2019 7th International Conference on Information and Communication Technology (ICoICT)最新文献_第9页

The MSOF-DTW Method for Checking Timeseries Similarities 时间序列相似度检测的msofdtw方法

2019 7th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2019-07-01 DOI: 10.1109/ICoICT.2019.8835360

F. A. Yulianto, Kuspriyanto, R. Mengko

引用次数: 3

Online Political Engagement using Twitter among Malaysian Parliamentary Members 马来西亚国会议员使用Twitter进行在线政治参与

2019 7th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2019-07-01 DOI: 10.1109/ICoICT.2019.8835321

K. F. Hashim, Muhammad Fuad bn Othman, Shadi Atalla, Z. Othman, S. Ismail, S. Miniaoui

引用次数: 0

ICoICT 2019 TOC

2019 7th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2019-07-01 DOI: 10.1109/icoict.2019.8835302

引用次数: 0

The Causality Effect on Vector Autoregressive Model: The Case for Rainfall Forecasting 向量自回归模型的因果效应:以降雨预报为例

2019 7th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2019-07-01 DOI: 10.1109/ICoICT.2019.8835379

A. A. Rohmawati, P. H. Gunawan

{"title":"The Causality Effect on Vector Autoregressive Model: The Case for Rainfall Forecasting","authors":"A. A. Rohmawati, P. H. Gunawan","doi":"10.1109/ICoICT.2019.8835379","DOIUrl":"https://doi.org/10.1109/ICoICT.2019.8835379","url":null,"abstract":"In recent decades, a stochastics modeling of weather prediction is gaining popularity among government and researchers. A devastating weather may have profound environmental, human and/or financial loss. It is a familiar fact that the series process over a fixed time horizon following historical time series model. A Vector Autoregressive (VAR) is one of time series models that explain current and past values of the multivariate variables as a linear model. Moreover, the instantaneous interaction between two or more variables deal with a causal relations between those variables. In particular application, observing the response of one variable to an impulse in the other is interesting, allowing a number of further variables as well. The explanation of impulse response is essential for a structural modeling, we explore a causality concept well known as Granger-Causality (G-causes) test. Thus, we shall examine the causal relations between rainfall and temperature, addressed by constructing and forecasting simultaneously bivariate VAR model. Henceforth, we consider the VAR(2) based on a parsimonious principle and Akaike Information Criteria (AIC). Evidently, the result present an exogenous shock of temperature has an effect for rainfall, we called temperature is \"G-causes\" for rainfall. Thus, we argue a temperature do Granger-cause for rainfall, and a vice versa is not properly true. A VAR model provide deeper and more accurate insight into rainfall prediction, shown by an RMSE 0.0021.","PeriodicalId":439440,"journal":{"name":"2019 7th International Conference on Information and Communication Technology (ICoICT)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130070737","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

A DASH Diet Recommendation System for Hypertensive Patients Using Machine Learning 基于机器学习的高血压患者DASH饮食推荐系统

2019 7th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2019-07-01 DOI: 10.1109/ICoICT.2019.8835323

Romeshwar Sookrah, Jaysree Devee Dhowtal, Soulakshmee Devi Nagowah

引用次数: 21

Predicting Staple Food Materials Price Using Multivariables Factors (Regression and Fourier Models with ARIMA) 多变量因素预测主粮价格(ARIMA回归与傅立叶模型)

2019 7th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2019-07-01 DOI: 10.1109/ICoICT.2019.8835193

Said Fadlan Asnhari, P. H. Gunawan, Yanti Rusmawati

{"title":"Predicting Staple Food Materials Price Using Multivariables Factors (Regression and Fourier Models with ARIMA)","authors":"Said Fadlan Asnhari, P. H. Gunawan, Yanti Rusmawati","doi":"10.1109/ICoICT.2019.8835193","DOIUrl":"https://doi.org/10.1109/ICoICT.2019.8835193","url":null,"abstract":"Staple food material prices can be a trending topic in the market. The fluctuation of the price is influenced by many factors. For instance, the weather, oil price, and etc are the external factors of the staple food price. Indeed, the prediction of staple food fluctuation price is important for the farmers, consumers, even government. In this paper, the Linear Regression and Fourier model with ARIMA (Autoregressive Integrated Moving Average) will be used to predict the staple food price which consider the external influences. Here, the results using those two methods are shown in a good agreement with the observation price at market. However, the highest accuracy in predicting price using Fourier regression with ARIMA is obtained for staple food onion which is 96.57%. Meanwhile, using multiple linear regression with ARIMA, the highest accuracy is obtained for staple food red chili with 99.84%. Overall, in this research, Fourier regression with ARIMA is observed better than multiple linear regression with ARIMA method, since the accuracy of Fourier regression with ARIMA is quite stable without disturbance of fluctuation existing data.","PeriodicalId":439440,"journal":{"name":"2019 7th International Conference on Information and Communication Technology (ICoICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129163870","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

Financial Network Approach for Modeling about Company Bankruptcy 公司破产建模的财务网络方法

2019 7th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2019-07-01 DOI: 10.1109/ICoICT.2019.8835189

Jorgie Bartelsi Permana, Yanti Rusmawati, Muhammad Arzaki

引用次数: 1

A Multi-label Classification on Topics of Quranic Verses (English Translation) Using Backpropagation Neural Network with Stochastic Gradient Descent and Adam Optimizer 基于随机梯度下降和Adam优化器的反向传播神经网络古兰经(英译)主题多标签分类

2019 7th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2019-07-01 DOI: 10.1109/ICoICT.2019.8835362

Nanang Saiful Huda, M. S. Mubarok, Adiwijaya

引用次数: 6

Smart Living Society Begins with A Holistic Digital Economy: A Multi-Level Insight 智慧生活社会始于整体数字经济:多层次视角

2019 7th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2019-07-01 DOI: 10.1109/ICoICT.2019.8835199

M.P Low, Chay Yoke Chung, Leng Yean Ung, P. Tee, Thiam-Yong Kuek

引用次数: 3

Mathematical Formulation and Implementation of Query Inversion Techniques in RDBMS for Tracking Data Provenance RDBMS中用于跟踪数据来源的查询反转技术的数学公式和实现

2019 7th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2019-07-01 DOI: 10.1109/ICoICT.2019.8835290

Anika Tabassum, Anannya Islam Nady, Mohammad Rezwanul Huq

{"title":"Mathematical Formulation and Implementation of Query Inversion Techniques in RDBMS for Tracking Data Provenance","authors":"Anika Tabassum, Anannya Islam Nady, Mohammad Rezwanul Huq","doi":"10.1109/ICoICT.2019.8835290","DOIUrl":"https://doi.org/10.1109/ICoICT.2019.8835290","url":null,"abstract":"Nowadays the massive amount of data is produced from different sources and lots of applications are processing these data to discover insights. Sometimes we may get unexpected results from these applications and it is not feasible to trace back to the data origin manually to find the source of errors. To avoid this problem, data must be accompanied by the context of how they are processed and analyzed. Especially, data-intensive applications like e-Science always require transparency and therefore, we need to understand how data has been processed and transformed. In this paper, we propose mathematical formulation and implementation of query inversion techniques to trace the provenance of data in a relational database management system (RDBMS). We build mathematical formulations of inverse queries for most of the relational algebra operations and show the formula for join operations in this paper. We, then, implement these formulas of inversion techniques and the experiment shows that our proposed inverse queries can successfully trace back to original data i.e. finding data provenance.","PeriodicalId":439440,"journal":{"name":"2019 7th International Conference on Information and Communication Technology (ICoICT)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121196612","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