The research emphasizes the effectiveness of Bayesian classification algorithms in predicting patient visits in healthcare settings. Bayesian algorithms examine past patient data to detect intricate patterns in admission dynamics, including demographic, clinical, and temporal factors. Through the use of Bayesian principles, prediction models are able to estimate the probability of certain patient demographics occurring at certain intervals, therefore assisting in the allocation of resources and the management of operations. Probabilities that have been estimated are used to make choices on staffing, resource allocation, and operational strategy. The variation in probability estimates across different observations improves the predictive usefulness, hence strengthening the effectiveness in healthcare management and planning.
{"title":"Probabilistic Model of Patient Classification Using Bayesian Model","authors":"P. Tansitpong","doi":"10.4018/ijrqeh.348579","DOIUrl":"https://doi.org/10.4018/ijrqeh.348579","url":null,"abstract":"The research emphasizes the effectiveness of Bayesian classification algorithms in predicting patient visits in healthcare settings. Bayesian algorithms examine past patient data to detect intricate patterns in admission dynamics, including demographic, clinical, and temporal factors. Through the use of Bayesian principles, prediction models are able to estimate the probability of certain patient demographics occurring at certain intervals, therefore assisting in the allocation of resources and the management of operations. Probabilities that have been estimated are used to make choices on staffing, resource allocation, and operational strategy. The variation in probability estimates across different observations improves the predictive usefulness, hence strengthening the effectiveness in healthcare management and planning.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828801","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}
Coronavirus has spread worldwide, with over 688 million confirmed cases and 6.8 million deaths. The results could be important as containment restrictions begin to be relaxed and we are not immune to new strains. They underscore the need to introduce increasingly effective techniques to deal with such a spread and help identify new infections more quickly, at a reasonable cost and with a minimum error rate. Machine learning models constitute a new approach, used increasingly in this field. In this proposed work, the authors built a classification model named CovStacknet based on StackNet metamodeling methodology combined with the deep convolutional neural network as the basis for feature extraction from x-ray images. Firstly, the proposed model used VGG16 as a transfer learning of deep convolutional neural networks and achieved an accuracy score of 98%. Secondly, the proposed model is extended to evaluate four other deep convolutional neural networks, ResNet-50, Inception-V3, MobileNet-V2 and DenseNet, and ResNet-50, has achieved the best performance.
{"title":"A New Classification Model Based on Transfer Learning of DCNN and Stacknet for Fast Classification of Pneumonia Through X-Ray Images","authors":"Jalal Rabbah, Mohammed Ridouani, L. Hassouni","doi":"10.4018/ijrqeh.326765","DOIUrl":"https://doi.org/10.4018/ijrqeh.326765","url":null,"abstract":"Coronavirus has spread worldwide, with over 688 million confirmed cases and 6.8 million deaths. The results could be important as containment restrictions begin to be relaxed and we are not immune to new strains. They underscore the need to introduce increasingly effective techniques to deal with such a spread and help identify new infections more quickly, at a reasonable cost and with a minimum error rate. Machine learning models constitute a new approach, used increasingly in this field. In this proposed work, the authors built a classification model named CovStacknet based on StackNet metamodeling methodology combined with the deep convolutional neural network as the basis for feature extraction from x-ray images. Firstly, the proposed model used VGG16 as a transfer learning of deep convolutional neural networks and achieved an accuracy score of 98%. Secondly, the proposed model is extended to evaluate four other deep convolutional neural networks, ResNet-50, Inception-V3, MobileNet-V2 and DenseNet, and ResNet-50, has achieved the best performance.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44880490","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}
Chanemougavally J., Shruthy K. M., S. Sudhakar, M. Sasirekha
Medical education is experimenting with different tools to make teaching-learning more compatible with the medical curriculum. One such addition is blended learning, which combines traditional teaching with e-learning. The study aims to assess the effectiveness of combining e-learning and traditional face-to-face gross anatomy teaching in undergraduate medical students. This collaborative study was done in the Department of Anatomy, A.C.S Medical College and Hospital, Dr. M.G.R. Educational and Research Institute (Deemed to be University). One hundred fourteen students volunteered to participate in the study. Six topics from the gross anatomy of the abdomen were chosen for the study. An overall pre-test questionnaire was delivered with the didactic lectures. Another pre-test questionnaire was given about the selected topic before sharing the online learning materials. A post-test questionnaire in Google form was collected at the end of the day. Feedback was collected from all study participants.
