For best-performing networks from 5G and above, it must support a wide range of needs. It is understood that more transmission, resource assistance and communication systems will be required. Achieving these tasks can be challenging as network infrastructure becomes more complex and massive. A good solution is to incorporate more robust AI technology that has been tested to provide answers ranging from channel prediction to autonomous network management, as well as network security. Today, however, the latest technology to integrate AI into wireless networks is limited to using a unique AI algorithm to solve a specific problem. A comprehensive framework that can fully utilize the power of AI in solving various network problems remains an open problem. Therefore, this paper introduces the idea of the spy pieces on which the AI unit is installed and delivers on one condition. Intelligence units are used to flexibly control the intelligence of AI algorithms with two comprehension strategies to perform different intellectual tasks: 1) Neural network-based channel predictions and 2) Industrial network-based security acquisition, to illustrate this framework.
Face image is an efficient biometric trait to recognize human beings without expecting any co-operation from a person. In this paper, we propose HVDLP: Horizontal Vertical Diagonal Local Pattern based face recognition using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The face images of different sizes are converted into uniform size of 108×990and color images are converted to gray scale images in pre-processing. The Discrete Wavelet Transform (DWT) is applied on pre-processed images and LL band is obtained with the size of 54×45. The Novel concept of HVDLP is introduced in the proposed method to enhance the performance. The HVDLP is applied on 9×9 sub matrix of LL band to consider HVDLP coefficients. The local Binary Pattern (LBP) is applied on HVDLP of LL band. The final features are generated by using Guided filters on HVDLP and LBP matrices. The Euclidean Distance (ED) is used to compare final features of face database and test images to compute the performance parameters.
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