Biblio

A good ecological environment is crucial to attracting talents, cultivating talents, retaining talents and making talents fully effective. This study provides a solution to the current mainstream problem of how to deal with excellent employee turnover in advance, so as to promote the sustainable and harmonious human resources ecological environment of enterprises with a shortage of talents.This study obtains open data sets and conducts data preprocessing, model construction and model optimization, and describes a set of enterprise employee turnover prediction models based on RapidMiner workflow. The data preprocessing is completed with the help of the data statistical analysis software IBM SPSS Statistic and RapidMiner.Statistical charts, scatter plots and boxplots for analysis are generated to realize data visualization analysis. Machine learning, model application, performance vector, and cross-validation through RapidMiner's multiple operators and workflows. Model design algorithms include support vector machines, naive Bayes, decision trees, and neural networks. Comparing the performance parameters of the algorithm model from the four aspects of accuracy, precision, recall and F1-score. It is concluded that the performance of the decision tree algorithm model is the highest. The performance evaluation results confirm the effectiveness of this model in sustainable exploring of enterprise employee turnover prediction in human resource management.

This paper presents the machine learning algorithm to detect whether an executable binary is benign or ransomware. The ransomware cybercriminals have targeted our infrastructure, businesses, and everywhere which has directly affected our national security and daily life. Tackling the ransomware threats more effectively is a big challenge. We applied a machine-learning model to classify and identify the security level for a given suspected malware for ransomware detection and prevention. We use the feature selection data preprocessing to improve the prediction accuracy of the model.

Multimedia data available in various disciplines are usually heterogeneous, containing representations in multi-views, where the cross-modal search techniques become necessary and useful. It is a challenging problem due to the heterogeneity of data with multiple modalities, multi-views in each modality and the diverse data categories. In this paper, we propose a novel multi-view cross-modal hashing method named Multi-view Collective Tensor Decomposition (MCTD) to fuse these data effectively, which can exploit the complementary feature extracted from multi-modality multi-view while simultaneously discovering multiple separated subspaces by leveraging the data categories as supervision information. Our contributions are summarized as follows: 1) we exploit tensor modeling to get better representation of the complementary features and redefine a latent representation space; 2) a block-diagonal loss is proposed to explicitly pursue a more discriminative latent tensor space by exploring supervision information; 3) we propose a new feature projection method to characterize the data and to generate the latent representation for incoming new queries. An optimization algorithm is proposed to solve the objective function designed for MCTD, which works under an iterative updating procedure. Experimental results prove the state-of-the-art precision of MCTD compared with competing methods.