Hybrid Artificial Intelligence Models for Enhanced Predictive Analytics

Abstract

One artificial intelligence technique has its limits when it comes to predictive analytics, especially with the increasing complexity of real-world data. Improved prediction accuracy, resilience, and flexibility can be achieved through the use of hybrid artificial intelligence models. These models integrate two or more computational methodologies. Hybrid models overcome the limitations of individual paradigms by combining the best features of many approaches, such as statistical methods, evolutionary algorithms, fuzzy logic, deep learning, and machine learning. the part that hybrid AI models play in improving predictive analytics in a wide range of contexts. It takes a look at some of the most popular hybrid designs, such as neuro-fuzzy systems, ensemble-based models, and hybrids of deep learning and classical machine learning. Representation of features, learning efficiency, and uncertainty decision-making are all enhanced by these models. Even in dynamic and complicated settings, the hybrid AI models always beat the standalone methods, especially when dealing with limited, noisy, or heterogeneous data. Building accurate and resilient predictive systems remains a difficulty when dealing with model complexity and interpretability. However, hybrid techniques provide a promising future in this regard. upcoming avenues for investigation that seek to enhance the transparency of predictive analytics and optimize the construction of hybrid models.