Browsing by Author "Goumiri, Soumia"

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    A new hybrid model of convolutional neural networks and hidden Markov chains for image classification
    (Springer, 2023-05) Goumiri, Soumia; Benboudjema , Dalila; Pieczynski, Wojciech
    Convolutional neural networks (CNNs) have lately proven to be extremely effective in image recognition. Besides CNN, hidden Markov chains (HMCs) are probabilistic models widely used in image processing. This paper presents a new hybrid model composed of both CNNs and HMCs. The CNN model is used for feature extraction and dimensionality reduction and the HMC model for classification. In the new model, named CNN-HMC, convolutional and pooling layers of the CNN model are applied to extract features maps. Also a Peano scan is applied to obtain several HMCs. Expectation–Maximization (EM) algorithm is used to estimate HMC’s parameters and to make the Bayesian Maximum Posterior Mode (MPM) classification method used unsupervised. The objective is to enhance the performances of the CNN models for the image classification task. To evaluate the performance of our proposal, it is compared to six models in two series of experiments. In the first series, we consider two CNN-HMC and compare them to two CNNs, 4Conv and Mini AlexNet, respectively. The results show that CNN-HMC model outperforms the classical CNN model, and significantly improves the accuracy of the Mini AlexNet. In the second series, it is compared to four models CNN-SVMs, CNN-LSTMs, CNN-RFs, and CNN-gcForests, which only differ from CNN-HMC by the second classification step. Based on five datasets and four metrics recall, precision, F1-score, and accuracy, results of these comparisons show again the interest of the proposed CNN-HMC. In particular, with a CNN model of 71% of accuracy, the CNN-HMC gives an accuracy ranging between 81.63% and 92.5%.
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    HMC-COVID-19: Hidden Markov Chains Model for COVID-19 Diagnosis
    (IEEE, 2023) Goumiri, Soumia; Benboudjema, Dalila
    Markov chains are probabilistic models that are useful in different image processing tasks. This paper presents an approach based on Hidden Markov Chain (HMCs) to diagnose COVID-19, that we named HMC-COVID-19. To assess the performance of the proposed solution, we use a public dataset of chest X-RAY images. Our solution has been evaluated using the accuracy of prediction. The preliminary results are promising and can be further enhanced.
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    Smart Mobility in Smart Cities: Emerging challenges, recent advances and future directions
    (Taylor & Francis, 2023-08-13) Goumiri, Soumia; Yahiaoui, Saïd; Djahel, Soufiene
    The world is witnessing a vivid race toward developing advanced solutions to enable smart, fast, affordable and environment friendly mobility for Smart Cities inhabitants. This led to the emergence of the Smart Mobility concept, attracting significant attention from major actors in the mobility sector including policy makers and traffic authorities. Therefore, this survey paper presents an overview of Smart Mobility and discusses the main challenges associated with its key building blocks, parking and traffic management, traffic routing in addition to emissions and road safety implications. Then, the most important works that attempted to address these challenges are presented, and their strengths and limitations are analyzed. Finally, the lessons learned from this study and the most promising future directions to tackle these challenges are presented.