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

Image Classification of Forage Plants in Fabaceae Family Using Scale Invariant Feature Transform Method 基于尺度不变特征变换的豆科饲料植物图像分类

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376824

Thidarat Pinthong, Worawut Yimyam, Narumol Chumuang, M. Ketcham, Patiyuth Pramkeaw, Nattavee Utakrit

{"title":"Image Classification of Forage Plants in Fabaceae Family Using Scale Invariant Feature Transform Method","authors":"Thidarat Pinthong, Worawut Yimyam, Narumol Chumuang, M. Ketcham, Patiyuth Pramkeaw, Nattavee Utakrit","doi":"10.1109/iSAI-NLP51646.2020.9376824","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376824","url":null,"abstract":"This paper proposes a novel method for the image classification of forage plants in fabaceae family by using Scale Invariant Feature Transform (SIFT) method. The color image extension jpeg color mode RGB adjust the image to 1000x1000 pixels to get a single image of the template file. All of the sample images, four prototype images were standard scaled and rotated. The image was obtained through the image extraction process using SIFT implements and matching dataset of Forage Plants leaves with matching points to evaluate the accuracy of flea leaf identification, it was found that Senna siamea, Clitoria ternatea and Pithecellobium dulce leaves 100% accuracy but Sesbania grandiflora Desv was obtained with 0% accuracy. The total accuracy of all 4 plants 75%, indicated that the photosynthesis of SIFT leaves was suitable for Senna siamea, Clitoria ternatea and Pithecellobium dulce Because it is 100% accurate, but not with Sesbania grandiflora Desv leaves. The accuracy is 0% because the leaves are dark green. The leaves are not clear. And the leaves are slender, evenly spaced leaves, which makes it a very rare feature. While Senna siamea, Clitoria ternatea and Pithecellobium dulce leaves are clear. Leaf edge is unique. Include appropriate techniques for recognition and classification.","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":"115642218","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: 12

Development a home electrical equipment control device via voice commands for elderly assistance 开发一种家用电器控制装置，通过语音命令来帮助老年人

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376818

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

引用次数: 10

Intelligent Medicine Identification System Using a Combination of Image Recognition and Optical Character Recognition 图像识别与光学字符识别相结合的智能药品识别系统

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376816

Nagorn Maitrichit, Narit Hnoohom

{"title":"Intelligent Medicine Identification System Using a Combination of Image Recognition and Optical Character Recognition","authors":"Nagorn Maitrichit, Narit Hnoohom","doi":"10.1109/iSAI-NLP51646.2020.9376816","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376816","url":null,"abstract":"This research aims to develop an automatic verification system with deep learning techniques to verify prescription dispensing accuracy. The proposed method will be able to help pharmacies to reduce errors that lead to patients receiving the wrong medicine to patients. The system consists of two models: image classification and text classification. The image classification model uses raw medicine blister pack images, then removes the background to interpret the features based on the pattern recognition for Histograms of Oriented Gradients (HOG) of the model. It is composed of Convolution Neural Network (CNN), Linear Regression, and Logistic Regression. The text classification model uses text extraction to obtain imprints appearing on the blister package then matches the words to a bag of word. The dataset collected two-hundred types of medicine blister packs images inside plastic zip bags as a dataset. It includes 300 high-quality images of front-side medicine blister packages for each type of package in light-controlled conditions with a black background, which are used for training the model. The automatic verification system uses the majority vote based on the confidence of the two models. Experimental results, indicate that the image classification model of CNN with HOG feature extraction has the highest accuracy at 95.83 percent. In-text classification results show that the method using Character Region Awareness For Text detection (CRAFT), Keras-OCR, and text correction gave the highest accuracy at 92 percent. Overall accuracy was 94.23 percent.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"30 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":"124684032","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: 6

Can Tweets predict ICO success? Sentiment Analysis for Success of ICO Whitepaper: evidence from Australia and Singapore Markets 推特能预测ICO成功吗?ICO成功的情绪分析白皮书:来自澳大利亚和新加坡市场的证据

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376810

Anchaya Chursook, Nathee Naktnasukanjn, Somsak Chaimaim, Piyachat Udomwong, Jutharut Chatsirikul, N. Chakpitak

引用次数: 1

iSAI-NLP 2020 Cover Page iSAI-NLP 2020封面

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/isai-nlp51646.2020.9376828

引用次数: 0

Statistical Machine Translation for Myanmar Language Paraphrase Generation 缅甸语释义生成的统计机器翻译

