A Hybrid Machine Learning and Deep Learning Model for Knowledge Discovery in Big Data

Abstract

The fast expansion of big data has resulted in the creation of innovative methods of effective knowledge discovery and smart decision-making. This paper also seeks to discuss the combination of the machine learning and deep learning methods in the extraction of knowledge in large-scale unstructured datasets. It suggests a hybrid analytical framework that is the form of integration of data preprocessing, feature selection, and model optimization methods to enhance the accuracy and efficiency of knowledge discovery processes. A range of machine learning and deep learning algorithms are tested and deployed on test big data environments to determine their effectiveness in accuracy, error rate, and computation efficiency. The findings indicate that hybrid models are more effective than the traditional single method approaches because they can effectively represent the complex pattern and relationships in the data of high dimensionality. Moreover, the suggested solution demonstrates enhanced scalability and strength in a wide range of application areas including cybersecurity, healthcare, and social media analytics. The results note the necessity of combining several learning paradigms to improve the predictive performance and knowledge extraction abilities. To sum up, the study represents a thorough framework of using hybrid machine learning methods in big data analytics, which have much implications to the real-life application and future research opportunities in intelligent data-driven systems.