ADAPTIVE DEEP LEARNING FRAMEWORK FOR INTELLIGENT DATA CLASSIFICATION

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Intelligent data classification has become one of the most significant research domains in artificial intelligence and machine learning due to the exponential growth of digital information across healthcare, finance, education, cybersecurity, and industrial automation. Traditional machine learning algorithms often struggle to process highly dynamic and large-scale datasets with varying structures and complexities. This paper proposes an Adaptive Deep Learning Framework (ADLF) for Intelligent Data Classification that integrates convolutional neural networks (CNNs), recurrent neural networks (RNNs), and adaptive optimization techniques to improve classification accuracy, scalability, and computational efficiency. The proposed framework dynamically adjusts model parameters based on incoming data characteristics and utilizes feature selection and attention mechanisms for enhanced learning performance. Experimental analysis demonstrates that the proposed framework achieves superior classification accuracy compared to conventional deep learning models across benchmark datasets. The framework also reduces training loss and improves adaptability in real-time environments. The results indicate that adaptive deep learning architectures can significantly improve intelligent classification systems in modern data-driven applications.

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Keywords

Deep Learning, Intelligent Classification, Adaptive Learning, Neural Networks, Data Mining, Artificial Intelligence.