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2023 15th International Conference on Knowledge and Smart Technology (KST)最新文献

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Multiple Words to Single Word Associations Using Masked Language Models 使用掩蔽语言模型的多词到单词关联
2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2023-02-21 DOI: 10.1109/KST57286.2023.10086780
Yuya Soma, Y. Horiuchi, S. Kuroiwa
{"title":"Multiple Words to Single Word Associations Using Masked Language Models","authors":"Yuya Soma, Y. Horiuchi, S. Kuroiwa","doi":"10.1109/KST57286.2023.10086780","DOIUrl":"https://doi.org/10.1109/KST57286.2023.10086780","url":null,"abstract":"In this paper, we examine a word association task that predicts a correct associative word from five stimulus words using Masked Language Models (hereafter referred to as MLMs). For MLMs, we used BERT and gMLP. Since our word association task uses only nouns for both stimulus and associative words, we trained new models by restricting masked tokens to nouns. In our experiment, we input sentences such as “The prefecture associated with Mt. Fuji, Lake Hamana, … and eels is MASK. (富士山、浜名湖、⋯、うなぎから連想する都道府県は MASK です。),” so that MASK outputs an associative word. In the experiments, we also examined adding Japanese quotation marks 「」 before and after the MASK, i.e., 「MASK」. The experiment results showed that the highest percentage of correct answers, 49%, was obtained by adding 「」 before and after the MASK (74% of the correct answers were within the top five words).","PeriodicalId":351833,"journal":{"name":"2023 15th International Conference on Knowledge and Smart Technology (KST)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127972056","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 GoogLeNet Performance Approach for COVID-19 Detection using Chest X-ray Images 基于胸部x线图像检测COVID-19的GoogLeNet性能方法
2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2023-02-21 DOI: 10.1109/KST57286.2023.10086817
Patipan Rattanawin, Tidatep Pakinsee, Pokpong Songmuang
{"title":"A GoogLeNet Performance Approach for COVID-19 Detection using Chest X-ray Images","authors":"Patipan Rattanawin, Tidatep Pakinsee, Pokpong Songmuang","doi":"10.1109/KST57286.2023.10086817","DOIUrl":"https://doi.org/10.1109/KST57286.2023.10086817","url":null,"abstract":"Coronavirus disease (COVID-19) is a major pandemic disease that has already infected millions of people worldwide and affects many aspects, especially public health. There are many clinical techniques for the diagnosis of this disease, such as RT-PCR and CT-Scan. X-ray image is one of the important techniques for medical diagnosis and easily accessible in classifying suspected cases of COVID-19 infection. In this study, we classified COVID-19 images with four classes: COVID-19, Normal, Lung opacity and Viral pneumonia by compared three models: EfficientNetB0, MobileNet and GoogLeNet for the performance of classification using 1,000 chest X-ray images from Kaggle dataset within scenario of resource limitations. The experiment reveals that GoogLeNet shows superiority over other models that produced the highest accuracy results of 88% and F1 score of 0.88 with a total time of 1 hour and 15 minutes. Along with its confusion matrix that shows model can better classify images than other models.","PeriodicalId":351833,"journal":{"name":"2023 15th International Conference on Knowledge and Smart Technology (KST)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132486253","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
Thai Dry-Evergreen Forest’s Biomass Estimation using Machine Learning Models 利用机器学习模型估算泰国干常绿森林的生物量
2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2023-02-21 DOI: 10.1109/KST57286.2023.10086748
Kamthorn Puntumapon, Aitsanart Wuthithanakul, Pedro Uria Recio, B. Vindevogel
{"title":"Thai Dry-Evergreen Forest’s Biomass Estimation using Machine Learning Models","authors":"Kamthorn Puntumapon, Aitsanart Wuthithanakul, Pedro Uria Recio, B. Vindevogel","doi":"10.1109/KST57286.2023.10086748","DOIUrl":"https://doi.org/10.1109/KST57286.2023.10086748","url":null,"abstract":"Above ground biomass (AGB) is the key measurement for carbon credit. To quantify AGB over large forests, it is essential to develop a method that can handle the growing forest area. In this study, we investigate the possibility of estimating AGB using satellite data and machine-learning models. Several machine learning models, linear, non-linear, and ensemble methods, are evaluated. Random forest algorithm achieved the best model performance. On validation data, the random forest model can predict AGB with 24.5 Mg per area in terms of RMSE. The results demonstrate that satellite data from Sentinel-1, Sentinel-2, and MODIS have the potential to predict AGB in Thai dry-evergreen forests.","PeriodicalId":351833,"journal":{"name":"2023 15th International Conference on Knowledge and Smart Technology (KST)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132078231","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
