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2017 9th International Conference on Knowledge and Smart Technology (KST)最新文献

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Multiple ARIMA subsequences aggregate time series model to forecast cash in ATM 多ARIMA子序列聚合时间序列模型用于自动取款机现金预测
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886096
Paisit Khanarsa, K. Sinapiromsaran
{"title":"Multiple ARIMA subsequences aggregate time series model to forecast cash in ATM","authors":"Paisit Khanarsa, K. Sinapiromsaran","doi":"10.1109/KST.2017.7886096","DOIUrl":"https://doi.org/10.1109/KST.2017.7886096","url":null,"abstract":"This paper proposes a new forecasting model for the amount of cash in ATM, called a multiple ARIMA subsequences aggregate time series model or MASA model. To assess the MASA model, the time series data is split into in-sample for building and out-sample data for evaluating. To build the MASA model, the aggregate in-sample data is subdivided into training time series data and validating time series data. The aggregate technique by its period is used to reduce a sway in the time series. A fixed number of subsequence patterns are generated and fitted using ARIMA models and then validated using the symmetric mean absolute percentage error or SMAPE to identify the best fitted model. The step of forecasting a future value in the aggregate group using the MASA model needs to identify the subsequence with the best fitted ARIMA model. The disaggregate process will distribute this group value into future values using a ratio from the past. The MASA model is compared using SMAPE with the SARIMA model and ETS exponential smoothing model. The results exhibit the better SMAPE than the SARIMA model and ETS exponential smoothing model.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115494467","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: 4
Fault detection for circulating water pump using time series forecasting and outlier detection 基于时间序列预测和离群值检测的循环水泵故障检测
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886095
Manassakan Sanayha, P. Vateekul
{"title":"Fault detection for circulating water pump using time series forecasting and outlier detection","authors":"Manassakan Sanayha, P. Vateekul","doi":"10.1109/KST.2017.7886095","DOIUrl":"https://doi.org/10.1109/KST.2017.7886095","url":null,"abstract":"In any power plants, it is crucial to perform a preventive maintenance to avoid unexpected breakdown of machinery, e.g., circulating water pump, using data collected from various sensors. There have been prior attempts using just traditional prediction techniques. In this paper, we propose a two-stage model that employs a technique from time series analysis to predict when the machine tends to be failed for one day in advance. The first stage focuses on forecasting trends of each sensor using “Auto-Regression Integrated Moving Average (ARIMA).” Then, the second stage aims to classify failure mode using the predicted sensor values. The experiment was conducted on data collected from eight sensors within one year. The result is shown that our proposed algorithm significantly outperforms an existing technique, Regression Artificial Neural Network.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116968863","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
Applying One-Versus-One SVMs to classify multi-label data with large labels using spark 使用spark实现一对一svm对大标签的多标签数据进行分类
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886091
Suthipong Daengduang, P. Vateekul
{"title":"Applying One-Versus-One SVMs to classify multi-label data with large labels using spark","authors":"Suthipong Daengduang, P. Vateekul","doi":"10.1109/KST.2017.7886091","DOIUrl":"https://doi.org/10.1109/KST.2017.7886091","url":null,"abstract":"Multi-Label classification aims to classify an example that can belong to many classes. Although One-versus-All (OVA) is the most common approach, our prior work has shown that the proposed One-versus-One (OVO) always gives higher prediction accuracy than OVA. However, OVO requires an extremely high computational cost when there are a large number of labels. In this paper, we apply our OVO SVMs on the proposed Spark framework along with a mechanism to split a job into a set of small jobs and then process them in parallel. The framework can induce OVO SVMs very fast, while maintaining the prediction accuracy even though there are a large number of classes. The experiment was conducted on six standard benchmarks. The result shows that our framework can really reduce computing time on Spark environment, while significantly outperforms OVA in terms of F1 on all data.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124901263","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: 8
Improving Thai-English word alignment for interrogative sentences in SMT by grammatical knowledge 利用语法知识改进SMT中疑问句的泰英对齐
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886115
Kanyalag Phodong, R. Kongkachandra
