Based on the findings of previous studies, a new theoretical model of the influence of hospital's online healthcare information services to its elderly patients' information adoption intention was formulated, analyzed, and developed in this present study. Using an online data collection method through a self-administered questionnaire, this study made use of structural equation modeling (SEM) to determine the information adoption intention of elderly patients in China. Results showed that the total effects of elderly patients' information adoption intention revolved around the quality of the online healthcare information channel and its service quality followed by the patients' cognition behaviors such as perceived ease of use and usefulness. Practical implications and recommendations for the improvement of online healthcare information services and information adoption intention in China are discussed further in this present paper.
{"title":"The Influence of Hospital Online Healthcare Information Services on Information Adoption Intention","authors":"L. Liang","doi":"10.4018/ijrqeh.308805","DOIUrl":"https://doi.org/10.4018/ijrqeh.308805","url":null,"abstract":"Based on the findings of previous studies, a new theoretical model of the influence of hospital's online healthcare information services to its elderly patients' information adoption intention was formulated, analyzed, and developed in this present study. Using an online data collection method through a self-administered questionnaire, this study made use of structural equation modeling (SEM) to determine the information adoption intention of elderly patients in China. Results showed that the total effects of elderly patients' information adoption intention revolved around the quality of the online healthcare information channel and its service quality followed by the patients' cognition behaviors such as perceived ease of use and usefulness. Practical implications and recommendations for the improvement of online healthcare information services and information adoption intention in China are discussed further in this present paper.","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":"45243404","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}
This research aims to devise a method of encrypting medical images based on chaos map, Knight's travel map, affine transformation, and DNA cryptography to prevent attackers from accessing the data. The proposed DMIES cryptographic system performs the chaos intertwining logistic map diffusion and confusion process on chosen pixels of medical images. The DNA structure of the medical image has generated using all eight DNA encoding rules that are dependent on the pixel positions in the medical image. Knight's travel map is decomposed, which helps to prevent tampering and certification after the diffusion process. Finally, to avoid the deformity of medical data, a shear-based affine transformation is used. Compared to existing standard image encryption systems, the extensive and complete security assessment highlights the relevance and benefits of the proposed DMIES cryptosystem. The proposed DMIES can also withstand various attacks like statistical, differential, exhaustive, cropping, and noise attack.
{"title":"A DNA Sequencing Medical Image Encryption System (DMIES) Using Chaos Map and Knight's Travel Map","authors":"Adithya B., Santhi G.","doi":"10.4018/ijrqeh.308803","DOIUrl":"https://doi.org/10.4018/ijrqeh.308803","url":null,"abstract":"This research aims to devise a method of encrypting medical images based on chaos map, Knight's travel map, affine transformation, and DNA cryptography to prevent attackers from accessing the data. The proposed DMIES cryptographic system performs the chaos intertwining logistic map diffusion and confusion process on chosen pixels of medical images. The DNA structure of the medical image has generated using all eight DNA encoding rules that are dependent on the pixel positions in the medical image. Knight's travel map is decomposed, which helps to prevent tampering and certification after the diffusion process. Finally, to avoid the deformity of medical data, a shear-based affine transformation is used. Compared to existing standard image encryption systems, the extensive and complete security assessment highlights the relevance and benefits of the proposed DMIES cryptosystem. The proposed DMIES can also withstand various attacks like statistical, differential, exhaustive, cropping, and noise attack.","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":"43228313","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}
Millions of smart devices and sensors continuously produce and transmit data to control real-world infrastructures using complex networks in the internet of things (IoT). These devices have limited computing, processing, storage, and communication resources to perform time-critical and rigorous computing tasks. Edge computing has emerged as a new model to resolve the above problems by performing computation near IoT devices. The IoT revolution is reshaping the modern healthcare system with promising technological, economic, and social prospects. IoT in healthcare not only helps patients but also doctors to monitor the patient's health condition from a remote place. Software-defined networking (SDN) is an effective and promising solution to overcome issues such as IoT device management, control, interoperability, and maintenance. In this paper, the authors perform an extensive survey to analyze the role of SDN and edge computing in healthcare. Finally, the paper is concluded with the ongoing research on SDN and edge computing to solve various issues in IoT based healthcare domain.
