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

Question Classification for Thai Conversational Chatbots Using Artificial Neural Networks and Multilingual BERT Models 基于人工神经网络和多语言BERT模型的泰语会话聊天机器人问题分类

2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2023-02-21 DOI: 10.1109/KST57286.2023.10086784

Kit Thananukhun, S. Jaiyen, Kulsawasd Jitkajornwanich, Anantaporn Hanskunatai

{"title":"Question Classification for Thai Conversational Chatbots Using Artificial Neural Networks and Multilingual BERT Models","authors":"Kit Thananukhun, S. Jaiyen, Kulsawasd Jitkajornwanich, Anantaporn Hanskunatai","doi":"10.1109/KST57286.2023.10086784","DOIUrl":"https://doi.org/10.1109/KST57286.2023.10086784","url":null,"abstract":"Question-Answering (QA) models are part of Natural Language Processing (NLP) field used for ensuring questions match the answers appropriately. QA consists of several steps, one of which is called Question Classification, which is to classify the context of communication. In this step, it categorizes group of questions based on what users need to know in order to combine answers within the same category and respond accurately. It helps saving us time to search for answers as well. In this paper, we present a question classification model for Thai Conversational Chatbot using Artificial Neural Network and Multilingual Bidirectional Encoder Representations from Transformer (BERT) models using BERT-base multilingual cased combined with Multilayer Perceptron (MLP). The method yields the highest accuracy of 92.57%, compared to the BERT-base multilingual cased combined with other classification models, including Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN) and Decision Trees (DTs) with the accuracy scores of 88.57%, 80.00%, 78.57% and 60.29%, respectively. In addition, we also compare the performance of our proposed BERT model with another well-known Thai word embedding model, called Thai2Vec, which also combines with other classification models including MLP, SVM, NB, KNN and DTs, and their results of accuracies are: 85.71%, 85.71%, 75.71%, 75.71% and 58.86%, respectively. From the experiments, the BERT model combined with MLP can achieve the highest performance in term of accuracy among other methods.","PeriodicalId":351833,"journal":{"name":"2023 15th International Conference on Knowledge and Smart Technology (KST)","volume":"14 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":"114943590","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

Traditional Vietnamese Herbal Medicine Image Recognition by CNN 传统越南草药图像识别由CNN

2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2023-02-21 DOI: 10.1109/KST57286.2023.10086725

Trung Nguyen Quoc, Vinh Truong Hoang

引用次数: 0

S-Edge: Smart Edge Computing Framework for Real-time Heterogeneous Vehicular Network S-Edge:面向实时异构车辆网络的智能边缘计算框架

2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2023-02-21 DOI: 10.1109/KST57286.2023.10086800

Anuj Sachan, Yash Daultani, Neetesh Kumar

{"title":"S-Edge: Smart Edge Computing Framework for Real-time Heterogeneous Vehicular Network","authors":"Anuj Sachan, Yash Daultani, Neetesh Kumar","doi":"10.1109/KST57286.2023.10086800","DOIUrl":"https://doi.org/10.1109/KST57286.2023.10086800","url":null,"abstract":"With the rapid growth of transportation vehicles, urban centers are becoming overcrowded due to limited road infrastructure. Several queue length-based traffic light controllers have been developed to address this problem. Due to excessive congestion on the road during peak hours, the existing system suffers from the starvation problem at any intersection. This results in numerous instances where longer green phase duration is assigned to the same lane, increasing vehicle waiting time in other lanes. This issue is addressed by an efficient Smart Edge (S-Edge) lane pressure-based traffic light controller framework that accounts for the real-time heterogeneous vehicular dynamics. Additionally, this work proposes a method that uses average queue length and waiting time to estimate lane pressure for the Edge-controller that allocates phase duration effectively. This light-weighted actuated traffic light controller determines the cycle and phase (R/Y/G) durations of traffic lights. To validate the effectiveness of the proposed S-Edge controller, a detailed analysis has been carried out against the same line of state-of-the-art models that are based on a well-known open-source simulator called Simulation of Urban MObility (SUMO).","PeriodicalId":351833,"journal":{"name":"2023 15th International Conference on Knowledge and Smart Technology (KST)","volume":"35 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":"126799165","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

