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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Indonesian Journal of Electrical Engineering and Computer Science
Arumugam, Vignesh
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Computer Science & IT

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Enhanced time series forecasting using hybrid ARIMA and machine learning models Arumugam, Vignesh; Natarajan, Vijayalakshmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1970-1979

Abstract

Accurate energy demand forecasting is essential for optimizing resource management and planning within the energy sector. Traditional time series models, such as ARIMA and SARIMA, have long been employed for this purpose. However, these methods often face limitations in handling nonstationary data, complexity in model tuning, and susceptibility to overfitting. To address these challenges, this study proposes a hybrid approach that integrates traditional statistical models with advanced computational methods. By combining the strengths of both approaches, the proposed models aim to enhance predictive accuracy, improve computational efficiency, and maintain robustness across varied energy datasets. Experimental results demonstrate that these hybrid models consistently outperform standalone traditional methods, providing more reliable and precise forecasts. These findings underscore the potential of hybrid methodologies in advancing energy demand forecasting and supporting more effective decision-making in energy management.
A meta-learning framework for leaf disease detection using vision transformer-based feature extraction, PCA, and tuned SVM classifier Venkataraman, Jayalakshmi; Devi Potluri, Bhargavi; Arumugam, Vignesh; Balasubramaniyam, Shoba; Subramani, Banumathi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp267-275

Abstract

A hybrid meta-learning approach is proposed for effective leaf disease detection by integrating vision transformer (ViT), principal component analysis (PCA), and support vector classifier (SVC). The objective of this study is to accurately classify plant leaf conditions into three categories: healthy, angular leaf spot, and bean rust. The dataset consists of 1,167 labeled leaf images, divided into training (974 images), validation (133 images), and testing (60 images) sets. A pretrained ViT model is employed for feature extraction, producing a feature vector of shape (974, 64) for the training data. To mitigate the curse of dimensionality and improve classification performance, PCA is applied, reducing the features to 41 principal components while retaining 98% of the original variance and accuracy 97.85%. For the classification task, an SVC is used and fine-tuned using the Optuna hyperparameter optimization framework to enhance accuracy and generalization. A distributed deep learning strategy is incorporated to accelerate training and scale computation, while the tf.data API is utilized to construct an efficient and scalable data input pipeline. The hybrid model demonstrates strong classification performance on the test set, indicating that combining deep transformer-based feature extraction with dimensionality reduction and optimized classical machine learning classifiers is effective for plant disease identification. This approach offers a robust and computationally efficient solution for precision agriculture, enabling automated and accurate leaf disease diagnosis and supporting early intervention strategies in crop management.
Co-Authors Balasubramaniyam, Shoba Devi Potluri, Bhargavi Natarajan, Vijayalakshmi Subramani, Banumathi Venkataraman, Jayalakshmi