2021 3rd International Conference on Advancements in Computing (ICAC)最新文献_第3页

Virtual Dressing Room: Smart Approach to Select and Buy Clothes 虚拟试衣间:选择和购买衣服的智能方法

2021 3rd International Conference on Advancements in Computing (ICAC) Pub Date : 2021-12-09 DOI: 10.1109/ICAC54203.2021.9671198

Weerasinghe S. W. P. N. M., Rajapaksha R. M. D. D., Sathsara L. G. I., Gunasekara H. S. D. N., D. Wijendra, D. D. De Silva

{"title":"Virtual Dressing Room: Smart Approach to Select and Buy Clothes","authors":"Weerasinghe S. W. P. N. M., Rajapaksha R. M. D. D., Sathsara L. G. I., Gunasekara H. S. D. N., D. Wijendra, D. D. De Silva","doi":"10.1109/ICAC54203.2021.9671198","DOIUrl":"https://doi.org/10.1109/ICAC54203.2021.9671198","url":null,"abstract":"The clothing industry portrays a major part of a respective country`s economy. Due to the predilection for clothing items of the people have led to the increasing of physical and online clothing stores in all around the world. Most of the people are used to go to the physical shopping and purchase their desired clothing items. But, as a consequence of the current pandemic situation, most of the people are unable to step out from their homes. This application is intended to cater an opportunity to the customers, who are not able to reach the physical clothing stores due to a pandemic situation and mobility difficulties. In addition, this application diminishes the time wastage, clothing size mismatches and the lesser user satisfaction ratio inside a physical clothing store. A customized 3D model has featured in the application to cater the virtual fitting experience to the customer. And the AI chatbot assistant in the application interacts with the user while catering virtual assistance for a better cloth selection process. In addition to that, this application has concentrated on the clothing shop by providing a future sales prediction component utilizing the K-Nearest Neighbors algorithm to provide an aid to their business commitments.","PeriodicalId":227059,"journal":{"name":"2021 3rd International Conference on Advancements in Computing (ICAC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121376986","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}

引用次数: 2

IoT-based Monitoring System for Oyster Mushroom Farming 基于物联网的香菇养殖监控系统

2021 3rd International Conference on Advancements in Computing (ICAC) Pub Date : 2021-12-09 DOI: 10.1109/ICAC54203.2021.9671112

Y. D. Surige, Perera W. S. M, Gunarathna P. K. N, Ariyarathna K. P. W, N. Gamage, D. Nawinna

{"title":"IoT-based Monitoring System for Oyster Mushroom Farming","authors":"Y. D. Surige, Perera W. S. M, Gunarathna P. K. N, Ariyarathna K. P. W, N. Gamage, D. Nawinna","doi":"10.1109/ICAC54203.2021.9671112","DOIUrl":"https://doi.org/10.1109/ICAC54203.2021.9671112","url":null,"abstract":"Agriculture plays a major segment in the economy of Sri Lanka, a developing country. Mushrooms, farming is a popular option among the farmers as it consumes less space and less time for growing while offering a high nutritional value, but most farmers fail to obtain the best yield from their cultivations due to the defects and inefficiencies in the manual methods that are being presently used. This paper presents an ICT solution to avoid inefficiencies in the mushroom farming process. The system is developed focusing one of the popular mushroom type ‘Oyster Mushrooms’. The system offers four functionalities to perform mushroom farming precisely The system offers four functionalities to perform mushroom farming precisely. The Environmental Monitoring function is built with the support of a Long Short Term Memory (LSTM), Harvest time detection function is developed with the support of Convolutional Neural Networks (CNN) with Mobile Net V2 model, The Disease detection and control recommendation function is based on the support of CNN with mobile Net V2 model and the Yield prediction function is developed using the support of Long Short Term Memory (LSTM), The farmer is connected to the system through a mobile application. The system can monitor the environmental factors with an accuracy of 89% and the harvest time can be detected with an accuracy of 92%. Also, the system detects the mushroom diseases with an accuracy of 99% and predicts the monthly yield of a mushroom cultivation with an accuracy of 97%. The intense use of precise farming will eventually lead to high mushroom yields.","PeriodicalId":227059,"journal":{"name":"2021 3rd International Conference on Advancements in Computing (ICAC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127798976","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}

