Pub Date : 2023-04-01DOI: 10.18178/ijml.2023.13.2.1130
{"title":"A Cell Tracking Method for Dynamic Analysis of Immune Cells Based on Deep Learning","authors":"","doi":"10.18178/ijml.2023.13.2.1130","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.2.1130","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76078988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.18178/ijml.2023.13.2.1131
{"title":"Big Data Applications in Supply Chain Management: SCOPUS Based Review","authors":"","doi":"10.18178/ijml.2023.13.2.1131","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.2.1131","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84283071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.18178/ijml.2023.13.2.1134
Sebastien Cohen, Florence Leve, Harold Trannois, Wafa Badreddine, Florian Legendre
Abstract —Nowadays, the medical sector faces several challenges due to different factors including the increase in the number of patients to be taken care of, the economic crisis and the saturation of hospitals. Hence, hospital administrations aim to develop new strategies to handle these issues as remote patient monitoring. In this context, we propose a decision-making Spiking Neural Network (SNN) model regarding patient health conditions to integrate to patient monitoring systems. Our model offers, based on the measurements of the physiological parameters of the patient, a feedback of the patient’s health condition and a raising of the alert if necessary. To do so, we construct an SNN model that represents the rules provided by a group of doctors and that allow this model to be representative of one patient. The results obtained by our model as well as those of a rule-based model validated by physicians have an error rate of less than 10%. Our goal is to reduce this error rate associating the two models and not to put the two models in competition.
{"title":"A Decision-Making Model Based on Spiking Neural Network (SNN) for Remote Patient Monitoring","authors":"Sebastien Cohen, Florence Leve, Harold Trannois, Wafa Badreddine, Florian Legendre","doi":"10.18178/ijml.2023.13.2.1134","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.2.1134","url":null,"abstract":" Abstract —Nowadays, the medical sector faces several challenges due to different factors including the increase in the number of patients to be taken care of, the economic crisis and the saturation of hospitals. Hence, hospital administrations aim to develop new strategies to handle these issues as remote patient monitoring. In this context, we propose a decision-making Spiking Neural Network (SNN) model regarding patient health conditions to integrate to patient monitoring systems. Our model offers, based on the measurements of the physiological parameters of the patient, a feedback of the patient’s health condition and a raising of the alert if necessary. To do so, we construct an SNN model that represents the rules provided by a group of doctors and that allow this model to be representative of one patient. The results obtained by our model as well as those of a rule-based model validated by physicians have an error rate of less than 10%. Our goal is to reduce this error rate associating the two models and not to put the two models in competition.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80816059","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}
Abstract —Being exposed to offensive language on social media platforms is relatively higher because of anonymity and distant self-expression compared to real communication. Billions of contents are shared daily on these platforms, making it impossible to detect offensive posts with manual editorial processes. This situation arises the need for automatic detection of offensive language in social media posts to provide users' online safety. In this paper, we applied different Machine Learning (ML) models on over manually annotated 36,000 Turkish tweets to detect the use of offensive language messages automatically. According to the results, the most successful model for predicting offensive language is pre-trained transformer-based ELECTRA model with 0.8216 F-1 score. We also obtained the highest F-1 score with 0.8342 in this dataset up to now by combining transformer-based ELECTRA and BERT models in an ensemble model.
{"title":"Offensive Language Detection in Social Media Using Transformers and Importance of Pre-training","authors":"Beyzanur Saraçlar, Birol Kuyumcu, Selman Delil, Cüneyt Aksakalli","doi":"10.18178/ijml.2023.13.2.1133","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.2.1133","url":null,"abstract":" Abstract —Being exposed to offensive language on social media platforms is relatively higher because of anonymity and distant self-expression compared to real communication. Billions of contents are shared daily on these platforms, making it impossible to detect offensive posts with manual editorial processes. This situation arises the need for automatic detection of offensive language in social media posts to provide users' online safety. In this paper, we applied different Machine Learning (ML) models on over manually annotated 36,000 Turkish tweets to detect the use of offensive language messages automatically. According to the results, the most successful model for predicting offensive language is pre-trained transformer-based ELECTRA model with 0.8216 F-1 score. We also obtained the highest F-1 score with 0.8342 in this dataset up to now by combining transformer-based ELECTRA and BERT models in an ensemble model.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82086523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.18178/ijml.2023.13.2.1132
{"title":"UNMMIT: A Unified Framework on Unsupervised Multimodal Multi-domain Image-to-Image Translation","authors":"","doi":"10.18178/ijml.2023.13.2.1132","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.2.1132","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82542604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-05DOI: 10.53759/7669/jmc202303005
B. P., S. K
Energy efficiency plays biggest role in the wireless sensor network as the sensors are smaller and restricted in their resource capacity. Due to their nature of restricted resource capacity, data transmission would become more complex. So, it is required to concentrate more on data transmission strategies, to ensure the interruption avoided data transmission. There are different examination strategies has been presented before for performing data transmission. Among them most of research methodologies focused on attaining energy consumption reduced optimal data transmission. The working principlee and processing flow of previous research methodologies has been discussed here in details. This survey article is to discuss the research techniques which attempts to perform energy consumption reduced data handling, so that network lifetime of various sensor nodes can be utilized effectively along with increased data transmission rate. And this research work discussed the merits and demerits analysed over each research techniques discussed here. Finally, this research work is concluded with the performance analysis over varying number of nodes. The examination of the analysis work is done in the matlab. The mathematical qualities have been examined to predict the exhibition level of various examination procedures as far as their packet transmission rate, delay and energy utilization.
