Outputs (4095)

Information literacy impact framework: Final project report (2022)
Report
Cruickshank, P., Ryan, B., & Milosheva, M. (2022). Information literacy impact framework: Final project report

This report presents findings from a review of literature reporting on information literacy (IL) impact. It is intended to deliver considerations towards a framework for impactful IL interventions, including development of parameters to guide impact... Read More about Information literacy impact framework: Final project report.

A Multirobot Cooperative Area Coverage Search Algorithm Based on Bioinspired Neural Network in Unknown Environments (2022)
Journal Article
Chen, B., Zhang, W., Zhang, F., Liu, Y., & Yu, H. (2023). A Multirobot Cooperative Area Coverage Search Algorithm Based on Bioinspired Neural Network in Unknown Environments. IEEE Systems Journal, 17(2), 1995-2006. https://doi.org/10.1109/jsyst.2022.3198712

This article proposes a novel approach based on a bioinspired neural network (BIN) for multirobot area coverage search in unknown environments. We obtain the dynamic environment information in the search process of the multirobot by combining the BIN... Read More about A Multirobot Cooperative Area Coverage Search Algorithm Based on Bioinspired Neural Network in Unknown Environments.

Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique (2022)
Journal Article
Moezzi, S. A. R., Ghaedi, A., Rahmanian, M., Mousavi, S. Z., & Sami, A. (2023). Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique. Journal of Digital Imaging, 36(1), 80-90. https://doi.org/10.1007/s10278-022-00692-x

Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is difficult. In these circumstances, natural language processing (NLP) techniqu... Read More about Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique.

A Testing Methodology for the Internet of Things Affordable IP Cameras (2022)
Book Chapter
Dzwigala, G., Ghaleb, B., Aldhaheri, T. A., Wadhaj, I., Thomson, C., & Al-Zidi, N. M. (2022). A Testing Methodology for the Internet of Things Affordable IP Cameras. In H. Sharma, V. Shrivastava, K. Kumari Bharti, & L. Wang (Eds.), . Springer. https://doi.org/10.1007/978-981-19-2130-8_37

IP cameras are becoming a cheaper and more convenient option for a lot of households, whether it being for outdoor, indoor security, or as baby or pet monitors. Their security, however, is often lacking, and there is currently no testbed that focuses... Read More about A Testing Methodology for the Internet of Things Affordable IP Cameras.

Semi-supervised Representative Learning for Measuring Epidermal Thickness in Human Subjects in Optical Coherence Tomography by Leveraging Datasets from Rodent Models (2022)
Journal Article
Ji, Y., Yang, S., Zhou, K., Lu, J., Wang, R., Rocliffe, H. R., Pellicoro, A., Cash, J. L., Li, C., & Huang, Z. (2022). Semi-supervised Representative Learning for Measuring Epidermal Thickness in Human Subjects in Optical Coherence Tomography by Leveraging Datasets from Rodent Models. Journal of Biomedical Optics, 27(8), Article 085002. https://doi.org/10.1117/1.JBO.27.8.085002

Aim: Morphological changes in the epidermis layer are critical for the diagnosis and assessment of various skin diseases. Due to its non-invasiveness, optical coherence tomography (OCT) is a good candidate for observing microstructural changes of ski... Read More about Semi-supervised Representative Learning for Measuring Epidermal Thickness in Human Subjects in Optical Coherence Tomography by Leveraging Datasets from Rodent Models.

A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis (2022)
Journal Article
Rehman, M. U., Shafique, A., Ghadi, Y. Y., Boulila, W., Jan, S. U., Gadekallu, T. R., Driss, M., & Ahmad, J. (2022). A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis. IEEE Transactions on Network Science and Engineering, 9(6), 4322-4337. https://doi.org/10.1109/tnse.2022.3199235

Early cancer identification is regarded as a challenging problem in cancer prevention for the healthcare community. In addition, ensuring privacy-preserving healthcare data becomes more difficult with the growing demand for sharing these data. This s... Read More about A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis.

A Mixed Approach for Aggressive Political Discourse Analysis on Twitter (2022)
Journal Article
Torregrosa, J., D’Antonio-Maceiras, S., Villar-Rodríguez, G., Hussain, A., Cambria, E., & Camacho, D. (2023). A Mixed Approach for Aggressive Political Discourse Analysis on Twitter. Cognitive Computation, 15, 440-465. https://doi.org/10.1007/s12559-022-10048-w

Political tensions have grown throughout Europe since the beginning of the new century. The consecutive crises led to the rise of different social movements in several countries, in which the political status quo changed. These changes included an in... Read More about A Mixed Approach for Aggressive Political Discourse Analysis on Twitter.

Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques (2022)
Journal Article
Aamir, S., Rahim, A., Aamir, Z., Abbasi, S. F., Khan, M. S., Alhaisoni, M., Khan, M. A., Khan, K., & Ahmad, J. (2022). Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques. Computational and Mathematical Methods in Medicine, 2022, Article 5869529. https://doi.org/10.1155/2022/5869529

Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various com... Read More about Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques.

A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem (2022)
Presentation / Conference Contribution
Marrero, A., Segredo, E., León, C., & Hart, E. (2022, September). A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem. Presented at Parallel Problem Solving from Nature – PPSN XVII, 17th International Conference, Dortmund, Germany

We propose a new approach to generating synthetic instances in the knapsack domain in order to fill an instance-space. The method uses a novelty-search algorithm to search for instances that are diverse with respect to a feature-space but also elicit... Read More about A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem.

Evolutionary Approaches to Improving the Layouts of Instance-Spaces (2022)
Presentation / Conference Contribution
Sim, K., & Hart, E. (2022, September). Evolutionary Approaches to Improving the Layouts of Instance-Spaces. Presented at 17th International Conference, PPSN 2022, Dortmund, Germany

We propose two new methods for evolving the layout of an instance-space. Specifically we design three different fitness metrics that seek to: (i) reward layouts which place instances won by the same solver close in the space; (ii) reward layouts that... Read More about Evolutionary Approaches to Improving the Layouts of Instance-Spaces.