{"title":"The Effect of E-Learning and Traditional Teaching Done Hand-in-Hand for First-Year M.B.B.S. Students","authors":"Chanemougavally J., Shruthy K. M., S. Sudhakar, M. Sasirekha","doi":"10.4018/ijrqeh.325354","DOIUrl":"https://doi.org/10.4018/ijrqeh.325354","url":null,"abstract":"Medical education is experimenting with different tools to make teaching-learning more compatible with the medical curriculum. One such addition is blended learning, which combines traditional teaching with e-learning. The study aims to assess the effectiveness of combining e-learning and traditional face-to-face gross anatomy teaching in undergraduate medical students. This collaborative study was done in the Department of Anatomy, A.C.S Medical College and Hospital, Dr. M.G.R. Educational and Research Institute (Deemed to be University). One hundred fourteen students volunteered to participate in the study. Six topics from the gross anatomy of the abdomen were chosen for the study. An overall pre-test questionnaire was delivered with the didactic lectures. Another pre-test questionnaire was given about the selected topic before sharing the online learning materials. A post-test questionnaire in Google form was collected at the end of the day. Feedback was collected from all study participants.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48616082","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}
Bipin Kumar Rai, Pranjali Sharma, Sagar Singhal, Basavaraj S. Paruti
In recent years, there have been many attempts to introduce blockchain-based identity management solutions, which allow the user to take over control of his/her own identity. In this paper, the authors have reviewed in-depth existing blockchain-based identity management papers and patents published online. Based on that analysis of the literature, a system will be implemented which will come up with the current issues and try to minimize them. Being transparent, immutable, and decentralized in nature, blockchain mechanism is found to be a better technology which can reduce the corruption in the experimental scenario. The objective is to develop a decentralized system which can be used for the verification of the employees in an organization. This is done to stop or reduce the cases of identity theft and data leakage in recent time. This system will be using Ethereum blockchain platform for monitoring the information and smart contract for authentication.
{"title":"Decentralized Blockchain-Enabled Employee Authentication System","authors":"Bipin Kumar Rai, Pranjali Sharma, Sagar Singhal, Basavaraj S. Paruti","doi":"10.4018/ijrqeh.323570","DOIUrl":"https://doi.org/10.4018/ijrqeh.323570","url":null,"abstract":"In recent years, there have been many attempts to introduce blockchain-based identity management solutions, which allow the user to take over control of his/her own identity. In this paper, the authors have reviewed in-depth existing blockchain-based identity management papers and patents published online. Based on that analysis of the literature, a system will be implemented which will come up with the current issues and try to minimize them. Being transparent, immutable, and decentralized in nature, blockchain mechanism is found to be a better technology which can reduce the corruption in the experimental scenario. The objective is to develop a decentralized system which can be used for the verification of the employees in an organization. This is done to stop or reduce the cases of identity theft and data leakage in recent time. This system will be using Ethereum blockchain platform for monitoring the information and smart contract for authentication.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46745186","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}
A. Yadav, Vinod Kumar, D. Joshi, D. Rajput, Haripriya Mishra, Basavaraj S. Paruti
COVID-19 prediction models are highly welcome and necessary for authorities to make informed decisions. Traditional models, which were used in the past, were unable to reliably estimate death rates due to procedural flaws. The genetic algorithm in association with an artificial neural network (GA-ANN) is one of the suitable blended AI strategies that can foretell more correctly by resolving this difficult COVID-19 phenomena. The genetic algorithm is used to simultaneously optimise all of the ANN parameters. In this work, GA-ANN and ANN models were performed by applying historical daily data from sick, recovered, and dead people in India. The performance of the designed hybrid GA-ANN model is validated by comparing it to the standard ANN and MLR approach. It was determined that the GA-ANN model outperformed the ANN model. When compared to previous examined models for predicting mortality rates in India, the hypothesized hybrid GA-ANN model is the most competent. This hybrid AI (GA-ANN) model is suggested for the prediction due to reasonably better performance and ease of implementation.
