A HYBRID LEARNING FRAMEWORK COMBINING MACHINE LEARNING AND DEEP NEURAL MODELS FOR COMPLEX DATA PREDICTION

Abstract

The increasing amount of complex data in diverse domains has created an important challenge for future modeling, as traditional machine learning approaches often struggle with high-dimensional, non-linear patterns and unnecessary information. The deep nerve models, while powerful in capturing the hierarchical representatives, often demand comprehensive computational resources and large training datasets, which can limit their gratuity in real -world scenarios. To resolve these challenges, this study proposes a hybrid learning framework that combines traditional machine learning techniques and strength of deep nervous networks to increase the future accurate accurate, efficiency and scalability. Framework integrates the feature of machine learning and preprising capabilities of the machine learning with the ability to learn the deeper model, which creates a collective pipeline that optimizes the structured, semi-corresponding and unnecessary data environment.

The functioning supports machine learning algorithms such as random forests, gradients boosting, and vector machines, supporting vector machines to select alleviation, noise filtering, and features, which are later used as inputs of dark nerve architecture, including firm neural network, recurring nervous networks and transformer-based models. By organizing these models in a supplementary manner, the framework not only reduces overfiting, but also reduces computational complexity, which makes it suitable for real -time predictive functions in domains such as healthcare diagnostics, financial forecasting, smart manufacturing and social media analytics.

Comprehensive experiments conducted on benchmark dataset suggest that hybrid framework improves intensive learning models in terms of frequent standalone machine learning and accuracy, strength and interpretation, reducing the training time by about 20 percent to improve the future performance. In addition, the hybridization strategy increases generalization by dynamic adjusting data variability and domain-specific obstacles, thus ensuring flexibility in noise and incomplete conditions.

Conclusions highlight that the proposed hybrid learning paradigm not only bridges the gap between traditional machine learning and deep nervous networks, but also provides a scalable solution to handle asymmetrical and high-dimensional datasets. This research contributes to pursue intelligent prediction systems by offering a flexible architecture that is adaptable to the domain and is capable of developing with emerging data complications. Future work will expand the structures with learning reinforcement and self-prescribed teaching system, so that adaptability can be improved further, while the future deciding decisions can include clear AI techniques to increase transparency and confidence in making future decisions.