引用次数: 2

Dynamic User Interface Personalization Based on Deep Reinforcement Learning 基于深度强化学习的动态用户界面个性化

2021 3rd International Conference on Advancements in Computing (ICAC) Pub Date : 2021-12-09 DOI: 10.1109/ICAC54203.2021.9671076

K. Silva, W. A. P. S. Abeyasekare, D. Dasanayake, T. B. Nandisena, D. Kasthurirathna, Archchana Kugathasan

{"title":"Dynamic User Interface Personalization Based on Deep Reinforcement Learning","authors":"K. Silva, W. A. P. S. Abeyasekare, D. Dasanayake, T. B. Nandisena, D. Kasthurirathna, Archchana Kugathasan","doi":"10.1109/ICAC54203.2021.9671076","DOIUrl":"https://doi.org/10.1109/ICAC54203.2021.9671076","url":null,"abstract":"Personalization is one of the most sought out and popular methods for brand recognition and consumer attraction. The usage of deep reinforcement learning due to its' ability to learn actions the way humans learn from experience, if utilized and evaluated properly it can result in a revolutionary effect on personalization. The methodology proposed in this research utilizes deep reinforcement learning where an artificial agent may be trained by interacting with its environment. Utilizing the experience gathered, the agent is able optimize in the form of rewards. The approach explained, can be utilized across applications which can be personalized. Several scenarios ranging from changing the layout of webpages, to rearranging icons on mobile home screens are discussed. The main objective is to develop an API for the web developers and smartphone manufacturers to utilize so that depending on the application personalization can be achieved by enhancing saliency, minimizing selection time, increasing engagement, or an arrangement of these. The technique can manage a variety of adaptations, such as how graphical elements are shown and how they behave. An experiment was conducted which showcased improved user experience considering the position change of the user interface elements thereby personalizing the layout.","PeriodicalId":227059,"journal":{"name":"2021 3rd International Conference on Advancements in Computing (ICAC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132441590","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}

引用次数: 1

Human and Organizational Threat Profiling Using Machine Learning 使用机器学习的人类和组织威胁分析

2021 3rd International Conference on Advancements in Computing (ICAC) Pub Date : 2021-12-09 DOI: 10.1109/ICAC54203.2021.9671194

P. M. I. N. Kumara, K. G. S. Dananjaya, N. Amarasena, H. M. S. Pinto, K. Yapa, L. Rupasinghe

{"title":"Human and Organizational Threat Profiling Using Machine Learning","authors":"P. M. I. N. Kumara, K. G. S. Dananjaya, N. Amarasena, H. M. S. Pinto, K. Yapa, L. Rupasinghe","doi":"10.1109/ICAC54203.2021.9671194","DOIUrl":"https://doi.org/10.1109/ICAC54203.2021.9671194","url":null,"abstract":"The usage of online social networking sites is increasing rapidly. But the downside is that the growth of various kinds of ongoing social media threats such as fake profiles, cyberbullying, and fake news. Many important observations can be made to increase the existing knowledge about social media threats by studying various information exchanged through public and organizations. One direction is to conduct studies on human behavior and personality traits using public user profile data and the organizational threat classifying. This research aims to build a system to predict human personality behaviors on social media profiles based on the OCEAN Model and company-based threat profiling. All the data collected relating to everyone in the consumer’s friend list is analyzed to obtain the threatening behaviors and classified according to the OCEAN to generate a threat report. Organizational network gathered log data for filtered log protection against malware. Logs received from these endpoints will be collected by collectors. Those logs will be forwarded to our filter, made of a Machine Learning Algorithm (MLA). This will be a custom MLA specially designed for this purpose. MLA will classify and categorize threats according to their severity, filtered log protection system against malware and other threats.","PeriodicalId":227059,"journal":{"name":"2021 3rd International Conference on Advancements in Computing (ICAC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132480960","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}

