Outputs (18)

Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science (2024)
Journal Article
Goddard, T. R., Brookes, K. J., Sharma, R., Moemeni, A., & Rajkumar, A. P. (2024). Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science. Cells, 13(3), Article 223. https://doi.org/10.3390/cells13030223

Dementia with Lewy bodies (DLB) is a significant public health issue. It is the second most common neurodegenerative dementia and presents with severe neuropsychiatric symptoms. Genomic and transcriptomic analyses have provided some insight into dise... Read More about Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science.

Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities (2022)
Journal Article
Calderon-Ramirez, S., Yang, S., Elizondo, D., & Moemeni, A. (2022). Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities. Applied Soft Computing, 123, Article 108983. https://doi.org/10.1016/j.asoc.2022.108983

In the context of the global coronavirus pandemic, different deep learning solutions for infected subject detection using chest X-ray images have been proposed. However, deep learning models usually need large labelled datasets to be effective. Semi-... Read More about Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities.

Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets (2022)
Journal Article
Calderon-Ramirez, S., Oala, L., Torrentes-Barrena, J., Yang, S., Elizondo, D., Moemeni, A., …Lopez-Rubio, E. (2023). Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets. IEEE Transactions on Artificial Intelligence, 4(2), 282-291. https://doi.org/10.1109/tai.2022.3168804

Semi-supervised deep learning (SSDL) is a popular strategy to leverage unlabelled data for machine learning when labelled data is not readily available. In real-world scenarios, different unlabelled data sources are usually available, with varying de... Read More about Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets.

A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica (2022)
Journal Article
Calderon-Ramirez, S., Murillo-Hernandez, D., Rojas-Salazar, K., Elizondo, D., Yang, S., Moemeni, A., & Molina-Cabello, M. (2022). A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica. Medical and Biological Engineering and Computing, 60(4), 1159-1175. https://doi.org/10.1007/s11517-021-02497-6

The implementation of deep learning-based computer-aided diagnosis systems for the classification of mammogram images can help in improving the accuracy, reliability, and cost of diagnosing patients. However, training a deep learning model requires a... Read More about A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica.

Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images (2021)
Journal Article
Calderon-Ramirez, S., Yang, S., Moemeni, A., Elizondo, D., Colreavy-Donnelly, S., Chavarría-Estrada, L. F., & Molina-Cabello, M. A. (2021). Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images. Applied Soft Computing, 111, Article 107692. https://doi.org/10.1016/j.asoc.2021.107692

A key factor in the fight against viral diseases such as the coronavirus (COVID-19) is the identification of virus carriers as early and quickly as possible, in a cheap and efficient manner. The application of deep learning for image classification o... Read More about Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images.

Improving Uncertainty Estimation With Semi-supervised Deep Learning for COVID-19 Detection Using Chest X-ray Images (2021)
Journal Article
Calderon-Ramirez, S., Yang, S., Moemeni, A., Colreavy-Donnelly, S., Elizondo, D. A., Oala, L., …Molina-Cabello, M. A. (2021). Improving Uncertainty Estimation With Semi-supervised Deep Learning for COVID-19 Detection Using Chest X-ray Images. IEEE Access, 9, 85442 - 85454. https://doi.org/10.1109/ACCESS.2021.3085418

In this work we implement a COVID-19 infection detection system based on chest Xray images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer aided diagnosis tools in medical applications. Model estimations with h... Read More about Improving Uncertainty Estimation With Semi-supervised Deep Learning for COVID-19 Detection Using Chest X-ray Images.

Revising ICT Programmes Through Learning Outcome Alignment: A Practical Exercise in Belarusian Universities (2019)
Book Chapter
Moemeni, A., Gatward, R., Kankeviciene, I., & Pyko, A. (2019). Revising ICT Programmes Through Learning Outcome Alignment: A Practical Exercise in Belarusian Universities. In J. Carter, & C. Rosen (Eds.), Transnational Higher Education In Computing Courses: Experiences and Reflections (61-69). (1). UK: Springer Nature. https://doi.org/10.1007/978-3-030-28251-6

EU-Funded ERASMUS Capacity Building in Higher Education project called ‘Innovative ICT Education for Socio-Economic Development (IESED 2017–2019)’ has been established in the consortium of five Belarusian (BY) Higher Education Institutions (HEIs) as... Read More about Revising ICT Programmes Through Learning Outcome Alignment: A Practical Exercise in Belarusian Universities.

Revising ICT Programmes Through Learning Outcome Alignment: A Practical Exercise in Belarusian Universities (2019)
Book Chapter
Moemeni, A., Gatward, R., Kankeviciene, L., & Pyko, A. (2019). Revising ICT Programmes Through Learning Outcome Alignment: A Practical Exercise in Belarusian Universities. In J. Carter, & C. Rosen (Eds.), Transnational Higher Education in Computing Courses: Experiences and Reflections (61-69). Springer. https://doi.org/10.1007/978-3-030-28251-6_5

EU-Funded ERASMUS Capacity Building in Higher Education project called ‘Innovative ICT Education for Socio-Economic Development (IESED 2017–2019)’ has been established in the consortium of five Belarusian (BY) Higher Education Institutions (HEIs) as... Read More about Revising ICT Programmes Through Learning Outcome Alignment: A Practical Exercise in Belarusian Universities.

A Quantisation of Cognitive Learning Process by Computer Graphics-Games: Towards More Efficient Learning Models (2016)
Journal Article
Orun, A. B., Seker, H., Rose, J., Moemeni, A., & Fidan, M. (2016). A Quantisation of Cognitive Learning Process by Computer Graphics-Games: Towards More Efficient Learning Models. OALib Journal, 03(01), 1-12. https://doi.org/10.4236/oalib.1102329

With the latest developments in computer technologies and artificial intelligence (AI) techniques, more opportunities of cognitive data acquisition and stimulation via game-based systems have become available for computer scientists and psychologists... Read More about A Quantisation of Cognitive Learning Process by Computer Graphics-Games: Towards More Efficient Learning Models.

Hybrid Marker-less Camera Pose Tracking with Integrated Sensor Fusion (2014)
Thesis
Moemeni, A. (2014). Hybrid Marker-less Camera Pose Tracking with Integrated Sensor Fusion. (Thesis). Centre for Computational Intelligence (CCI) - De Montfort University. Retrieved from https://nottingham-repository.worktribe.com/output/4780253

This thesis presents a framework for a hybrid model-free marker-less inertial-visual camera pose tracking with an integrated sensor fusion mechanism. The proposed solution addresses the fundamental problem of pose recovery in computer vision and robo... Read More about Hybrid Marker-less Camera Pose Tracking with Integrated Sensor Fusion.