{"title":"A Certain Investigations on Energy Efficient Techniques on Wireless Sensor Networks over Smart Grid","authors":"B. P., S. K","doi":"10.53759/7669/jmc202303005","DOIUrl":"https://doi.org/10.53759/7669/jmc202303005","url":null,"abstract":"Energy efficiency plays biggest role in the wireless sensor network as the sensors are smaller and restricted in\u0000their resource capacity. Due to their nature of restricted resource capacity, data transmission would become more complex.\u0000So, it is required to concentrate more on data transmission strategies, to ensure the interruption avoided data transmission.\u0000There are different examination strategies has been presented before for performing data transmission. Among them most of\u0000research methodologies focused on attaining energy consumption reduced optimal data transmission. The working principlee\u0000and processing flow of previous research methodologies has been discussed here in details. This survey article is to discuss\u0000the research techniques which attempts to perform energy consumption reduced data handling, so that network lifetime of\u0000various sensor nodes can be utilized effectively along with increased data transmission rate. And this research work discussed\u0000the merits and demerits analysed over each research techniques discussed here. Finally, this research work is concluded with\u0000the performance analysis over varying number of nodes. The examination of the analysis work is done in the matlab. The\u0000mathematical qualities have been examined to predict the exhibition level of various examination procedures as far as their\u0000packet transmission rate, delay and energy utilization.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"2007 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83053138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-05DOI: 10.53759/7669/jmc202303004
Shruthishree S.H ., Harshvardhan Tiwari, D. Verma
Breast cancer represents one of the leading cancer-related diseases worldwide, affecting mostly women after puberty. Even though the illness is fatal and kills thousands of people each year, it is mostly curative if found quickly. As a result, prompt and precise detection methods are critical to patient survival. Previously, doctors used manual detection systems for this objective. However, such techniques have been slow and frequently dependent on the physician's expertise. As technology advanced, these primitive methodologies were supplemented by computer-aided detection (CAD) algorithms. Deep learning is extremely common because of the massive development in large data, the Internet of Things (IoT), linked devices, and high-performance computers using GPUs and TPUs. The Internet of Things (IoT) has advanced recently, and the healthcare industry is benefiting from this growth. Sensors that gather data for required analysis are crucial components utilized in the Internet of Things. Physicians and medical staff will be able to carry out their tasks with ease and intelligence thanks to the Internet of Things. The proposed research focus on integrating Alexnet and ResNet101 for accurate prediction of Breast malignancy from mammogram data. This methodology will target the features more precisely than any other combination of the pre-trained model. Finally, to resolve the computational burden issue, the feature reduction ReliefF methodology is used. To demonstrate the proposed method, an online publicly released set of data of 750 BU images is used. For training and testing the models, the set of data has been further split into 80 and 20% ratios. Following extensive testing and analysis, it was discovered that the DenseNet-201 and MobileNet-v2 trained SVMs to have an accuracy of 98.39 percent for the original and augmented Mammo images online datasets, respectively. This research discovered that the proposed approach is efficient and simple to implement to assist radiographers and physicians in diagnosing breast cancer in females.
