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IEEE/ICACT20260241 Slide.14        [Big Slide]	Oral Presentation
	Now, let me walk you through the exact steps of how we implement and train our machine learning models. Our dataset is simulated with input signals (4-QAM, AWGN channel) across low SNR values from -19 to -11 dB. We split the data 80/20 for training/testing, normalize the features, and train models using MATLAB functions like the fitcsvm() function for classification and the fitrsvm() function for regression. Predictions are made with the predict() function, and performance is evaluated using metrics derived from the confusionmat() function, including accuracy, precision, Pd, Pf, and sensing error.

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