Gender Classification Using BP Neural Network Based on Student Physical Data

We can utilize height and weight information from 30 male and 30 female students as training data to develop a BP neural network with two inputs and one output. This network implements gender classification for given physical data with 90% accuracy. The neural network learns patterns and relationships between height-weight characteristics and gender through backpropagation algorithm. The implementation typically involves normalized input preprocessing, sigmoid activation functions, and gradient descent optimization. This model enables gender prediction by inputting any individual's height and weight measurements, with the network processing these features through hidden layers to generate binary classification output. The high accuracy rate makes this approach reliable for practical gender identification tasks, where the model can be deployed using Python with libraries like TensorFlow or MATLAB's Neural Network Toolbox, incorporating cross-validation techniques to ensure robustness.