Izvorni znanstveni članak
https://doi.org/10.32985/ijeces.15.7.3
FedExLSA: Design and Development of algorithms on Federated Data Exploration of Topic Prediction Using Latent Semantic Analysis
Saranya M
; SRM Institute of Science and Technology, Kattankulathur Research Scholar, Department of Computing Technologies, School of Computing Kattankulathur, Chennai, India
*
Amutha B
; SRM Institute of Science and Technology, Kattankulathur Professor, Department of Computing Technologies, School of Computing Kattankulathur, Chennai, India
* Dopisni autor.
Sažetak
Every government in the world has multiple departments that must function and operate to address the various inquiries raised by the population. The government's diverse range of websites offers citizens a platform to submit inquiries, thereby facilitating the fulfilling of their requirements. Comprehending the subjects addressed in People Query is essential for government services. Unstructured query data is analyzed using extracting information from text techniques such as allocation of Latent diffuser (LDA) and analysis of hidden semantics (LSA). LSA outperforms other methods in terms of performance because of its minimal complexity and quick installation process. Research on decentralized learning techniques for natural language processing (NLP) is necessary due to concerns about limited data availability and privacy. Federated learning (FL) employs methods that enable different users to collectively train an integrated broad model while maintaining their information regionally stored and accessible. Nevertheless, the current body of literature lacks a thorough examination and evaluation of FL techniques. Data federation is an approach to data integration that allows the government to access and query data from multiple diverse sources as if they were a single, unified repository. Functioning as a form of data virtualization, it facilitates the creation of a comprehensive representation of data, thereby enhancing operational efficiency and the accuracy of decision-making. FedEx utilizes Federated Learning to apply topic modelling techniques to common NLP tasks. The proposed structure integrates the FL Methodology with Latent Semantic Analysis to deliver outcomes for intelligent data analysis and management.
Ključne riječi
Natural Language Processing; Topic Modeling; Federated Learning; Latent Semantic analysis; Text Mining;
Hrčak ID:
319161
URI
Datum izdavanja:
12.7.2024.
Posjeta: 198 *