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Shobha, Shobha
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Enhancing radar signal processing through LVQ-Kalman fusion: a tsunami prediction perspective Shobha, Shobha; Narasimhaiah, Nalini
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp282-289

Abstract

In radar signal processing, the pursuit of precise prediction algorithms motivates the exploration of innovative methodologies. This study introduces a pioneering fusion of learning vector quantization (LVQ)- Kalman, merging LVQ with the advanced Kalman filter. The primary aim is to enhance adaptability and robustness, vital in weather monitoring and military surveillance. LVQ, known for its efficacy in pattern recognition and prediction, adjusts prototype vectors iteratively based on input data, ideal for radar signal intricacies. Various LVQ types are incorporated, tailored meticulously for specific radar applications. The Kalman filter, originally for aerospace, excels in tracking and predicting dynamic systems, seamlessly integrated to address uncertainties in radar data. By combining LVQ’s pattern recognition with the Kalman filter’s adaptability, the fusion aims to create a versatile system navigating radar data intricacies. Applications range from airborne target tracking to weather analysis and military surveillance. The integrated approach offers adaptability and robustness, vital for real-world implementations, particularly in tsunami detection. Future research may explore deep learning to further enhance adaptability. This fusion technique presents significant potential for advancing radar signal processing, promising accurate and adaptive systems, especially in critical applications like tsunami detection.
Meta-model integration with attention mechanisms for advanced decision-level fusion in machine learning Shobha, Shobha; Narasimhaiah, Nalini
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1325-1336

Abstract

This work proposes an advanced meta-model approach that incorporates forecasts from multiple machine learning models to improve classification accuracy in complex tasks. The approach employs decision-level data fusion, where predictions from random forest (RF), XGBoost, neural networks (NN), and support vector machine (SVM) are combined within a meta-model framework. The meta-model incorporates an attention mechanism and a gated model selection process to dynamically emphasize the most relevant model outputs based on input features. The results demonstrate superior accuracy in predicting explicit content compared to traditional fusion methods. This research highlights the potential of attention-enhanced meta-models in improving interpretability and accuracy across various domains. The integration of meta-models with attention mechanisms has the potential to significantly enhance decision-level fusion in machine learning applications. This study investigates the development of an advanced fusion framework leveraging attention mechanisms to improve decision-making accuracy in multi-source data environments. The proposed method is evaluated across multiple datasets, demonstrating its efficacy in increasing predictive performance and robustness.
Co-Authors Narasimhaiah, Nalini