Outputs (237)

Blended Experience Narratives (2022)
Presentation / Conference Contribution
Flint, T., O'Keefe, B., Mastermaker, M., Sturdee, M., & Benyon, D. (2022, July). Blended Experience Narratives. Presented at 35th International BCS Human-Computer Interaction Conference, Keele

Our work employs conceptual integration, otherwise known as blending, as a tool for designing interactions that work across and between the digital/physical divide. Theoretical approaches to blends can be difficult to navigate and a hard subject to g... Read More about Blended Experience Narratives.

Digital Exclusion Cyber Security - Improving The Online Safety Of Digitally Excluded Users (2022)
Report
Koikkalainen, H., Lapok, P., Johnston, L., & Lawson, A. (2022). Digital Exclusion Cyber Security - Improving The Online Safety Of Digitally Excluded Users. University of St Andrews/ Scottish Government Cyber Resilliance Unit

This exploratory study was designed to explore cyber security issues experienced by vulnerable adults who do not regularly have direct access to digital devices and the internet, with a special focus on people who are financially insecure and experie... Read More about Digital Exclusion Cyber Security - Improving The Online Safety Of Digitally Excluded Users.

A novel security and authentication method for infrared medical image with discrete time chaotic systems (2022)
Journal Article
Boyraz, O. F., Guleryuz, E., Akgul, A., Yildiz, M. Z., Kiran, H. E., & Ahmad, J. (2022). A novel security and authentication method for infrared medical image with discrete time chaotic systems. Optik, 267, Article 169717. https://doi.org/10.1016/j.ijleo.2022.169717

Objective:
Hand vein images have become important biometric signs used for identification systems. Also, dorsal hand vein images have noteworthy advantages in terms of reliability and contactless procedure. Surgically changing the vascular pattern u... Read More about A novel security and authentication method for infrared medical image with discrete time chaotic systems.

Arabic sentiment analysis using dependency-based rules and deep neural networks (2022)
Journal Article
Diwali, A., Dashtipour, K., Saeedi, K., Gogate, M., Cambria, E., & Hussain, A. (2022). Arabic sentiment analysis using dependency-based rules and deep neural networks. Applied Soft Computing, 127, Article 109377. https://doi.org/10.1016/j.asoc.2022.109377

With the growth of social platforms in recent years and the rapid increase in the means of communication through these platforms, a significant amount of textual data is available that contains an abundance of individuals’ opinions. Sentiment analysi... Read More about Arabic sentiment analysis using dependency-based rules and deep neural networks.

Ensemble learning-based IDS for sensors telemetry data in IoT networks (2022)
Journal Article
Naz, N., Khan, M. A., Alsuhibany, S. A., Diyan, M., Tan, Z., Khan, M. A., & Ahmad, J. (2022). Ensemble learning-based IDS for sensors telemetry data in IoT networks. Mathematical Biosciences and Engineering, 19(10), 10550-10580. https://doi.org/10.3934/mbe.2022493

The Internet of Things (IoT) is a paradigm that connects a range of physical smart devices to provide ubiquitous services to individuals and automate their daily tasks. IoT devices collect data from the surrounding environment and communicate with ot... Read More about Ensemble learning-based IDS for sensors telemetry data in IoT networks.

Minimising line segments in linear diagrams is NP-hard (2022)
Journal Article
Chapman, P., Sim, K., & Hao Chen, H. (2022). Minimising line segments in linear diagrams is NP-hard. Journal of Computer Languages, 71, Article 101136. https://doi.org/10.1016/j.cola.2022.101136

Linear diagrams have been shown to be an effective method of representing set-based data. Moreover, a number of guidelines have been proven to improve the efficacy of linear diagrams. One of these guidelines is to minimise the number of line segments... Read More about Minimising line segments in linear diagrams is NP-hard.

A novel multiple kernel fuzzy topic modeling technique for biomedical data (2022)
Journal Article
Rashid, J., Kim, J., Hussain, A., Naseem, U., & Juneja, S. (2022). A novel multiple kernel fuzzy topic modeling technique for biomedical data. BMC Bioinformatics, 23(1), Article 275. https://doi.org/10.1186/s12859-022-04780-1

Background: Text mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data is in text format. Topic modeling is one of the popular methods among text mining techniques used t... Read More about A novel multiple kernel fuzzy topic modeling technique for biomedical data.

Evaluation Mechanism for Decentralised Collaborative Pattern Learning in Heterogeneous Vehicular Networks (2022)
Journal Article
Qiao, C., Qiu, J., Tan, Z., Min, G., Zomaya, A. Y., & Tian, Z. (2023). Evaluation Mechanism for Decentralised Collaborative Pattern Learning in Heterogeneous Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems, 24(11), 13123 - 13132. https://doi.org/10.1109/TITS.2022.3186630

Collaborative machine learning, especially Feder-ated Learning (FL), is widely used to build high-quality Machine Learning (ML) models in the Internet of Vehicles (IoV). In this paper, we study the performance evaluation problem in an inherently hete... Read More about Evaluation Mechanism for Decentralised Collaborative Pattern Learning in Heterogeneous Vehicular Networks.

Novel single and multi-layer echo-state recurrent autoencoders for representation learning (2022)
Journal Article
Chouikhi, N., Ammar, B., Hussain, A., & Alimi, A. M. (2022). Novel single and multi-layer echo-state recurrent autoencoders for representation learning. Engineering Applications of Artificial Intelligence, 114, Article 105051. https://doi.org/10.1016/j.engappai.2022.105051

Representation learning impacts the performance of Machine Learning (ML) models. Feature extraction-based methods such as Auto-Encoders (AEs) are used to find new, more accurate data representations from original ones. They perform efficiently on a s... Read More about Novel single and multi-layer echo-state recurrent autoencoders for representation learning.

Educational data mining to predict students' academic performance: A survey study (2022)
Journal Article
Batool, S., Rashid, J., Nisar, M. W., Kim, J., Kwon, H.-Y., & Hussain, A. (2023). Educational data mining to predict students' academic performance: A survey study. Education and Information Technologies, 28(1), 905-971. https://doi.org/10.1007/s10639-022-11152-y

Educational data mining is an emerging interdisciplinary research area involving both education and informatics. It has become an imperative research area due to many advantages that educational institutions can achieve. Along these lines, various da... Read More about Educational data mining to predict students' academic performance: A survey study.