,"abstract":"Anemia is the leading cause of transfusion in premature infants. Anemia can be caused by an excess loss of blood through laboratory testing and phlebotomy. Heart Rate Variability (HRV) has been known to be an indicator of distress within the body and research has been conducted showing association between HRV and transfusion. This paper presents the current state of knowledge regarding transfusion in the premature population and literature assessing what association of HRV and transfusion is known","PeriodicalId":170083,"journal":{"name":"2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116861889","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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