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A Hybrid Feature Selection Method for Effective Data Classification in Data Mining Applications 数据挖掘应用中有效数据分类的混合特征选择方法
IF 1
International Journal of Grid and High Performance Computing Pub Date : 2019-01-01 DOI: 10.4018/IJGHPC.2019010101
Ilangovan Sangaiya, A. V. A. Kumar
{"title":"A Hybrid Feature Selection Method for Effective Data Classification in Data Mining Applications","authors":"Ilangovan Sangaiya, A. V. A. Kumar","doi":"10.4018/IJGHPC.2019010101","DOIUrl":"https://doi.org/10.4018/IJGHPC.2019010101","url":null,"abstract":"In data mining, people require feature selection to select relevant features and to remove unimportant irrelevant features from a original data set based on some evolution criteria. Filter and wrapper are the two methods used but here the authors have proposed a hybrid feature selection method to take advantage of both methods. The proposed method uses symmetrical uncertainty and genetic algorithms for selecting the optimal feature subset. This has been done so as to improve processing time by reducing the dimension of the data set without compromising the classification accuracy. This proposed hybrid algorithm is much faster and scales well to the data set in terms of selected features, classification accuracy and running time than most existing algorithms.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"7 1","pages":"1-16"},"PeriodicalIF":1.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86407916","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
A High Performance Parallel Ranking SVM with OpenCL on Multi-core and Many-core Platforms 基于OpenCL的多核和多核平台上的高性能并行排序支持向量机
IF 1
International Journal of Grid and High Performance Computing Pub Date : 2019-01-01 DOI: 10.4018/IJGHPC.2019010102
Huming Zhu, Peidao Li, P. Zhang, Zheng Luo
{"title":"A High Performance Parallel Ranking SVM with OpenCL on Multi-core and Many-core Platforms","authors":"Huming Zhu, Peidao Li, P. Zhang, Zheng Luo","doi":"10.4018/IJGHPC.2019010102","DOIUrl":"https://doi.org/10.4018/IJGHPC.2019010102","url":null,"abstract":"A ranking support vector machine (RSVM) is a typical pairwise method of learning to rank, which is effective in ranking problems. However, the training speed of RSVMs are not satisfactory, especially when solving large-scale data ranking problems. Recent years, many-core processing units (graphics processing unit (GPU), Many Integrated Core (MIC)) and multi-core processing units have exhibited huge superiority in the parallel computing domain. With the support of hardware, parallel programming develops rapidly. Open Computing Language (OpenCL) and Open Multi-Processing (OpenMP) are two of popular parallel programming interfaces. The authors present two high-performance parallel implementations of RSVM, an OpenCL version implemented on multi-core and many-core platforms, and an OpenMP version implemented on multi-core platform. The experimental results show that the OpenCL version parallel RSVM achieved considerable speedup on Intel MIC 7110P, NVIDIA Tesla K20M and Intel Xeon E5-2692v2, and it also shows good portability.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"30 1","pages":"17-28"},"PeriodicalIF":1.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79355947","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: 5
Statically Optimal Binary Search Tree Computation Using Non-Serial Polyadic Dynamic Programming on GPU's GPU上非串行多进动态规划的静态最优二叉搜索树计算
IF 1
International Journal of Grid and High Performance Computing Pub Date : 2019-01-01 DOI: 10.4018/IJGHPC.2019010104
Mohsin Altaf Wani, Manzoor Ahmad
{"title":"Statically Optimal Binary Search Tree Computation Using Non-Serial Polyadic Dynamic Programming on GPU's","authors":"Mohsin Altaf Wani, Manzoor Ahmad","doi":"10.4018/IJGHPC.2019010104","DOIUrl":"https://doi.org/10.4018/IJGHPC.2019010104","url":null,"abstract":"Modern GPUs perform computation at a very high rate when compared to CPUs; as a result, they are increasingly used for general purpose parallel computation. Determining if a statically optimal binary search tree is an optimization problem to find the optimal arrangement of nodes in a binary search tree so that average search time is minimized. Knuth's modification to the dynamic programming algorithm improves the time complexity to O(n2). We develop a multiple GPU-based implementation of this algorithm using different approaches. Using suitable GPU implementation for a given workload provides a speedup of up to four times over other GPU based implementations. We are able to achieve a speedup factor of 409 on older GTX 570 and a speedup factor of 745 is achieved on a more modern GTX 1060 when compared to a conventional single threaded CPU based implementation.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"32 1","pages":"49-70"},"PeriodicalIF":1.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76428020","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
Smart Traffic Management System for Anticipating Unexpected Road Incidents in Intelligent Transportation Systems 智能交通系统中预测道路意外事故的智能交通管理系统
IF 1
International Journal of Grid and High Performance Computing Pub Date : 2018-10-01 DOI: 10.4018/IJGHPC.2018100104
Sahraoui Abdelatif, Derdour Makhlouf, P. Roose
