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All Journal International Journal of Electrical and Computer Engineering
Ennaama, Faouzia
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Computer Science & IT Electrical & Electronics Engineering

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Exploring the frontiers of trajectory outlier detection: an in-depth review and comparative analysis Chakri, Sana; Mouhni, Naoual; Ennaama, Faouzia
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5984-5997

Abstract

This paper provides a review and comparative analysis of trajectory outlier detection methods. It presents the definition of outliers in trajectory data and the existing types to further examine the advanced approaches. Basic steps for detecting an outlier, which include data preprocessing, feature extraction, modeling, and similar, have been presented. Moreover, advanced methods such as autoencoders and the use of deep learning for outlier detection have been explored. In the end, this paper evaluates the techniques and compares them using common metrics, mainly focusing on the techniques based on autoencoders or deep learning. It covers applications in real life and practice along with any limitations, challenges, and perspective ideas for the future. Ultimately, it can be a useful resource for expanding the understanding of domain researchers and practitioners.
Evaluating geometrically-approximated principal component analysis vs. classical eigenfaces: a quantitative study using image quality metrics Ennaama, Faouzia; Ennaama, Sara; Chakri, Sana
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.pp311-318

Abstract

Principal component analysis (PCA) is essential for diminishing the number of dimensions across various fields, preserving data integrity while simplifying complexity. Eigenfaces, a notable application of PCA, illustrates the method's effectiveness in facial recognition. This paper introduces a novel PCA approximation technique based on maximizing distance and compares it with the traditional eigenfaces approach. We employ several image quality metrics including Euclidean distance, mean absolute error (MAE), peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), and structural similarity index measure (SSIM) for a quantitative assessment. Experiments conducted on the Brazilian FEI database reveal significant differences between the approximated and classical eigenfaces. Despite these differences, our approximation method demonstrates superior performance in retrieval and search tasks, offering faster and parallelizable implementation. The results underscore the practical advantages of our approach, particularly in scenarios requiring rapid processing and expansion capabilities.
Co-Authors Chakri, Sana Ennaama, Sara Mouhni, Naoual