Employing Artificial Intelligence and Machine Learning to Create Adaptive Models for Improved Predictive Accuracy in Dynamical Real-World Applications
Midhila A Asok, V. Samuthira Pandi, N. Yuvaraj, S Supriya, Arokia Suresh Kumar Joseph, T. M. Thiyagu · 2025
The complexity and dynamism of real-world systems are constantly increasing, necessitating the development of adaptive predictive models capable of handling non-linearity, uncertainty, and changing surroundings. This research delves into the potential of building adaptive models that enhance prediction accuracy in various dynamical applications by utilizing AI and ML techniques. We offer a platform for realtime data analysis and model adaptation using advanced algorithms including Deep Neural Networks (DNNs), Random Forest, Support Vector Machines (SVM), and ensemble learning methods. In this study, we test these models in many real-world settings, such as autonomous systems, healthcare diagnostics, financial projections, and climate prediction. These are all domains where standard approaches often fall short when it comes to making broad generalizations. Our results show that AI-based models, especially deep neural networks (DNNs) and ensemble techniques, outperform traditional methods in acquiring hierarchical patterns, managing high-dimensional data, and adapting to real-time data fluctuation. Metrics like accuracy, precision, and error reduction, among others, are significantly improved by models that use adaptive learning methods, according to a comparative study. In addition, the study elucidates the role that computing efficiency, scalability, and model interpretability have in guaranteeing the successful implementation. Improving predictive modeling's resilience and dependability is the goal of this work, which tackles issues including data drift, non-stationary situations, and noise. The implementation of adaptive algorithms allows for this to be achieved. The results show how potential AI and ML are for revolutionary change in predictive applications. The results provide light on how to optimize hyperparameters, choose models, and learn in real-time for datasets that are constantly changing. Integrating hybrid AI methods with reinforcement learning will be the focus of future research aimed at enhancing decision-making and flexibility even further. Improved, more efficient, and more flexible solutions for real-world problems are on the horizon because to this research's advancements in intelligent predictive systems.