An Approach to Find the Serotypes of Rotavirus Using Self-Organizing Feature Map - Vijayalakshmi, R; Isabella, S (International Journal of Computer Sciences and Engineering, 2025)
Self-organizing maps (SOM) are different from other artificial neural networks in the sense that they use a neighbourhood function to preserve the topological properties of the input space. It converts complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. As it thereby compresses information while preserving the most important topological and metric relationships of the primary data items on the display, it may also be thought to produce some kind of abstractions. In this works, classifying the virus type using the SOM Toolbox. Self-organizing feature maps (SOFM) learn to classify input vectors according to how they are grouped in the input space. They differ from competitive layers in that neighboring neurons in the self-organizing map learn to recognize neighbouring sections of the input space