Stacking Ensemble of Capsule Network and Multi-Hidden Layer Extreme Learning Machine for Enhanced Lumpy Skin Disease Diagnosis - Mallikarjun, Goddeti; Narayana, V.A. (Informatica, 2024)
production and raise chances of calf mortality. This has raised the alarm on disease detection, in livestock. A novel model has been developed on the problem of skin disease, for cattle industry using stacking ensemble methods. The described approach is somewhat a transition of base models such as adjusted capsule networks and multi layered extreme learning machines which has the function of extracting important data required for the correct disease determination. Extreme Learning Machines are great for feature spaces and capsule networks are great for structures. The output will be then passed to level 2, which will use a processed random forest model as the final prediction. This complete methodology made its accuracy rate to be at 98.3 percent. When identifying and applying procedures associated with sophisticated algorithms, formers can reduce economical risks and protect herd’s health and gains to the maximum extent. This improves on sustainability and systems’ ability to cope with disease threats and enhances sustainability and resiliency in the face of disease threats.