Efficiency Improvisation of Large-Scale Knowledge Systems in Feature Determination using Proposed HVGAN Architecture

Senthil Murugan Nagarajan (), M. Asha Jerlin (), Ganesh Gopal Devarajan, R. Sivakami () and Ravi Shekhar Tiwari ()
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Senthil Murugan Nagarajan: Department of Mathematics, Faculty of Science, Technology and Medicine, University of Luxembourg, Esch Sur Alzette, Luxembourg
M. Asha Jerlin: ��School of Computer Science and Engineering, Vellore Institute of Technology University, Chennai, India
Ganesh Gopal Devarajan: ��Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, Uttar Pradesh 201204, India
R. Sivakami: �Department of Computer Science and Engineering, Sona College of Technology, Salem, India
Ravi Shekhar Tiwari: �Faculty of Management Studies, SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, Uttar Pradesh 201204, India

Journal of Information & Knowledge Management (JIKM), 2024, vol. 23, issue 02, 1-19

Abstract: Knowledge Management (KM) is crucial for efficient information retrieval and forms the backbone of implementing an Information System (IS). Efficiently extracting meaningful trends from headlines aids in visualising demographic situations and addressing emergency handling needs. Understanding Information Resource Management (IRM) and Knowledge Resource Management (KRM) concepts benefits end users. This proposed KRM concept organises knowledge, ensuring global resource control through a neural decision framework. Traditional methods like Structural Equation Modelling (SEM) have higher computational steps, whereas neural network strategies in KM reduce steps and improve data prediction accuracy. The paucity of input data is a common challenge, which can be overcome by using Generative Adversarial Network (GAN) architecture for probabilistic generative processes. This research evaluates acquisition, spreading, vulnerability, and application using GAN-based knowledge systems that mitigate data scarcity through generative methods. The proposed Hierarchical Vanilla Generative Adversarial Network (HVGAN) utilises gradient functions and hierarchical computation algorithms to achieve desired knowledge extraction with lower time complexity and higher accuracy. Implementation improves efficiency in accessing stored knowledge, yielding an AUC score of 96% compared to other GAN architectures.

Keywords: General adversarial network; hierarchical computation; knowledge resource management; contextual analysis; probabilistic methodology (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219649224500060

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