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To create a final equation or predicted value based on an ANN model, researchers must first train the network using a dataset with input-output pairs. Once the ANN is trained and validated, it can be used to predict output values for new input data. The predicted values are obtained by passing the input data through the trained ANN, and the output layer neurons provide the predicted values. It's crucial to evaluate the performance of the ANN using metrics such as mean squared error or correlation coefficient to assess prediction accuracy and reliability. Additionally, validation with independent datasets and sensitivity analysis can help ensure the robustness of the ANN predictions in real-world applications.