THE COMPARISON OF DISTRIBUTED P2P TRUST MODELS BASED ON QUANTITATIVE PARAMETERS IN THE FILE DOWNLOADING SCENARIOS

The Comparison of Distributed P2P Trust Models Based on Quantitative Parameters in the File Downloading Scenarios

Varied P2P trust models have been proposed recently; it is necessary to develop an effective method to evaluate these trust models to resolve the commonalities (guiding the newly generated trust models in theory) and individuality (assisting a decision maker in choosing an optimal trust model to implement in specific context) issues.A new method fo

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A Novel Predictive Modeling for Student Attrition Utilizing Machine Learning and Sustainable Big Data Analytics

Student attrition poses significant societal and economic challenges, leading to unemployment, lower earnings, and other adverse outcomes for individuals and communities.To address this, predictive systems leveraging Spinner Mount machine learning and big data aim to identify at-risk students early and intervene effectively.This study leverages big

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