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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) AGRIVITA, Journal of Agricultural Science
Md Ghani, Nor Azura
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Agriculture, Biological Sciences & Forestry Computer Science & IT Engineering

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Hybrid embedded and filter feature selection methods in big-dimension mammary cancer and prostatic cancer data Md Noh, Siti Sarah; Ibrahim, Nurain; M. Mansor, Mahayaudin; Md Ghani, Nor Azura; Yusoff, Marina
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3101-3110

Abstract

The feature selection method enhances machine learning performance by enhancing learning precision. Determining the optimal feature selection method for a given machine learning task involving big-dimension data is crucial. Therefore, the purpose of this study is to make a comparison of feature selection methods highlighting several filters (information gain, chi-square, ReliefF) and embedded (Lasso, Ridge) hybrid with logistic regression (LR). A sample size of n=100, 75 is chosen randomly, and the reduction features d=50, 22, and 10 are applied. The procedure for feature reduction makes use of the entire sample sizes. Each sample size's results are compared, including tests with no feature selection process. The results indicate that LR+ReliefF is the best method for mammary cancer data, whereas LR+IG is the best for prostatic cancer data, making the filter more suitable than embedded for big-dimension data. This study revealed that the sample's features and size influence the most effective method for selecting features from big-dimension data. Therefore, it provides insight into the most effective methods for particular features and sample sizes in high-dimensional data.
Support Vector Machine (SVM) for Tomato Leaf Disease Detection Ibrahim, Shafaf; Mohd Fuad, Nur Afiqah; Md Ghani, Nor Azura; Aminuddin, Raihah; Sunarko, Budi
AGRIVITA Journal of Agricultural Science Vol 47, No 2 (2025)
Publisher : Faculty of Agriculture University of Brawijaya in collaboration with PERAGI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17503/agrivita.v47i2.3746

Abstract

Tomatoes rank among the top five most globally demanded crops and serve as a key ingredient in numerous dishes. However, productivity may decline due to challenges such as diseases, pest infestations, and climate change. Therefore, automatic disease detection is essential to identify early signs of illness during the growth period. This study proposes a method for detecting tomato leaf diseases using image processing techniques. The approach involves image enhancement, feature extraction, and classification. Initially, leaf disease images were enhanced using the Contrast Adjustment technique. Subsequently, color and texture features were extracted using Color Moments and the Gray-Level Co-occurrence Matrix (GLCM), respectively. Disease detection was carried out using a Support Vector Machine (SVM). The method was tested on 50 images each for healthy leaves and four types of tomato leaf diseases: Bacterial Spot, Yellow Leaf Curl Virus, Early Blight, and Late Blight. The performance of the disease detection system was evaluated using a confusion matrix, achieving an overall accuracy, sensitivity, and specificity of 96%, 90%, and 97.5%, respectively. These results demonstrate the effectiveness of the proposed SVM-based approach for tomato leaf disease detection.
Co-Authors Aminuddin, Raihah Budi Sunarko Ibrahim, Nurain Ibrahim, Shafaf M. Mansor, Mahayaudin Md Noh, Siti Sarah Mohd Fuad, Nur Afiqah Yusoff, Marina