Sparrow Search Algorithm based BGRNN Model for Animal Healthcare Monitoring in Smart IoT

Rural regions rely heavily on agriculture for their economic survival. Therefore, it is crucial farmers to implement effective and technical solutions raise production, lessen the impact of issues associated animal husbandry, improve agricultural yields. Because technological developments in computers data storage, huge volumes information are now available. The difficulty extracting useful from this mountain has prompted development novel approaches tools, such as mining, that can help close informational gap. To evaluate mining methods put them use Animal database create meaningful connections was goal suggested system. study's primary objective develop an IoT-based Integrated Health Care System. Various sensors were used research tool collect physical environmental animals habitats. Temperature, heart rate, air quality readings types collected. This contributes field health monitoring by introducing Optimised Bidirectional Gated Recurrent Neural Network approach. BiGRNN improved form Unit (GRU) which input sent both forward backward through a network resulting outputs connected same output layer. Since method employs number hyper-parameters, optimised means Sparrow Search Algorithm (SSA). originality study demonstrated SSA technique hyperparameter optimisation BiGRNN, with focus forecasting. Hyperparameters like momentum, learning weight decay may all be adjusted method. In conclusion, results demonstrate tactic more than current methods.