2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)最新文献_第4页

Government Data Sharing Framework based on DIKW Hierarchy Model 基于DIKW层次模型的政府数据共享框架

2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE) Pub Date : 2019-11-01 DOI: 10.1109/ICTKE47035.2019.8966872

A. Tungkasthan, Pitaya Poompuang, S. Intarasema

引用次数: 1

Stock Market Sentiment Classification from FinTech News 金融科技新闻的股票市场情绪分类

2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE) Pub Date : 2019-11-01 DOI: 10.1109/ICTKE47035.2019.8966841

Suntarin Sangsavate, Suparatana Tanthanongsakkun, S. Sinthupinyo

引用次数: 3

CMOS Programmable Full-Wave Rectifier Using Current Conveyor Analogue Switches 采用电流输送模拟开关的CMOS可编程全波整流器

2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE) Pub Date : 2019-11-01 DOI: 10.1109/ICTKE47035.2019.8966844

Thanat Nonthaputha, M. Kumngern

引用次数: 0

Nighttime Vehicle Routing for Sustainable Urban Logistics 可持续城市物流的夜间车辆路径

2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE) Pub Date : 2019-11-01 DOI: 10.1109/ICTKE47035.2019.8966825

Alfan Kurnia Yudha, S. Starita

{"title":"Nighttime Vehicle Routing for Sustainable Urban Logistics","authors":"Alfan Kurnia Yudha, S. Starita","doi":"10.1109/ICTKE47035.2019.8966825","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966825","url":null,"abstract":"Vehicle routing problems play a critical role in logistics distribution, allowing companies to minimize operational parameters such as cost, fuel consumption, emissions etc. This paper studies a customized vehicle routing problem incorporating nighttime delivery options for a heavily congested urban area. The aim is to identify the optimal combination of day and night routes by trading off between fuel and staff costs. A Linear Programming (LP) formulation for the Night Time Vehicle Routing Problem (NTVRP) is introduced. The model is then applied to a case study using real data from a department store in Bangkok, Thailand. A fuel consumption model is used alongside an emission model to estimate the beneficial impact of NTVRP on both costs and emissions. Results show that when demand is high and 55 tonne heavy goods vehicles are used, the cost savings are about 16.7 percent. More significantly, CO2 emissions are reduced by more than 30 percent. With low demand, cost savings are more than 30.8 percent, together with a 28.2 percent reduction in CO2 emissions. Overall, the case study shows that nighttime delivery is a viable option to increase efficiency and sustainability of a logistics company.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130515846","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

Provision and Visualization of Solar Radiation Data for Energy Management System 面向能源管理系统的太阳辐射数据提供与可视化

2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE) Pub Date : 2019-11-01 DOI: 10.1109/ICTKE47035.2019.8966805

Yoshiro Yamamoto, T. Funayama, Kazuki Konda, H. Takenaka, K. Murata, T. Nakajima

引用次数: 0

Stock Closing Price Prediction Using Machine Learning 利用机器学习预测股票收盘价

2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE) Pub Date : 2019-11-01 DOI: 10.1109/ICTKE47035.2019.8966836

Pawee Werawithayaset, Suratose Tritilanunt

引用次数: 6

Gamification-Driven Process: Financial Literacy in Thailand 游戏化驱动的过程:泰国的金融素养

2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE) Pub Date : 2019-11-01 DOI: 10.1109/ICTKE47035.2019.8966885

Wilawan Inchamnan, Winyu Niranatlamphong, Natlapat Engbunmeesakul

引用次数: 2

Long Short-Term Memory Deep Neural Network Model for PM2.5 Forecasting in the Bangkok Urban Area 曼谷市区PM2.5预测的长短期记忆深度神经网络模型

2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE) Pub Date : 2019-11-01 DOI: 10.1109/ICTKE47035.2019.8966854