{"title":"Improving Thai-English word alignment for interrogative sentences in SMT by grammatical knowledge","authors":"Kanyalag Phodong, R. Kongkachandra","doi":"10.1109/KST.2017.7886115","DOIUrl":"https://doi.org/10.1109/KST.2017.7886115","url":null,"abstract":"This paper presents a method to improve Thai-English word alignment in statistical machine translation (SMT) for interrogative sentences in a parallel corpus. We utilize the Thai and English grammatical knowledge i.e. tense, part of speech (POS), and question inversion pattern. The proposed method handles the difference of Thai and English interrogative sentences using sentence transformation, interrogative grammatical attribute extraction, and interrogative grammatical attribute annotation. This method works as a pre-processing of GIZA, a standard word co-occurrence alignment tool in SMT. We hypothesize that using grammatical knowledge as a pre-processing of GIZA can provide higher accuracy. We experiment by using 43,500 interrogative sentences to compare alignment result between interrogative sentences attached an interrogative grammatical label and interrogative sentences unattached interrogative grammatical label. The experimental results yield 95% of accuracy with significant improvement than the conventional one. With the increasing accuracy of word alignment, the translation accuracy is consequently improved.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121150596","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
User preferences profiling based on user behaviors on Facebook page categories 基于Facebook页面类别用户行为的用户偏好分析
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886077
Rachsuda Jiamthapthaksin, T. Aung
{"title":"User preferences profiling based on user behaviors on Facebook page categories","authors":"Rachsuda Jiamthapthaksin, T. Aung","doi":"10.1109/KST.2017.7886077","DOIUrl":"https://doi.org/10.1109/KST.2017.7886077","url":null,"abstract":"User preference profiling is important in both social networking mining and recommender systems. Facebook provides information of page category over two hundred relating to user preferences, but the predefined categories may not fit application well. Explicitly mapping those categories to a desirable set of user preferences is a tedious task. This paper proposes an effective user profiling technique using features constructed from user behaviours on Facebook page in different categories. The models created from three well known classification algorithms: Naïve Bayes (NB), Artificial Neural Network (ANN), and Support Vector Machine (SVM) turn the raw data of user behaviours into a set of user preferences defined by application. The experiments performed on Facebook dataset show that the constructed features are implicitly reflecting user preferences and can be used to tailor the preferences as needed. Among the three algorithms SVM leverages classification performance the most with accuracy over seventy-two percent.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134538037","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: 4
Automatic text imprint analysis from pill images 从药丸图像自动文本印记分析
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886081
Siroratt Suntronsuk, S. Ratanotayanon
{"title":"Automatic text imprint analysis from pill images","authors":"Siroratt Suntronsuk, S. Ratanotayanon","doi":"10.1109/KST.2017.7886081","DOIUrl":"https://doi.org/10.1109/KST.2017.7886081","url":null,"abstract":"Pill identification is a serious concern for pharmacists due to similarity of pill appearances. Pill imprints usually contain important information that can be used to add or search for pill information on existing pill databases. However, current techniques for extracting imprints often give results as vectors which cannot be used with existing databases. Thus, this paper proposed an approach for automatically extracting text on imprints. This approach relied on a set of rules to locate imprint locations and a noise elimination technique to remove problematic pixels in binary images obtained by OTSU's thresholding. Our approach can achieve an F-measure of 0.77 for printed imprints and 0.57 overall.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130766420","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: 7
Volatile urine biomarkers detection in type II diabetes towards use as smart healthcare application 挥发性尿液生物标志物检测在II型糖尿病中的智能医疗应用
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886086
P. Choden, T. Seesaard, Tanthip Eamsa-ard, Chutintorn Sriphrapradang, T. Kerdcharoen
{"title":"Volatile urine biomarkers detection in type II diabetes towards use as smart healthcare application","authors":"P. Choden, T. Seesaard, Tanthip Eamsa-ard, Chutintorn Sriphrapradang, T. Kerdcharoen","doi":"10.1109/KST.2017.7886086","DOIUrl":"https://doi.org/10.1109/KST.2017.7886086","url":null,"abstract":"In this work, we fabricated six chemiresistive sensors, employed in a portable e-nose and performed tests with urine samples from two groups of population, namely type II diabetes and healthy subjects. To identify sensitivity and selectivity of chemiresistive gas sensors, the first test was performed towards five volatile organic compounds (VOCs) which are particularly found in human urine profiles and the second test with real urine samples from the volunteers. Principal component analysis (PCA) and cluster analysis (CA) applied to validate the obtained sensing response successfully spilt urinary volatile odors into two separate groups of diabetes and healthy status. A hypothesis testing (p-value approach) demonstrated that S3 and S4 (p˂0.05) responded specifically to the urine odors from diabetic patients and healthy subjects. Our findings suggest the possibility of using chemiresistive gas sensors in e-nose as an alternative diagnostic tool for diabetes detection through analysis of volatile urine odors.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124657739","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
