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Int. J. Rough Sets Data Anal.最新文献

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Modified LexRank for Tweet Summarization 修改了Tweet摘要的LexRank
Int. J. Rough Sets Data Anal. Pub Date : 2016-10-01 DOI: 10.4018/IJRSDA.2016100106
Avinash Samuel, D. Sharma
{"title":"Modified LexRank for Tweet Summarization","authors":"Avinash Samuel, D. Sharma","doi":"10.4018/IJRSDA.2016100106","DOIUrl":"https://doi.org/10.4018/IJRSDA.2016100106","url":null,"abstract":"Summary generation is an important process in those conditions where the user needs to obtain the key features of the document without having to go through the whole document itself. The summarization process is of basically two types: 1 Single document Summarization and, 2 Multiple Document Summarization. But here the microblogging environment is taken into account which have a restriction on the number of characters contained within a post. Therefore, single document summarizers are not applicable to this condition. There are many features along which the summarization of the microblog post can be done for example, post's topic, it's posting time, happening of the event, etc. This paper proposes a method that includes the temporal features of the microblog posts to develop an extractive summary of the event from each and every post, which will further increase the quality of the summary created as it includes all the key features in the summary.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116583166","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: 23
Olympics Big Data Prognostications 奥运大数据预测
Int. J. Rough Sets Data Anal. Pub Date : 2016-10-01 DOI: 10.4018/IJRSDA.2016100103
Arushi Jain, Vishal Bhatnagar
{"title":"Olympics Big Data Prognostications","authors":"Arushi Jain, Vishal Bhatnagar","doi":"10.4018/IJRSDA.2016100103","DOIUrl":"https://doi.org/10.4018/IJRSDA.2016100103","url":null,"abstract":"Data is continuously snowballing over the years, gradually a huge growth is seen in data to store and tame to yield meticulous result. It gives rise to a concept nowadays, reckoned as big data analytics. With the summer Olympics at Rio de Janeiro, Brazil in the year 2016 round the corner, we, the authors have implemented a mathematical model by implementing efficient map reduce program to predict the number of medals each country might bag at the games. Based on a number of factors such as historical performance of the country in terms of medals won, the performance of athletes, financial scenario in the country, fitness levels and nutrition of athletes along with familiarity to the playing conditions can be used to come up with a reliable estimate.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132501044","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: 25
Modeling Rumors in Twitter: An Overview 推特中的谣言建模:概述
Int. J. Rough Sets Data Anal. Pub Date : 2016-10-01 DOI: 10.4018/IJRSDA.2016100104
Rhythm Walia, M. Bhatia
{"title":"Modeling Rumors in Twitter: An Overview","authors":"Rhythm Walia, M. Bhatia","doi":"10.4018/IJRSDA.2016100104","DOIUrl":"https://doi.org/10.4018/IJRSDA.2016100104","url":null,"abstract":"With the advent of web 2.0 and anonymous free Internet services available to almost everyone, social media has gained immense popularity in disseminating information. It has become an effective channel for advertising and viral marketing. People rely on social networks for news, communication and it has become an integral part of our daily lives. But due to the limited accountability of users, it is often misused for the spread of rumors. Such rumor diffusion hampers the credibility of social media and may spread social panic. Analyzing rumors in social media has gained immense attention from the researchers in the past decade. In this paper the authors provide a survey of work in rumor analysis, which will serve as a stepping-stone for new researchers. They organized the study of rumors into four categories and discussed state of the art papers in each with an in-depth analysis of results of different models used and a comparative analysis between approaches used by different authors.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124010877","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: 14
Gene Expression Analysis based on Ant Colony Optimisation Classification 基于蚁群优化分类的基因表达分析
Int. J. Rough Sets Data Anal. Pub Date : 2016-07-01 DOI: 10.4018/IJRSDA.2016070104
G. Schaefer
