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

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[iSAI-NLP 2020 Keynote Speakers - 3 abstracts] [iSAI-NLP 2020主题演讲- 3个摘要]
2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/isai-nlp51646.2020.9376827
{"title":"[iSAI-NLP 2020 Keynote Speakers - 3 abstracts]","authors":"","doi":"10.1109/isai-nlp51646.2020.9376827","DOIUrl":"https://doi.org/10.1109/isai-nlp51646.2020.9376827","url":null,"abstract":"","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"31 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":"116782855","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
Face Recognition System for Financial Identity Theft Protection 人脸识别系统在金融身份盗窃保护中的应用
2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376826
Thidarat Pinthong, Worawut Yimyam, Narumol Chumuang, M. Ketcham
{"title":"Face Recognition System for Financial Identity Theft Protection","authors":"Thidarat Pinthong, Worawut Yimyam, Narumol Chumuang, M. Ketcham","doi":"10.1109/iSAI-NLP51646.2020.9376826","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376826","url":null,"abstract":"the paper propose a part of image processing for applying on security and authentication. This system looks to be about image processing techniques applied to work in security and data access authentication information through application on mobile. In regard to financial transactions by using Eigen face for face recognition to process and verify its validity. The paper is an idea for prototype applications, the system may not be complete and correct, it can be used practically. However, our work will provide the technical know-only.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"334 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":"123329282","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
Utilization-Weighted Algorithm for Spreading Factor Assignment in LoRaWAN LoRaWAN中传播因子分配的利用加权算法
2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376786
Kasama Kamonkusonman, R. Silapunt
{"title":"Utilization-Weighted Algorithm for Spreading Factor Assignment in LoRaWAN","authors":"Kasama Kamonkusonman, R. Silapunt","doi":"10.1109/iSAI-NLP51646.2020.9376786","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376786","url":null,"abstract":"Long Range Wide Area Network (LoRaWAN) is one of the leading low power wireless networks that can support thousands of Internet of Things (IoT) devices. To enhance the scalability of LoRaWAN, this paper proposes the UtilizationWeighted (UW) algorithm, which is the spreading factor management algorithm designed based on the M/D/1 queue theory. The main concept of this algorithm is channel utilization balancing that helps form groups of nodes assigned with different spreading factors (SFs). The simulations are performed under two scenarios that are similar and various uplink time interval among SFs. The results show that our UW algorithm can outperform the traditional Min-airtime method in both scenarios. The packet received rate (PRR) of the UW algorithm is clearly higher than that of the Min-airtime method for all number of nodes and time intervals. Especially in the various time interval simulation of the networks of 120, 600, and 1,200 nodes, the maximum PRR improvements occur at 1, 3, and 5 times of the minimum time interval between uplinks, T0ffl, respectively, and are around 34%, 36%, and 35%, respectively.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"55 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":"134009740","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
A Machine Learning Approach for the Classification of Methamphetamine Dealers on Twitter in Thailand. 泰国Twitter上甲基苯丙胺经销商分类的机器学习方法。
2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376817
Punnavich Khowrurk, R. Kongkachandra
{"title":"A Machine Learning Approach for the Classification of Methamphetamine Dealers on Twitter in Thailand.","authors":"Punnavich Khowrurk, R. Kongkachandra","doi":"10.1109/iSAI-NLP51646.2020.9376817","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376817","url":null,"abstract":"This research presents a method to classify messages from Twitter (tweet) related to Methamphetamine. The messages are classified into three classes: normal, seller, buyer. The models presented in this research are Multinomial Naive Bayes, Multi-Class LSTM, and Hierarchical LSTM. Model training uses a balanced and imbalanced dataset. The text used for Model training is tokenized from four tokenizers: Tlex+, Lexto+, Attacut, and Deepcut. To study the model performance’s effect, we divide the data with a different dataset and tokenizer. The results showed that all models could classify the messages into the three classes. The most effective model built from a balanced dataset is the Hierarchical LSTM model using the Lexto+ Tokenizer provides the highest Accuracy, and the most effective model build from an imbalanced dataset is the Multi-Class LSTM model using the Lexto+ Tokenizer. This model gave the highest Accuracy, but the Fl-Score of the Hierarchical LSTM model gave better Accuracy in each class.The creation of a text classification model related to Methamphetamine uses Twitter messages. Most of them are Thai grammatical errors and has many slang usage. We found that Lexto+ is the best tokenizer to build a model. However, it is not much different from other tokenizers. On the other hand, the best dataset to build the model is a balanced dataset that significantly affects model performance.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"16 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133136503","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
Cryptocurrencies Asset Pricing Analysis: evidence from Thailand markets 加密货币资产定价分析:来自泰国市场的证据
2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376813
Kanyawut Ariya, Nathee Naktnasukanjn, Tanarat Rattanadamrongaksorn, Piyachat Udomwong, Saronsad Sokantika, N. Chakpitak
{"title":"Cryptocurrencies Asset Pricing Analysis: evidence from Thailand markets","authors":"Kanyawut Ariya, Nathee Naktnasukanjn, Tanarat Rattanadamrongaksorn, Piyachat Udomwong, Saronsad Sokantika, N. Chakpitak","doi":"10.1109/iSAI-NLP51646.2020.9376813","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376813","url":null,"abstract":"Can cryptocurrencies price variations be explained by exogenous classical market prices? We evaluate this issue by using daily data on some of the most important asset prices and indexes in Thailand i.e. Gold, Oil, SET50 index, Tourism index, Mutual fund, and THB/USD exchange rate in comparison with digital asset prices i.e. Bitcoin, Ethereum, Litecoin, Ripple, DASH, and Stellar. By performing both direct and inverse relationships using correlation matrix to find distance relationship and using minimum spanning tree to find the closest path between assets, we found strong direct relationship among cryptocurrencies in digital market with SET50 index and oil price in classical markets. We also found that THB-USD exchange rate has inverse relationship with Bitcoin price, SET50 index and oil price. There is a link between cryptocurrencies asset price and some classical assets’ market price.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"23 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":"124077992","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