{"title":"Edge Computing in SDN-Enabled IoT-Based Healthcare Frameworks","authors":"Malaram Kumhar, Jitendra B. Bhatia","doi":"10.4018/ijrqeh.308804","DOIUrl":"https://doi.org/10.4018/ijrqeh.308804","url":null,"abstract":"Millions of smart devices and sensors continuously produce and transmit data to control real-world infrastructures using complex networks in the internet of things (IoT). These devices have limited computing, processing, storage, and communication resources to perform time-critical and rigorous computing tasks. Edge computing has emerged as a new model to resolve the above problems by performing computation near IoT devices. The IoT revolution is reshaping the modern healthcare system with promising technological, economic, and social prospects. IoT in healthcare not only helps patients but also doctors to monitor the patient's health condition from a remote place. Software-defined networking (SDN) is an effective and promising solution to overcome issues such as IoT device management, control, interoperability, and maintenance. In this paper, the authors perform an extensive survey to analyze the role of SDN and edge computing in healthcare. Finally, the paper is concluded with the ongoing research on SDN and edge computing to solve various issues in IoT based healthcare domain.","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":"44494449","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}
Sudhakar Sengan, O. Khalaf, Vidya Sagar P., D. Sharma, Arokia Jesu Prabhu L., A. A. Hamad
Existing methods use static path identifiers, making it easy for attackers to conduct DDoS flooding attacks. Create a system using Dynamic Secure aware Routing by Machine Learning (DAR-ML) to solve healthcare data. A DoS detection system by ML algorithm is proposed in this paper. First, to access the user to see the authorized process. Next, after the user registration, users can compare path information through correlation factors between nodes. Then, choose the device that will automatically activate and decrypt the data key. The DAR-ML is traced back to all healthcare data in the end module. In the next module, the users and admin can describe the results. These are the outcomes of using the network to make it easy. Through a time interval of 21.19% of data traffic, the findings demonstrate an attack detection accuracy of over 98.19%, with high precision and a probability of false alarm.
{"title":"Secured and Privacy-Based IDS for Healthcare Systems on E-Medical Data Using Machine Learning Approach","authors":"Sudhakar Sengan, O. Khalaf, Vidya Sagar P., D. Sharma, Arokia Jesu Prabhu L., A. A. Hamad","doi":"10.4018/ijrqeh.289175","DOIUrl":"https://doi.org/10.4018/ijrqeh.289175","url":null,"abstract":"Existing methods use static path identifiers, making it easy for attackers to conduct DDoS flooding attacks. Create a system using Dynamic Secure aware Routing by Machine Learning (DAR-ML) to solve healthcare data. A DoS detection system by ML algorithm is proposed in this paper. First, to access the user to see the authorized process. Next, after the user registration, users can compare path information through correlation factors between nodes. Then, choose the device that will automatically activate and decrypt the data key. The DAR-ML is traced back to all healthcare data in the end module. In the next module, the users and admin can describe the results. These are the outcomes of using the network to make it easy. Through a time interval of 21.19% of data traffic, the findings demonstrate an attack detection accuracy of over 98.19%, with high precision and a probability of false alarm.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70461569","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}
Fadi Othman Elham AlBashtawy Mohammed Ahmad Abu Alfware Fawaris
Introduction: Healthcare workers face incomparable work and psychological demands that are amplified throughout the COVID-19 pandemic. Aim: This study aimed to investigate the psychological impact of the COVID-19 pandemic on health care workers in Jordan. Method: A cross-sectional design was used. Data was collected using an online survey during the outbreak of COVID-19. Results: Overall, of the 312 healthcare workers, almost 38% and 36% presented with moderate to severe anxiety and depression consecutively. Nurses reported more severe symptoms than other healthcare workers. And both anxiety and depression were negatively correlated with well-being. Getting infected was not an immediate worry among healthcare workers; however, they were worried about carrying the virus to their families. Implications for Practice: Stakeholders must understand the impact of COVID-19 on healthcare workers and plan to provide them with the required psychological support and interventions at an early stage.