Kankamon Thaweephol, Nuwee Wiwatwattana

{"title":"Long Short-Term Memory Deep Neural Network Model for PM2.5 Forecasting in the Bangkok Urban Area","authors":"Kankamon Thaweephol, Nuwee Wiwatwattana","doi":"10.1109/ICTKE47035.2019.8966854","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966854","url":null,"abstract":"Accurately forecasting fine particulate matter of less than a 2.5 micrometer diameter (PM2.5) concentration levels is important to better manage the air pollution situation and to give advance warnings to residents and officials. In this paper, a Long Short-Term Memory (LSTM) deep neural network model and a Seasonal AutoRegressive Integrated Moving Average with eXogenous regressor (SARIMAX) were trained on air quality and meteorological time series data at the Chokchai metropolitan police station area in Bangkok from 2017 to 2018. After figuring out the best configuration of both algorithms, the performance of the LSTM model to predict PM2.5 concentrations for 24 hours was evaluated and compared against the SARIMAX model. Our experiments indicated that LSTM had a better prediction accuracy as indicated by the RMSE and MAE values for each of the time steps. LSTM could forecast one hour ahead at a very low RMSE of 3.11 micrograms per cubic meter on average, and a MAE of 2.36 micrograms per cubic meter on average, while SARIMAX errors were more than doubled. When the time steps were farther apart, the number of errors were higher for both models.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"466 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123877495","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: 9

Using Social Network Mining for Speech Behavior Analysis of Couples Sitting on a Sofa: (A Semantic Comparison between Happy and Unhappy Relationships) 基于社会网络挖掘的夫妇坐在沙发上的言语行为分析(幸福与不幸福关系的语义比较)

2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE) Pub Date : 2019-11-01 DOI: 10.1109/ICTKE47035.2019.8966888

P. Porouhan, W. Premchaiswadi

{"title":"Using Social Network Mining for Speech Behavior Analysis of Couples Sitting on a Sofa: (A Semantic Comparison between Happy and Unhappy Relationships)","authors":"P. Porouhan, W. Premchaiswadi","doi":"10.1109/ICTKE47035.2019.8966888","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966888","url":null,"abstract":"This study is an extension of our another research entitled “Using Process Mining for Predicting Relationships of Couples Sitting on a Sofa”, whereas the 5 most frequent/possible sitting positions for Happy Couples were identified/discovered as well as the 2 most frequent/possible sitting positions for Unhappy Couples. The main focus and emphasis of the current work is on Speech Behavior Analysis of the both Happy and Unhappy Couples, for each of the above-discussed sitting positions, in a semantic approach. To do this, 8 semantic keywords (i.e., in order to convey/represent the emotional status of the verbal words and phrases exchanged between the couples) were initially defined, and two Process Mining (process discovery) techniques/algorithms were later applied on the (previously collected) Sofa Data as the following: (1) Social Network Miner algorithm (based on the Subcontracting metric) supported by the ProM 6 Package Manager. (2) Fuzzy Miner algorithm (via frequency-based metric) supported by the Disco Fluxicon. Accordingly, the results showed that the occurrence of the keywords “Happy”, “Excited”, “Satisfied” and “In Love” was more frequent/possible in the following sitting positions: “Cuddling in the middle”, “Cuddling in the corner, “Side-by-Side (touching without cuddling)”, “Corner cuddle with tucked leggs” and “Legs on lap”. Alternatively, the occurrence of the keywords “Irritated”, “Sad”, “Angry” and “Worried” was more frequent/possible in the following sitting positions: “Opposite sides of the sofa” and “Sat on different sofas”. This study provides groundwork for further and future studies.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131967355","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

Enhancing Security in Biometric Authentication Systems using Dynamic Third-factor 利用动态第三方因素增强生物特征认证系统的安全性

2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE) Pub Date : 2019-11-01 DOI: 10.1109/ICTKE47035.2019.8966809

Sheena I. Sapuay, B. Gerardo, A. Hernandez

引用次数: 0