All Outputs (20)

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.

Wavelet and multiwavelet watermarking (2007)
Journal Article
Serdean, C., Ibrahim, M., Moemeni, A., & Al-Akaidi, M. (2007). Wavelet and multiwavelet watermarking. IET Image Processing, 1(2), 223-230. https://doi.org/10.1049/iet-ipr%3A20060214

The main objective of the paper is to provide a like-with-like performance comparison between the wavelet domain and the multiwavelet domain watermarking, under a variety of attacks. The investigation is restricted to balanced multiwavelets. Furtherm... Read More about Wavelet and multiwavelet watermarking.

Inertial-visual pose tracking using optical flow-aided particle filtering
Presentation / Conference Contribution
Moemeni, A., & Tatham, E. (2014, December). Inertial-visual pose tracking using optical flow-aided particle filtering. Presented at 2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP), Orlando, FL, USA

This paper proposes an algorithm for visual-inertial camera pose tracking, using adaptive recursive particle filtering. The method benefits from the agility of inertial-based and robustness of vision-based tracking. A proposal distribution has been d... Read More about Inertial-visual pose tracking using optical flow-aided particle filtering.

Quality Assessment of Dental Photostimulable Phosphor Plates with Deep Learning
Presentation / Conference Contribution
Bermudez, A., Calderon-Ramirez, S., Thang, T., Tyrrell, P., Moemeni, A., Yang, S., & Torrents-Barrena, J. (2020, July). Quality Assessment of Dental Photostimulable Phosphor Plates with Deep Learning. Presented at 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK

Photostimulable Phosphor Plates are commonly used in digital X-ray imaging for dentistry. During its usage, these plates get damaged, influencing the diagnosis performance and confidence of the dentistry professional. We propose a deep learning based... Read More about Quality Assessment of Dental Photostimulable Phosphor Plates with Deep Learning.

Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data
Presentation / Conference Contribution
Rostami-Shahrbabaki, M., Bogenberger, K., Safavi, A. A., & Moemeni, A. (2020, January). Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data. Presented at Transportation Research Board (TRB) Annual Meeting 2020, Washington DC, USA

Current traffic management systems in urban networks require real-time estimation of the traffic states.With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement... Read More about Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data.

Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images
Presentation / Conference Contribution
Calderon-Ramirez, S., Giri, R., Moemeni, A., Umaña, M., Elizondo, D., Torrents-Barrena, J., & Molina-Cabello, M. A. (2021, January). Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy

Coronavirus (Covid-19) is spreading fast, infecting people through contact in various forms including droplets from sneezing and coughing. Therefore, the detection of infected subjects in an early, quick and cheap manner is urgent. Currently availabl... Read More about Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images.

Low-Cost Automatic Ambient Assisted Living System
Presentation / Conference Contribution
Malekmohamad, H., Moemeni, A., Orun, A., & Purohit, J. K. (2018, March). Low-Cost Automatic Ambient Assisted Living System. Presented at 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Athens, Greece

The recent increase in ageing population in countries around the world has brought a lot of attention toward research and development of ambient assisted living (AAL) systems. These systems should be inexpensive to be installed in elderly homes, prot... Read More about Low-Cost Automatic Ambient Assisted Living System.

A framework for camera pose tracking using stochastic data fusion
Presentation / Conference Contribution
Moemeni, A., & Tatham, E. (2010, December). A framework for camera pose tracking using stochastic data fusion. Presented at 2010 2nd International IEEE Consumer Electronics Society's Games Innovations Conference (ICE-GIC 2010), Hong Kong, China

A novel camera pose tracking system using a stochastic inertial-visual sensor fusion has been proposed. A method based on the Particle Filtering concept has been adapted for inertial and vision data fusion, which benefits from the agility of inertial... Read More about A framework for camera pose tracking using stochastic data fusion.

Improving Uncertainty Estimations for Mammogram Classification Using Semi-Supervised Learning
Presentation / Conference Contribution
Calderon-Ramırez, S., Murillo-Hernandez, D., Rojas-Salazar, K., Calvo-Valverde, L.-A., Yang, S., Moemeni, A., Elizondo, D., Lopez-Rubio, E., & Molina-Cabello, M. (2021, July). Improving Uncertainty Estimations for Mammogram Classification Using Semi-Supervised Learning. Presented at International Joint Conference on Neural Networks (IJCNN 2021), Online

Computer aided diagnosis for mammogram images have seen positive results through the usage of deep learning architectures. However, limited sample sizes for the target datasets might prevent the usage of a deep learning model under real world scenari... Read More about Improving Uncertainty Estimations for Mammogram Classification Using Semi-Supervised Learning.

Comparing a Graphical User Interface, Hand Gestures and Controller in Virtual Reality for Robot Teleoperation
Presentation / Conference Contribution
Chen, J., Moemeni, A., & Caleb-Solly, P. (2023, March). Comparing a Graphical User Interface, Hand Gestures and Controller in Virtual Reality for Robot Teleoperation. Presented at Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, Stockholm, Sweden

Robot teleoperation is being explored in a number of application areas, where combining human adaptive intelligence and high precision of robots can provide access to dangerous or inaccessible places, or augment human dexterity. Using virtual reality... Read More about Comparing a Graphical User Interface, Hand Gestures and Controller in Virtual Reality for Robot Teleoperation.

Robustness of Deep Learning Methods for Occluded Object Detection - A Study Introducing a Novel Occlusion Dataset
Presentation / Conference Contribution
Wu, Z., Moemeni, A., Caleb-Solly, P., & Castle-Green, S. (2023, June). Robustness of Deep Learning Methods for Occluded Object Detection - A Study Introducing a Novel Occlusion Dataset. Presented at 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia

A large number of deep learning based object detection algorithms have been proposed and applied in a wide range of domains such as security, autonomous driving and robotics. In practical usage, objects being occluded are common, and can result in re... Read More about Robustness of Deep Learning Methods for Occluded Object Detection - A Study Introducing a Novel Occlusion Dataset.