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International Journal of Big Data Intelligence and Applications最新文献

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Considerations for the Effective National CDO Policy 对有效的国家CDO政策的考虑
International Journal of Big Data Intelligence and Applications Pub Date : 2022-01-01 DOI: 10.4018/ijbdia.315767
Jinmyeong Lee, S. Yoon, Beopyeon Kim, Hunyeong Kwon
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引用次数: 0
A LIGHTWEIGHT SYSTEM TOWARDS VIEWING ANGLE AND CLOTHING VARIATION IN GAIT RECOGNITION 一种面向视角和服装变化的轻量级步态识别系统
International Journal of Big Data Intelligence and Applications Pub Date : 2021-01-01 DOI: 10.4018/ijbdia.287616
{"title":"A LIGHTWEIGHT SYSTEM TOWARDS VIEWING ANGLE AND CLOTHING VARIATION IN GAIT RECOGNITION","authors":"","doi":"10.4018/ijbdia.287616","DOIUrl":"https://doi.org/10.4018/ijbdia.287616","url":null,"abstract":"In this paper, the authors have proposed a computationally efficient, robust, and lightweight system for gait recognition. The proposed system contains two main stages: In the first stage, a classification network identifies optical flow corners in the normalized silhouette and calculates the distances traveled in every viewpoint which is further used by a regression model to identify the viewing angle. In the second stage, a feature extraction network computes the Gait Energy Image (GEI) for every viewpoint and then uses Principal Component Analysis (PCA) to extract low dimensional feature vectors from these GEI images. Finally, a multi-layer perceptron model is trained using the extracted principal components for every viewing angle. The performance of a system is comprehensively evaluated on the CASIA B and OULP gait dataset. The experimental results demonstrate the superior performance of a proposed system in viewing angle classification (100% accuracy), gait recognition (100% accuracy in normal walk), computational efficiency, robustness to clothing, and viewing angle variation.","PeriodicalId":272065,"journal":{"name":"International Journal of Big Data Intelligence and Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128176379","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
Classifying UNSW-NB15 Network Traffic in the Big Data Framework using Random Forest in Spark 基于Spark随机森林的大数据框架下UNSW-NB15网络流量分类
International Journal of Big Data Intelligence and Applications Pub Date : 2021-01-01 DOI: 10.4018/ijbdia.287617
{"title":"Classifying UNSW-NB15 Network Traffic in the Big Data Framework using Random Forest in Spark","authors":"","doi":"10.4018/ijbdia.287617","DOIUrl":"https://doi.org/10.4018/ijbdia.287617","url":null,"abstract":"The focus of this work is on detecting and classifying attacks in network traffic using a binary as well as multi-class machine learning classifier, Random Forest, in a distributed Big Data environment using Apache Spark. The classifier is tested using the UNSW-NB15 dataset. Major problems in these types of datasets include high dimensionality and imbalanced data. To address the issue of high dimensionality, both Information Gain as well as Principal Components Analysis (PCA) were applied before training and testing the data using Random Forest in Apache Spark. Binary as well as multi-class Random Forest classifiers were compared in a distributed environment, with and without using PCA, using various number of Spark cores and Random Forest trees, in terms of performance time and statistical measures. The highest accuracy was obtained by the binary classifier at 99.94%, using 8 cores and 30 trees. This study obtained higher accuracy and lower FAR rates than previously achieved, with low testing times.","PeriodicalId":272065,"journal":{"name":"International Journal of Big Data Intelligence and Applications","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131938389","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
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