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 Journal of Statistics and Management Systems cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
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The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

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Open Access Research Article

Mathematical enhancement of unsupervised machine learning algorithms for optimal lifeline donor knowledge extraction

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* Corresponding author · click or hover a name for details

pp. 465–477Vol. 27Issue 2March 2024DOI: 10.47974/JSMS-1282XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1282
Pages:
465–477

Abstract

Lifeline Donor groups are crucial in the field of humanitarian aid because they provide lifesaving aid in times of crisis. For effective resource management and aid coordination, it is crucial to draw relevant insights from donor data in a timely manner. Scalability, precision, and flexibility in responding to changing donor dynamics are all areas where traditional techniques of knowledge extraction fall short. This study offers a fresh method for optimizing knowledge extraction from Lifeline Donor databases by utilizing improved unsupervised machine learning methods. In order to extract, categorize, and prioritize donor knowledge from disparate data sources, this research presents a multi-faceted framework that includes cutting-edge machine learning algorithms. By merging cutting-edge developments in NLP and data mining, the proposed framework improves upon classic clustering and topic modelling methods. The program is able to capture subtle semantic links and underlying patterns in donor communication data by using methods like word embedding models and graph-based clustering.

Keywords

Subject Classifications

Primary 93A30Secondary 49K15

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