Nonlinear Analysis And Processing Of Software Development, Financial Data, And Marketing Insights Under Internet Of Things Monitoring System

Abstract

The skyrocketing amount of data in software engineering, finance and marketing means that intelligent systems are needed that can manage irregular trends and keep monitoring everything in real time. In this study, we suggest a join approach that uses IoT monitoring with nonlinear models to find meaningful trends in multidimensional data. Long Short-Term Memory (LSTM), Support Vector Machine (SVM), K-Means Clustering and Gradient Boosting were used to work with and analyze data from IoT-connected systems. According to the experiment, LSTM had the highest prediction accuracy of 94.6%, mainly used for financial forecasting and Gradient Boosting achieved 91.3%, mainly used for software defect prediction. K-Means separated the marketing data into clusters using 0.82 as the silhouette score, while SVM correctly classified 90.1% of multidimensional anomalies. Traditional models performed an average of 18% lower than the models proposed here. This setup facilitates fast choices, adapts to any scale and ensures tasks are automated, making it a strong choice for data-based strategies in complex situations. This study shows how blending IoT with nonlinear analysis can greatly improve business intelligence and digital operations.