Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{162205,
        author = {Anjana S  and Albin S S},
        title = {Concept-Adaptive Deep Learning for Efficient Short Text Stream Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {8},
        pages = {320-326},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162205},
        abstract = {This research introduces Concept-Adaptive Deep Learning, a novel approach addressing the challenges posed by short text streams in real-world applications, particularly within the dynamic context of social media. Focused on brevity, high volume, and rapid data influx, short text streams present difficulties for existing classification algorithms due to data sparsity and concept drift. Leveraging external resources and pretrained embedding models, Convolutional Neural Networks (CNNs) capture local patterns, while a flexible Long Short-Term Memory (LSTM) network adapts to varying characteristics of text streams, such as high volume and velocity. Distributed computing techniques enhance efficiency and scalability, with a unique concept drift factor facilitating dynamic adjustments in classification strategies. Rigorous experimental validation on real datasets showcases the proposed approach's effectiveness and efficiency, positioning it as a robust solution for accurate short text stream classification in dynamic, high-velocity data environments.},
        keywords = {Concept-Adaptive Deep Learning, Short Text Stream Classification, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) Networks},
        month = {},
        }