RANDES: A Ransomware Detection System based on Machine Learning 基于机器学习的勒索软件检测系统
2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2023-02-21 DOI: 10.1109/KST57286.2023.10086910
Tanasart Phuangtong, Nitipoom Jaroonchaipipat, Nontawat Thanundonsuk, Parich Sakda, S. Fugkeaw
{"title":"RANDES: A Ransomware Detection System based on Machine Learning","authors":"Tanasart Phuangtong, Nitipoom Jaroonchaipipat, Nontawat Thanundonsuk, Parich Sakda, S. Fugkeaw","doi":"10.1109/KST57286.2023.10086910","DOIUrl":"https://doi.org/10.1109/KST57286.2023.10086910","url":null,"abstract":"Ransomware is one of the most prevalent cybercrimes where an attacker steals or freezes the organizational data through the data encryption. Thus, the task of ransomware detection has great importance in the field of cyber security. One thing in common with the existing models today is that they treated the assemblies as one long text. While in the execution of real code, the program counter may jump in between lines, making it more like graph traversal than linear. Thus, we proposed a new deep learning model for ransomware detection based on the executable file disassembling analysis. We split the assemblies into non-branching sequences and apply per-sequence embedding. Then, we employed Graph Attention Network (GAT) to classify whether a suspect executable file is a ransomware. Finally, we conducted experiments to show that our proposed system is efficient for real deployment.","PeriodicalId":351833,"journal":{"name":"2023 15th International Conference on Knowledge and Smart Technology (KST)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121121828","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
Development of Low-cost and Robust IoT Field Station for Coffee Plantation 低成本、强大的咖啡种植园物联网现场站开发
2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2023-02-21 DOI: 10.1109/KST57286.2023.10086759
S. Siyang, T. Kerdcharoen
{"title":"Development of Low-cost and Robust IoT Field Station for Coffee Plantation","authors":"S. Siyang, T. Kerdcharoen","doi":"10.1109/KST57286.2023.10086759","DOIUrl":"https://doi.org/10.1109/KST57286.2023.10086759","url":null,"abstract":"Thailand has a growing demand in coffee consumption every year. Currently, the nation’s capacity for producing coffee is limited, necessitating a significant amount of imports from overseas. The Arabica coffee in the north of Thailand, where the scent and taste of coffee have a distinct identity based on varied cultivation locations, has reasonably good quality. Because of the impact of the environment, it is still challenging to maintain the quality of coffee beans from season to season. Moreover, fierce competition with nearby nations like Laos and Vietnam leads to lower prices of coffee beans. This forces Thai farmers and entrepreneurs to embrace more advanced technologies in order to comprehend the environment of each plantation region and boost their level of competition. This project aims to develop a trustworthy and cost-effective IoT field monitoring solution to assist farmers in understanding the climate conditions in various plantation areas. In order to examine the relationship between the environments, the data collected by the devices can be transferred back to the public cloud server. Then, it can be used as knowledge to raise Thai coffee beans’ quality to a premium level so they can continue to compete with coffee beans from other countries.","PeriodicalId":351833,"journal":{"name":"2023 15th International Conference on Knowledge and Smart Technology (KST)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125512513","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
Tentative Program 初步计划
2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 1948-09-01 DOI: 10.1109/KST57286.2023.10086829
Rome Italy, Chen-Yong Lin, Meng Yu, Aamir Ahmad, Norbert Żołek, Fatema Alzahraa, Amin
{"title":"Tentative Program","authors":"Rome Italy, Chen-Yong Lin, Meng Yu, Aamir Ahmad, Norbert Żołek, Fatema Alzahraa, Amin","doi":"10.1109/KST57286.2023.10086829","DOIUrl":"https://doi.org/10.1109/KST57286.2023.10086829","url":null,"abstract":"pGlcNAc Nanofibers Derived from a Marine Polymer Stimulate Regenerative Wound Repair via Activation of a TLR4/type I IFN pathway in Combination with an Integrin/Akt1","PeriodicalId":351833,"journal":{"name":"2023 15th International Conference on Knowledge and Smart Technology (KST)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1948-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114898147","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
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