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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Siwoo Byun
Anyang University
Computer Science & IT Electrical & Electronics Engineering

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Performance evaluation of adaptive offloading model using hybrid machine learning and statistic prediction Siwoo Byun; Seok-Woo Jang; Joonho Byun
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp463-471

Abstract

We introduce fast sensor diagnosis and focuses on intelligent offloading skills to enhance the sensor data screening efficiency. This study proposed the adaptive offloading model based on statistics-based prediction feedback and sensor candidate filtering. For the statistics-based filtering, sliding sensor grids and compounded sensor context were devised. This study also proposed hybrid prediction model using support vector machine (SVM) and k-nearest neighbors (KNN) machine training for the adaptive offloading. Therefore, the sensor information that is highly likely to be the cause of the actual device faults can be selected and transmitted, resulting in improved offloading performance. The test results through Google Colab show that the fault prediction accuracy of proposed models is 95%.
Semantic-aligned multimodal human activity recognition using visual and audio data Yeeun Park; Junhoo Byun; Siwoo Byun
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2087-2095

Abstract

Human activity recognition (HAR) requires robust performance under heterogeneous sensing conditions for practical deployment. However, single-modality approaches are limited in capturing the rich contextual information inherent in complex human behaviors. This paper presents a semantic-aligned multimodal HAR framework that integrates visual and audio information without assuming instance-level synchronization. To address dataset heterogeneity, samples from the HMDB51 video dataset and the ESC-50 audio dataset are aligned by mapping fine-grained classes into a shared high-level activity label space. For each modality, ResNet-18-based models are trained independently using frame-based visual inputs and 64-bin Mel-spectrogram-based audio representations. During inference, the output logits of the two models are combined through score-level weighted linear fusion. Experimental results show that the proposed multimodal approach consistently outperforms unimodal baselines in terms of accuracy and Macro-F1 score, with particularly notable improvements in activity groups where environmental context plays a significant role. These findings indicate that semantic-aligned score-level fusion can improve recognition robustness even under mismatched dataset conditions.
Co-Authors Joonho Byun Junhoo Byun Seok-Woo Jang Yeeun Park