{"title":"Gene Expression Analysis based on Ant Colony Optimisation Classification","authors":"G. Schaefer","doi":"10.4018/IJRSDA.2016070104","DOIUrl":"https://doi.org/10.4018/IJRSDA.2016070104","url":null,"abstract":"Microarray studies and gene expression analysis have received significant attention over the last few years and provide many promising avenues towards the understanding of fundamental questions in biology and medicine. In this paper, the authors investigate the application of ant colony optimisation ACO based classification for the analysis of gene expression data. They employ cAnt-Miner, a variation of the classical Ant-Miner classifier, which is capable of interpreting the numerical gene expression data. Experimental results on well-known gene expression datasets show that the ant-based approach is capable of extracting a compact rule base while providing good classification performance.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125097153","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: 10
Theoretical Analysis of Different Classifiers under Reduction Rough Data Set: A Brief Proposal 简约粗糙数据集下不同分类器的理论分析
Int. J. Rough Sets Data Anal. Pub Date : 2016-07-01 DOI: 10.4018/IJRSDA.2016070101
Shamim Ripon, Md. Sarwar Kamal, S. Hossain, N. Dey
{"title":"Theoretical Analysis of Different Classifiers under Reduction Rough Data Set: A Brief Proposal","authors":"Shamim Ripon, Md. Sarwar Kamal, S. Hossain, N. Dey","doi":"10.4018/IJRSDA.2016070101","DOIUrl":"https://doi.org/10.4018/IJRSDA.2016070101","url":null,"abstract":"Rough set plays vital role to overcome the complexities, vagueness, uncertainty, imprecision, and incomplete data during features analysis. Classification is tested on certain dataset that maintain an exact class and review process where key attributes decide the class positions. To assess efficient and automated learning, algorithms are used over training datasets. Generally, classification is supervised learning whereas clustering is unsupervised. Classifications under mathematical models deal with mining rules and machine learning. The Objective of this work is to establish a strong theoretical and manual analysis among three popular classifier namely K-nearest neighbor K-NN, Naive Bayes and Apriori algorithm. Hybridization with rough sets among these three classifiers enables enable to address larger datasets. Performances of three classifiers have tested in absence and presence of rough sets. This work is in the phase of implementation for DNA Deoxyribonucleic Acid datasets and it will design automated system to assess classifier under machine learning environment.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128677270","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: 28
A Disaster Management Specific Mobility Model for Flying Ad-hoc Network 飞行Ad-hoc网络灾害管理专用机动性模型
Int. J. Rough Sets Data Anal. Pub Date : 2016-07-01 DOI: 10.4018/IJRSDA.2016070106
Amartya Mukherjee, N. Dey, N. Kausar, A. Ashour, Redha Taïar, A. Hassanien
{"title":"A Disaster Management Specific Mobility Model for Flying Ad-hoc Network","authors":"Amartya Mukherjee, N. Dey, N. Kausar, A. Ashour, Redha Taïar, A. Hassanien","doi":"10.4018/IJRSDA.2016070106","DOIUrl":"https://doi.org/10.4018/IJRSDA.2016070106","url":null,"abstract":"The extended Mobile Ad-hoc Network architecture is a paramount research domain due to a wide enhancement of smart phone and open source Unmanned Aerial Vehicle UAV technology. The novelty of the current work is to design a disaster aware mobility modeling for a Flying Ad-hoc network infrastructure, where the UAV group is considered as nodes of such ecosystem. This can perform a collaborative task of a message relay, where the mobility modeling under a \"Post Disaster\" is the main subject of interest, which is proposed with a multi-UAV prototype test bed. The impact of various parameters like UAV node attitude, geometric dilution precision of satellite, Global Positioning System visibility, and real life atmospheric upon the mobility model is analyzed. The results are mapped with the realistic disaster situation. A cluster based mobility model using the map oriented navigation of nodes is emulated with the prototype test bed.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115606128","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: 81
Accident Causation Factor Analysis of Traffic Accidents using Rough Relational Analysis 基于粗糙关联分析法的交通事故成因分析
Int. J. Rough Sets Data Anal. Pub Date : 2016-07-01 DOI: 10.4018/IJRSDA.2016070105
C. Erden, N. Çelebi
{"title":"Accident Causation Factor Analysis of Traffic Accidents using Rough Relational Analysis","authors":"C. Erden, N. Çelebi","doi":"10.4018/IJRSDA.2016070105","DOIUrl":"https://doi.org/10.4018/IJRSDA.2016070105","url":null,"abstract":"The aim of this study is to show that the decision rules generated from Rough Sets Theory can be used for a new relational analysis. Rough Sets Theory generally works with small datasets more than big data. If we can deal with the decision rules and its complexities, it is still possible to analyze big data with Rough Set Theory. That is why in this study the authors offer a statistical method to overdue problems which belongs to big data. According statistical methods, a lots of decision rules generated from rough sets theory become useful information. Using a real case data on the traffic accident which were taken place in USA in 2013, this paper finds the relationships between accident causation factors which may be referred to decision makers in the field of traffic.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121381005","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: 27
