Metaheuristic Deep Learning-Driven Wireless Communication Security Adaptation Using Multivariate Analysis of Variance (MANOVA)

The implementation of accurate models to improve access technologies, communication transmission, and network slicing is anticipated play a big part in the edge computing approach, as demands needs individuals are quickly evolving. Deep learning have tended deliver more benefits wide range applications; it also assists data providers demonstrating considerable improvements tackling complex real-world challenges. While integration connected wireless networks deep still its infancy, increasingly focused on sophisticated technologies meet current future end users. Based theoretical practical aspect that ranges from basic applications communication, this study intended addressing opportunities adopting metaheuristic learning-driven communication. researchers intend apply descriptive design enables understanding critical aspects an elaborate manner. authors use both primary sources secondary for performing analysis. source sourced understand application area, research used gather respondents test hypothesis provide conclusions based analyses. scope work utilize quantitative model undertake analysis evaluating key parameters which will allow interpretation findings.