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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
El Hami, Norelislam
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset Rhouas, Sara; El Attaoui, Anas; El Hami, Norelislam
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3177-3186

Abstract

In the ever-evolving landscape of global commerce, marked by the convergence of digital transformation and borderless markets, this research addresses the intricate challenges of currency exchange and risk management. Leveraging Bayesian optimization, the study fine-tunes the random forest algorithm using the extensive Klarna E-commerce dataset. Through systematic analysis, the research uncovers insights into managing currency prediction amid dynamic global markets. Emphasizing the role of Bayesian optimization parameters, the study reveals nuanced trade-offs in model performance. Notably, the optimal simulation, conducted with 14 iterations, 1 job, and a random state set to 684, exhibits a standout performance, showcasing a negative mean squared error (MSE) of approximately -0.9891 and an accuracy rate of 74.63%. The primary objective is to assess the impact of Bayesian optimization in enhancing the random forest algorithm's predictive capabilities, particularly in currency prediction within international e-commerce. These findings offer refined strategies for businesses navigating the intricate landscape of global finance, empowering decision-making through a comprehensive understanding of data, algorithms, and challenges in international commerce.
Android malware detection using the random forest algorithm El Attaoui, Anas; El Hami, Norelislam; Koulou, Younes
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1876-1883

Abstract

The rapid growth in Android device usage has resulted in a significant increase in malware targeting this platform, posing serious threats to user security and privacy. This research tackles the challenge of Android malware detection by leveraging advanced machine learning techniques, with a particular emphasis on the random forest (RF) algorithm. Our primary objective is to accurately identify and classify malicious applications to enhance the security of Android devices. In this study, we employed the RF algorithm to analyze a comprehensive dataset of Android applications, where the classification of each application as either malware or benign is known. The method was rigorously tested, yielding impressive results: an average accuracy of 98.47%, a sensitivity of 98.60%, and an F-score of 98.60%. These metrics underscore the effectiveness of our approach. Moreover, we conducted a comparative analysis of the RF algorithm against other malware detection methods. The results demonstrate that the RF algorithm outperforms these alternative methods, offering superior detection capabilities and contributing to more robust Android security measures.
Analysis of big data from New York taxi trip 2023: revenue prediction using ordinary least squares solution and limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithms Rhouas, Sara; El Hami, Norelislam
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp711-718

Abstract

This study explores the prediction of taxi trip fares using two linear regression methods: normal equations (ordinary least squares solution (OLS)) and limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). Utilizing a dataset of New York City yellow taxi trips from 2023, the analysis involves data cleaning, feature engineering, and model training. The data consists of over 12 million records, managed, and processed that involves configuring the Spark driver and executor memory to efficiently process the Parquet-format data stored on hadoop distributed file system (HDFS). Key features influencing fare amount, such as passenger count, trip distance, fare amount, and tip amount, were analyzed for correlation. Models were trained on an 80-20 train-test split, and their performance was evaluated using root-mean-square error (RMSE) and mean squared error (MSE). Results show that both methods provide comparable accuracy, with slight differences in coefficients and training time. Additionally, vendor performance metrics, including total trips, average trip distance, fare amount, and tip amount, were analyzed to reveal trends and inform strategic decisions for fleet management. This comprehensive analysis demonstrates the efficacy of linear regression techniques in predicting taxi fares and offers valuable insights for optimizing taxi operations.
Unified and evolved approach based on neural network and deep learning methods for intrusion detection Boukhalfa, Alaeddine; El Attaoui, Anas; Rhouas, Sara; El Hami, Norelislam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4071-4079

Abstract

Currently, network security has become a major concern for all entities around the world. Attackers employ various methods to disrupt services, which requires new methods to stop them all in one way. Moreover, these intrusions can evolve and overcome security measures and devices, which pushes to use new evolving methods able to accompany the evolution of these threats, to block them. In our paper, we propose a new approach for intrusion detection, founded on neural network (NN) and deep learning (DL) methods. This approach is planned to not only identify threats, but also to develop a long-term memory of them, in order to detect new ones resembling these memorized attacks, and simultaneously, to provide a single way to stop all kinds of intrusions. To test our model, we have chosen the most recently employed methods in literature, NN and DL algorithms: feedforward neural network (FNN), convolutional neural network (CNN), and long short-term memory (LSTM), then we have applied them on network security layer-knowledge discovery in databases (NSL KDD) intrusions dataset. The results of experiments were impressive for all the algorithms, with maximum performances noted by LSTM, which affirms the efficacy of our proposed method for intrusion detection.
Co-Authors Boukhalfa, Alaeddine El Attaoui, Anas Koulou, Younes Rhouas, Sara