Jong-Yih Kuo^*, Hui-Chi Lin, Chien-Hung Liu

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 53-68, 2021, DOI:10.32604/iasc.2021.014437 - 07 January 2021

Abstract Predicting salary trends of students after employment is vital for helping students to develop their career plans. Particularly, salary is not only considered employment information for students to pursue jobs, but also serves as an important indicator for measuring employability and competitiveness of graduates. This paper considers salary prediction as an ordinal regression problem and uses deep learning techniques to build a salary prediction model for determining the relative ordering between different salary grades. Specifically, to solve this problem, the model uses students’ personal information, grades, and family data as input features and employs a More >

Xiaoping Zhao¹, Jiaxin Wu^1,*, Yonghong Zhang², Yunqing Shi³, Lihua Wang²

CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 223-242, 2018, DOI:10.32604/cmc.2018.02490

Abstract With the rapid development of mechanical equipment, mechanical health monitoring field has entered the era of big data. Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities, this also brings influence to the mechanical fault diagnosis field. Therefore, according to the characteristics of motor vibration signals (nonstationary and difficult to deal with) and mechanical ‘big data’, combined with deep learning, a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed. The frequency domain signals obtained by the Fourier transform More >