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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Soma, Shridevi
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Computer Science & IT Engineering

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Enhancing text classification through novel deep learning sequential attention fusion architecture Shilpa, Shilpa; Soma, Shridevi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4642-4653

Abstract

Text classification is a pivotal task within natural language processing (NLP), aimed at assigning semantic labels to text sequences. Traditional methods of text representation often fall short in capturing intricacies in contextual information, relying heavily on manual feature extraction. To overcome these limitations, this research work presents the sequential attention fusion architecture (SAFA) to enhance the features extraction. SAFA combines deep long sort-term memory (LSTM) and multi-head attention mechanism (MHAM). This model efficiently preserves data, even for longer phrases, while enhancing local attribute understanding. Additionally, we introduce a unique attention mechanism that optimizes data preservation, a crucial element in text classification. The paper also outlines a comprehensive framework, incorporating convolutional layers and pooling techniques, designed to improve feature representation and enhance classification accuracy. The model's effectiveness is demonstrated through 2-dimensional convolution processes and advanced pooling, significantly improving prediction accuracy. This research not only contributes to the development of more accurate text classification models but also underscores the growing importance of NLP techniques.
Enhancing document text classification using hybrid deep contextual and correlation network Shilpa, Shilpa; Soma, Shridevi
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1100-1108

Abstract

Document analysis involves the extraction and processing of information from documents, a task increasingly automated through the use of deep learning (DL) technologies. Despite the high predictive power of DL models, their black-box nature poses challenges to transparency and interpretability, hindering their integration into the industry. This paper introduces the hybrid deep contextual and correlation network (HDCCNet), a novel methodology designed to improve both the accuracy and interpretability of multi-category classification tasks. HDCCNet leverages a hybrid layer category correlation module to deepen category connections, thereby enhancing the understanding and prediction of category interrelations. To address potential prediction divergence, residual connections are incorporated, ensuring stable and reliable performance. Furthermore, HDCCNet reduces model parameters, accelerating convergence and making the model more efficient. This efficiency is particularly beneficial for practical applications, allowing faster deployment and scalability. By bridging the gap between DL’s capabilities and industry needs for transparency, HDCCNet provides a robust solution for automated document processing, paving the way for broader adoption of DL technologies in business environments.
Co-Authors Shilpa, Shilpa