{"title":"Smart Traffic Management System for Anticipating Unexpected Road Incidents in Intelligent Transportation Systems","authors":"Sahraoui Abdelatif, Derdour Makhlouf, P. Roose","doi":"10.4018/IJGHPC.2018100104","DOIUrl":"https://doi.org/10.4018/IJGHPC.2018100104","url":null,"abstract":"This article describes how anticipating unforeseen road events reveal a serious problem in intelligent transportation systems. Due to the diversity of causes, road incidents do not require regular traffic conditions and accurate prediction of these incidents in real-time becomes a complicated task not defined so far. In this article, a smart traffic management system based cloud-assisted service is proposed to preserve the traffic safety by controlling the road segments and predicts the probability of incoming incidents. The proposed cloud-assisted service includes a predictive model based on logistic regression to predict the occurrence of unforeseen incidents. The sudden slowdown of vehicles speeds is the practical case of the article. The classification task of the predictive model incorporates four explained variables, including vehicle speed, the travel time and estimated delay time. The prediction accuracy is proved by checking the model relevance according to the quality of fit and the statistical significance of each explained variable.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"55 1","pages":"67-82"},"PeriodicalIF":1.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86814499","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: 4
A Predictive Map Task Scheduler for Optimizing Data Locality in MapReduce Clusters 一种用于优化MapReduce集群数据局部性的预测映射任务调度程序
IF 1
International Journal of Grid and High Performance Computing Pub Date : 2018-10-01 DOI: 10.4018/IJGHPC.2018100101
Mohamed Merabet, S. Benslimane, M. Barhamgi, Christine Bonnet
{"title":"A Predictive Map Task Scheduler for Optimizing Data Locality in MapReduce Clusters","authors":"Mohamed Merabet, S. Benslimane, M. Barhamgi, Christine Bonnet","doi":"10.4018/IJGHPC.2018100101","DOIUrl":"https://doi.org/10.4018/IJGHPC.2018100101","url":null,"abstract":"This article describes how data locality is becoming one of the most critical factors to affect performance of MapReduce clusters because of network bisection bandwidth becomes a bottleneck. Task scheduler assigns the most appropriate map tasks to nodes. If map tasks are scheduled to nodes without input data, these tasks will issue remote I/O operations to copy the data to local nodes that decrease execution time of map tasks. In that case, prefetching mechanism can be useful to preload the needed input data before tasks is launching. Therefore, the key challenge is how this article can accurately predict the execution time of map tasks to be able to use data prefetching effectively without any data access delay. In this article, it is proposed that a Predictive Map Task Scheduler assigns the most suitable map tasks to nodes ahead of time. Following this, a linear regression model is used for prediction and data locality based algorithm for tasks scheduling. The experimental results show that the method can greatly improve both data locality and execution time of map tasks.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"126 1","pages":"1-14"},"PeriodicalIF":1.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80786900","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
Sketch-Based 3D Model Retrieval Using Attributes 基于草图的3D模型检索使用属性
IF 1
International Journal of Grid and High Performance Computing Pub Date : 2018-07-01 DOI: 10.4018/IJGHPC.2018070105
Haopeng Lei, Guoliang Luo, Yuhua Li, Jianming Liu, Jihua Ye
{"title":"Sketch-Based 3D Model Retrieval Using Attributes","authors":"Haopeng Lei, Guoliang Luo, Yuhua Li, Jianming Liu, Jihua Ye","doi":"10.4018/IJGHPC.2018070105","DOIUrl":"https://doi.org/10.4018/IJGHPC.2018070105","url":null,"abstract":"With the rapid growth of available 3D models on the Internet, how to retrieve 3D models based on hand-drawn sketch retrieval are becoming increasingly important. This article proposes a new sketch-based 3D model retrieval approach. This approach is different from current methods that make use of low-level visual features to capture the search intention of users. The proposed method uses two kinds of semantic attributes, including pre-defined attributes and latent attributes. Specifically, pre-defined attributes are defined manually which can provide prior knowledge about different sketch categories and latent-attributes are more discriminative which can differentiate sketch categories at a finer level. Therefore, these semantic attributes can provide a more descriptive and discriminative meaningful representation than low-level feature descriptors. The experiment results demonstrate that this proposed method can achieve superior performance over previously proposed sketch-based 3D model retrieval methods.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"126 1","pages":"60-75"},"PeriodicalIF":1.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78060106","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
Design and Application of a Containerized Hybrid Transaction Processing and Data Analysis Framework 一个容器化混合事务处理和数据分析框架的设计与应用
IF 1
International Journal of Grid and High Performance Computing Pub Date : 2018-07-01 DOI: 10.4018/IJGHPC.2018070106
Ye Tao, Xiaodong Wang, Xiaowei Xu