Information Security Awareness in Higher Education Institutes: A Work in Progress 高等教育机构的信息安全意识:一项正在进行的工作

2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2023-02-21 DOI: 10.1109/KST57286.2023.10086884

Rohani Rohan, Suree Funilkul, W. Chutimaskul, P. Kanthamanon, B. Papasratorn, Debajyoti Pal

引用次数: 1

AIX Implementation in Image-Based PM2.5 Estimation: Toward an AI Model for Better Understanding AIX在基于图像的PM2.5估计中的实现:面向更好理解的AI模型

2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2023-02-21 DOI: 10.1109/KST57286.2023.10086917

Sapdo Utomo, A. John, Ayush Pratap, Zhi-Sheng Jiang, P. Karthikeyan, Pao-Ann Hsiung

{"title":"AIX Implementation in Image-Based PM2.5 Estimation: Toward an AI Model for Better Understanding","authors":"Sapdo Utomo, A. John, Ayush Pratap, Zhi-Sheng Jiang, P. Karthikeyan, Pao-Ann Hsiung","doi":"10.1109/KST57286.2023.10086917","DOIUrl":"https://doi.org/10.1109/KST57286.2023.10086917","url":null,"abstract":"In accordance with the Sustainable Development Goals, the exponential expansion of machine learning (ML) and artificial intelligence (AI) presents an excellent chance to build more effective tools and solutions and generate positive social impact. According to the WHO report, global PM pollution causes more than 8 million deaths annually. This is the fundamental reason we are performing this research. This research proposes estimating air quality using deep learning. The proposed model can surpass the state-of-the-art model in terms of RMSE, R-squared, and accuracy, which have respective values of 30.10, 0.83, and 76.92%. In order to explain the model’s output, LIME has been implemented. According to LIME’s explanation, the proposed model’s output is trustworthy. Because it reveals that the sky, and not other places such as buildings, was the source of the most impactful superpixels on the model’s decision. We hope that with this discovery, we can contribute to the theme of “AI for social good,” notably in the domains of the environment and human welfare.","PeriodicalId":351833,"journal":{"name":"2023 15th International Conference on Knowledge and Smart Technology (KST)","volume":"1 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":"129995180","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

Classifying Skin Cancer and Acne using CNN 使用CNN分类皮肤癌和痤疮

2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2023-02-21 DOI: 10.1109/KST57286.2023.10086873

Kshitiza Vasudeva, S. Chandran

{"title":"Classifying Skin Cancer and Acne using CNN","authors":"Kshitiza Vasudeva, S. Chandran","doi":"10.1109/KST57286.2023.10086873","DOIUrl":"https://doi.org/10.1109/KST57286.2023.10086873","url":null,"abstract":"Several hospitals and dermatological clinics have adopted computer-vision-based diagnosis tools to aid in the early identification of skin cancer. The most frequent skin diseases are acne vulgaris and skin cancer. Acne Vulgaris affects 85% of the population in their lives, usually during adolescence. Benign Skin Cancer is the cancer commonly affecting people among all other types in developed and developing countries. To measure the success of medical treatment techniques, an objective evaluation of the lesion is required. Traditionally, dermatologists manually count the number of lesions by visual examination or scanning obtained photographs of the patient’s skin and divide them into several categories. This old procedure is time intensive and necessitates a significant amount of work on the part of the physician. Using computer vision, automated the lesion detection, lesion classification, counting of Acne, counting of benign skin cancer and tracking of Acne Severity, making it simple for patients to analyse and track the results of their acne treatment. The goal of this study is to develop a Convolutional Neural network model to classify the lesions into acne and benign skin cancer. The proposed model is developed and trained on acne and different types of benign skin cancer images and achieved an accuracy of 96.4%.","PeriodicalId":351833,"journal":{"name":"2023 15th International Conference on Knowledge and Smart Technology (KST)","volume":"23 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":"131563052","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

Heart Rate Measurement on Smartphone using Cardiography: A Scoping Review 在智能手机上使用心脏图测量心率:范围审查

2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2023-02-21 DOI: 10.1109/KST57286.2023.10086930

