Training the SVM to Larger Dataset Applications using the SVM Sampling Technique

In light of the COVID-19 pandemic, this research examines supply chain disruptions critically, highlighting their devastating effect on businesses like as drugs and perishable food. The report emphasizes the need of durable supply networks, encouraging firms to invest in strong risk mitigation measures and cutting-edge technology to maintain continuity and adaptation in an ever-changing business environment. Deep learning and machine learning approaches have emerged as critical tools for solving these supply chain difficulties. Decision trees, random forests, k-nearest neighbors, and support vector machines (SVM) have all been shown to be effective in mitigating supply chain issues, with SVM achieving an impressive accuracy rate of 96.6%. Furthermore, the random forest model has a respectable accuracy of 95.00%. Notably, the study discovers that the gated recurrent unit architecture outperforms the long short-term memory design, with an accuracy rate of 98.01%. This investigation provides useful insights for the difficulties confronting contemporary supply networks.

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Training the SVM to Larger Dataset Applications using the SVM Sampling Technique

Cited by 3 publications

References 5 publications

An Automatic and Robust System for Identification of Problematic Call Centre Conversations

An Automatic and Robust System for Identification of Problematic Call Centre Conversations

Outfit of Exemplar-SVMs for Object Detection and Beyond

Predictive Modeling of Supply Chain Disruptions in the COVID-19 Pandemic Using Advanced Machine Learning Approaches

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