{"title":"Design and Application of a Containerized Hybrid Transaction Processing and Data Analysis Framework","authors":"Ye Tao, Xiaodong Wang, Xiaowei Xu","doi":"10.4018/IJGHPC.2018070106","DOIUrl":"https://doi.org/10.4018/IJGHPC.2018070106","url":null,"abstract":"This article describes how rapidly growing data volumes require systems that have the ability to handle massive heterogeneous unstructured data sets. However, most existing mature transaction processing systems are built upon relational databases with structured data. In this article, the authors design a hybrid development framework, to offer greater scalability and flexibility of data analysis and reporting, while keeping maximum compatibility and links to the legacy platforms on which transaction business logics run. Data, service and user interfaces are implemented as a toolset stack, for developing applications with functionalities of information retrieval, data processing, analyzing and visualizing. A use case of healthcare data integration is presented as an example, where information is collected and aggregated from diverse sources. The workflow and simulation of data processing and visualization are also discussed, to validate the effectiveness of the proposed framework.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"48 1","pages":"76-90"},"PeriodicalIF":1.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75346867","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
An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care 深度学习工具在医疗保健大数据分析中的有效性研究
IF 1
International Journal of Grid and High Performance Computing Pub Date : 2018-07-01 DOI: 10.4018/IJGHPC.2018070101
R. Priyadarshini, Rabindra Kumar Barik, C. Panigrahi, Harishchandra Dubey, B. K. Mishra
{"title":"An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care","authors":"R. Priyadarshini, Rabindra Kumar Barik, C. Panigrahi, Harishchandra Dubey, B. K. Mishra","doi":"10.4018/IJGHPC.2018070101","DOIUrl":"https://doi.org/10.4018/IJGHPC.2018070101","url":null,"abstract":"This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"31 1","pages":"1-13"},"PeriodicalIF":1.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72737721","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: 18
Multimedia Feature Mapping and Correlation Learning for Cross-Modal Retrieval 跨模态检索的多媒体特征映射和相关学习
IF 1
International Journal of Grid and High Performance Computing Pub Date : 2018-07-01 DOI: 10.4018/IJGHPC.2018070103
Xu Yuan, Hua Zhong, Zhikui Chen, Fangming Zhong, Yueming Hu
{"title":"Multimedia Feature Mapping and Correlation Learning for Cross-Modal Retrieval","authors":"Xu Yuan, Hua Zhong, Zhikui Chen, Fangming Zhong, Yueming Hu","doi":"10.4018/IJGHPC.2018070103","DOIUrl":"https://doi.org/10.4018/IJGHPC.2018070103","url":null,"abstract":"This article describes how with the rapid increasing of multimedia content on the Internet, the need for effective cross-modal retrieval has attracted much attention recently. Many related works ignore the latent semantic correlations of modalities in the non-linear space and the extraction of high-level modality features, which only focuses on the semantic mapping of modalities in linear space and the use of low-level artificial features as modality feature representation. To solve these issues, the authors first utilizes convolutional neural networks and topic modal to obtain a high-level semantic feature of various modalities. Sequentially, they propose a supervised learning algorithm based on a kernel with partial least squares that can capture semantic correlations across modalities. Finally, the joint model of different modalities is learnt by the training set. Extensive experiments are conducted on three benchmark datasets that include Wikipedia, Pascal and MIRFlickr. The results show that the proposed approach achieves better retrieval performance over several state-of-the-art approaches.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"33 1","pages":"29-45"},"PeriodicalIF":1.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84411320","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
An Efficient Geometric Method for 3D Reconstruction Based on Images 一种有效的基于图像的三维重建几何方法
IF 1
International Journal of Grid and High Performance Computing Pub Date : 2018-04-01 DOI: 10.4018/IJGHPC.2018040103
Ying Zhu, Jingyue Jiang, Zhiling Tang, Fang Wang, Rong Wang, Si Zhong, Xiaonan Luo
{"title":"An Efficient Geometric Method for 3D Reconstruction Based on Images","authors":"Ying Zhu, Jingyue Jiang, Zhiling Tang, Fang Wang, Rong Wang, Si Zhong, Xiaonan Luo","doi":"10.4018/IJGHPC.2018040103","DOIUrl":"https://doi.org/10.4018/IJGHPC.2018040103","url":null,"abstract":"Inthisarticle,anewgeometricmethodfor3Dreconstructionbasedonimagesispresented.Themain advantageofthismethodislowdatastoragerequirementandhighcomputationprocessefficiency. Thekeymathematicalmethodsarecurveandsurfacemodelingtechnology.Inordertoimplement geometricmethodfor3Dreconstruction,anewsurfaceisdevelopednamedasrationalW-spline surfaceviatensorproduct,throughtheimportantpropertiesofthebasisfunction,theauthorsget theimportantpropertiesoftherationalsurface.Bygivingthenewcontrolmatrixandthechangeof theparametersbasedontheimages,3Dreconstructioneffectcanbeobtained.Thissurfaceincludes non-rationalsurfaceandrationalsurface,andtheir tensorproductformsareadopted.Inorder to expressthisalgorithmintuitively,thebilinearW-splinesurfaceisrealizedbytheexperiment.The experimentalresultsshowedthatthegeometricmethodcanbeusedtothe3Dreconstruction. KEywoRDS 3D Reconstruction, Geometric, Image Representation, Tensor Product, W-Spline Surface","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"168 1","pages":"34-46"},"PeriodicalIF":1.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88286123","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
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