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376783

Myint Myint Htay, Ye Kyaw Thu, Hninn Aye Thant, T. Supnithi

{"title":"Statistical Machine Translation for Myanmar Language Paraphrase Generation","authors":"Myint Myint Htay, Ye Kyaw Thu, Hninn Aye Thant, T. Supnithi","doi":"10.1109/iSAI-NLP51646.2020.9376783","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376783","url":null,"abstract":"In this paper, we applied a statistical machine translation (SMT) approach to generate Burmese paraphrases of input sentences and words in Burmese. The system trained 89K sentence pairs that are manually collected from Facebook Comments and daily conversation corpus and also 89K Burmese Paraphrase Words are collected from Burmese Wiktionary. We implemented three different statistical machine translation models; phrase-based, hierarchical phrase based, and the operation sequence model. Moreover, we used two segmentation units; character and syllable segmentation for comparing the machine translation performance. The performance of machine translation or paraphrase generation was measured in terms of BLEU, RIBES, chrF++, and WER scores for all experiments. However, automatic evaluation metrics are weak for judging whether the generated Burmese sentences and words “is a paraphrase” or “is not a paraphrase’: And thus, we also conducted a human evaluation on both sentence-to-sentence and word-toword paraphrase generation results. We found that the results obtained using the BLEU and RIBES automatic evaluation metrics were misleading and as the human evaluation result the machine translation approach is suitable for Burmese paraphrase generation.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"90 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":"116354321","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

Myanmar POS Resource Extension Effects on Automatic Tagging Methods 缅甸POS资源扩展对自动标注方法的影响

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376835

Zar Zar Hlaing, Ye Kyaw Thu, Myat Myo Nwe Wai, T. Supnithi, P. Netisopakul

{"title":"Myanmar POS Resource Extension Effects on Automatic Tagging Methods","authors":"Zar Zar Hlaing, Ye Kyaw Thu, Myat Myo Nwe Wai, T. Supnithi, P. Netisopakul","doi":"10.1109/iSAI-NLP51646.2020.9376835","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376835","url":null,"abstract":"Part-of-speech (POS) tagging is the process of assigning the part-of-speech tag or other lexical class marker to each word in a sentence. It is also one of the most important steps in Natural Language Processing (NLP) task pipeline. There are several research works in Myanmar POS tagging implemented with different approaches. However, there is only one publicly available tagged corpus named myPOS corpus. The size of this corpus is only 11 thousand sentences. It is not enough to train downstream NLP tasks, such as machine learning. For this reason, we manually extended the original myPOS corpus as myPOS version 2.0 and the size of the extended corpus becomes approximately triple size of the original myPOS corpus. To evaluate the effects of the extended corpus versus the original corpus, the accuracies of four supervised tagging algorithms, namely, Conditional Random Fields (CRFs), Hidden Markov Model (HMM), Ripple Down Rules based (RDR), and neural sequence labeling approach of Conditional Random Fields $(mathrm{NCRF}^{++})$ are compared. The results showed that the extended myPOS version 2.0 improved the accuracies of automatic POS tagging methods compared with the original myPOS.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"46 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":"133592535","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: 6

Mushroom Classification by Physical Characteristics by Technique of k-Nearest Neighbor 基于k-最近邻技术的蘑菇物理特性分类

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376820

Narumol Chumuang, Kittisak Sukkanchana, M. Ketcham, Worawut Yimyam, Jiragorn Chalermdit, Nawarat Wittayakhom, Patiyuth Pramkeaw

引用次数: 17

Behavioral Analysis of Transformer Models on Complex Grammatical Structures 复杂语法结构转换模型的行为分析

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376782

Kanyanut Kriengket, Kanchana Saengthongpattana, Peerachet Porkaew, Vorapon Luantangsrisuk, P. Boonkwan, T. Supnithi

引用次数: 0

Daily Health Monitoring Chatbot with Linear Regression 每日健康监测聊天机器人与线性回归

2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2020-11-18 DOI: 10.1109/iSAI-NLP51646.2020.9376822

K. Wongpatikaseree, A. Ratikan, Chaianun Damrongrat, Katiyaporn Noibanngong

{"title":"Daily Health Monitoring Chatbot with Linear Regression","authors":"K. Wongpatikaseree, A. Ratikan, Chaianun Damrongrat, Katiyaporn Noibanngong","doi":"10.1109/iSAI-NLP51646.2020.9376822","DOIUrl":"https://doi.org/10.1109/iSAI-NLP51646.2020.9376822","url":null,"abstract":"Nowadays, elderly people harm their health from daily routines such as the habits of eating junk food, lack of time for exercising, and so on. When age increases, most of elderly people often have encountered with high blood pressure disease. This disease can lead to many dangerous diseases. Additionally, population of the doctors in hospital is not so high. The doctor usually takes a short note about symptoms for quick services that might not be enough for diagnosis. Therefore, we proposed daily health monitoring chatbot for the elderly people. We need to collect information from the elderly people to create personal health record (PHR). We develop conversational chatbot to interact with the elderly people via LINE application. Outcomes of this research support the doctor’s work because after reading daily PHR, the doctor diagnoses the diseases and gives advice for medical treatment more accurately. Furthermore, Linear regression technique was developed to monitor the blood pressure’s trend of elderly. Thus, they can prevent or relieve some diseases from chatbot warning and taking care the health.","PeriodicalId":311014,"journal":{"name":"2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"19 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":"132835516","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: 3