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All Journal JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science
Ahmad, Azlin
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Computer Science & IT

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Image classification based on few-shot learning algorithms: a review Qi, Qiao; Ahmad, Azlin; Ke, Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp933-943

Abstract

Image classification is a critical task in the field of computer vision, and its importance has significantly increased over the past few years. Machine learning and deep learning techniques have demonstrated immense potential in this field. However, traditional image classification models require a vast amount of training data, which can be challenging and expensive to obtain. To overcome this limitation, researchers are turning to few-shot learning, which aims to classify images with limited training samples. This paper presents a detailed analysis of the field of image classification using few-shot learning. First, it investigates the use of data augmentation, transfer learning, and meta-learning methods in this field. Then, it introduces several commonly used datasets and evaluation metrics in few-shot classification, compares several classical few-shot classification methods, and summarizes the experimental results obtained from public datasets. Finally, this paper analyzes the current challenges in few-shot image classification and suggests potential future directions.
HCRF: an improved random forest algorithm based on hierarchical clustering Zhuo, Wang; Ahmad, Azlin
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp578-586

Abstract

Random forest (RF) selects feature subsets randomly. Useless and redundant features will lower the quality of the selected features and subsequently affect the overall classification accuracy of the RF. This study proposes an improved RF algorithm based on hierarchical clustering (HCRF). The algorithm uses hierarchical clustering algorithms to optimize the feature selection process, by establishing similar feature groups based on the GINI index, and then selecting features from each group proportionally to construct the feature subset. The feature subset is then used to construct a single classifier. This process increases the filtering of feature subsets, reducing the negative impact of useless and redundant features on the model, and improving the model's generalization ability and overall performance. In the experimental verification, ten datasets of different sizes and domains were selected, and the accuracy, precision, recall, F1 score, and running time of HCRF, support vector machine (SVM), RF, classification and regression tree (CART) were compared using 10-fold cross-validation. Combining all the results, the HCRF algorithm showed significant improvements in all evaluation indicators, proving that its performance is superior to the other three classifiers. Therefore, this algorithm has broad application areas and value, and effectively improves the overall performance of the classifier within a lower complexity range.
Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models Muhamad Krishnan, Nor Farisha; Zukarnain, Zuriani Ahmad; Ahmad, Azlin; Jamaludin, Marhainis
JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.6.3.788

Abstract

Dengue fever is well-known as a potentially fatal disease, and the number of cases in some areas remains uncontrolled. Despite efforts to prevent the dengue outbreak from spreading further, vectors may be to blame. Identifying what weather characteristics contribute to dengue outbreaks is important to predict the dengue outbreak. This study proposes Artificial Neural Network (ANN) and Decision Tree (DT) models based on maximum temperature, minimum temperature, total rainfall, and average humidity to predict the dengue outbreak in Kota Bharu. Different numbers of hidden nodes were used in ANN to optimize the model. Both models, ANN and DT are evaluated based on accuracy, sensitivity and specificity showing that ANN (Accuracy = 68.85%, Sensitivity = 99.71%, Specificity = 1.27%), performed better than DT (Accuracy = 67.46%, Sensitivity = 98.82%, Specificity = 2.53%). This means that ANN outperforms DT when predicting a dengue outbreak in Kota Bharu. Based on the ANN model, it can be concluded that the number of hidden nodes affects the model's accuracy. Selecting the ideal number of hidden nodes for modeling the ANN model is appropriate. Even though ANN accuracy for prediction models is greater than DT, it is still low. It can be inferred that selecting a prediction model appropriate for a variety of dataset types and levels of complexity is important. Based on these models, the government may take pre-emptive actions to enhance public awareness about climate change.
Co-Authors Jamaludin, Marhainis Ke, Wang Muhamad Krishnan, Nor Farisha Qi, Qiao Zhuo, Wang Zukarnain, Zuriani Ahmad