引用次数: 0

LUMOZ – A Real Time Augmented Reality Based Tool for Media Production LUMOZ -一个实时增强现实的媒体制作工具

2021 3rd International Conference on Advancements in Computing (ICAC) Pub Date : 2021-12-09 DOI: 10.1109/ICAC54203.2021.9671102

M. P. W. P. A. Wanigasekara, A. Vihanga Nivarthana, R. M. B. awantha Thilan, G. M. J. U. Gankanda, Thusithanjana Thilakarthna, Shyam Reyal

{"title":"LUMOZ – A Real Time Augmented Reality Based Tool for Media Production","authors":"M. P. W. P. A. Wanigasekara, A. Vihanga Nivarthana, R. M. B. awantha Thilan, G. M. J. U. Gankanda, Thusithanjana Thilakarthna, Shyam Reyal","doi":"10.1109/ICAC54203.2021.9671102","DOIUrl":"https://doi.org/10.1109/ICAC54203.2021.9671102","url":null,"abstract":"Globalization has created vast competition among media content productions. Gaining an audience for these products depends on the quality, correctness, timely content and should be produced according to the target audience’s comprehension level. The aforementioned factors can be achieved by using new technology in the process of content production. This trend also affects the local news productions. At present, news delivery and other digital media creations are transforming from traditional delivery methods into more advanced technology-based delivery. For this transformation, Augmented Reality and 3D technologies play significant roles which help improving content’s attractiveness and correctness. And using this new technology can create a new fan base and increase the popularity of the content. The use of these technologies in an industrial base or startup base still provides many challenges. LUMOZ provides a solution for these challenges. This tool provides easier access to point tracking,3D object placement, gesture controls, 3D data visualization, and 3D model library. Simply LUMOZ can be considered as a budget-friendly and time-efficient tool that can handle 3D objects in real-time in a live streaming environment.","PeriodicalId":227059,"journal":{"name":"2021 3rd International Conference on Advancements in Computing (ICAC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133498961","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}

引用次数: 0

Application of RFID and IoT technology into specimen logistic system in the healthcare sector RFID和物联网技术在医疗行业标本物流系统中的应用

2021 3rd International Conference on Advancements in Computing (ICAC) Pub Date : 2021-12-09 DOI: 10.1109/ICAC54203.2021.9671088

Mya Myet Thwe Chit, Wattanasak Srisiri, A. Siritantikorn, N. Kongruttanachok, W. Benjapolakul

{"title":"Application of RFID and IoT technology into specimen logistic system in the healthcare sector","authors":"Mya Myet Thwe Chit, Wattanasak Srisiri, A. Siritantikorn, N. Kongruttanachok, W. Benjapolakul","doi":"10.1109/ICAC54203.2021.9671088","DOIUrl":"https://doi.org/10.1109/ICAC54203.2021.9671088","url":null,"abstract":"The invention and innovation of RFID technology changed the world and many sectors (such as logistics, railways, healthcare, and so on) are now deployed with RFID technology instead of using barcode systems. With the numerous advantages, Radio Frequency Identification (RFID) got many expectations in the healthcare sector. The main objective of this research work is to implement the RFID technology in Specimen collection in the healthcare sector and the IoT (Internet of Things) network supports the transaction while the specimen test box is being delivered. The system uses a Sparkfun RFID reader to read/write patient information to the Gen2 RFID tag, which is attached to the test tube collected from the patients. When the test box is delivered to another laboratory, we develop an IoT network to know the box’s temperature, humidity, and GPS location instantly, with the help of an NB-IoT shield. The major advantage of the combination between IoT technology and RFID is that the management of test box overall condition becomes much easier. To summarize, this method is highly competent in identifying the location of medical devices in real-time and reduces the time-consuming of data logging than the barcode system.","PeriodicalId":227059,"journal":{"name":"2021 3rd International Conference on Advancements in Computing (ICAC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114435893","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}

