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

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.