{"title":"Integrated IoT-based Healthcare System for the Early Detection of Breast Cancer Using Intelligent Diagnostic System","authors":"Shruthishree S.H ., Harshvardhan Tiwari, D. Verma","doi":"10.53759/7669/jmc202303004","DOIUrl":"https://doi.org/10.53759/7669/jmc202303004","url":null,"abstract":"Breast cancer represents one of the leading cancer-related diseases worldwide, affecting mostly women after puberty. Even though the illness is fatal and kills thousands of people each year, it is mostly curative if found quickly. As a result, prompt and precise detection methods are critical to patient survival. Previously, doctors used manual detection systems for this objective. However, such techniques have been slow and frequently dependent on the physician's expertise. As technology advanced, these primitive methodologies were supplemented by computer-aided detection (CAD) algorithms. Deep learning is extremely common because of the massive development in large data, the Internet of Things (IoT), linked devices, and high-performance computers using GPUs and TPUs. The Internet of Things (IoT) has advanced recently, and the healthcare industry is benefiting from this growth. Sensors that gather data for required analysis are crucial components utilized in the Internet of Things. Physicians and medical staff will be able to carry out their tasks with ease and intelligence thanks to the Internet of Things. The proposed research focus on integrating Alexnet and ResNet101 for accurate prediction of Breast malignancy from mammogram data. This methodology will target the features more precisely than any other combination of the pre-trained model. Finally, to resolve the computational burden issue, the feature reduction ReliefF methodology is used. To demonstrate the proposed method, an online publicly released set of data of 750 BU images is used. For training and testing the models, the set of data has been further split into 80 and 20% ratios. Following extensive testing and analysis, it was discovered that the DenseNet-201 and MobileNet-v2 trained SVMs to have an accuracy of 98.39 percent for the original and augmented Mammo images online datasets, respectively. This research discovered that the proposed approach is efficient and simple to implement to assist radiographers and physicians in diagnosing breast cancer in females.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87749568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-05DOI: 10.53759/7669/jmc202303003
Anand N Patil, Sujata V. Mallapur
Intelligent transportation system (ITS) is a technique to improve the driving conditions and safety through collaborative exchange of information between vehicles. Ensuring the authenticity and secure exchange of the events is an important functionality of ITS. Recently blockchain based decentralized solutions are proposed to address event’s authenticity and secure exchange instead of traditional centralized trusted third-party solutions. Along these lines, this work proposes a block chain based decentralized architecture to realize additional functionalities of fine-grained access control to events, revocation of access to events and ensuring the trustworthiness of the events. Block chain along with IPFS is used to realize these functionalities in a fully distributed manner using smart contracts. Performance comparison of proposed solution with state of art demonstrates a better resilience to attacks and comparatively lower execution costs for smart contracts.
{"title":"Integrated Blockchain Manufacturing Design for Distributed Authentication, Validation and Secure Sharing of Events in VANET","authors":"Anand N Patil, Sujata V. Mallapur","doi":"10.53759/7669/jmc202303003","DOIUrl":"https://doi.org/10.53759/7669/jmc202303003","url":null,"abstract":"Intelligent transportation system (ITS) is a technique to improve the driving conditions and safety through collaborative exchange of information between vehicles. Ensuring the authenticity and secure exchange of the events is an important functionality of ITS. Recently blockchain based decentralized solutions are proposed to address event’s authenticity and secure exchange instead of traditional centralized trusted third-party solutions. Along these lines, this work proposes a block chain based decentralized architecture to realize additional functionalities of fine-grained access control to events, revocation of access to events and ensuring the trustworthiness of the events. Block chain along with IPFS is used to realize these functionalities in a fully distributed manner using smart contracts. Performance comparison of proposed solution with state of art demonstrates a better resilience to attacks and comparatively lower execution costs for smart contracts.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"362 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76515582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-05DOI: 10.53759/7669/jmc202303006
Umamaheswaran Arumugam, Suganthi Perumal
An entirely new and trendy peer-to-peer modern communications graph is called a Mobile Ad-hoc Network (MANET). The MANETs form their network without any infrastructure facilities, whenever needed. Military activities frequently need the quick and secure transfer of large quantities of data. The radio spectrum has been used by the military up until now for good communication but might have a chance to impact security problems. The security of data transfer is a major issue given the natural component of wireless networks in real-time situations. The main challenge is confirming trust across MANET nodes, as well as dealing with bandwidth, energy, and changing topology. By degrading the trust level between nodes, the malicious attitude increases poor data transmission, increases energy use, and reduces the duration of the network. To address this issue, we proposed a new protocol, Trust-based Secure and Reliable Routing Protocol (TSRRP), to increase the trust between nodes in MANETs and predict anomalous activity. This is done with the help of certain Quality of Service (QoS) metrics, such as the result analysis phase. NS2 is used to simulate the result. The simulation outcomes demonstrate how the suggested protocol performs better than the existing protocols.
