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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Balouki, Youssef
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Airport infrastructure and runway precision aids for forecasting flight arrival delays Alla, Hajar; Balouki, Youssef
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.pp1038-1050

Abstract

Recent research has concentrated on using machine learning approaches to forecast flight delays. The majority of prior prediction algorithms were based on simple and standard attributes collected from the database from which the data were pulled. This article is the first attempt to propose novel features linked to airport capacity and infrastructure. The total runways, the total runway intersections, the longest runway length, the shortest runway length, the runway precision rate, the total terminals, and the total gates were all examined. In this paper, we suggest an optimized multilayer perceptron to predict flight arrival retards implementing data for domestic flights operated in United States airports. We employed data normalization, sampling techniques, and hyper-parameter tuning to strengthen the reliability of the suggested model. The experimental findings demonstrated that data normalization, sampling approaches, and Bayesian optimization produced the most accurate model with 92.49% accuracy. The achievements of the study were compared to other benchmark research from literature. The time complexity for the proposed model was computed and presented at the end of the investigation.
Detecting human fall using internet of things devices for healthcare applications Benhaili, Zakaria; Balouki, Youssef; Moumoun, Lahcen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp561-569

Abstract

Falls pose a significant threat to unintentional injuries, particularly impacting the independence of older individuals. Existing detection methods suffer from drawbacks, including inaccuracies, wearer discomfort, complex setup, resource-intensive computation, and limitations in detecting falls outside a specific setting. In response, our innovative fall detection system integrates with a pneumatic solution, analyzing fundamental human activities like running, walking, and sitting, both indoors and outdoors. This approach combines wearable sensors with a vision-based solution, utilizing a smart belt with embedded accelerometer and gyroscope, alongside wall-installed cameras in a smart house. The system triggers an airbag and sends an emergency alarm upon fall detection. To achieve this, we propose FallMixer a lightweight deep learning model, combined with ‘you only look once’ version 8 (YOLOv8) algorithm, fine-tuned on a collected video dataset to enable real-time detection. We found that the models result in competitive performance, as demonstrated on SisFall, UCI human activity recognition (HAR), and mobile health (MHEALTH) datasets with a remarkable mean average precision. Subsequently, we assess the hardware performance of our solution on edge devices.
Co-Authors Alla, Hajar Benhaili, Zakaria Moumoun, Lahcen