{"title":"Hybrid Artificial Intelligence-Based Models for Prediction of Death Rate in India Due to COVID-19 Transmission","authors":"A. Yadav, Vinod Kumar, D. Joshi, D. Rajput, Haripriya Mishra, Basavaraj S. Paruti","doi":"10.4018/ijrqeh.320480","DOIUrl":"https://doi.org/10.4018/ijrqeh.320480","url":null,"abstract":"COVID-19 prediction models are highly welcome and necessary for authorities to make informed decisions. Traditional models, which were used in the past, were unable to reliably estimate death rates due to procedural flaws. The genetic algorithm in association with an artificial neural network (GA-ANN) is one of the suitable blended AI strategies that can foretell more correctly by resolving this difficult COVID-19 phenomena. The genetic algorithm is used to simultaneously optimise all of the ANN parameters. In this work, GA-ANN and ANN models were performed by applying historical daily data from sick, recovered, and dead people in India. The performance of the designed hybrid GA-ANN model is validated by comparing it to the standard ANN and MLR approach. It was determined that the GA-ANN model outperformed the ANN model. When compared to previous examined models for predicting mortality rates in India, the hypothesized hybrid GA-ANN model is the most competent. This hybrid AI (GA-ANN) model is suggested for the prediction due to reasonably better performance and ease of implementation.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46149896","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}
Breast cancer is the second major cause of cancer deaths in women. Machine learning classification techniques can be used to increase the precision of diagnosis and bring it closer to 100%, thus saving the lives of many people. This paper proposed four different models, built using different combinations of selected features and applying five ML classification techniques to all the models to identify the best model with the highest accuracy. It analyzes five machine learning techniques, namely logistic regression (LR), support vector machines (SVM), naive bayes (NB), decision trees (DT), and k-nearest neighbor (KNN), for prediction of breast cancer using the Wisconsin Diagnostic Breast Cancer Dataset on these four models. The objective of the paper is to find the best ML algorithm that can most accurately predict breast cancer for a particular model. The outcome of this paper helps the doctors to improvise the diagnosis by knowing the effect of combinations of symptoms with the growth of breast cancer.
{"title":"Classifying Malignant and Benign Tumors of Breast Cancer","authors":"Meshwa Rameshbhai Savalia, J. V. Verma","doi":"10.4018/ijrqeh.318483","DOIUrl":"https://doi.org/10.4018/ijrqeh.318483","url":null,"abstract":"Breast cancer is the second major cause of cancer deaths in women. Machine learning classification techniques can be used to increase the precision of diagnosis and bring it closer to 100%, thus saving the lives of many people. This paper proposed four different models, built using different combinations of selected features and applying five ML classification techniques to all the models to identify the best model with the highest accuracy. It analyzes five machine learning techniques, namely logistic regression (LR), support vector machines (SVM), naive bayes (NB), decision trees (DT), and k-nearest neighbor (KNN), for prediction of breast cancer using the Wisconsin Diagnostic Breast Cancer Dataset on these four models. The objective of the paper is to find the best ML algorithm that can most accurately predict breast cancer for a particular model. The outcome of this paper helps the doctors to improvise the diagnosis by knowing the effect of combinations of symptoms with the growth of breast cancer.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43094898","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}
An illness that lasts longer and has continual repercussions is known as a chronic illness. Adults all across the world die as a result of chronic sickness. Diabetes disease prediction by improvising support vector machine is a platform that predicts diabetes based on the data entered into the system and offers reliable results based on that data. Earlier, the dataset consisted of a smaller number of features comprising the patients' medical details that were useful in determining the patient's health condition and was mainly focused on gestational diabetes, which only deals with pregnant women. In this work, the authors build a system that is more efficient than the previous system because of these reasons. It provides more accurate results by improvising the support vector machine, which includes more datasets and can predict the possibility of diabetes disease in both males and females.
{"title":"DDPIS: Diabetes Disease Prediction by Improvising SVM","authors":"Shivani Sharma, Bipin Kumar Rai, Mahak Gupta, Muskan Dinkar","doi":"10.4018/ijrqeh.318090","DOIUrl":"https://doi.org/10.4018/ijrqeh.318090","url":null,"abstract":"An illness that lasts longer and has continual repercussions is known as a chronic illness. Adults all across the world die as a result of chronic sickness. Diabetes disease prediction by improvising support vector machine is a platform that predicts diabetes based on the data entered into the system and offers reliable results based on that data. Earlier, the dataset consisted of a smaller number of features comprising the patients' medical details that were useful in determining the patient's health condition and was mainly focused on gestational diabetes, which only deals with pregnant women. In this work, the authors build a system that is more efficient than the previous system because of these reasons. It provides more accurate results by improvising the support vector machine, which includes more datasets and can predict the possibility of diabetes disease in both males and females.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45587177","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}
In the healthcare industry, providing a vital backbone for services is critical. The supply chain is a complex network that crosses organizational and geographical borders. In the healthcare business, counterfeit pills are one of the primary reasons for the harmful impact on human health and financial loss. Thus, pharmaceutical supply chains and end-to-end tracking systems are the recent research in healthcare. In this paper, the authors propose blockchain-based traceability of counterfeited drugs (BBTCD) that implements tracking of counterfeited drugs using smart contracts on the Ethereum blockchain. They offer a solution to fully decentralize the tracking by storing BBTCD on IPFS (inter planetary file system) to provide transparency and cost-effectiveness.
