Biography commercial serial crime analysis using enhanced dynamic neural network

In sphere of criminology, suspect prediction analysis has been the point of convergence for many researchers. The focus of this study is on three prime attributes of next serial suspect's biography including nationality, age and time. Generally, to prevent the uncertainty in dynamic systems by nonlinear methods, a predictor is required in Time Delay Neural Network (TDNN). However, existing TDNN with single activation function is less effective to predict labeled class due to lower accuracy. Poor approximation of smooth mapping in single hidden layer makes it less effective. This study aims to propose a combined transfer functions to improve Nonlinear Autoregressive Time Series for performance prediction with exogenous (external) input (NARX)'s by utilizing Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) algorithms. Consequently Hyperbolic Tangent Sigmoid (Tansig) and Radial Basis Function (RBF) are used in LM and SCG algorithms as bi-transfer functions for prediction of next suspect's biography in commercial serial case. The results of NARX model with combination of Tansig and RBF as two objective of transfer functions of LM and SCG, presented better performance for prediction of next serial crime suspect's biography in comparison to single activation function of Tansig and RBF.