2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)最新文献_第4页

Medicine Identification System on Mobile Devices for the Elderly 基于移动设备的老年人药品识别系统

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376837

Pitchaya Chotivatunyu, Narit Hnoohom

{"title":"Medicine Identification System on Mobile Devices for the Elderly","authors":"Pitchaya Chotivatunyu, Narit Hnoohom","doi":"10.1109/iSAI-NLP51646.2020.9376837","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376837","url":null,"abstract":"This research develops an application that helps the elderly to identify medicine from a mobile image, to reduce confusion in taking medication, and thus to reduce the rate of medication errors. The data used in this research are collected from the medicine blister packs for the elderly consisting of 14 types of medicine, which are taken with the smartphone cameras and amounting to a total of 56,000 single medicine blister pack images for image classification model training. For object detection model training, there are a total of 21,000 single medicine blister pack images with added multiple medicine blister pack images amounting to 120 images from the image dataset. Text recognition is used to identify the medicine type using Keras-OCR. For all experimental results in the image classification model experiments reveal that the MobileNet V2 with 14-class detection has the highest accuracy at 93.79 percent. The object detection model is the MobileNet V1 with the highest mAP of 0.875 with the Average Precision with 0.5 IoU and 0.75 IoU at 0.998 and 0.91, respectively.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"94 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":"126229328","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 Development Heat Stroke Detection System Integrated with Infrared Camera 一种集成红外摄像机的中暑探测系统的研制

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376814

Worawut Yimyam, M. Ketcham, Narumol Chumuang, Nattavee Utakrit, Montean Rattanasiriwongwut, Sansanee Hiranchan

引用次数: 10

Feature Extraction with SHAP Value Analysis for Student Performance Evaluation in Remote Collaboration 基于SHAP值分析的特征提取在远程协作学生成绩评价中的应用

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376830

Mako Komatsu, Chihiro Takada, Chihiro Neshi, Teruhiko Unoki, M. Shikida

引用次数: 2

Estimation of Oil Content in Oil Palm Fresh Fruit Bunch by Its Surface Color 油棕鲜果串表面颜色测定含油量

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376834

Sutat Sae-Tang

{"title":"Estimation of Oil Content in Oil Palm Fresh Fruit Bunch by Its Surface Color","authors":"Sutat Sae-Tang","doi":"10.1109/iSAI-NLP51646.2020.9376834","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376834","url":null,"abstract":"Oil palm is one of the potential tree crops in Thailand. However, the production of oil palm has been experienced many aspects. Price factor is also one of the problems. Price of oil palm depends on the amount of oil content in the oil palm fruit which are estimated by an expert. The main consideration is the ripeness of the oil palm fresh fruit bunches. An expert determines using its surface color. A different experience of experts leads to a different estimation. The problem may be solved using the chemical analysis methods which more accurate. However, it takes time and uncomfortable. In this research, artificial intelligence (AI) will be applied to estimate the oil content in a fresh fruit bunch (FFB). Two popular types of oil palms in Thailand are used in this work. The Nigrescene fruit, color varies from dark purple to red orange depending on its gene and ripeness. The Virescene fruit, color changes from green to orange. The surface color of an oil palm fruit and structure of the bunch were considered as the feature set. An oil palm FFB image from a smartphone camera was fed to the model for predicting the oil content in FFB. Several models such as multi linear regression, artificial neural network and convolution neural network will be observed. The measure of the quality’s model uses the root mean square error (RMSE). The convolution neural network produces the average of RMSE at 727 for Nigrescene and at 4.83 for Virescene.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"33 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":"134237319","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

COVID-19: Data Analysis and the situation Prediction Using Machine Learning Based on Bangladesh perspective COVID-19:基于孟加拉国视角的数据分析和使用机器学习的情况预测

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376812

Abir Abdullha, Sheikh Abujar

引用次数: 6

Direction of Arrival Identification Using MUSIC Method and NLMS Beamforming 基于MUSIC方法和NLMS波束形成的到达方向识别

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376838

R. Suleesathira

引用次数: 1

About this Publication 关于本刊物

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/isai-nlp51646.2020.9376821

引用次数: 0

A Surveillance System for Children Stuck Inside the Car with Embedded System Technology 一种基于嵌入式系统技术的车内儿童监控系统

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376781

Arthit Yooyen, Siriruang Phatchuay

引用次数: 0

iSAI-NLP 2020 Organizers

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/isai-nlp51646.2020.9376811

引用次数: 0

A Study of Three Statistical Machine Translation Methods for Myanmar (Burmese) and Shan (Tai Long) Language Pair 缅甸语和掸邦语对的三种统计机器翻译方法研究

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376832

Nang Aeindray Kyaw, Ye Kyaw Thu, Hlaing Myat Nwe, Phyu Phyu Tar, N. Min, T. Supnithi

{"title":"A Study of Three Statistical Machine Translation Methods for Myanmar (Burmese) and Shan (Tai Long) Language Pair","authors":"Nang Aeindray Kyaw, Ye Kyaw Thu, Hlaing Myat Nwe, Phyu Phyu Tar, N. Min, T. Supnithi","doi":"10.1109/iSAI-NLP51646.2020.9376832","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376832","url":null,"abstract":"Shan is said to be the second-largest ethnic group of Myanmar. The main motivation is to break down the communication barrier between Shan people and Myanmar people. This paper contributes to the first evaluation of the quality of machine translation between Myanmar (Burmese) and Shan (Tai Long). We also built a Myanmar-Shan parallel corpus (around 11K sentences) based on the Myanmar language of the ASEAN MT corpus. In this research, three different statistical machine translation approaches were used to carry out the experiment: phrase-based, hierarchical phrase-based, and the operation sequence model. Furthermore, two different segmentation schemes were studied, these were syllable segmentation and word segmentation. Translating with syllable segmentation achieved higher quality machine translation for both Myanmar and Shan languages. BLEU and RIBES scoring techniques are used to measure the performance of the machine translations. The operation sequence model gave the highest scores (41.85 BLEU and 0.88031 RIBES) for Shan to Myanmar syllable translation. For Myanmar to Shan syllable translation, hierarchical phrase-based machine translation gave the highest BLEU score of 34.72 and the operation sequence model gave the highest RIBES score of 0.87012. Our experimental results with syllable segmentation produced promising results even with low data resources and we expect this can be developed into a useful translation system as more data comes available in the future.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"22 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":"126722407","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