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All Outputs (19)

The Intersection of Generative AI and Healthcare: Addressing Challenges to Enhance Patient Care (2024)
Conference Proceeding
Albaroudi, E., Mansouri, T., & Alameer, A. (2024). The Intersection of Generative AI and Healthcare: Addressing Challenges to Enhance Patient Care. In 2024 Seventh International Women in Data Science Conference at Prince Sultan University (WiDS PSU). https://doi.org/10.1109/wids-psu61003.2024.00039

This research analyses the evolving intersection of generative AI and healthcare. It explores the transformative potential of integrating generative AI in healthcare, particularly in process automation, patient care, patient monitoring, and diagnosis... Read More about The Intersection of Generative AI and Healthcare: Addressing Challenges to Enhance Patient Care.

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.

Deep learning-based artificial vision for grasp classification in myoelectric hands (2017)
Journal Article
Ghazaei, G., Alameer, A., Degenaar, P., Morgan, G., & Nazarpour, K. (2017). Deep learning-based artificial vision for grasp classification in myoelectric hands. Journal of Neural Engineering, 14(3), 036025. https://doi.org/10.1088/1741-2552/aa6802

Objective. Computer vision-based assistive technology solutions can revolutionise the quality of care for people with sensorimotor disorders. The goal of this work was to enable trans-radial amputees to use a simple, yet efficient, computer vision sy... Read More about Deep learning-based artificial vision for grasp classification in myoelectric hands.

Biologically-inspired object recognition system for recognizing natural scene categories (2016)
Presentation / Conference
Alameer, A., Degenaar, P., & Nazarpour, K. (2016, October). Biologically-inspired object recognition system for recognizing natural scene categories. Presented at International Conference for Students on Applied Engineering (ISCAE), Newcastle upon Tyne

Visual processing has attracted a lot of attention in the last decade. Hierarchical approaches for object recognition are gradually becoming widely-accepted. Generally, they are inspired by the ventral stream of human visual cortex, which is in charg... Read More about Biologically-inspired object recognition system for recognizing natural scene categories.

An exploratory study on the use of convolutional neural networks for object grasp classification
Presentation / Conference
Ghazaei, G., Alameer, A., Degenaar, P., Morgan, G., & Nazarpour, K. An exploratory study on the use of convolutional neural networks for object grasp classification. Presented at 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)

The loss of hand profoundly affects an individual's quality of life. Prosthetic hands can provide a route to functional rehabilitation by allowing the amputees to undertake their daily activities. However, the performance of current artificial hands... Read More about An exploratory study on the use of convolutional neural networks for object grasp classification.

An elastic net-regularized HMAX model of visual processing
Presentation / Conference
Alameer, A., Ghazaeil, G., Degenaar, P., & Nazarpour, K. An elastic net-regularized HMAX model of visual processing. Presented at 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)

The hierarchical MAX (HMAX) model of human visual system has been used in robotics and autonomous systems widely. However, there is still a stark gap between human and robotic vision in observing the environment and intelligently categorizing the obj... Read More about An elastic net-regularized HMAX model of visual processing.

Automatic recognition of feeding and foraging behaviour in pigs using deep learning
Journal Article
Alameer, A., Kyriazakis, I., Dalton, H., Miller, A., & Bacardit, J. Automatic recognition of feeding and foraging behaviour in pigs using deep learning. Biosystems Engineering, 197, 91-104. https://doi.org/10.1016/j.biosystemseng.2020.06.013

Highlights • An automated detection method of pig feeding and foraging behaviour was developed. • The automated method is based on convolutional deep neural networks. • The automated method does not rely on pig tracking to e... Read More about Automatic recognition of feeding and foraging behaviour in pigs using deep learning.

Automated recognition of postures for the detection of compromised health pig
Book Chapter
Alameer, A. Automated recognition of postures for the detection of compromised health pig. In 71st Annual Meeting of European Federation of Animal Science. http://www.eaap.org/EAAP2020_Book_of_Abstracts.pdf

Changes in pig behaviours may be used to detect early signs of problems, such as in animal health. Automated vision-based early warning systems have been developed to detect behavioural changes in groups of pigs to monitor their health and welfare st... Read More about Automated recognition of postures for the detection of compromised health pig.

Classification of handwritten Chinese numbers with convolutional neural networks
Presentation / Conference
Ameri, R., Alameer, A., Ferdowski, S., Abolghasemi, V., & Nazarpour, K. Classification of handwritten Chinese numbers with convolutional neural networks. Presented at 5th International Conference on Pattern Recognition and Image Analysis (IPRIA)

Deep learning methods have become the key ingredient in the field of computer vision; in particular, convolutional neural networks (CNNs). Appropriating the network architecture and data pre-processing have significant impact on performance. This pap... Read More about Classification of handwritten Chinese numbers with convolutional neural networks.