A Systematic Literature review: Role of Deep Learning in Big Data Applications

Abstract

Deep learning (DL) has changed how we handle huge amount of data like big data. The ability of DL to handle large scale (Volume), speed (Velocity), diversity (variance), and structured data (accuracy) is the focus of this paper, and DL helps perform important aspects of solving complex problems. Pattern recognition, real-time speech processing, and hybrid data processing (e.g., text, images, and video) are all feasible for DL models such as convolutional neural networks (e.g., CNNs) and convolutional neural networks (RNNs). Research shows that DL can improve decision-making and efficiency in areas such as cybersecurity, healthcare and finance with the help of big data. It also focuses on finding the right balance between controlling energy costs and achieving the right results. The study identified solutions to this problem, including energy conservation, simple design and recycling methods. It also explains how DL helps clean and sanitize big data, identify important patterns, and manage irrelevant data. The future of DL is improving data management, enabling real-time analytics and working with emerging technologies such as fog computing and intelligent connectivity (IoT). This study shows that DL is a powerful tool that can shape the future of society.