Swarm Intelligence for Automatic Video Image Contrast Adjustment 基于群体智能的视频图像对比度自动调整
Int. J. Rough Sets Data Anal. Pub Date : 2016-07-01 DOI: 10.4018/IJRSDA.2016070102
R. Aparna
{"title":"Swarm Intelligence for Automatic Video Image Contrast Adjustment","authors":"R. Aparna","doi":"10.4018/IJRSDA.2016070102","DOIUrl":"https://doi.org/10.4018/IJRSDA.2016070102","url":null,"abstract":"Video surveillance has become an integrated part of today's life. We are surrounded by video cameras in all the public places and organizations in our day to day life. Many useful information like face detection, traffic analysis, object classification, crime analysis can be assessed from the recorded videos. Image enhancement plays a vital role to extract any useful information from the images. Enhancing the video frames is a major part as it serves the further analysis of video sequences. The proposed paper discusses the automatic contrast adjustment in the video frames. A new hybrid algorithm was developed using the spatial domain method and Artificial Bee Colony Algorithm ABC, a swarm intelligence based technique for image enhancement. The proposed algorithm was tested using the traffic surveillance images. The proposed method produced good results and better quality picture for varied levels of poor quality video frames.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125872139","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: 10
A Study of Sub-Pattern Approach in 2D Shape Recognition Using the PCA and Ridgelet PCA 基于主成分分析和脊波主成分分析的二维形状识别子模式方法研究
Int. J. Rough Sets Data Anal. Pub Date : 2016-04-01 DOI: 10.4018/IJRSDA.2016040102
Muzameel Ahmed, Manjunath Aradhya
{"title":"A Study of Sub-Pattern Approach in 2D Shape Recognition Using the PCA and Ridgelet PCA","authors":"Muzameel Ahmed, Manjunath Aradhya","doi":"10.4018/IJRSDA.2016040102","DOIUrl":"https://doi.org/10.4018/IJRSDA.2016040102","url":null,"abstract":"In the area of computer vision and machine intelligence, image recognition is a prominent field. There have been several approaches in use for 2D shape recognition using shape features extraction. This paper suggest, subspace method approach. Normally in the earlier methods proposed so far, an entire image is considered in the training and matching operation, with sub pattern approach a given image is partitioned in to many sub images. The recognition process is carried out in two steps, in the first step the Ridgelet transform is used to feature extraction, in the second step PCA is used for dimensionality reduction. For recognition efficiency rate a test study is conducted by using seventeen different distance measure technique. The training and testing process is conducted using leave-one-out strategy. The proposed method is tested on the standard MPEG-7 dataset. The results of Ridgelet PCA are compared with PCA results.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117106104","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: 27
Movie Analytics for Effective Recommendation System using Pig with Hadoop 基于Pig和Hadoop的电影分析推荐系统
Int. J. Rough Sets Data Anal. Pub Date : 2016-04-01 DOI: 10.4018/IJRSDA.2016040106
Arushi Jain, Vishal Bhatnagar
{"title":"Movie Analytics for Effective Recommendation System using Pig with Hadoop","authors":"Arushi Jain, Vishal Bhatnagar","doi":"10.4018/IJRSDA.2016040106","DOIUrl":"https://doi.org/10.4018/IJRSDA.2016040106","url":null,"abstract":"Movies have been a great source of entertainment for the people ever since their inception in the late 18th century. The term movie is very broad and its definition contains language and genres such as drama, comedy, science fiction and action. The data about movies over the years is very vast and to analyze it, there is a need to break away from the traditional analytics techniques and adopt big data analytics. In this paper the authors have taken the data set on movies and analyzed it against various queries to uncover real nuggets from the dataset for effective recommendation system and ratings for the upcoming movies.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"361 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122764418","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: 30
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