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All Journal Journal of Robotics and Control (JRC)
Phimphisan, Songgrod
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Journal : Journal of Robotics and Control (JRC)

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An Explainable CNN–LSTM Framework for Monthly Crude Oil Price Forecasting Using WTI Time Series Data Thongjamroon, Joompol; Phimphisan, Songgrod; Sriwiboon, Nattavut
Journal of Robotics and Control (JRC) Vol. 6 No. 5 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i5.26609

Abstract

Crude oil price forecasting has posed significant challenges due to its volatility and nonlinear dynamics. This study has proposed an explainable CNN–LSTM framework to predict monthly West Texas Intermediate (WTI) crude oil prices. The model has captured both local and sequential patterns without using external inputs or decomposition. Trained over 50 epochs across three data splits, it has been evaluated using RMSE, MAE, MASE, SMAPE, and directional accuracy. A classification accuracy of 92.4% and directional accuracy of up to 87.4% have been achieved. The model has consistently outperformed classical and hybrid baselines, with statistical significance confirmed by the Friedman–Nemenyi test. Saliency-based interpretability has further enhanced transparency, making the framework suitable for real-world energy forecasting.
A Transformer-Enhanced CNN Framework for EEG Emotion Detection with Lightweight Gray Wolf Optimization and SHAP Analysis Sriwiboon, Nattavut; Phimphisan, Songgrod
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26725

Abstract

Emotion recognition from electroencephalogram (EEG) signals has been recognized as critical for enhancing human–computer interaction and mental health monitoring. In this paper, an explainable and real-time dual-stream deep learning framework has been proposed for EEG-based emotion classification. The model integrates a 1D convolutional neural network (1D-CNN) for local feature extraction and a transformer encoder for global dependency modeling, with multi-head attention used for feature fusion. Lightweight Gray Wolf Optimization (LGWO) has been employed for selecting optimal features, and an ensemble of lightweight classifiers has been applied to improve robustness. Experiments conducted on DEAP, SEED, BrainWave, and INTERFACE datasets have demonstrated superior performance, achieving accuracies of 96.90%, 94.25%, 93.70%, and 92.80%, respectively. An average inference delay of 5.2 milliseconds per trial has confirmed real-time applicability. Furthermore, SHAP analysis has been incorporated to interpret the model’s decision-making process by identifying influential EEG channels and frequency components. The results have validated the proposed model as a robust, accurate, and explainable solution for EEG-based emotion recognition, establishing a new benchmark for future research in affective computing and clinical applications.
Co-Authors Sriwiboon, Nattavut Thongjamroon, Joompol