In this study, we investigated the inhibitory effects of silk extracts derived from the cocoon shell, silk cocoon, silkworm pupa and non-sericin extract, on blocking HSV-1 and HSV-2 binding to host cells, resulting in the inhibition of the virus infection in Vero cells. The silk cocoon derived from silkworm, Bombyx mori, has been recognized for its broad-spectrum biological activity, including antiviral activity however, its effects against HSV infection are unknown. Therefore, the development of an alternative approach is needed. Standard treatment with antiviral drugs, such as acyclovir, could prevent serious complications however, resistance has been reported specifically among immunocompromised patients. Herpes simplex virus (HSV) infections are prevalent worldwide and are the cause of life- threatening diseases. Application of ELM would be particularly useful, when the relation between production and its attributing characters is complex and non-linear. Outcomes emanated from this experiment have revealed the clear advantages of employing extreme learning machines (ELMs) for weather-based modelling of silk cocoon production. As the weather variables are found to be correlated, principal components are obtained and fed into the linear (principal component regression) and non-linear models (back propagation-artificial neural network and extreme learning machine) as inputs. With this backdrop, this study evaluates the suitability of neural network models in comparison with the linear regression models in predicting silk cocoon production of the selected six districts (Kolar, Chikballapur, Ramanagara, Chamarajanagar, Mandya and Mysuru) of Karnataka by utilising weather variables for ten consecutive years (2009-2018). In spite of the immense popularity and sheer power of the neural network models, their application in sericulture is still very much limited.
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