Peng Tang^∗, Yu Wang, Ning Shen

Computer Systems Science and Engineering, Vol.35, No.4, pp. 237-246, 2020, DOI:10.32604/csse.2020.35.237

Abstract In today’s modern society, the physical fitness of college students is gradually declining. In this paper, a prediction model for college students’ physical fitness is established, in which support vector regression (SVR) and k-means clustering are combined together for the prediction of college students’ fitness. Firstly, the physical measurement data of college students are classified according to gender and class characteristics. Then, the k-means clustering method is used to classify the physical measurement data of college students. Next, the physical characteristics of college students are extracted by SVR to establish the prediction model of physical indicators, and the model for… More >

C. Raja Kumar¹, VE. Jayanthi²

Computer Systems Science and Engineering, Vol.34, No.3, pp. 123-129, 2019, DOI:10.32604/csse.2019.34.123

Abstract Collaborative Filtering (CF) is one of the popular methodology in recommender systems. It suffers from the data sparsity problem, recommendation inaccuracy and big-error in predictions. In this paper, the efficient advisory tool is implemented for the younger generation to choose their right career based on their knowledge. It acquires the notions of indiscernible relation from Fuzzy Rough Sets Theory (FRST) and propose a novel algorithm named as Fuzzy Rough Set Theory Based Collaborative Filtering Algorithm (FRSTBCF). To evaluate the model, data is prepared using the cross validation method. Based on that, ratings are evaluated by calculating the MAE (mean average… More >

Zhenhua Guo^1,2, Lixin Zhang^1,*, Xue Hu¹, Huanmei Chen²

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 625-630, 2020, DOI:10.32604/iasc.2020.013941

Abstract Wind speed prediction is an important part of the wind farm management and wind power grid connection. Having accurate prediction of short-term wind speed is the basis for predicting wind power. This paper proposes a short-term wind speed prediction strategy based on the wavelet analysis and the multilayer perceptual neural network for the Dabancheng area, in China. Four wavelet neural network models using the Morlet function as the wavelet basis function were developed to forecast short-term wind speed in January, April, July, and October. Predicted wind speed was compared across the four models using the mean square error and regression.… More >

Meng Wei^*, Ningxin Zhang, Yuan Tong, Yu Song

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 597-607, 2020, DOI:10.32604/iasc.2020.013938

Abstract The traditional radar signal detection mode of the analog digital converter (ADC) has a low prediction efficiency. Therefore, the advanced prediction model of the tunnel geological radar based on the cluster computing was designed. The completeness factor of the detection radar signal was calculated by the computer cluster effect, and then the information extraction and information integration of the radar pulse for the radar detection signal was determined. Moreover, the multi-order nonlinear regression forecasting model restructured the received signal. Thus, the prediction of the radar detection signal was achieved. In order to ensure the effectiveness of the design, the simulation… More >

Hongsheng Su^1,2,∗, Shuangshuang Wang¹, Dengfei Wang²

Computer Systems Science and Engineering, Vol.33, No.5, pp. 351-360, 2018, DOI:10.32604/csse.2018.33.351

Abstract To better solve the issue with abnormal failure of electric motor unit (EMU) brake pad resulted from various random factors in the ever-changing operating environment, in this paper, a new evaluation method of performance prediction and abnormity decision is proposed based on the Multivariate integrated random walk (MIRW) model. In this method, the state space model of the EMU brake pad performance degradation is firstly established. And then based on the observed data, the brake pad performance degradation trend is extracted by the fixed interval forward - backward smoothing algorithm. Based on it, the future degradation state can be predicted… More >

Ruiguo Yu^1,2, Zhiqiang Liu^1,2, Jianrong Wang^1,3, Mankun Zhao^1,2, Jie Gao^1,3, Mei Yu^1,3,*

Computer Systems Science and Engineering, Vol.33, No.4, pp. 267-274, 2018, DOI:10.32604/csse.2018.33.267

Abstract The spatio-temporal feature with historical wind power information and spatial information can effectively improve the accuracy of wind power prediction, but the role of the spatio-temporal feature has not yet been fully discovered. This paper investigates the variance of the spatio-temporal feature. Based on this, a hybrid machine learning method for wind power prediction is designed. First, the training set is divided into several groups according to the variance of the input pattern, and then each group is used to train one or more predictors respectively. Multiple machine learning methods, such as the support vector machine regression and the decision… More >

Jianji Ren¹, Shan Zhao¹, Junding Sun¹, Ding Li², Song Wang³, Zongpu Jia¹

Computer Systems Science and Engineering, Vol.33, No.4, pp. 259-265, 2018, DOI:10.32604/csse.2018.33.259

Abstract In the Content-Centric Networking (CCN) architecture, popular content can be cached in some intermediate network devices while being delivered, and the following requests for the cached content can be efficiently handled by the caches. Thus, how to design in-network caching is important for reducing both the traffic load and the delivery delay. In this paper, we propose a caching framework of Prefix-based Popularity Prediction (PPP) for efficient caching in CCN. PPP assigns a lifetime (in a cache) to the prefix of a name (of each cached object) based on its access history (or popularity), which is represented as a Prefix-Tree… More >

Jen-Yuan Yeh^∗

Computer Systems Science and Engineering, Vol.33, No.1, pp. 41-52, 2018, DOI:10.32604/csse.2018.33.041

Abstract As a crucial task in information retrieval, ranking defines the preferential order among the retrieved documents for a given query. Supervised learning has recently been dedicated to automatically learning ranking models by incorporating various models into one effective model. This paper proposes a novel supervised learning method, in which instances are represented as bags of contexts of features, instead of bags of features. The method applies rank-order correlations to measure the correlation relationships between features. The feature vectors of instances, i.e., the 1st-order raw feature vectors, are then mapped into the feature correlation space via projection to derive the context-level… More >

Leyao Chen, Lei Hong^*, Jiayin Liu

Journal of New Media, Vol.2, No.2, pp. 91-98, 2020, DOI:10.32604/jnm.2020.010125

Abstract This paper aims to analyze the microblog data published by the official account in a certain province of China, and finds out the rule of Weibo that is easier to be forwarded in the new police media perspective. In this paper, a new topic-based model is proposed. Firstly, the LDA topic clustering algorithm is used to extract the topic categories with forwarding heat from the microblogs with high forwarding numbers, then the Naive Bayesian algorithm is used to topic categories. The sample data is processed to predict the type of microblog forwarding. In order to evaluate this method, a large… More >

Jianwei Chen, Jianbo Li^*, Ying Li

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 847-864, 2020, DOI:10.32604/cmes.2020.010240

Abstract Predicting human mobility has great significance in Location based Social Network applications, while it is challenging due to the impact of historical mobility patterns and current trajectories. Among these challenges, historical patterns tend to be crucial in the prediction task. However, it is difficult to capture complex patterns from long historical trajectories. Motivated by recent success of Convolutional Neural Network (CNN)-based methods, we propose a Union ConvGRU (UCG) Net, which can capture long short-term patterns of historical trajectories and sequential impact of current trajectories. Specifically, we first incorporate historical trajectories into hidden states by a shared-weight layer, and then utilize… More >