Gender and age prediction are the key areas of research in the biometric as well as human face recognition applications aimed at effective future prediction and the knowledge discovery about the specific person. The process makes use of assorted approaches and algorithms whereby the deep learning is also the prime in usage patterns. Our research presents a new idea based on modifying the deep network structure and using learning methods of the two other researchers. We made some modification on the structure of the convolutional neural network (CNN) that was used by the first researcher, then, we used two learning methods, which were adopted by the second researcher, Single-Task Learning (STL) and Deep Multi-Task Learning (DMTL) approach, and we present new structure of CNN according to the above two modifications, implemented and evaluated, and the results show the effective performance of our proposed structure. The proposed net presented in this work has the association of Adience-Face Benchmark face dataset, samples of a test and training set, the implementation is performed by Python software.

We developed old designed of a Back-Propagation neural network (BPNN), which it was designed by other researchers, and we made modification in their structure. The 1 st velocity ratio was discriminated by lowest speed, and highest twist. The 6 th velocity ratio was discriminated by highest speed, and lowest twist. The aim of this paper is to design neural structure get best performance to control an electrical automotive transportation six-speed gearbox of the vehicle. We focus on the evaluation of the BPNN to select the suitable number of layers and neurons. Experimentally, the structure of the proposed BPNN are constructed from four layers: eight input nodes in the first layer that received data in binary number, 45 neurons in 1 st hidden-layer, 25 neurons in 2 nd hidden-layer, and 6 neurons in the fourth layer. The MSE and number of Epochs are the main factors used for the evaluation of the proposed structure, and compared with the other structures which was designed by other researchers. Experimentally, we discovered that the best value of Epoch and MSE was chosen when the BPNN consisted of two hidden-layers, 45, and 25 neurons in the 1 st and 2 nd hidden-layer respectively. The implementation was applied using MATLAB software.

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