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Outputs (18)

A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring (2024)
Journal Article
Albaroudi, E., Mansouri, T., & Alameer, A. (2024). A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring. AI and Ethics, 5(1), 383-404. https://doi.org/10.3390/ai5010019

The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, i... Read More about A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring.

Toward the automated detection of behavioral changes associated with the post-weaning transition in pigs (2023)
Journal Article
Kyriazakis, I., Alameer, A., Bučková, K., & Muns, R. (2023). Toward the automated detection of behavioral changes associated with the post-weaning transition in pigs. Frontiers in Veterinary Science, 9, https://doi.org/10.3389/fvets.2022.1087570

We modified an automated method capable of quantifying behaviors which we then applied to the changes associated with the post-weaning transition in pigs. The method is data-driven and depends solely on video-captured image data without relying on se... Read More about Toward the automated detection of behavioral changes associated with the post-weaning transition in pigs.

Automated detection and quantification of contact behaviour in pigs using deep learning (2022)
Journal Article
Alameer, A., Buijs, S., O'Connell, N., Dalton, L., Lilian Vestbjerg Larsen, M., Juul Pedersen, L., & Kyriazakis, I. (2022). Automated detection and quantification of contact behaviour in pigs using deep learning. Biosystems Engineering, 224, 118-130. https://doi.org/10.1016/j.biosystemseng.2022.10.002

Change in the frequency of contact between pigs within a group may be indicative of a change in the physiological or health status of one or more pigs within a group, or indicative of the occurrence of abnormal behaviour, e.g. tail-biting. Here, we d... Read More about Automated detection and quantification of contact behaviour in pigs using deep learning.

Labeled projective dictionary pair learning: application to handwritten numbers recognition (2022)
Journal Article
Ameri, R., Alameer, A., Ferdowsi, S., Nazarpour, K., & Abolghasemi, V. (2022). Labeled projective dictionary pair learning: application to handwritten numbers recognition. Information Sciences, 609, 489-506. https://doi.org/10.1016/j.ins.2022.07.070

Dictionary learning was introduced for sparse image representation. Today, it is a cornerstone of image classification. We propose a novel dictionary learning method to recognise images of handwritten numbers. Our focus is to maximise the sparse-repr... Read More about Labeled projective dictionary pair learning: application to handwritten numbers recognition.

Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs (2020)
Journal Article
Alameer, A., Kryiazakis, I., & Bacardit, J. (2020). Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs. Scientific reports, 10, 13665. https://doi.org/10.1038/s41598-020-70688-6

Changes in pig behaviours are a useful aid in detecting early signs of compromised health and welfare. In commercial settings, automatic detection of pig behaviours through visual imaging remains a challenge due to farm demanding conditions, e.g., oc... Read More about Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs.

Automatic recognition of feeding and non-nutritive feeding behaviour in pigs using deep learning (2019)
Conference Proceeding
Alameer, A., Dalton, H., & Kyriazakis, I. (2019). Automatic recognition of feeding and non-nutritive feeding behaviour in pigs using deep learning. . https://doi.org/10.3920/978-90-8686-890-2

Automated vision-based early warning systems have been developed to detect behavioural changes in groups of pigs to monitor their health and welfare status. However, automatic feed detection remains a problem in precision pig farming due to problems... Read More about Automatic recognition of feeding and non-nutritive feeding behaviour in pigs using deep learning.

Context-Based Object Recognition: Indoor Versus Outdoor Environments (2019)
Book Chapter
Alameer, A., Degenaar, P., & Nazarpour, K. (2019). Context-Based Object Recognition: Indoor Versus Outdoor Environments. In K. Arai, & S. Kapoor (Eds.), CVC 2019: Advances in Computer Vision (437-490). Springer Nature. https://doi.org/10.1007/978-3-030-17798-0_38

Object recognition is a challenging problem in high-level vision. Models that perform well for the outdoor domain, perform poorly in the indoor domain and the reverse is also true. This is due to the dramatic discrepancies of the global properties of... Read More about Context-Based Object Recognition: Indoor Versus Outdoor Environments.

Incoherent dictionary pair learning : application to a novel open-source database of chinese numbers (2018)
Journal Article
Abolghasemi, V., Chen, M., Alameer, A., Ferdowsi, S., Chambers, J., & Nazarpour, K. (2018). Incoherent dictionary pair learning : application to a novel open-source database of chinese numbers. IEEE Signal Processing Letters, 25(4), 472-476. https://doi.org/10.1109/LSP.2018.2798406

We enhance the efficacy of an existing dictionary pair learning algorithm by adding a dictionary incoherence penalty term. After presenting an alternating minimization solution, we apply the proposed incoherent dictionary pair learning (InDPL) method... Read More about Incoherent dictionary pair learning : application to a novel open-source database of chinese numbers.

Processing occlusions using elastic-net hierarchical MAX model of the visual cortex (2017)
Presentation / Conference
Alameer, A., Degenaar, P., & Nazarpour, K. (2017, July). Processing occlusions using elastic-net hierarchical MAX model of the visual cortex. Presented at 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), Gdynia, Poland

Humans can recognise objects under partial occlusion. Machine-based approaches cannot reliably recognise objects and scenes in the presence of occlusion. This paper investigates the use of the elastic net hierarchical MAX (En-HMAX) model to handle oc... Read More about Processing occlusions using elastic-net hierarchical MAX model of the visual cortex.