Zobrazeno 1 - 10 of 48 pro vyhledávání: '"Abdelmalik Ouamane"'
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Akademický článek
AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions
Autor: Yassine Habchi, Yassine Himeur, Hamza Kheddar, Abdelkrim Boukabou, Shadi Atalla, Ammar Chouchane, Abdelmalik Ouamane, Wathiq Mansoor
Publikováno v: Systems, Vol 11, Iss 10, p 519 (2023)
Artificial intelligence (AI) has significantly impacted thyroid cancer diagnosis in recent years, offering advanced tools and methodologies that promise to revolutionize patient outcomes. This review provides an exhaustive overview of the contemporar
Externí odkaz: https://doaj.org/article/de16e0197b9a403389a51546c52ef544
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2
Akademický článek
A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs)
Autor: Khaled Telli, Okba Kraa, Yassine Himeur, Abdelmalik Ouamane, Mohamed Boumehraz, Shadi Atalla, Wathiq Mansoor
Publikováno v: Systems, Vol 11, Iss 8, p 400 (2023)
The growing interest in unmanned aerial vehicles (UAVs) from both the scientific and industrial sectors has attracted a wave of new researchers and substantial investments in this expansive field. However, due to the wide range of topics and subdomai
Externí odkaz: https://doaj.org/article/e95d6264618c4c08aa99279d735038cd
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5
Multilinear subspace learning using handcrafted and deep features for face kinship verification in the wild
Autor: Elhocine Boutellaa, Ammar Chouchane, Mohcene Bessaoudi, Abdelmalik Ouamane
Publikováno v: Applied Intelligence. 51:3534-3547
In this paper, we propose a new multilinear and multiview subspace learning method called Tensor Cross-view Quadratic Discriminant Analysis for face kinship verification in the wild. Most of the existing multilinear subspace learning methods straight
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e4c950f3d9da3d264003baade081b20a
https://doi.org/10.1007/s10489-020-02044-0
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6
Tensor cross-view quadratic discriminant analysis for kinship verification in the wild
Autor: Abdelmalik Ouamane, Oualid Laiadi, Abdenour Hadid, Abdelmalik Taleb-Ahmed, Abdelhamid Benakcha
Publikováno v: Neurocomputing
Neurocomputing, Elsevier, 2020, 377, pp.286-300. ⟨10.1016/j.neucom.2019.10.055⟩
Neurocomputing, 2020, 377, pp.286-300. ⟨10.1016/j.neucom.2019.10.055⟩
International audience; This paper presents a new Tensor Cross-view Quadratic Discriminant Analysis (TXQDA) method based on the XQDA method for kinship verification in the wild. Many researchers used metric learning methods and have achieved reasonab
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::31c78d792b93f95936645f4e0cede993
https://doi.org/10.1016/j.neucom.2019.10.055
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7
A weighted exponential discriminant analysis through side-information for face and kinship verification using statistical binarized image features
Autor: Abdelhamid Benakcha, Abdenour Hadid, Abdelmalik Taleb-Ahmed, Abdelmalik Ouamane, Oualid Laiadi
Publikováno v: International journal of machine learning and cybernetics
International journal of machine learning and cybernetics, 2021, 12 (1), pp.171-185. ⟨10.1007/s13042-020-01163-x⟩
International journal of machine learning and cybernetics, Springer, 2021, 12 (1), pp.171-185. ⟨10.1007/s13042-020-01163-x⟩
International audience; Side-information based exponential discriminant analysis (SIEDA) is more efficient than side-information based linear discriminant analysis (SILDA) in computing the discriminant vectors because it maximizes the Fisher criterio
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c8db430ab26b63526a11934540accc8
https://hal.science/hal-03322498
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8
Multi-view deep features for robust facial kinship verification
Autor: Abdelhamid Benakcha, Abdelmalik Ouamane, Abdelmalik Taleb-Ahmed, Oualid Laiadi, Abdenour Hadid
Publikováno v: 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG)
15th IEEE International Conference on Automatic Face and Gesture Recognition (FG), Nov 2020, Buenos Aires, Argentina. ⟨10.1109/FG47880.2020.00118⟩
FG
15th IEEE International Conference on Automatic Face and Gesture Recognition (FG), Nov 2020, Buenos Aires, Argentina. pp.743-747, ⟨10.1109/FG47880.2020.00118⟩
Automatic kinship verification from facial images is an emerging research topic in machine learning community. In this paper, we proposed an effective facial features extraction model based on multi-view deep features. Thus, we used four pre-trained
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b6c9e48d2741524c7ec31b674a8933d0
https://hal.archives-ouvertes.fr/hal-03322818
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9
Feature fusion via deep random forest for facial age estimation
Autor: Abdelmalik Ouamane, Abdelmalik Taleb-Ahmed, Fadi Dornaika, O. Guehairia
Publikováno v: Neural Networks
Neural Networks, Elsevier, 2020, 130, pp.238-252. ⟨10.1016/j.neunet.2020.07.006⟩
Neural Networks, 2020, 130, pp.238-252. ⟨10.1016/j.neunet.2020.07.006⟩
International audience; In the last few years, human age estimation from face images attracted the attention of many researchers in computer vision and machine learning fields. This is due to its numerous applications. In this paper, we propose a new
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::201c2fee38e586a77012cd7d39945300
https://hal.archives-ouvertes.fr/hal-03321565
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10
Deep Random Forest for Facial Age Estimation Based on Face Images
Autor: Abdelmalik Ouamane, Abdelmalik Taleb-Ahmed, Fadi Dornaika, Oussama Guehairia
Publikováno v: 1st International Conference on Communications, Control Systems and Signal Processing (CCSSP 2020)
1st International Conference on Communications, Control Systems and Signal Processing (CCSSP 2020), May 2020, El Oued, Algeria. pp.305-309, ⟨10.1109/CCSSP49278.2020.9151621⟩
ISBN 978-1-7281-5836-5 ; e-ISBN 978-1-7281-5835-8; International audience; The face image of an individual is important for most biometrics systems. The face picture gives loads of helpful informations, including the individual's personal identity, g
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a63c2ad8d584aaa68a1563160938bce
https://uphf.hal.science/hal-03566350/document
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