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All Journal International Journal of Advances in Intelligent Informatics Science in Information Technology Letters International Journal of Robotics and Control Systems
Abdalla, Modawy Adam Ali
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Mapping dengue vulnerability: spatial cluster analysis reveals patterns in Central Java, Indonesia Fithriyyah, Anisahtul; Purwaningsih, Tuti; Konate, Siaka; Abdalla, Modawy Adam Ali
Science in Information Technology Letters Vol 4, No 2 (2023): November 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i2.1203

Abstract

In Indonesia, where the interplay between climate variability and infectious diseases is pronounced, Dengue Fever poses a significant threat, particularly in Central Java, ranking as the province with the third-highest incidence of Dengue cases nationwide. This study adopts a proactive approach, employing cluster analysis techniques—single linkage, average linkage, and Ward’s method—to categorize cities and regencies in Central Java based on their susceptibility to Dengue outbreaks. The comparative analysis, facilitated by standard deviation values, reveals nuanced vulnerability patterns, with the single linkage method presenting the most refined categorization, yielding four distinct vulnerability clusters: very low (0.097), low (0.150), medium (0.205), and high (0.303). Furthermore, spatial analysis utilizing Moran’s Index indicates a positive spatial autocorrelation among Dengue cases (Moran’s I = 0.62, p 0.05), underscoring the spatial homogeneity in case distribution across regions. These findings emphasize the critical need for targeted interventions and evidence-based policymaking to effectively combat Dengue transmission in Central Java and mitigate its public health impact.
Comparative Electromagnetic Performance Analysis of Double Stator and Single Stator Superconducting Generators for Direct-Drive Wind Turbines Elhindi, Mohamed; Abdalla, Modawy Adam Ali; Omar, Abdalwahab; Pranolo, Andri; Mirghani, Abdelhameed; Omer, Abduelrahman Adam
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1385

Abstract

Superconducting synchronous generators, especially for 10-MW direct-drive wind power systems, are gaining prominence due to their lightweight, compact design, lowering energy generation costs compared to conventional generators. With the ability to generate high magnetic fields. various approaches are exist for designing such generators for example modular superconducting generators which allow for easier assembly, maintenance, and scalability by dividing the generator into smaller, interchangeable components and single stator which simplifying the generator's design and reducing manufacturing costs. This study introduces a novel concept of a double-stator superconducting generator alongside a conventional single-stator superconducting generator, aiming to investigate and contrast the electromagnetic performance of both machine types considering different number pole pairs. Booth of the machines has been designed and studied applying 2d finite element model (COMSOL Multiphysics). The compared machine parameters include: the flux linkage and electromagnetic torque. Our study and compression of the two machines reveal that the double stator superconducting generator is characterized by high electromagnetic torque compared to its single-stator counterpart. the analysis also reveals that increasing the pole pairs number leads to high electromagnetic torque and higher magnetic flux density.
Machine learning-based residential load demand forecasting: Evaluating ELM, XGBoost, RF, and SVM for enhanced energy system and sustainability Abdalla, Modawy Adam Ali; Ishaga, Ahmed Mohamed; Osman, Hassan Ahmed; Elhindi, Mohamed; Ibrahim, Nasreldin; Snani, Aissa; Hamid, Gomaa Haroun Ali; Hammad, Abdallah
Science in Information Technology Letters Vol 6, No 1 (2025): May 2025
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v6i1.1866

Abstract

Accurate forecasting of electrical power load is essential for properly planning, operating, and integrating energy systems to accommodate renewables and achieve environmental sustainability. Therefore, this study introduces different machine learning (ML) methods, including support vector machines (SVM), random forests (RF), extreme learning machines (ELM), and extreme gradient boosting (XGBoost) to predict hourly electricity demand using electricity consumption and temperature data for train and test ML models. The data is processed by autocorrelation function (ACF) and cross-correlation function (CCF) to determine the appropriate lag time for the inputs. Furthermore, ML model accuracy is assessed using coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). Results show that the ELM model achieved the highest R² in both summer (0.971) and winter (0.868), outperforming the other models in accuracy R² and error reduction (MAE and RMSE). ELM also more effectively captured load fluctuations. The result of this research has applications for load demand forecasting in the proper planning and operation of the residential grid. The results help estimate load demand and provide useful guidance for residential grid planning and management by determining the best techniques for precisely estimating load demand and identifying domestic energy consumption patterns
GAN-Enhanced multimodal fusion and ensemble learning for imbalanced chest X-Ray classification Snani, Aissa; Khadir, Mohammed Tarek; Pranolo, Andri; Abdalla, Modawy Adam Ali
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.2092

Abstract

Chest X-ray (CXR) classification tasks often suffer from severe class imbalance, resulting in biased predictions and suboptimal diagnostic performance. To address this challenge, we propose an integrated framework that combines high-fidelity data augmentation using Generative Adversarial Networks (GANs), ensemble learning via hard and soft voting, and multimodal feature fusion. The method begins by partitioning the majority class into multiple subsets, which are individually balanced through GAN-generated synthetic images. Deep learning models, specifically DenseNet201 and EfficientNetV2B3, are trained separately on each balanced subset. These models are then combined using ensemble voting to improve robustness. Additionally, features extracted from the most performant models are fused and used to train traditional classifiers such as Logistic Regression, Multilayer Perceptron, CatBoost, and XGBoost. Evaluations on a publicly available CXR dataset demonstrate consistent improvements across key metrics, including accuracy, precision, recall, F1-score, AUROC, AUPRC, MCC, and G-mean. This framework shows superior performance in multiclass scenarios.
Co-Authors Andri Pranolo Elhindi, Mohamed Fithriyyah, Anisahtul Hamid, Gomaa Haroun Ali Hammad, Abdallah Ibrahim, Nasreldin Ishaga, Ahmed Mohamed Khadir, Mohammed Tarek Konate, Siaka Mirghani, Abdelhameed Omar, Abdalwahab Omer, Abduelrahman Adam Osman, Hassan Ahmed Snani, Aissa Tuti Purwaningsih, Tuti