{"title":"Trust based Secure and Reliable Routing Protocol of Military Communication on MANETs","authors":"Umamaheswaran Arumugam, Suganthi Perumal","doi":"10.53759/7669/jmc202303006","DOIUrl":"https://doi.org/10.53759/7669/jmc202303006","url":null,"abstract":"An entirely new and trendy peer-to-peer modern communications graph is called a Mobile Ad-hoc Network (MANET). The MANETs form their network without any infrastructure facilities, whenever needed. Military activities frequently need the quick and secure transfer of large quantities of data. The radio spectrum has been used by the military up until now for good communication but might have a chance to impact security problems. The security of data transfer is a major issue given the natural component of wireless networks in real-time situations. The main challenge is confirming trust across MANET nodes, as well as dealing with bandwidth, energy, and changing topology. By degrading the trust level between nodes, the malicious attitude increases poor data transmission, increases energy use, and reduces the duration of the network. To address this issue, we proposed a new protocol, Trust-based Secure and Reliable Routing Protocol (TSRRP), to increase the trust between nodes in MANETs and predict anomalous activity. This is done with the help of certain Quality of Service (QoS) metrics, such as the result analysis phase. NS2 is used to simulate the result. The simulation outcomes demonstrate how the suggested protocol performs better than the existing protocols.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74414699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-05DOI: 10.53759/7669/jmc202303001
Lavanya Shinangaram, Santhosh Kumar Dhatrika
Corona virus (COVID-19) is an infectious disease, now this COVID-19 pandemic got spread all over the world which causes illness in the respiratory system in humans, it can spread widely in a short time. In this paper the concept of wireless sensor network (WSN) for Internet of things (IoT) is allocated to the healthcare and detection system for COVID-19 is used to design the biomedical sensors with microcontrollers which are used to collect the data, biosensor based low-cost sensitive portable devices for COVID-19 testing kit which is based on Screen printed electrode sensor (SPEs), this is the complete model of health professionals are observe patients information at the ThingSpeak with help of Wi-Fi, Bluetooth module, professionals workload is minimizing to reducing the possibility of the infected COVID-19 condition. the performance of this work is the data is monitored by the patient’s status, the output of these sensors is communicated via wireless sensing node and acquiring for same data has to be send to the remote wireless monitor for the observed patients status via IoT, If in case of any emergency patients can also control the conditions. The stage of infection disease patients can also monitor system data is to inform the medical professionals at the time being finished. Hence the optimistic results show that the biomedical sensors and SPEs are in beneficial process for identification of COVID-19 so it can be situating the results on ThingSpeak and Bluetooth module, The clinical centers to help conditions behind its conformation with additional biomedical sensors.
{"title":"Detection of COVID-19 Using Screen Printed Electrode based Biosensor","authors":"Lavanya Shinangaram, Santhosh Kumar Dhatrika","doi":"10.53759/7669/jmc202303001","DOIUrl":"https://doi.org/10.53759/7669/jmc202303001","url":null,"abstract":"Corona virus (COVID-19) is an infectious disease, now this COVID-19 pandemic got spread all over the world\u0000which causes illness in the respiratory system in humans, it can spread widely in a short time. In this paper the concept of\u0000wireless sensor network (WSN) for Internet of things (IoT) is allocated to the healthcare and detection system for COVID-19\u0000is used to design the biomedical sensors with microcontrollers which are used to collect the data, biosensor based low-cost\u0000sensitive portable devices for COVID-19 testing kit which is based on Screen printed electrode sensor (SPEs), this is the\u0000complete model of health professionals are observe patients information at the ThingSpeak with help of Wi-Fi, Bluetooth\u0000module, professionals workload is minimizing to reducing the possibility of the infected COVID-19 condition. the performance\u0000of this work is the data is monitored by the patient’s status, the output of these sensors is communicated via wireless sensing\u0000node and acquiring for same data has to be send to the remote wireless monitor for the observed patients status via IoT, If in\u0000case of any emergency patients can also control the conditions. The stage of infection disease patients can also monitor system\u0000data is to inform the medical professionals at the time being finished. Hence the optimistic results show that the biomedical\u0000sensors and SPEs are in beneficial process for identification of COVID-19 so it can be situating the results on ThingSpeak and\u0000Bluetooth module, The clinical centers to help conditions behind its conformation with additional biomedical sensors.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90919223","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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