Outputs (839)

Tailored risk assessment and forecasting in intermittent claudication (2024)
Journal Article
Ravindhran, B., Prosser, J., Lim, A., Lathan, R., Mishra, B., Hitchman, L., Smith, G. E., Carradice, D., Thakker, D., Chetter, I. C., & Pymer, S. (2024). Tailored risk assessment and forecasting in intermittent claudication. BJS Open, 8(1), Article zrad166. https://doi.org/10.1093/bjsopen/zrad166

Background: Guidelines recommend cardiovascular risk reduction and supervised exercise therapy as the first line of treatment in intermittent claudication, but implementation challenges and poor patient compliance lead to significant variation in man... Read More about Tailored risk assessment and forecasting in intermittent claudication.

Application of Artificial Intelligence and Data Science in Detecting the Impact of Usability from Evaluation of Mobile Health Applications (2024)
Journal Article
Kayode, O., Al Jaber, T., & Gordon, N. (2024). Application of Artificial Intelligence and Data Science in Detecting the Impact of Usability from Evaluation of Mobile Health Applications. International Journal on Engineering Technologies and Informatics, 5(1), 1-9. https://doi.org/10.51626/ijeti.2024.05.00070

Mobile health (mHealth) applications have demonstrated immense potential for facilitating preventative care and disease management through intuitive platforms. However, realizing transformational health objectives relies on creating accessible tools... Read More about Application of Artificial Intelligence and Data Science in Detecting the Impact of Usability from Evaluation of Mobile Health Applications.

Evaluating Asthma Symptoms in Relation to Indoor Air Quality: Insights from IoT-enabled Monitoring (2024)
Presentation / Conference Contribution
Mishra, B. K., Thakker, D., John, R., Kureshi, R. R., Ahmad, B., Jones, W., & Li, X. (2023, December). Evaluating Asthma Symptoms in Relation to Indoor Air Quality: Insights from IoT-enabled Monitoring. Presented at 4th International Conference on Distributed Sensing and Intelligent Systems (ICDSIS 2023), Dubai, UAE

Air pollution appears in the form of outdoor air quality and indoor air quality (IAQ). Particulate Matters (PM2.5 and PM10) and CO2, among many air pollutants, are responsible for worsening IAQ. IAQ has been linked to lung illnesses such as asthma, c... Read More about Evaluating Asthma Symptoms in Relation to Indoor Air Quality: Insights from IoT-enabled Monitoring.

Improving Rice Yield Prediction Accuracy Using Regression Models with Climate Data (2024)
Presentation / Conference Contribution
Mohamad Mohsin, M. F., Umana, M. K., Hassan, M. G., Sharif, K. I. M., Ismail, M. A., Salleh, K., Zahari, S. M., Sarmani, M. A., & Gordon, N. Improving Rice Yield Prediction Accuracy Using Regression Models with Climate Data. Presented at International Conference on Computing and Informatics 2023, Kuala Lumpur, Malaysia

Rice production is critical to food security, and accurate yield predictions are required for planning and decision-making. However, precisely predicting rice yields using machine learning models can be difficult due to the complicated interactions o... Read More about Improving Rice Yield Prediction Accuracy Using Regression Models with Climate Data.

A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy (2024)
Journal Article
Xue, Y., Kambhampati, C., Cheng, Y., Mishra, N., Wulandhari, N., & Deutz, P. (2024). A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy. International Journal of Computational Intelligence Systems, 17(1), Article 8. https://doi.org/10.1007/s44196-023-00375-7

The mass production of plastic waste has caused an urgent worldwide public health crisis. Although government policies and industrial innovation are the driving forces to meet this challenge, trying to understand public attitudes may improve the effi... Read More about A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy.

Using outlier elimination to assess learning-based correspondence matching methods (2024)
Journal Article
Ding, X., Luo, Y., Jie, B., Li, Q., & Cheng, Y. (2024). Using outlier elimination to assess learning-based correspondence matching methods. Information Sciences, 659, Article 120056. https://doi.org/10.1016/j.ins.2023.120056

Recently, deep learning (DL) technology has been widely used in correspondence matching. The learning-based models are usually trained on benign image pairs with partial overlaps. Since DL model is usually data-dependent, non-overlapping images may b... Read More about Using outlier elimination to assess learning-based correspondence matching methods.

Exploring the Impact of Conceptual Bottlenecks on Adversarial Robustness of Deep Neural Networks (2024)
Journal Article
Rasheed, B., Abdelhamid, M., Khan, A., Menezes, I., & Masood Khatak, A. (2024). Exploring the Impact of Conceptual Bottlenecks on Adversarial Robustness of Deep Neural Networks. IEEE Access, 12, 131323-131335. https://doi.org/10.1109/ACCESS.2024.3457784

Deep neural networks (DNNs), while powerful, often suffer from a lack of interpretability and vulnerability to adversarial attacks. Concept bottleneck models (CBMs), which incorporate intermediate high-level concepts into the model architecture, prom... Read More about Exploring the Impact of Conceptual Bottlenecks on Adversarial Robustness of Deep Neural Networks.

NeuFG: Neural Fuzzy Geometric Representation for 3D Reconstruction (2024)
Journal Article
Hong, Q., Yang, C., Chen, J., Li, Z., Wu, Q., Li, Q., & Tian, J. (2024). NeuFG: Neural Fuzzy Geometric Representation for 3D Reconstruction. IEEE Transactions on Fuzzy Systems, https://doi.org/10.1109/TFUZZ.2024.3447088

3D reconstruction from multi-view images is considered as a longstanding problem in computer vision and graphics. In order to achieve high-fidelity geometry and appearance of 3D scenes, this paper proposes a novel geometric object learning method for... Read More about NeuFG: Neural Fuzzy Geometric Representation for 3D Reconstruction.

Fairness, Bias, and Ethics in AI: Exploring the Factors Affecting Student Performance (2024)
Journal Article
Omughelli, D., Gordon, N., & Al Jaber, T. (2024). Fairness, Bias, and Ethics in AI: Exploring the Factors Affecting Student Performance. Journal of Intelligent Communication, 4(1), 100-110. https://doi.org/10.54963/jic.v4i1.306

The use of artificial intelligence (AI) as a data science tool for education has enormous potential for increasing student performance and course outcomes. However, the growing concern about fairness, bias, and ethics in AI systems requires a careful... Read More about Fairness, Bias, and Ethics in AI: Exploring the Factors Affecting Student Performance.

Redefining Digital Twins - A Wind Energy Operations and Maintenance Perspective (2024)
Presentation / Conference Contribution
Tuton, E., Ma, X., & Dethlefs, N. (2024, May). Redefining Digital Twins - A Wind Energy Operations and Maintenance Perspective. Presented at The Science of Making Torque from Wind (TORQUE 2024), Florence, Italy

Digital Twin (DT) technology has seen an explosion in popularity, with wind energy no exception. This is particularly true for Operations & Maintenance (O&M) applications. However, this expanded use has been accompanied by loose, conflicting, definit... Read More about Redefining Digital Twins - A Wind Energy Operations and Maintenance Perspective.