In this scientific manuscript, a robust framework of deep learning predictive modeling is introduced. The prime aim of this computational system is to determine and predict wireless spectrum data set with lower computational cost. The cost-effective design of the formulated system applies training of convolutional neural network (CNN) to strengthen the prediction accuracy. The computational modeling and design optimization is carried out considering ANN stacks along with its corresponding feature neuron sets. It also implies non-recursive and less iterative design solution which makes it more scalable and robust and also determines better classification accuracy as compared to conventional approaches. The model validation is carried out with respect to a set of performance matrices such as Mean Absolute Error (MAE), Mean Relative Error (MRE), Correlation Density Function (CDF) and Root Mean Square Error (RMSE) in a numerical computing environment.
Deep Learning , Predictive Modeiling ,Wireless Signals , Preprocessing , Classification.