Tashfiq Rahman, Worarat Krathu, C. Arpnikanondt

{"title":"Heart Rate Measurement on Smartphone using Cardiography: A Scoping Review","authors":"Tashfiq Rahman, Worarat Krathu, C. Arpnikanondt","doi":"10.1109/KST57286.2023.10086930","DOIUrl":"https://doi.org/10.1109/KST57286.2023.10086930","url":null,"abstract":"Background: Smartphones nowadays are equipped with the necessary hardware that can be used to provide consumers with mobile health monitoring and healthcare management. Apps that assess Heart Rate (HR) utilizing the built-in hardware of a smartphone are among these intelligent technologies. This study sought to identify previous research on employing smartphones to estimate vital signs using cardiography procedures.Methods: For this study, a scoping review was completed. Title and abstract were used to initially screen 175 papers and articles, then the eligibility was determined based on the notion that the papers and articles dealt with cardiography and the use of smartphones to estimate pulse.Results: A total of 35 papers and articles were included after screening and eligibility checks, as well as backward and forward literature searches, of which 9 items were listed and characterized as being of interest to smartphone-implemented cardiography research.Conclusions: Although cardiography and its practice in heartbeat measurement have been extensively studied, there is surprisingly little research that looks at the same techniques applied to smartphones from a Computer Science perspective. This paper seeks to serve as a starting point for additional research on the subject of smartphone-based cardiography as well as encourage further in-depth research. Paper Classification: General review.","PeriodicalId":351833,"journal":{"name":"2023 15th International Conference on Knowledge and Smart Technology (KST)","volume":"1 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":"130961063","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

Facial Emotion Detection for Thai Elderly People using YOLOv7 基于YOLOv7的泰国老年人面部情绪检测

2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2023-02-21 DOI: 10.1109/KST57286.2023.10086786

Thanapong Khajontantichaikun, S. Jaiyen, S. Yamsaengsung, P. Mongkolnam, Thanitsorn Chirapornchai

引用次数: 0

PM2.5 Forecasting Model based on Linear and Non-linear Hybrid Algorithm 基于线性与非线性混合算法的PM2.5预测模型

2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2023-02-21 DOI: 10.1109/KST57286.2023.10086907

Anupong Banjongkan, Nittaya Kerdprasop, Anusara Hirunyawanakul, Kittisak Kerdprasop

{"title":"PM2.5 Forecasting Model based on Linear and Non-linear Hybrid Algorithm","authors":"Anupong Banjongkan, Nittaya Kerdprasop, Anusara Hirunyawanakul, Kittisak Kerdprasop","doi":"10.1109/KST57286.2023.10086907","DOIUrl":"https://doi.org/10.1109/KST57286.2023.10086907","url":null,"abstract":"Air pollution is one of the harmful problems that the world has focused on and needs to be solved urgently because air pollution has a direct impact on humans leading to premature death caused by various diseases such as asthma inflammatory respiratory disease, lung cancer, and so on. The air pollutants, especially tiny particulate matter (PM), are currently receiving attention because they are a major problem in many large and populated cities around the world. This paper proposed a time-series model for forecasting PM2.5 in advance through a machine learning process with a linear and non-linear hybrid algorithm. A hybrid algorithm that brings together the capabilities of autoregressive integrated moving average (ARIMA) and the adaptive-neuro fuzzy inference system (ANFIS) is used to find the linear and nonlinear correlation of the PM2.5 time-series data. The proposed model is called ARIMA-FIS which uses the gradient descent (GD) method in the learning process. The dataset used in this research is the daily recorded of PM2.5 values in Rayong province, which is the industrial city in Thailand. The results showed that the ARIMA-FIS model had the best performance in forecasting PM2.5 in advance with the least error at 3.46 of mean absolute error (MAE) and 5.11 of root mean square error (RMSE). The proposed model gave the percentage of RMSE almost 3% better than the other standard time-series models.","PeriodicalId":351833,"journal":{"name":"2023 15th International Conference on Knowledge and Smart Technology (KST)","volume":"17 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":"133090928","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-Time Detection and Classification of Facial Emotions 面部情绪的实时检测与分类

2023 15th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2023-02-21 DOI: 10.1109/KST57286.2023.10086866

Teerapong Winyangkun, Noparut Vanitchanant, Varin Chouvatut, Benjamas Panyangam

引用次数: 0