A Deep Learning-Based Framework for Human Resource Recommendation. (22nd July 2022)

• Record Type: Journal Article
• Authors: Ming, Li
• Other Names: Lakshmanna Kuruva Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-07-22
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/2377143
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : Recently, small- and medium-scale organizations have gained favorable responses due to the dramatic economic growth in China. This rise is due to international collaborations and social-economic growth by providing good quality and quantity services. However, when compared with other developing countries, small- and medium-scale organizations in China face many restrictions considering size and contributions. Few organizations are still facing a challenge because of the difficulties and lesser quality and also with lack of human resources. The prime objective of this study is to identify the current scenario of human resources for small- and medium-scale organizations, the factors affecting it, and the steps that can be effective in overcoming these challenges. In this study, human resource data is analyzed and managed using deep learning. The functionalities of human resources are realized by the deep learning approach, and further, business volume is reduced for enhancing the efficiency of human resources. The forecasting model is proposed and tested in human resource data by implementing a gradient descent process. Additionally, a deep neural network is implemented to enhance the accuracy of the proposed model. Experimental analysis is conducted by considering several neurons at the hidden layer, iteration count, and different types of decent gradient processes. The training accuracy and validation accuracy of the proposed model by implementing a deep neural network are observed as 95.67% and 94.53%. The experimental observations reveal the potential and significance of the proposed model.