2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)最新文献_第2页

Welcome Message from the Chair 主席致欢迎辞

2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA) Pub Date : 2020-07-01 DOI: 10.1109/databia50434.2020.9190587

E. Yiridoe

引用次数: 0

Attribute Selection Effect on Tree-Based Classifiers for Letter Recognition 属性选择对基于树的字母识别分类器的影响

2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA) Pub Date : 2020-07-01 DOI: 10.1109/DATABIA50434.2020.9190393

Rizal Dwi Prayogo, N. Ikhsan

{"title":"Attribute Selection Effect on Tree-Based Classifiers for Letter Recognition","authors":"Rizal Dwi Prayogo, N. Ikhsan","doi":"10.1109/DATABIA50434.2020.9190393","DOIUrl":"https://doi.org/10.1109/DATABIA50434.2020.9190393","url":null,"abstract":"This study presents evaluation measures for attribute selection effect on classification performance in classifying the 26 uppercase letters in the English alphabet. Attribute selection is an essential method in the classification phase to measure the attribute significance related to the class label since not all attributes are significant for letter recognition. Therefore, insignificant attributes should be reduced by applying dimensionality reduction. The filter-based attribute selection methods using Information Gain, Gain Ratio, Correlation, and Chi-square are proposed. The performances of attribute selection are evaluated by tree-based classifiers using J48, CART, and Random Forest algorithms with the measures of accuracy, precision, recall, F-measure, and processing time. The results indicate that the use of attribute selection methods provides the increase of classification performances for letter recognition. The reduction of insignificant attributes is discussed in terms of the effect on classification accuracy and the processing time. The optimal number of selected attributes is determined for each attribute selection, it provides better classification accuracy with more time-efficient.","PeriodicalId":165106,"journal":{"name":"2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114211578","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

Predicting a Hit Song with Machine Learning: Is there an apriori secret formula? 用机器学习预测热门歌曲:是否有一个先验的秘密公式?

2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA) Pub Date : 2020-07-01 DOI: 10.1109/DATABIA50434.2020.9190613

Agha Haider Raza, Krishnadas Nanath

引用次数: 11

Air Pollution Monitoring System Using Waspmote Gases Sensor Board in Wireless Sensor Network 无线传感器网络中使用水蒸气传感器板的空气污染监测系统

2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA) Pub Date : 2020-07-01 DOI: 10.1109/DATABIA50434.2020.9190503

B. Siregar, Azmi Nur Nasution, D. Arisandi

引用次数: 3

HealFavor: Dataset and A Prototype System for Healthcare ChatBot HealFavor:医疗聊天机器人的数据集和原型系统

2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA) Pub Date : 2020-07-01 DOI: 10.1109/DATABIA50434.2020.9190281

Abdullah Faiz Ur Rahman Khilji, Sahinur Rahman Laskar, Partha Pakray, R. A. Kadir, M. S. Lydia, Sivaji Bandyopadhyay

引用次数: 6

Multi-Objective Feature Selection based on Clustering and Principal Component Analysis by Enhanced Electromagnetic-likes Algorithm 基于聚类和增强类电磁算法主成分分析的多目标特征选择

2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA) Pub Date : 2020-07-01 DOI: 10.1109/DATABIA50434.2020.9190226

Majid Abdolrazzagh, Shokooh Pour Mahyabadi, Somaye Jalali-Poor, Erna Budhiarti Nababan

{"title":"Multi-Objective Feature Selection based on Clustering and Principal Component Analysis by Enhanced Electromagnetic-likes Algorithm","authors":"Majid Abdolrazzagh, Shokooh Pour Mahyabadi, Somaye Jalali-Poor, Erna Budhiarti Nababan","doi":"10.1109/DATABIA50434.2020.9190226","DOIUrl":"https://doi.org/10.1109/DATABIA50434.2020.9190226","url":null,"abstract":"Given the rapid growth of data and the reduced implementation quality of data mining and pattern extraction techniques, the use of feature reduction has become an important challenge of data mining and pattern recognition. An important goal of data reduction techniques is to make the minimum effort and achieve the maximum efficiency of data selection for the implementation of data mining process. The two primary objectives of feature selection are to minimize the errors of the patterns identified in the reduced subset and minimize the number of features. The majority of available feature selection algorithms adopts a single-objective approach. This is the first paper focused on clustering used as the identifier of unsupervised hidden patterns. It is also focused on the principal component analysis (PCA) to analyze the values of the features. The goals of the new multi-objective feature selection problem are to minimize the coefficient of PCA, maximize the accuracy of k-medoids clustering, and minimize the number of selected features. Another innovation of this study was to select the best subset of features at the best performance by using the electromagnetism-like mechanism (EM) algorithm. The proposed method was tested on 14 standard UCI datasets. The results indicated the competitive advantage of this algorithm over other algorithms implemented to solve this problem.","PeriodicalId":165106,"journal":{"name":"2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)","volume":"155 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128694067","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 similarity for new meanings 新含义的相似性

2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA) Pub Date : 2020-07-01 DOI: 10.1109/DATABIA50434.2020.9190316

M. K. Nasution, Opim Salim Sitompul, M. Elveny, Rahmad Syah, Romi Fadillah Rahmat

引用次数: 3

Implementation of Best First Search Algorithm in Determining Best Route Based on Traffic Jam Level in Medan City 棉兰市基于交通拥堵程度确定最佳路径的最佳优先搜索算法实现

2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA) Pub Date : 2020-07-01 DOI: 10.1109/DATABIA50434.2020.9190626

D. Rachmawati, P. Sihombing, Billy Halim

{"title":"Implementation of Best First Search Algorithm in Determining Best Route Based on Traffic Jam Level in Medan City","authors":"D. Rachmawati, P. Sihombing, Billy Halim","doi":"10.1109/DATABIA50434.2020.9190626","DOIUrl":"https://doi.org/10.1109/DATABIA50434.2020.9190626","url":null,"abstract":"Traffic congestion is a problem for almost everyone in big cities. Based on the 2017 traffic condition report released by Inrix, a transportation analysis company, on average, Indonesians were wasting time about 51 hours a year stuck in traffic congestion. Therefore, one of the solutions to overcome this traffic jam problem is by creating an application or system which can find routes with the lowest possible level of a traffic jam from the origin location to the destination. Best First Search algorithm works by selecting the best nodes (with the most economical cost) among other generated nodes from the initial node to the goal node. The route generated by the system will be shown on the map, along with the distance, travel time, algorithm running time, and traffic flow condition of the route. The implementation and testing on the system showed that the distance traveled by walking was less than or equal to the distance traveled by driving. On the other hand, using the same travel mode, the route from origin to destination had different distances and travel time than the vice-versa because of the Best First Search algorithm itself. Nevertheless, in some cases, the distance from the origin to the destination may be the same as from destination to origin because both of them are closed together. The average distance, travel time, and algorithm running time generated from the testing were 2.8 km, 20.375 minutes, and 0.182 seconds. However, the routes generated by the system weren't always optimal because the Best First Search algorithm wasn't taking into account the total travel time taken.","PeriodicalId":165106,"journal":{"name":"2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133525289","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

Investing in Applications Based on Predictive Modeling 基于预测建模的应用投资

2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA) Pub Date : 2020-07-01 DOI: 10.1109/DATABIA50434.2020.9190301

S. Saad, Krishnadas Nanath

引用次数: 0