Applications Of Nonlinear Analysis Transforming Communication, Finance, And Marketing Paradigms For Seamless Connectivity

Abstract

The study investigates how nonlinear analysis, supported by AI algorithms, has changed communication, finance and marketing systems significantly. Linear models are not able to represent well the complexity and changing features found in the real world, so nonlinear approaches are needed. Four nonlinear algorithms—Nonlinear Support Vector Machine (NSVM), Recurrent Neural Network (RNN), Nonlinear Time Series Analysis (NTSA) and Fuzzy Logic Systems (FLS)—were applied in this research to analyze and improve data in the three areas. Results from experiments showed that NSVM achieved 92.4% in classification accuracy, while RNN accomplished 94.1% in predictive accuracy. Compared to linear models, NTSA made forecasting trend data improve by 38% and FLS improved the accuracy of marketing decisions by 31.6%. They work much better than traditional approaches in dealing with unexpected and inconsistent patterns in data. The use of AI-driven tools with nonlinear models provides an integrated system that allows easy connection, strong prediction and smart automation in today’s changing worlds. Nonlinear methods are shown to be key in making systems respond well and support decision-making during uncertainty.