A Novel Approach to Multimodal Data Fusion: LSTM-Based Pattern Recognition for Improved Classification

Registration ID: IJNRD_214850

Published ID: IJNRD2403062

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Arvind Panwar , Hemant Bhardwaj

Multimodal Data Fusion, Long Short-Term Memory (LSTM), Pattern Recognition, Heterogeneous Data Integration, Deep Learning Models

This work presents a pattern recognition strategy that utilizes Long Short-Term Memory (LSTM) to tackle the difficulties related to integrating different types of data and improving feature learning. The main goal is to improve the accuracy of categorization by combining deep learning models that are designed for different types of input. A unified pattern recognition model is produced by association analysis.The method commences by training categorization models specifically designed for different sorts of data. The LSTM utilizes its ability to retain information over long periods of time in order to capture the temporal patterns present in the data. Next, the fusion approach is examined, and a method for determining adaptive weight fusion is introduced.The algorithmic workflow involves the manipulation of data before training a model and combining different types of data. Empirical evidence confirms that the suggested approach surpasses models that only rely on individual data types, demonstrating higher accuracy in categorization.

Published Paper   E-Certificate

Arvind Panwar & Hemant Bhardwaj (March-2024). A Novel Approach to Multimodal Data Fusion: LSTM-Based Pattern Recognition for Improved Classification. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), a581-a596. https://ijnrd.org/papers/IJNRD2403062.pdf

Volume 9 Issue 3, March-2024

Pages : a581-a596

Paper Reg. ID: IJNRD_214850

Published Paper Id: IJNRD2403062

Downloads: 000121988

Research Area: Engineering

Country: Ghaziabad, Uttar Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403062.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403062

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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