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Towards Autonomous Document Classification: Leveraging Deep Learning for Intelligent Data Organization
Subject area: Science,Engineering and Technology · Area of research: Deep Learning
Abstract
The exponential growth of unstructured data has amplified the need for efficient and autonomous document classification systems. This study explores the transformative potential of deep learning in revolutionizing document organization through intelligent, automated approaches. By leveraging state-of-the-art neural networks, including Convolutional Neural Networks (CNNs) and Transformer-based architectures, this research proposes a robust framework for classifying diverse document types with high accuracy and minimal human intervention. The model integrates advanced natural language processing (NLP) techniques and contextual embeddings to capture semantic nuances and hierarchical relationships within text data. Experimental results demonstrate the system's adaptability to varying datasets and its scalability for large-scale implementations. This work also addresses challenges related to class imbalance, domain-specific terminology, and computational efficiency, offering comprehensive strategies to mitigate these barriers. The findings highlight the efficacy of deep learning in enabling autonomous document classification, paving the way for intelligent data management systems across industries.
Keywords
Autonomous Document Classification, Deep Learning, Intelligent Data Organization, Transformer Models, BERT
How to cite this paper
@article{1705284,
author = {Nagaraj Bhadurgatte Revanasiddappa},
title = {Towards Autonomous Document Classification: Leveraging Deep Learning for Intelligent Data Organization},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {7},
number = {6},
pages = {414-422},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705284.pdf},
abstract = {The exponential growth of unstructured data has amplified the need for efficient and autonomous document classification systems. This study explores the transformative potential of deep learning in revolutionizing document organization through intelligent, automated approaches. By leveraging state-of-the-art neural networks, including Convolutional Neural Networks (CNNs) and Transformer-based architectures, this research proposes a robust framework for classifying diverse document types with high accuracy and minimal human intervention. The model integrates advanced natural language processing (NLP) techniques and contextual embeddings to capture semantic nuances and hierarchical relationships within text data. Experimental results demonstrate the system's adaptability to varying datasets and its scalability for large-scale implementations. This work also addresses challenges related to class imbalance, domain-specific terminology, and computational efficiency, offering comprehensive strategies to mitigate these barriers. The findings highlight the efficacy of deep learning in enabling autonomous document classification, paving the way for intelligent data management systems across industries.},
keywords = {Autonomous Document Classification, Deep Learning, Intelligent Data Organization, Transformer Models, BERT},
month = {December},
}