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Analyzing the results from the experimental research environment to the user environment of technology products in the computer science industry is very necessary. In this study, firstly, we applied the local binary pattern and K-Nearest Neighbor method to create a facial gender recognition application with a database of 6000 images divided by male and female, the degree to which the accuracy of the model on recognition is 95.4%. Secondly, we use a 2-layer research model to analyze the results of the user survey about facial gender recognition technology products. Most of the previous studies on facial gender recognition techniques focused on analyzing the impact of factors affecting applications using single-step structure equation modeling (SEM). The purpose of this study, based on the technology acceptance method (TAM) theory, describes the artificial neural network (ANN) method to perform in-depth analysis, yielding more accurate results than the SEM model. The study measures the relationship between the readiness for new technologies (optimism, innovation, discomfort, and insecurity). Technology acceptance (Perceived ease of use, Perceived usefulness). Expectations confirmed and Information systems acceptance (service quality, system quality, and information quality) and user satisfaction on facial gender recognition systems such as personal information declaration systems at customs gates at domestic and international airports. This paper outlines the research model of the multi-analysis approach by combining Partial Least Squares - Structure Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis. First, the PLS-SEM model evaluates the factors affecting the intention to use the facial gender recognition system. Second, ANN ranks the impact factors of important predictors from the PLS-SEM model. The findings from the PLS-SEM and ANN approach research model confirm the results obtained from PLS-SEM by ANN. In addition, ANN performs linear and non-linear relational modeling with high prediction accuracy compared with the SEM model. In addition, An Importance Performance Map Analysis (IPMA) analyzes the results accurately for factors' important performance. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI), machine learning (ML) and deep learning (DL) techniques have made a breakthrough in many different research fields, such as computer vision, natural language processing, network management and mobile communication. One of the primary goals in 5G network pursues high efficiency, higher spectrum and energy efficiency, higher system capacity and data rates, and lower latency with cost reduction. The papers on the topic of "AI-enabled intelligent networks for 5G and beyond" contain novel network architectures and mechanisms by using AI/ML/DL approaches for 5G/B5G networks. [Extracted from the article]