Deep Learning Applications in Big Data: Expanding Horizons with AI-Driven Solutions

EasyChair Preprint 11687

Abstract

The synergy between deep learning and big data has spurred a transformative revolution across industries. This paper delves into the expansive landscape of deep learning applications within the realm of big data analytics, elucidating the pivotal role played by artificial intelligence (AI) in harnessing vast data repositories for actionable insights. The amalgamation of deep learning algorithms with big data frameworks has empowered organizations to unravel intricate patterns, extract meaningful information, and derive predictive models from colossal datasets. From image and speech recognition to natural language processing and recommendation systems, the versatility of deep learning within the ambit of big data has unlocked novel avenues for innovation and problem-solving. This paper surveys the diverse spectrum of applications where deep learning thrives in handling the challenges posed by big data. It explores how convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and other deep learning architectures have revolutionized data processing, analysis, and decision-making. Furthermore, it examines the integration of deep learning models with big data technologies such as Hadoop, Spark, and distributed computing frameworks to enable scalable and efficient computations. It highlights real-world use cases where AI-powered big data analytics have significantly enhanced operational efficiency, personalized experiences, and decision support systems.

Keyphrases: Big Data Analytics, Predictive Analytics, informed decision making

@booklet{EasyChair:11687,
  author    = {Smith Milson and Kerem Levent},
  title     = {Deep Learning Applications in Big Data: Expanding Horizons with AI-Driven Solutions},
  howpublished = {EasyChair Preprint 11687},
  year      = {EasyChair, 2024}}