Improvement of container scheduling for Docker using Ant Colony Optimization 基于蚁群优化的Docker容器调度改进
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886112
Chanwit Kaewkasi, Kornrathak Chuenmuneewong
{"title":"Improvement of container scheduling for Docker using Ant Colony Optimization","authors":"Chanwit Kaewkasi, Kornrathak Chuenmuneewong","doi":"10.1109/KST.2017.7886112","DOIUrl":"https://doi.org/10.1109/KST.2017.7886112","url":null,"abstract":"Docker, a software container implementation, has emerged not only as an operating-system level virtualization but also an application delivery platform for today Internet. However, the scheduling algorithm shipped with SwarmKit, the orchestration engine behind Docker, is suboptimal when resources are nonuniform. The use of meta-heuristic, like Ant Colony Optimization (ACO), is feasible to improve the scheduler's optimality. This paper presents a study of ACO to implement a new scheduler for Docker. The main contribution of this paper is an ACO-based algorithm, which distributes application containers over Docker hosts. It is to balance the resource usages and finally l eads to the better performance of applications. The experimental results showed that workloads placed by ACO performed better than those of the greedy algorithm by approximately 15% on the same host configuration.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121086579","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: 87
Majority voting based on q-Gaussian activation function circular extreme learning machine 基于q-高斯激活函数的多数投票循环极值学习机
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886083
Sarutte Atsawaraungsuk
{"title":"Majority voting based on q-Gaussian activation function circular extreme learning machine","authors":"Sarutte Atsawaraungsuk","doi":"10.1109/KST.2017.7886083","DOIUrl":"https://doi.org/10.1109/KST.2017.7886083","url":null,"abstract":"This paper presents a new approach of Extreme Learning Machine (ELM) ensembles that use majority voting with the q-Gaussian Activation function Circular Extreme Learning Machine (QCELM) to make the final decision for classification problems. For each QCELM is work on the CELM using q-Gaussian activation functions based on Tsallis distribution that varies the different parameter q values (called the entropic index) and selects the best parameter q to take the highest accuracy; As a result, QCELM is more accurate than traditional and applied ELMs. In the experiment, the results of the simulation on 46 datasets from UCI repository shows that Majority Voting based on q-Gaussian Circular Extreme Learning Machine (V-QCELM) outperforms several Extreme Learning Machine ensembles. Wilcoxon signed rank test is used to confirm statistical differences between V-QCELM and the compared ELM ensembles that can indicate V-QCELM is the higher-performance ensemble of Extreme Learning Machines.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"90 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120825251","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
SVM-based web content mining with leaf classification unit from DOM-tree 基于svm的dom树叶分类单元web内容挖掘
2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886134
Yeongsu Kim, Seungwoo Lee
{"title":"SVM-based web content mining with leaf classification unit from DOM-tree","authors":"Yeongsu Kim, Seungwoo Lee","doi":"10.1109/KST.2017.7886134","DOIUrl":"https://doi.org/10.1109/KST.2017.7886134","url":null,"abstract":"In order to analyze a news article dataset, we first extract important information such as title, date, and paragraph of the body. At the same time, we remove unnecessary information such as image, caption, footer, advertisement, navigation and recommended-news. The problem is that the formats of news articles are changing according to time and also they vary according to news source and even section of it. So, it is important for a model to generalize when predicting unseen formats of news articles. We confirmed that a machine learning based model is better to predict new data than a rule-based model by some experiments. Also, we suggest that noise information in the body possibly can be removed because we define a classification unit as a leaf node itself. On the other hand, general machine learning based models cannot remove noise information. Since they consider the classification unit as an intermediate node which consists of the set of leaf nodes, they cannot classify a leaf node itself.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121863929","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
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