EXPLOITING SYNERGIES FOR IMPROVED APPLICATIONS THROUGH THE FUSION OF DEEP LEARNING AND MACHINE LEARNING

Abstract

Deep learning and machine learning are two prominent fields within the domain of artificial intelligence that have revolutionized various industries. This abstract explores the concept of fusion, focusing on the integration of deep learning and machine learning techniques to exploit synergies and enhance applications across diverse domains.The abstract begins with an overview of deep learning and machine learning, highlighting their unique characteristics, strengths, and limitations. It then delves into the concept of fusion, emphasizing the potential benefits of combining these approaches for improved performance, robustness, and interpretability in real-world applications.The paper discusses fusion techniques at different levels, including data fusion, model fusion, and decision fusion. Data fusion involves integrating heterogeneous data sources and modalities, such as text, images, and sensor data, to create comprehensive and informative input representations. Model fusion focuses on combining deep learning and machine learning models, leveraging their complementary strengths in representation learning and generalization. Decision fusion involves combining outputs from multiple models or algorithms to make more accurate and reliable predictions or decisions.Moreover, the abstract presents case studies and examples where deep learning and machine learning fusion has demonstrated promising results. These case studies span various domains, such as healthcare diagnostics, autonomous systems, natural language processing, financial forecasting, and personalized recommendation systems. It showcases how fusion techniques have enhanced accuracy, scalability, interpretability, and efficiency in these applications. Furthermore, the abstract highlights the challenges and considerations associated with deep learning and machine learning fusion, such as model compatibility, training complexity, feature engineering, and computational requirements. It also discusses ongoing research efforts and potential future directions to address these challenges and further leverage the power of fusion techniques..)