{"title":"The Psychological Impact of the COVID-19 Pandemic on Jordanian Healthcare Workers","authors":"Fadi Othman Elham AlBashtawy Mohammed Ahmad Abu Alfware Fawaris","doi":"10.4018/ijrqeh.289635","DOIUrl":"https://doi.org/10.4018/ijrqeh.289635","url":null,"abstract":"Introduction: Healthcare workers face incomparable work and psychological demands that are amplified throughout the COVID-19 pandemic. Aim: This study aimed to investigate the psychological impact of the COVID-19 pandemic on health care workers in Jordan. Method: A cross-sectional design was used. Data was collected using an online survey during the outbreak of COVID-19. Results: Overall, of the 312 healthcare workers, almost 38% and 36% presented with moderate to severe anxiety and depression consecutively. Nurses reported more severe symptoms than other healthcare workers. And both anxiety and depression were negatively correlated with well-being. Getting infected was not an immediate worry among healthcare workers; however, they were worried about carrying the virus to their families. Implications for Practice: Stakeholders must understand the impact of COVID-19 on healthcare workers and plan to provide them with the required psychological support and interventions at an early stage.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70462031","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}
Medical tourism attracts medical vacationers by promoting its uniform vacation ease, healthcare know-how, proficiency and comprehensible amenities. With the upsurge in Covid-19 cases and no therapeutic treatment, non-pharmaceutical intrusions are the utmost priority. Unprecedented travel limitations and homestay restrictions are posing a huge economic burden to the tourism industry. The present study aims to identify determinants inciting sustainable e-medical tourism post Covid-19 pandemic. The study is advanced from the theoretical outlook, systematically determining and scrutinizing the prior literature to discuss the determinants which encourage e-medical tourism. The results of the study highlight that resource & management assistance, electronic supporting facilities, demand issues, technological intervention and situational glitches act as major aspects of perseverance of e-medical tourism. An apparent limitation of the present study is the absence of contributions based on empirical data.
{"title":"A Systematic Review on Determinants Inciting Sustainable E-Medical Tourism","authors":"Pooja Kansra","doi":"10.4018/ijrqeh.299962","DOIUrl":"https://doi.org/10.4018/ijrqeh.299962","url":null,"abstract":"Medical tourism attracts medical vacationers by promoting its uniform vacation ease, healthcare know-how, proficiency and comprehensible amenities. With the upsurge in Covid-19 cases and no therapeutic treatment, non-pharmaceutical intrusions are the utmost priority. Unprecedented travel limitations and homestay restrictions are posing a huge economic burden to the tourism industry. The present study aims to identify determinants inciting sustainable e-medical tourism post Covid-19 pandemic. The study is advanced from the theoretical outlook, systematically determining and scrutinizing the prior literature to discuss the determinants which encourage e-medical tourism. The results of the study highlight that resource & management assistance, electronic supporting facilities, demand issues, technological intervention and situational glitches act as major aspects of perseverance of e-medical tourism. An apparent limitation of the present study is the absence of contributions based on empirical data.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45591229","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 objective of the paper is to present the techniques of Artificial Intelligence based on deep learning that can be applied to detect fractures in bones on X-rays. The paper comprises of discussions of various entities. Initially, there is a discussion on data formulation and processing. Following which, distinguished image processing techniques are presented for fracture detection. Later, there is an analysis of conventional and current neural network methodologies for fracture detection techniques. Furthermore, there is a comparative analysis for the same. Finally, in the end, a discussion is presented in the paper regarding problems and challenges confronted by researchers for fracture detection. The study shows, deep learning techniques provide accuracy in the diagnosis than the conventional methods in fracture detection on X-rays. The paper leads to a path for the researchers to deal with difficulties and issues encountered with the fracture detection on X-rays while using deep learning techniques.
{"title":"Evolution of Artificial Intelligence in Bone Fracture Detection","authors":"Deepti Mishra, G. Bajaj","doi":"10.4018/ijrqeh.299958","DOIUrl":"https://doi.org/10.4018/ijrqeh.299958","url":null,"abstract":"The objective of the paper is to present the techniques of Artificial Intelligence based on deep learning that can be applied to detect fractures in bones on X-rays. The paper comprises of discussions of various entities. Initially, there is a discussion on data formulation and processing. Following which, distinguished image processing techniques are presented for fracture detection. Later, there is an analysis of conventional and current neural network methodologies for fracture detection techniques. Furthermore, there is a comparative analysis for the same. Finally, in the end, a discussion is presented in the paper regarding problems and challenges confronted by researchers for fracture detection. The study shows, deep learning techniques provide accuracy in the diagnosis than the conventional methods in fracture detection on X-rays. The paper leads to a path for the researchers to deal with difficulties and issues encountered with the fracture detection on X-rays while using deep learning techniques.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42189468","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 novel coronavirus named COVID-19 has spread speedily and has triggered a worldwide outbreak of respiratory illness. Early diagnosis is always crucial for pandemic control. Compared to RT-PCR, chest computed tomography (CT) imaging is the more consistent, concrete, and prompt method to identify COVID-19 patients. For clinical diagnostics, the information received from computed tomography scans is critical. So there is a need to develop an image analysis technique for detecting viral epidemics from computed tomography scan pictures. Using DenseNet, ResNet, CapsNet, and 3D-ConvNet, four deep machine learning-based architectures have been proposed for COVID-19 diagnosis from chest computed tomography scans. From the experimental results, it is found that all the architectures are providing effective accuracy, of which the COVID-DNet model has reached the highest accuracy of 99%. Proposed architectures are accessible at https://github.com/shamiktiwari/CTscanCovi19 can be utilized to support radiologists and reserachers in validating their initial screening.
