2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)最新文献_第3页

An Efficiency Random Forest Algorithm for Classification of Patients with Kidney Dysfunction 肾功能不全患者分类的高效随机森林算法

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376785

Narumol Chumuang, Nuttawoot Meesang, M. Ketcham, Worawut Yimyam, Jiragorn Chalermdit, Nawarat Wittayakhom, Patiyuth Pramkeaw

引用次数: 13

A Framework of IoT Platform for Autonomous Mobile Robot in Hospital Logistics Applications 自主移动机器人在医院物流应用中的物联网平台框架

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376823

K. Thamrongaphichartkul, N. Worrasittichai, Teeraya Prayongrak, S. Vongbunyong

引用次数: 6

Enhancing and Evaluating an Impact of OCR and Ontology on Financial Document Checking Process 增强和评估OCR和本体对财务文件审核过程的影响

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376808

Worawut Yimyam, M. Ketcham, Tanapon Jensuttiwetchakult, Sansanee Hiranchan, Patiyuth Pramkeaw, Narumol Chumuang

引用次数: 10

Developed Credit Card Fraud Detection Alert Systems via Notification of LINE Application 通过LINE应用通知开发信用卡欺诈检测警报系统

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/isai-nlp51646.2020.9376829

Narumol Chumuang, Sansanee Hiranchan, M. Ketcham, Worawut Yimyam, Patiyuth Pramkeaw, Sakchai Tangwannawit

{"title":"Developed Credit Card Fraud Detection Alert Systems via Notification of LINE Application","authors":"Narumol Chumuang, Sansanee Hiranchan, M. Ketcham, Worawut Yimyam, Patiyuth Pramkeaw, Sakchai Tangwannawit","doi":"10.1109/isai-nlp51646.2020.9376829","DOIUrl":"https://doi.org/10.1109/isai-nlp51646.2020.9376829","url":null,"abstract":"As nowadays, prevention of fraud is another important issue, researcher have initiated the idea of applying suspicious frauds in credit cards to line application. The objectives of this research are: 1) for developing the suspected credit card fraud via API LINE Notify. 2) Measure the accuracy of the developed system in the notification to prevent suspicious fraud credit card. The measurement method is comprised of five steps which are: 1) Analysis of work systems is a study and analysis of problems to determine needs. 2) System design is the process of designing research tools. 3) Developing a system is the process of developing research tools. 4) A test of the tools is executed 5) Summary of results, discussion results, and suggestions. The measurement results of efficiency, accuracy, and completeness of the data were in a very good level, equal to 86.67%. The results of the measurement of efficiency to the conditions set are very good, equal to 80.00 %. The results of the measurement on time very good, equal to 86.67%. In conclusion, the developed system accomplishes all research goals.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122023929","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: 13

Optimization of Prediction Method of Chronic Kidney Disease Using Machine Learning Algorithm 基于机器学习算法的慢性肾病预测方法优化

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376787

Pronab Ghosh, F M Javed Mehedi Shamrat, Shahana Shultana, Saima Afrin, A. Anjum, Aliza Ahmed Khan

{"title":"Optimization of Prediction Method of Chronic Kidney Disease Using Machine Learning Algorithm","authors":"Pronab Ghosh, F M Javed Mehedi Shamrat, Shahana Shultana, Saima Afrin, A. Anjum, Aliza Ahmed Khan","doi":"10.1109/iSAI-NLP51646.2020.9376787","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376787","url":null,"abstract":"Chronic Kidney disease (CKD), a slow and late-diagnosed disease, is one of the most important problems of mortality rate in the medical sector nowadays. Based on this critical issue, a significant number of men and women are now suffering due to the lack of early screening systems and appropriate care each year. However, patients’ lives can be saved with the fast detection of disease in the earliest stage. In addition, the evaluation process of machine learning algorithm can detect the stage of this deadly disease much quicker with a reliable dataset. In this paper, the overall study has been implemented based on four reliable approaches, such as Support Vector Machine (henceforth SVM), AdaBoost (henceforth AB), Linear Discriminant Analysis (henceforth LDA), and Gradient Boosting (henceforth GB) to get highly accurate results of prediction. These algorithms are implemented on an online dataset of UCI machine learning repository. The highest predictable accuracy is obtained from Gradient Boosting (GB) Classifiers which is about to 99.80% accuracy. Later, different performance evaluation metrics have also been displayed to show appropriate outcomes. To end with, the most efficient and optimized algorithms for the proposed job can be selected depending on these benchmarks.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114580558","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: 33

Face Detection System for Public Transport Service Based on Scale-Invariant Feature Transform 基于尺度不变特征变换的公交服务人脸检测系统

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376819

Narumol Chumuang, Sansanee Hiranchan, M. Ketcham, Worawut Yimyam, Patiyuth Pramkeaw, Tanapon Jensuttiwetchakult

引用次数: 14

A Conversational Agent for Database Query: A Use Case for Thai People Map and Analytics Platform 数据库查询的会话代理:泰国人地图和分析平台的用例

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376833

Thikamporn Simud, S. Ruengittinun, Navaporn Surasvadi, Nuttapong Sanglerdsinlapachai, Anon Plangprasopchok

{"title":"A Conversational Agent for Database Query: A Use Case for Thai People Map and Analytics Platform","authors":"Thikamporn Simud, S. Ruengittinun, Navaporn Surasvadi, Nuttapong Sanglerdsinlapachai, Anon Plangprasopchok","doi":"10.1109/iSAI-NLP51646.2020.9376833","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376833","url":null,"abstract":"Since 2018, Thai People Map and Analytics Platform (TPMAP) has been developed with the aims of supporting government officials and policy makers with integrated household and community data to analyze strategic plans, implement policies and decisions to alleviate poverty. However, to acquire complex information from the platform, non-technical users with no database background have to ask a programmer or a data scientist to query data for them. Such a process is time-consuming and might result in inaccurate information retrieved due to miscommunication between non-technical and technical users. In this paper, we have developed a Thai conversational agent on top of TPMAP to support self-service data analytics on complex queries. Users can simply use natural language to fetch information from our chatbot and the query results are presented to users in easy-to-use formats such as statistics and charts. The proposed conversational agent retrieves and transforms natural language queries into query representations with relevant entities, query intentions, and output formats of the query. We employ Rasa, an open-source conversational AI engine, for agent development. The results show that our system yields Fl-score of 0.9747 for intent classification and 0.7163 for entity extraction. The obtained intents and entities are then used for query target information from a graph database. Finally, our system achieves end-to-end performance with accuracies ranging from 57.5%-80.0%, depending on query message complexity. The generated answers are then returned to users through a messaging channel.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120981089","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

Comparison of Face Classification with Single and Multi-model base on CNN 基于CNN的单模型与多模型人脸分类比较

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376825

S. Watcharabutsarakham, Supphachoke Suntiwichaya, Chanchai Junlouchai, Apichon Kitvimorat

引用次数: 2

Survey of Query correction for Thai business-oriented information retrieval 面向业务的泰语信息检索查询纠错研究

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376809

Phongsathorn Kittiworapanya, Nuttapong Saelek, Anuruth Lertpiya, Tawunrat Chalothorn

引用次数: 1

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/isai-nlp51646.2020.9376836

引用次数: 0