Maximizing Big Data Insights with Neural Network Advancements: an Exploration of Data-Driven Analytical Strategies

EasyChair Preprint 12751

Abstract

In the era of big data, harnessing the power of neural networks has become imperative for maximizing insights and uncovering hidden patterns within vast datasets. This paper presents an exploration of data-driven analytical strategies that leverage neural network advancements to extract valuable insights from big data. We delve into the principles of neural network architectures and their application in various domains of big data analytics. By examining case studies and real-world examples, we highlight the efficacy of neural networks in handling complex data structures and extracting meaningful patterns. Additionally, we discuss the challenges and considerations associated with implementing neural network-based approaches in big data analytics, including scalability, interpretability, and computational resources. Through this comprehensive analysis, we aim to provide researchers and practitioners with a deeper understanding of how neural networks can be effectively utilized to maximize the insights derived from big data.

Keyphrases: Big Data Analytics, Data-driven approaches, Ethical Considerations, Optimization Techniques, Scalability, deep learning, interpretability, neural networks

@booklet{EasyChair:12751,
  author    = {Mr Juli},
  title     = {Maximizing Big Data Insights with Neural Network Advancements: an Exploration of Data-Driven Analytical Strategies},
  howpublished = {EasyChair Preprint 12751},
  year      = {EasyChair, 2024}}