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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Mohamed, Asmaa
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Computer Science & IT Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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The effect of feature selection with optimization on taxi fare prediction A. Naim, Amany; Hekal Omar, Asmaa; A. Ibrahim, Asmaa; Mohamed, Asmaa; M. Mostafa, Naglaa
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.v14i3.8658

Abstract

Feature selection plays a key influence in machine learning (ML); the main objective of feature selection is to eliminate irrelevant and redundant variables in different classification problems to improve the performance of the learning algorithms. Classification accuracy is improved by reducing the number of selected features. Many real-world problems, such as taxi fare can be predicted by ML. This paper proposes feature selection using genetic algorithm (GA) optimization to predict taxi fare. Experiments are performed on real datasets of taxi fare, and this paper uses eight classifiers to evaluate the selected features. The performance of the classifiers is assessed using various performance metrics. The results are compared with feature selection without optimization. The proposed method records high classification accuracy when evaluated by three types of classifiers (random forest, AdaBoost, and Gradient Boost). The results indicate that the prediction accuracy of the proposed method is 99.7% on taxi fare dataset.
An improved round robin time sharing algorithm for optimizing data mapping in cloud computing environments Abdelkader, Afaf; Mohamed, Asmaa; Ghazy, Nermeen
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.8087

Abstract

Cloud computing in recent years has been widely applied in a wide number of applications and fields. However, allocating tasks to virtual machines (VMs) remains a part that needs enhancement. Task scheduling algorithms in heterogeneous computing system are required to satisfy high-performance data mapping requirements. The efficient allocation between resources and tasks decreases waiting time (WT), turnaround time (TT) and maximizes resource utilization. Various task scheduling algorithms, including round robin (RR) and some improved RR algorithm are used for cloud environment. A novel time-sharing algorithm (NRRTSA) is introduced, demonstrating enhancements in WT and TT. Simulation findings indicate that the NRRTSA algorithm effectively schedules multiple requests (cloudlets) among several VMs, the proposed NRRTSA outperforms RR and other algorithms in terms of the average of both TT and WT. The average turnaround time (ATT) is enhanced with a ratio of 10.8% to 45%, the average waiting time (AWT) is enhanced with a ratio of 10.9% to 45%.
Co-Authors A. Elsharawy, Gaber A. Ibrahim, Asmaa A. Naim, Amany Abdelkader, Afaf Desuky, Abeer S. El-Sayed, Rania Salah Ghazy, Nermeen Hekal Omar, Asmaa M. Mostafa, Naglaa Shams, Somia A.