引用次数: 2

Symptomatic Analysis Prediction of Kidney Related Diseases using Machine Learning 基于机器学习的肾脏相关疾病症状分析预测

2021 3rd International Conference on Advancements in Computing (ICAC) Pub Date : 2021-12-09 DOI: 10.1109/ICAC54203.2021.9671129

Dulitha Lansakara, Thinusha Gunasekera, Chamara Niroshana, Imali Weerasinghe, P. Bandara, D. Wijendra

{"title":"Symptomatic Analysis Prediction of Kidney Related Diseases using Machine Learning","authors":"Dulitha Lansakara, Thinusha Gunasekera, Chamara Niroshana, Imali Weerasinghe, P. Bandara, D. Wijendra","doi":"10.1109/ICAC54203.2021.9671129","DOIUrl":"https://doi.org/10.1109/ICAC54203.2021.9671129","url":null,"abstract":"Sri Lanka has been witnessing an increase in kidney disease issues for a while. Elderly kidney patients, kidney transplant patients who passed the risk level after the surgery are not treated in the emergency clinic. These patients are handed over to their families to take care of them. In any case, it is impossible to tackle a portion of the issues that emerge regarding the patient at home. It is hoped to enter patient’s data from home every day and to develop a system that can use that entered data to predict whether a patient is in an essential circumstance or not. Additionally, individuals in high-hazard regions cannot know whether they are in danger of creating kidney disappointments or not and individuals in danger of creating kidney sickness because of Diabetes Mellitus. Thus, we desire to emphasize the framework to improve answers for this issue. The research focuses on developing a system that includes early kidney disease prediction models involving machine learning classification algorithms by considering the relevant variables. In predictive analysis, six machine learning methods are used: Support Vector Machine (SVM with kernels), Random Forest (RF), Decision Tree, Logistic Regression, and Multilayer Perceptron. These classification algorithms' performance is evaluated using statistical measures such as sensitivity (recall), precision, accuracy, and F-score. In categorizing, accuracy determines which examples are accurate. The experimental results reveal that Support Vector Machine outperforms other classification algorithms in terms of accuracy.","PeriodicalId":227059,"journal":{"name":"2021 3rd International Conference on Advancements in Computing (ICAC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121649905","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}

引用次数: 0

Sales Optimization Solution for Fashion Retail 时尚零售销售优化解决方案

2021 3rd International Conference on Advancements in Computing (ICAC) Pub Date : 2021-12-09 DOI: 10.1109/ICAC54203.2021.9671152

N. Ganhewa, S. M. L. B. Abeyratne, G. Chathurika, Dilani Lunugalage, D. D. De Silva

{"title":"Sales Optimization Solution for Fashion Retail","authors":"N. Ganhewa, S. M. L. B. Abeyratne, G. Chathurika, Dilani Lunugalage, D. D. De Silva","doi":"10.1109/ICAC54203.2021.9671152","DOIUrl":"https://doi.org/10.1109/ICAC54203.2021.9671152","url":null,"abstract":"The Fashion industry is one of the extensive, changeable, and growing businesses to exist. It encompasses fashion retailing which functions as a mediator between the manufacturers and clients. On account of the inconsistency of this industry, maximizing sales has been a crucial task. The objective of this research study is to analyze and explore product and consumer behavior and thereby maximize sales in the fashion retail industry for women’s clothing to overcome the struggles regarding gaining sales confronted by the industry. The emergence of big data and machine learning has a positive influence on fashion retailing. ML has been utilized in this research to implement a web application that aids in optimizing sales. It comprehends sales forecasting, customer segmentation, and customer demand analytics. Each research component obtains diverse inputs to initialize the prediction and visualization procedure. The models are built employing the Extra Trees Regressor algorithm, K-means algorithm, and Naïve Bayes algorithm. Finally, for specified inputs, results will be predicted that comprise sales forecasts for products, segmentation of consumers, and forecasts about most demanded fashion item’s characteristics. This paper portrays the proceedings of data preparation, model development, and results of each research component.","PeriodicalId":227059,"journal":{"name":"2021 3rd International Conference on Advancements in Computing (ICAC)","volume":"304 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123205743","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}