{"title":"Diagnosing COVID-19 from Chest CT Scan Images using Deep Learning Models","authors":"Shamik Tiwari","doi":"10.4018/ijrqeh.299961","DOIUrl":"https://doi.org/10.4018/ijrqeh.299961","url":null,"abstract":"A novel coronavirus named COVID-19 has spread speedily and has triggered a worldwide outbreak of respiratory illness. Early diagnosis is always crucial for pandemic control. Compared to RT-PCR, chest computed tomography (CT) imaging is the more consistent, concrete, and prompt method to identify COVID-19 patients. For clinical diagnostics, the information received from computed tomography scans is critical. So there is a need to develop an image analysis technique for detecting viral epidemics from computed tomography scan pictures. Using DenseNet, ResNet, CapsNet, and 3D-ConvNet, four deep machine learning-based architectures have been proposed for COVID-19 diagnosis from chest computed tomography scans. From the experimental results, it is found that all the architectures are providing effective accuracy, of which the COVID-DNet model has reached the highest accuracy of 99%. Proposed architectures are accessible at https://github.com/shamiktiwari/CTscanCovi19 can be utilized to support radiologists and reserachers in validating their initial screening.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48546639","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}
Machine learning (ML) has been instrumental in optimal decision making through relevant historical data, including the domain of Bioinformatics. In bioinformatics classification of natural genes and the genes that are infected by disease called invalid gene is a very complex task. In order to find the applicability of a Fresh Protein through Genomic research, DNA sequences are needed to be classified. The current work identifies classes of DNA sequence using Machine Learning algorithm. These classes are basically dependent on the sequence of nucleotides. With a fractional mutation in sequence there is a corresponding change in the class. Each numeric instance representing a class is linked to a Gene family including G protein coupled receptors, tyrosine kinase, synthase etc. In this paper, we applied the classification algorithm on three types of datasets to identify which gene class they belongs to. We converted sequences into substrings with a defined length. That ‘k value’ defines the length of substring which is one of the way to analyze the sequence.
{"title":"An Approach to DNA Sequence Classification through Machine Learning","authors":"Sapna Juneja","doi":"10.4018/ijrqeh.299963","DOIUrl":"https://doi.org/10.4018/ijrqeh.299963","url":null,"abstract":"Machine learning (ML) has been instrumental in optimal decision making through relevant historical data, including the domain of Bioinformatics. In bioinformatics classification of natural genes and the genes that are infected by disease called invalid gene is a very complex task. In order to find the applicability of a Fresh Protein through Genomic research, DNA sequences are needed to be classified. The current work identifies classes of DNA sequence using Machine Learning algorithm. These classes are basically dependent on the sequence of nucleotides. With a fractional mutation in sequence there is a corresponding change in the class. Each numeric instance representing a class is linked to a Gene family including G protein coupled receptors, tyrosine kinase, synthase etc. In this paper, we applied the classification algorithm on three types of datasets to identify which gene class they belongs to. We converted sequences into substrings with a defined length. That ‘k value’ defines the length of substring which is one of the way to analyze the sequence.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41478853","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}
Healthcare systems around the world are beset by problems due to the lack of effective communication. Significant problems relating to patient medical records access, transition, and storage have persisted due to the lack of resources to adequately interact and track records between all main participants. To overcome this challenge, a nationwide Electronic Health Record (EHR) solution may be utilized. To further enhance EHR efficiency, Blockchain technology can be used to improve security, performance, and cost. In this survey, various literature proposing Blockchain-based EHR systems are discussed, along with their benefits and potential research gaps. Also Authors proposed a comprehensive architecture that could bridge all the gaps.
{"title":"An Extensive Survey on Blockchain-Based Electronic Health Record System","authors":"Prahlad Kumar","doi":"10.4018/ijrqeh.299960","DOIUrl":"https://doi.org/10.4018/ijrqeh.299960","url":null,"abstract":"Healthcare systems around the world are beset by problems due to the lack of effective communication. Significant problems relating to patient medical records access, transition, and storage have persisted due to the lack of resources to adequately interact and track records between all main participants. To overcome this challenge, a nationwide Electronic Health Record (EHR) solution may be utilized. To further enhance EHR efficiency, Blockchain technology can be used to improve security, performance, and cost. In this survey, various literature proposing Blockchain-based EHR systems are discussed, along with their benefits and potential research gaps. Also Authors proposed a comprehensive architecture that could bridge all the gaps.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":"304 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41273408","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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