iSAI-NLP 2020 Committee
2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/isai-nlp51646.2020.9376831
{"title":"iSAI-NLP 2020 Committee","authors":"","doi":"10.1109/isai-nlp51646.2020.9376831","DOIUrl":"https://doi.org/10.1109/isai-nlp51646.2020.9376831","url":null,"abstract":"","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"24 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":"131468575","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
Real-life Human Activity Recognition with Tri-axial Accelerometer Data from Smartphone using Hybrid Long Short-Term Memory Networks 使用混合长短期记忆网络的智能手机三轴加速度计数据识别现实生活中的人类活动
2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376839
Narit Hnoohom, A. Jitpattanakul, S. Mekruksavanich
{"title":"Real-life Human Activity Recognition with Tri-axial Accelerometer Data from Smartphone using Hybrid Long Short-Term Memory Networks","authors":"Narit Hnoohom, A. Jitpattanakul, S. Mekruksavanich","doi":"10.1109/iSAI-NLP51646.2020.9376839","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376839","url":null,"abstract":"Human activity recognition (HAR) has an enthusiastic research field in time-series classification due to its variation of successful applications in various domains. The availability of affordable wearable devices have provided many challenging and interesting research HAR problems. Current researches suggest that deep learning approaches are suited to automated feature extraction from raw sensor data, instead of conventional machine learning approaches that reply on handcrafted features. Based on the recent success of Long Short-Term Memory (LSTM) networks for HAR domains, this work proposes a generic framework for accelerometer data based on LSTM networks for real-life HAR. Four hybrid LSTM networks have been comparatively studied on a public available real-life HAR dataset. Moreover, we take advantage of Bayesian optimization techniques for tuning hyperparameter of each LSTM networks. The experimental results indicate that the CNN-LSTM network surpasses other hybrid LSTM networks.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"27 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":"121514328","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: 19
A proposal of evaluation method using a pressure sensor for supporting auscultation training 一种使用压力传感器辅助听诊训练的评估方法的建议
2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376779
Yuki Kodera, Kunimasa Yagi, M. Shikida
{"title":"A proposal of evaluation method using a pressure sensor for supporting auscultation training","authors":"Yuki Kodera, Kunimasa Yagi, M. Shikida","doi":"10.1109/iSAI-NLP51646.2020.9376779","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376779","url":null,"abstract":"Japanese medical education has been focused on improving clinical skills lately. In clinical training, there are many training such as medical interview, palpation, and auscultation. However, assessment points of these training are not quantified. Therefore, it is difficult for a trainer to check clinical skills and attitudes of student doctors objectively. Auscultation is a fundamental skill, but it is difficult to assess objectively and, therefore, difficult to give appropriate feedback. In this paper, we proposed an evaluation method for auscultation pressure using a pressure sensor for a purpose of supporting auscultation training, which is one kind of clinical training. In addition, we implemented a prototype system, and collected pressure values during an actual doctor’s examination. Moreover, We discussed feature extraction method for supporting auscultation training from the collected data. Furthermore, we described that the proposed method is useful as one of ways for supporting the auscultation training.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"106 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":"122601409","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
A Memetic Algorithm for Tour Trip Design Problem 旅游行程设计问题的模因算法
2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376815
Apisit Cheng, A. Dumrongsiri
{"title":"A Memetic Algorithm for Tour Trip Design Problem","authors":"Apisit Cheng, A. Dumrongsiri","doi":"10.1109/iSAI-NLP51646.2020.9376815","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376815","url":null,"abstract":"to design a tour plan which provide a maximum satisfaction, before have any experiences with the destination can be hard and time consuming process. The goal of this study is to create an algorithm that efficiently generate a tour plan with high or maximum satisfaction within a reasonable processing time. The memetic algorithm which is a combination of genetics algorithm and local search algorithm would be created to solve this problem. This study used real data gathered from trusted tourist community in Thailand such as TripAdvisor.com, Wongnai.com, etc. The result of this study shown Memetic Algorithm (MA) approach could solve tour trip design problem efficiently since both saving in computation time and % gap are in a good shape and well-balanced.","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":"127075570","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
Simulation of Autonomous Mobile Robot System for Food Delivery in In-patient Ward with Unity 基于Unity的病房送餐自主移动机器人系统仿真
2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376784
S. Vongbunyong, Subarna Tripathi, K. Thamrongaphichartkul, N. Worrasittichai, A. Takutruea, Teeraya Prayongrak
{"title":"Simulation of Autonomous Mobile Robot System for Food Delivery in In-patient Ward with Unity","authors":"S. Vongbunyong, Subarna Tripathi, K. Thamrongaphichartkul, N. Worrasittichai, A. Takutruea, Teeraya Prayongrak","doi":"10.1109/iSAI-NLP51646.2020.9376784","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376784","url":null,"abstract":"Logistic management is crucial for effective and efficient transportation of various items in hospitals. During pandemic situations, especially COVID-19, special in-patient cohort ward is established to treat patients who require special treatment due to the quarantine protocol. Autonomous Mobile Robot (AMR) is used for delivering food and medical supplies to individual patients in order to keep the physical distance between patients and health workers. In this research, delivery by using multiple AMRs working in the in-patient ward is simulated. The simulation software is developed in Unity platform to study the operations of AMRs in various scenarios.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"166 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":"116058322","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: 5
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