引用次数: 2

Computer Vision Enabled Drowning Detection System 计算机视觉溺水检测系统

2021 3rd International Conference on Advancements in Computing (ICAC) Pub Date : 2021-12-09 DOI: 10.1109/ICAC54203.2021.9671126

Upulie Handalage, Nisansali Nikapotha, Chanaka Subasinghe, Tereen Prasanga, Thusithanjana Thilakarthna, D. Kasthurirathna

{"title":"Computer Vision Enabled Drowning Detection System","authors":"Upulie Handalage, Nisansali Nikapotha, Chanaka Subasinghe, Tereen Prasanga, Thusithanjana Thilakarthna, D. Kasthurirathna","doi":"10.1109/ICAC54203.2021.9671126","DOIUrl":"https://doi.org/10.1109/ICAC54203.2021.9671126","url":null,"abstract":"Safety is paramount in all swimming pools. The current systems expected to address the problem of ensuring safety at swimming pools have significant problems due to their technical aspects, such as underwater cameras and methodological aspects such as the need for human intervention in the rescue mission. The use of an automated visual-based monitoring system can help to reduce drownings and assure pool safety effectively. This study introduces a revolutionary technology that identifies drowning victims in a minimum amount of time and dispatches an automated drone to save them. Using convolutional neural network (CNN) models, it can detect a drowning person in three stages. Whenever such a situation like this is detected, the inflatable tube-mounted self-driven drone will go on a rescue mission, sounding an alarm to inform the nearby lifeguards. The system also keeps an eye out for potentially dangerous actions that could result in drowning. This system’s ability to save a drowning victim in under a minute has been demonstrated in prototype experiments' performance evaluations.","PeriodicalId":227059,"journal":{"name":"2021 3rd International Conference on Advancements in Computing (ICAC)","volume":"41 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131452730","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}

引用次数: 2

Enhance the Safety Measurements in Railways with the Aid of IoT and Image Processing 借助物联网和图像处理加强铁路安全措施

2021 3rd International Conference on Advancements in Computing (ICAC) Pub Date : 2021-12-09 DOI: 10.1109/ICAC54203.2021.9671190

Vedasingha K.S, Perera K. K. M. T., Hathurusinghe K. I., Akalanka H. W. I., N.C Amarasena, N. R. Dissanayake

{"title":"Enhance the Safety Measurements in Railways with the Aid of IoT and Image Processing","authors":"Vedasingha K.S, Perera K. K. M. T., Hathurusinghe K. I., Akalanka H. W. I., N.C Amarasena, N. R. Dissanayake","doi":"10.1109/ICAC54203.2021.9671190","DOIUrl":"https://doi.org/10.1109/ICAC54203.2021.9671190","url":null,"abstract":"Railways provide the most convenient and economically beneficial mode of transportation, and it has been the most popular transportation method. According to the past analyzed data, it reveals a considerable number of accidents which occurred at railways, caused damages to not only precious lives but also to the economy. The goal of this research is to minimize the railway accidents by developing \"Railway Process Automation System\" while ensuring human safety with use of Internet of Things (IoT) and image processing techniques. The system can detect the current location of the train and close the railway gate automatically. As usual, if the system fails to close the rail gate due to any failure, the proposed system can identify the current location and close the rail gate through decision making system by using past data. The proposed system introduces further two features which named as Railway track crack detection and motion detection which play a significant role in reducing the risk of railway accidents. Moreover, the system is capable of detecting rule violations at a level crossing by using sensors. The proposed system is implemented through a prototype and tested with real-world scenarios to gain the above 90% of accuracy.","PeriodicalId":227059,"journal":{"name":"2021 3rd International Conference on Advancements in Computing (ICAC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134017124","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}

引用次数: 0