Title: Reliable army knowledge management process using perception tacit knowledge with xenogeneic deep neural networks

Authors: P.R. Jasmine Jeni; R. Palson Kennedy; K. Sakthidasan Sankaran

Addresses: Department of Electronics and Communication Engineering, Chennai, Tamilnadu, India; Department of Computer Science Engineering, PERI Institute of Technology, Chennai – 600048, Tamilnadu, India ' Department of Electronics and Communication Engineering, Chennai, Tamilnadu, India; Department of Computer Science Engineering, PERI Institute of Technology, Chennai – 600048, Tamilnadu, India ' Department of Electronics and Communication Engineering, Hindustan Institute of Technology and Science, Chennai – 603103, Tamilnadu, India

Abstract: Knowledge management is the process of providing tacit knowledge for gathering and analysing data to perform particular examination in different applications such as soft-ware engineering, business industry, and education, military and so on. Among the various applications, reliability of the army management process is one of the major issues due to lack of details about particular commanding and training section. This paper introduces the reliable army knowledge management system by using the knowledge based machine learning approach. Initially, battle, training and commanding knowledgeable information's are collected by using perception tacit knowledge process. The collected army knowledge's are examined by applying the xenogeneic deep neural network which analyses the data by splitting different domains for making the developments in different knowledge process such as human resource analysis, army organisation, and their competitiveness. The collected knowledge information is used to improve the performance of army management.

Keywords: knowledge management; reliable army knowledge management; xenogenetic deep neural network; cultural tacit knowledge process.

DOI: 10.1504/IJKMS.2019.097124

International Journal of Knowledge Management Studies, 2019 Vol.10 No.1, pp.48 - 57

Received: 25 Jan 2018
Accepted: 02 Apr 2018
Published online: 21 Dec 2018 *

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