{"title":"Blockchain-Based Traceability of Counterfeited Drugs","authors":"Bipin Kumar Rai, Shivya Srivastava, Shruti Arora","doi":"10.4018/ijrqeh.318129","DOIUrl":"https://doi.org/10.4018/ijrqeh.318129","url":null,"abstract":"In the healthcare industry, providing a vital backbone for services is critical. The supply chain is a complex network that crosses organizational and geographical borders. In the healthcare business, counterfeit pills are one of the primary reasons for the harmful impact on human health and financial loss. Thus, pharmaceutical supply chains and end-to-end tracking systems are the recent research in healthcare. In this paper, the authors propose blockchain-based traceability of counterfeited drugs (BBTCD) that implements tracking of counterfeited drugs using smart contracts on the Ethereum blockchain. They offer a solution to fully decentralize the tracking by storing BBTCD on IPFS (inter planetary file system) to provide transparency and cost-effectiveness.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41824354","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}
IoT-based healthcare systems are becoming popular due to the extreme benefits patients, families, physicians, hospitals, and insurance companies are getting. Cloud is used traditionally for almost every IoT application, but cloud located far away from the devices resulted in an uncertain latency in providing services. At this point, fog computing emerged as the best alternative to provide such real-time services to delay-sensitive IoT applications. However, with the surge of patients, fog's limited resources may fail to handle the explosive growth in requests requiring advanced monitoring-based prioritization of tasks to meet the QoS requirements. To this end, in this paper, a level monitoring task scheduling (LMTS) algorithm is proposed for healthcare applications in fog to provide an immediate response to the delay-sensitive tasks with minimum delay and network usage. The proposed algorithm has been simulated using the Cloudsim simulator, and the results obtained demonstrated the efficacy of the proposed model.
{"title":"An Efficient Fog Layer Task Scheduling Algorithm for Multi-Tiered IoT Healthcare Systems","authors":"R. Behera, Amrut Patro, K. Reddy, D. S. Roy","doi":"10.4018/ijrqeh.308802","DOIUrl":"https://doi.org/10.4018/ijrqeh.308802","url":null,"abstract":"IoT-based healthcare systems are becoming popular due to the extreme benefits patients, families, physicians, hospitals, and insurance companies are getting. Cloud is used traditionally for almost every IoT application, but cloud located far away from the devices resulted in an uncertain latency in providing services. At this point, fog computing emerged as the best alternative to provide such real-time services to delay-sensitive IoT applications. However, with the surge of patients, fog's limited resources may fail to handle the explosive growth in requests requiring advanced monitoring-based prioritization of tasks to meet the QoS requirements. To this end, in this paper, a level monitoring task scheduling (LMTS) algorithm is proposed for healthcare applications in fog to provide an immediate response to the delay-sensitive tasks with minimum delay and network usage. The proposed algorithm has been simulated using the Cloudsim simulator, and the results obtained demonstrated the efficacy of the proposed model.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43164730","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 COVID-19 pandemic has crumbled health systems all over the world. Quick and accurate detection of coronavirus infection plays an important role in timely referral of physicians and control transmission of the disease. RT-PCR is the most widely test used for identification of COVID-19 patients, but it takes long to deliver the report. Researchers around the world are looking for alternative machine learning techniques including deep learning to assist the medical experts for early COVID-19 disease diagnosis from medical imaging such as chest films. This study proposes an enhanced convolutional neural network (EConvNet) model for the presence and absence of coronavirus disease from chest radiographs to contain this pandemic. The model is accurate compared to the traditional machine learning algorithms (RF, SVM, etc.). The suggested CNN model is approximately as accurate as the classifiers based on transfer learning (such as InceptionV3, VGG16, and Densenet121). Despite being simple in terms of number of parameters learnt, it takes less training time and demands less memory.
{"title":"Fine Tuning CNN for COVID-19 Patterns Detection From Chest Radiographs","authors":"Anju Jain, S. Ratnoo, D. Kumar","doi":"10.4018/ijrqeh.308801","DOIUrl":"https://doi.org/10.4018/ijrqeh.308801","url":null,"abstract":"The COVID-19 pandemic has crumbled health systems all over the world. Quick and accurate detection of coronavirus infection plays an important role in timely referral of physicians and control transmission of the disease. RT-PCR is the most widely test used for identification of COVID-19 patients, but it takes long to deliver the report. Researchers around the world are looking for alternative machine learning techniques including deep learning to assist the medical experts for early COVID-19 disease diagnosis from medical imaging such as chest films. This study proposes an enhanced convolutional neural network (EConvNet) model for the presence and absence of coronavirus disease from chest radiographs to contain this pandemic. The model is accurate compared to the traditional machine learning algorithms (RF, SVM, etc.). The suggested CNN model is approximately as accurate as the classifiers based on transfer learning (such as InceptionV3, VGG16, and Densenet121). Despite being simple in terms of number of parameters learnt, it takes less training time and demands less memory.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45915358","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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