Independent Researcher, University of the Cumberlands, USA.
Research Article
World Journal of Advanced Research and Reviews, 2022, 13(01), 863-866
Received on 28 November 2021; revised on 21 January 2022; accepted on 23 January 2022
The increasing volume of data in the digital era necessitates efficient automation of data analysis. This paper proposes an improved AI framework integrating advanced machine learning (ML) algorithms, deep learning (DL), and natural language processing (NLP) techniques to enhance automation in data analysis. The proposed framework ensures robust data preprocessing, feature selection, and predictive analytics while maintaining high accuracy and efficiency. By leveraging structured, semi-structured, and unstructured data, the AI-driven model reduces analysis time, minimizes human intervention, and increases reliability. The findings indicate that automated data analysis enables organizations to optimize decision-making, enhance productivity, and achieve a competitive advantage in their respective industries. Furthermore, the flexibility of this AI framework allows it to adapt to different domains, including finance, healthcare, and marketing, making it a versatile solution for diverse data-driven environments. Additionally, this framework can be integrated with cloud computing services and edge computing solutions, ensuring real-time data processing and analysis with minimal latency.
AI Framework; Neural Networks; Data Automation; Machine Learning; Decision-Making; Predictive Analytics; Cloud Computing; Edge Computing
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Rajesh Daruvuri. An improved AI framework for automating data analysis. World Journal of Advanced Research and Reviews, 2022, 13(1), 863-866. Article DOI: https://doi.org/10.30574/wjarr.2022.13.1.0749