Modernizing Workflows with Convolutional Neural Networks: Revolutionizing AI Applications
National General (An Allstate Company) Dallas, Texas, United States.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 3127–3136
Publication history:
Received on 19 August 2024; revised on 27 September 2024; accepted on 30 September 2024
Abstract:
Modernizing workflows is imperative to address labor-intensive tasks that hinder productivity and efficiency. Convolutional Neural Networks (CNNs), a prominent technique in Artificial Intelligence, offer transformative potential for automating complex processes and streamlining operations. This study explores the application of CNNs in building accurate classification models for diverse datasets, demonstrating their ability to significantly enhance decision-making processes and operational efficiency. By leveraging a dataset of images, an optimized CNN model has been developed, showcasing high accuracy and reliability in classification tasks. The findings underscore the ability of AI-powered approaches to reduce manual efforts, improve productivity, and support sustainable modernization across various domains. This study is relevant to professionals seeking to embed AI solutions into conventional workflows, offering a pathway to enhanced innovation and efficiency.
Keywords:
Convolutional Neural Networks (CNN); Workflow Modernization; Artificial Intelligence Applications; Deep Learning Techniques; Automation and Productivity; Image Classification Models; AI-Driven Modernization; Sustainable Solutions with AI; Operational Efficiency through AI; Decision-Making Optimization
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0