Soukaina Mjahed^1,*, Khadija Bouzaachane¹, Ahmad Taher Azar^2,3, Salah El Hadaj¹, Said Raghay¹

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 459-494, 2020, DOI:10.32604/cmes.2020.010791 - 12 October 2020

Abstract This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the “Higgs machine learning challenge 2014” data set. This unsupervised detection goes in this paper analysis through 4 steps: (1) selection of the most informative features from the considered data; (2) definition of the number of clusters based on the elbow criterion. The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters; (3) proposition of a new approach for hybridization of… More >

Yanqiu Cao¹, Chao Tan¹, Genlin Ji^{1, *}

Journal on Big Data, Vol.2, No.1, pp. 19-31, 2020, DOI:10.32604/jbd.2020.01003 - 07 September 2020

Abstract In the last few years, smartphone usage and driver sleepiness have been unanimously considered to lead to numerous road accidents, which causes many scholars to pay attention to autonomous driving. For this complexity scene, one of the major challenges is mining information comprehensively from massive features in vehicle video. This paper proposes a multi-label classification method MCM-VV (Multi-label Classification Method for Vehicle Video) for vehicle video to judge the label of road condition for unmanned system. Method MCM-VV includes a process of feature extraction and a process of multi-label classification. During feature extraction, grayscale, lane… More >

Lianggui Liu^1,*, Wei Li¹, Huiling Jia¹

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 97-106, 2018, DOI:10.32604/cmc.2018.03511

Abstract With the rapid development of mobile communication all over the world, the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities. Mobile phone communication data can be regarded as a type of time series and dynamic time warping (DTW) and derivative dynamic time warping (DDTW) are usually used to analyze the similarity of these data. However, many traditional methods only calculate the distance between time series while neglecting the shape characteristics of time series. In this paper, a novel hybrid method based on the More >