Future Computing and Informatics Journal最新文献_第5页

Applying spatial intelligence for decision support systems 将空间智能应用于决策支持系统

Future Computing and Informatics Journal Pub Date : 2018-12-01 DOI: 10.1016/j.fcij.2018.11.001

Amira M. Idrees , Mohamed H. Ibrahim , Ahmed I. El Seddawy

引用次数: 16

Antlion optimization and boosting classifier for spam email detection 垃圾邮件检测的Antlion优化和增强分类器

Future Computing and Informatics Journal Pub Date : 2018-12-01 DOI: 10.1016/j.fcij.2018.11.006

Amany A. Naem , Neveen I. Ghali , Afaf A. Saleh

{"title":"Antlion optimization and boosting classifier for spam email detection","authors":"Amany A. Naem , Neveen I. Ghali , Afaf A. Saleh","doi":"10.1016/j.fcij.2018.11.006","DOIUrl":"10.1016/j.fcij.2018.11.006","url":null,"abstract":"<div><p>Spam emails are not necessary, though they are harmful as they include viruses and spyware, so there is an emerging need for detecting spam emails. Several methods for detecting spam emails were suggested based on the methods of machine learning, which were submitted to reduce non-relevant emails and get results of high precision for spam email classification. In this work, a new predictive method is submitted based on antlion optimization (ALO) and boosting termed as ALO-Boosting for solving spam emails problem. ALO is a computational model imitates the preying technicality of antlions to ants in the life cycle. Where ALO was utilized to modify the actual place of the population in the separate seeking area, thus obtaining the optimum feature subset for the better classification submit based on boosting classifier. Boosting classifier is a classification algorithm that points to a group of algorithms which modifies soft learners into powerful learners. The proposed procedure is compared against support vector machine (SVM), k-nearest neighbours algorithm (KNN), and bootstrap aggregating (Bagging) on spam email datasets in a set of implementation measures. The experimental outcomes show the ability of the proposed method to successfully detect optimum features with the smallest value of selected features and a high precision of measures for spam email classification based on boosting classifier.</p></div>","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"3 2","pages":"Pages 436-442"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.fcij.2018.11.006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89340397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Time series forecasting using artificial neural networks methodologies: A systematic review 使用人工神经网络方法的时间序列预测:系统回顾

Future Computing and Informatics Journal Pub Date : 2018-12-01 DOI: 10.1016/j.fcij.2018.10.003

Ahmed Tealab

引用次数: 279

WITHDRAWN: Tabu search based optimization of PID parameters for temperature control of non-isothermal CSTR 基于禁忌搜索的非等温CSTR温度控制PID参数优化

Future Computing and Informatics Journal Pub Date : 2018-12-01 DOI: 10.1016/J.FCIJ.2018.11.007

Mohit Jain, Vijander Singh, A. Rani

引用次数: 0

A linguistic approach for opinionated documents summary 固执己见的文献摘要的语言学方法

Future Computing and Informatics Journal Pub Date : 2018-12-01 DOI: 10.1016/j.fcij.2017.10.004

Mahmoud Othman , Hesham Hassan , Ramadan Moawad , Amira M. Idrees

{"title":"A linguistic approach for opinionated documents summary","authors":"Mahmoud Othman , Hesham Hassan , Ramadan Moawad , Amira M. Idrees","doi":"10.1016/j.fcij.2017.10.004","DOIUrl":"10.1016/j.fcij.2017.10.004","url":null,"abstract":"<div><p>Now, the web pages contain opinions on almost anything, at review sites, forums, discussion groups, and blogs which called user generated content. They contain valuable information for different users such as persons or organizations, the processes of collecting, analyzing and classifying them to positive or negative opinions in addition to summarizing the opinions are considered a very important research issue. Summarizing opinions helps users to explore the opinion of others about the key aspects of a topic or an entity. The proposed opinion summarization system receives a document that contains sentences expressing opinions about an entity and generates a summary considering the important aspects, their relations, their sentiments and the textual evidences, as expressed in the reviews. In this paper we present a linguistic approach to summarize the opinionated documents across different domains, our evaluation based on a dataset of hotels, cars and various products reviews. The reviews collected from Tripadvisor, Amazon and Edmunds, each review document consist of a set of unordered, redundant reviews sentence, there are approximately 100 sentences per review document. The summary depends on the type of the opinion which is direct, comparative, or superlative. Each type is assigned to a specialist who is responsible for the summary.</p></div>","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"3 2","pages":"Pages 152-158"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.fcij.2017.10.004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73847188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

