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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Data Science Insights
Ku-Mahamud, Ku Ruhana
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Feature selection for sky image classification based on self adaptive ant colony system algorithm Petwan, Montha; Ku-Mahamud, Ku Ruhana
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp7037-7047

Abstract

Statistical-based feature extraction has been typically used to purpose obtaining the important features from the sky image for cloud classification. These features come up with many kinds of noise, redundant and irrelevant features which can influence the classification accuracy and be time consuming. Thus, this paper proposed a new feature selection algorithm to distinguish significant features from the extracted features using an ant colony system (ACS). The informative features are extracted from the sky images using a Gaussian smoothness standard deviation, and then represented in a directed graph. In feature selection phase, the self-adaptive ACS (SAACS) algorithm has been improved by enhancing the exploration mechanism to select only the significant features. Support vector machine, kernel support vector machine, multilayer perceptron, random forest, k-nearest neighbor, and decision tree were used to evaluate the algorithms. Four datasets are used to test the proposed model: Kiel, Singapore whole-sky imaging categories, MGC Diagnostics Corporation, and greatest common divisor. The SAACS algorithm is compared with six bio-inspired benchmark feature selection algorithms. The SAACS algorithm achieved classification accuracy of 95.64% that is superior to all the benchmark feature selection algorithms. Additionally, the Friedman test and Mann-Whitney U test are employed to statistically evaluate the efficiency of the proposed algorithms.
Deep residual bidirectional long short-term memory fusion: achieving superior accuracy in facial emotion recognition Munsarif, Muhammad; Ku-Mahamud, Ku Ruhana
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9090

Abstract

Facial emotion recognition (FER) is a crucial task in human communication. Various face emotion recognition models were introduced but often struggle with generalization across different datasets and handling subtle variations in expressions. This study aims to develop the deep residual bidirectional long short-term memory (Bi-LSTM) fusion method to improve FER accuracy. This method combines the strengths of convolutional neural networks (CNN) for spatial feature extraction and Bi-LSTM for capturing temporal dynamics, using residual layers to address the vanishing gradient problem. Testing was performed on three face emotion datasets, and a comparison was made with seventeen models. The results show perfect accuracy on the extended Cohn-Kanade (CK+) and the real-world affective faces database (RAF-DB) datasets and almost perfect accuracy on the face expression recognition plus (FERPlus) dataset. However, the receiver operating characteristic (ROC) curve for the CK+ dataset shows some inconsistencies, indicating potential overfitting. In contrast, the ROC curves for the RAF-DB and FERPlus datasets are consistent with the high accuracy achieved. The proposed method has proven highly efficient and reliable in classifying various facial expressions, making it a robust solution for FER applications.
Comparative analysis of xception and svm for brain tumor classification on mri images Huda, Nurul; Ku-Mahamud, Ku Ruhana
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2016

Abstract

Brain tumor classification from magnetic resonance imaging (MRI) plays a critical role in supporting radiologists during diagnosis and treatment planning. However, many existing automated approaches employ limited preprocessing, single-stage transfer learning, or evaluation on a single dataset, which restricts robustness and clinical applicability. This study proposes an enhanced transfer-learning framework based on the Xception architecture for multiclass brain tumor classification and compares its performance with baseline models under identical experimental conditions. The framework integrates a comprehensive preprocessing pipeline consisting of normalization, adaptive noise filtering, contrast enhancement, and targeted data augmentation, together with a structured two-phase fine-tuning strategy. A total of 6,537 MRI images were used, employing five-fold cross-validation, independent testing, and validation on an additional benchmark dataset. The proposed model achieved a mean cross-validation accuracy of 0.8994 ± 0.089 and 99.06% accuracy, precision, and recall on the independent test set, demonstrating strong stability and generalization ability. Evaluation on the CE-MRI Figshare dataset further confirmed robustness, yielding 98.45% accuracy, 98% precision, and 98% recall. In contrast, when re-evaluated in the same experimental setting, baseline models performed considerably worse: the SVM classifier achieved 21.41% accuracy, and ResNet50 reached 75.27%, both substantially lower than Xception. Although higher accuracies for these models have been reported in prior studies under different conditions, the present findings highlight their limited generalization under unified evaluation. Overall, the proposed Xception-based framework provides a reliable and generalizable solution for automated brain tumor classification, with strong potential to support clinical workflows such as triage prioritization and second-opinion assistance.
A Review on Support Vector Machine Problems and Solutions Al-Dulaimi, Hiba; Ku-Mahamud, Ku Ruhana
Data Science Insights Vol. 4 No. 1 (2026): Journal of Data Science Insights
Publisher : PT. Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v4i1.217

Abstract

In this paper, a review is presented particularly on Support Vector Machine (SVM) problems, so as to understand these problems and to identify the approaches to solve them. The aim is to organize the main SVM problems in a manner that provides a clear view for the readers. The approaches for solving SVM problems were classified into non-simultaneous and simultaneous approaches based on constraints considered in solving the problems. Algorithms for model selection and feature subset selection are classified into heuristic and non-heuristic approaches. Very promising result can be obtained if the bio-inspired algorithms are simultaneously applied with SVM for classification problem.
Co-Authors Al-Dulaimi, Hiba Munsarif, Muhammad Nurul Huda Petwan, Montha