WITHDRAWN: Forecasting of nonlinear time series using artificial neural network 摘要:非线性时间序列的人工神经网络预测

Future Computing and Informatics Journal Pub Date : 2018-12-01 DOI: 10.1016/j.fcij.2017.06.001

Ahmed Tealab , Hesham Hefny , Amr Badr

引用次数: 8

TGA: Team game algorithm TGA:团队游戏算法

Future Computing and Informatics Journal Pub Date : 2018-12-01 DOI: 10.1016/j.fcij.2018.03.002

M.J. Mahmoodabadi , M. Rasekh , T. Zohari

引用次数: 29

A fast SIFT based method for copy move forgery detection 基于SIFT的快速复制移动伪造检测方法

Future Computing and Informatics Journal Pub Date : 2018-12-01 DOI: 10.1016/j.fcij.2018.03.001

Hesham A. Alberry , Abdelfatah A. Hegazy , Gouda I. Salama

引用次数: 3

An optimization approach for automated unit test generation tools using multi-objective evolutionary algorithms 使用多目标进化算法的自动化单元测试生成工具的优化方法

Future Computing and Informatics Journal Pub Date : 2018-12-01 DOI: 10.1016/j.fcij.2018.02.004

Samar Ali Abdallah , Ramadan Moawad , Esaam Eldeen Fawzy

引用次数: 1

Partition based clustering of large datasets using MapReduce framework: An analysis of recent themes and directions 使用MapReduce框架的基于分区的大型数据集聚类:最近的主题和方向分析

Future Computing and Informatics Journal Pub Date : 2018-12-01 DOI: 10.1016/j.fcij.2018.06.002

Tanvir Habib Sardar, Zahid Ansari

{"title":"Partition based clustering of large datasets using MapReduce framework: An analysis of recent themes and directions","authors":"Tanvir Habib Sardar, Zahid Ansari","doi":"10.1016/j.fcij.2018.06.002","DOIUrl":"10.1016/j.fcij.2018.06.002","url":null,"abstract":"<div><p>Data clustering is one of the fundamental techniques in scientific analysis and data mining, which describes a dataset according to similarities among its objects. Partition based clustering algorithms are the most popular and widely used clustering technique. In this information era, due to the digitization of every field, the huge volume of data is available to data analysts. The quick growth of such datasets makes decade old computing platforms, programming paradigms, and clustering algorithms become inadequate to obtain knowledge from these datasets. To cluster such large datasets, Hadoop distributed platform, MapReduce programming paradigm and modified clustering algorithms are being used to shrink the computational time by distributing clustering job across multiple computing nodes. This paper provides a comprehensive review of Hadoop and MapReduce and their components. This paper aims to survey recent research works on partition based clustering algorithms which use MapReduce as their programming paradigm. In many recent works, the traditional partition based clustering algorithms like K-means, K-prototypes, K-medoids, K-modes and Fuzzy C-means are modified for MapReduce paradigm in order to obtain different clustering objectives on different datasets for reducing the computational time. The contribution of this paper is (1) to provide an overview of clustering challenges in real world large dataset clustering and the role of MapReduce programming paradigm and its supporting platforms in dealing the challenges for several tasks in different datasets and (2) to review recent works in partition based clustering using MapReduce paradigm for different clustering objectives for different datasets employing different strategies.</p></div>","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"3 2","pages":"Pages 247-261"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.fcij.2018.06.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78811683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 23