All Outputs (746)

Comprehensive Health Tracking Through Machine Learning and Wearable Technology (2025)
Journal Article
Yusuf, A., Jaber, T. A., & Gordon, N. (online). Comprehensive Health Tracking Through Machine Learning and Wearable Technology. Journal of Data Science and Intelligent Systems, https://doi.org/10.47852/bonviewjdsis52023588

The accurate interpretation of data from wearable devices is paramount in advancing personalized healthcare and disease prevention. This study explores the application of machine learning techniques to improve the interpretation of health metrics fro... Read More about Comprehensive Health Tracking Through Machine Learning and Wearable Technology.

Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback (2024)
Journal Article
Grey, S., & Gordon, N. (2024). Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback. New Directions in the Teaching of Physical Sciences, 19(1), 1-9. https://doi.org/10.29311/ndtns.vi19.4103

It is widely recognised that feedback is an important part of learning: effective feedback should result in a meaningful change in student behaviour (Morris et al., 2021). However, individual feedback takes time to produce, and for large cohorts-typi... Read More about Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback.

Digital Health and Indoor Air Quality: An IoT- Driven Human-Centred Visualisation Platform for Behavioural Change and Technology Acceptance (2024)
Presentation / Conference Contribution
Kureshi, R. R., Mazumdar, S., Mishra, B. K., Li, X., & Thakker, D. (2023, December). Digital Health and Indoor Air Quality: An IoT- Driven Human-Centred Visualisation Platform for Behavioural Change and Technology Acceptance. Presented at 4th International Conference on Distributed Sensing and Intelligent Systems (ICDSIS 2023), Dubai, UAE

The detrimental effects of air pollutants on human health have prompted increasing concerns regarding indoor air quality (IAQ). The emergence of digital health interventions and citizen science initiatives has provided new avenues for raising awarene... Read More about Digital Health and Indoor Air Quality: An IoT- Driven Human-Centred Visualisation Platform for Behavioural Change and Technology Acceptance.

Devising a Responsible Framework for Air Quality Sensor Placement (2024)
Presentation / Conference Contribution
Westcarr, J., Gunturi, V. M. V., Cabaneros, S. M., Raja, R., Thakker, D., & Porter, A. (2024, July). Devising a Responsible Framework for Air Quality Sensor Placement. Presented at 2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS), London, United Kingdom

A major challenge faced when developing smart, sustainable urban environments is the reduction of air pollutants that adversely impact citizens' health. The UK has implemented strategies such as clean air zones (CAZs) coupled with the use of sensor t... Read More about Devising a Responsible Framework for Air Quality Sensor Placement.

A new class of zero-truncated counting models and its application (2024)
Journal Article
Tang, X. P., Tian, Y. Z., Wu, C. H., Wang, Y., & Mian, Z. B. (2024). A new class of zero-truncated counting models and its application. Communications in Statistics - Simulation and Computation, https://doi.org/10.1080/03610918.2024.2384561

Count data is a type of data derived from the number of times an event occurs per unit of time, and zero-truncated count data refers to count data without zero, which often appears in various fields. In this paper, a new zero-truncated Bell (ZTBell)... Read More about A new class of zero-truncated counting models and its application.

Enquiry-based learning pedagogy – Design, development and delivery of a reproducible robotics framework (2024)
Presentation / Conference Contribution
Walker, A., Diaz, K. R. V., McKie, D., & Iqbal, J. (2024, February). Enquiry-based learning pedagogy – Design, development and delivery of a reproducible robotics framework. Presented at Ninth International Congress on Information and Communication Technology (ICICT 2024), London

Hardware-inspired enquiry-based learning (EBL) is an emerging pedagogy to develop transferable engineering skills in students. This paper is aimed at unleashing the potential of this pedagogy via the multidisciplinary domain of robotics to learn the... Read More about Enquiry-based learning pedagogy – Design, development and delivery of a reproducible robotics framework.

A hybrid contextual framework to predict severity of infectious disease: COVID-19 case study (2024)
Journal Article
Azam, M. M. B., Anwaar, F., Khan, A. M., Anwar, M., Ghani, H. B. A., Eisa, T. A. E., & Abdelmaboud, A. (2024). A hybrid contextual framework to predict severity of infectious disease: COVID-19 case study. Egyptian Informatics Journal, 27, Article 100508. https://doi.org/10.1016/j.eij.2024.100508

Infectious disease is a particular type of disorder triggered by organisms and transmitted directly or indirectly from an infected one like COVID-19. The global economy and public health are immensely affected by COVID-19, a recently emerging infecti... Read More about A hybrid contextual framework to predict severity of infectious disease: COVID-19 case study.

Global Knowledge, Local Impact: Domain Adaptation and Classification for Obesity in the UAE (2024)
Presentation / Conference Contribution
Raza, M., Khattak, A., Abbas, W., & Khan, A. (2024, June). Global Knowledge, Local Impact: Domain Adaptation and Classification for Obesity in the UAE. Presented at 37th IEEE International Symposium on Computer-Based Medical Systems (CBMS), Guadalajara, Mexico

Obesity, a global public health concern, is escalating rapidly, especially in the Middle East, with the United Arab Emirates (UAE) witnessing one of the highest prevalence rates among adults and children. This multifactorial health issue is influence... Read More about Global Knowledge, Local Impact: Domain Adaptation and Classification for Obesity in the UAE.

User Engagement Triggers in Social Media Discourse on Biodiversity Conservation (2024)
Journal Article
Dethlefs, N., & Cuayáhuitl, H. (online). User Engagement Triggers in Social Media Discourse on Biodiversity Conservation. ACM Transactions on Social Computing, https://doi.org/10.1145/3662685

Studies in digital conservation have increasingly used social media in recent years as a source of data to understand the interactions between humans and nature, model and monitor biodiversity, and analyse online discourse about the conservation of s... Read More about User Engagement Triggers in Social Media Discourse on Biodiversity Conservation.

An empirical analysis of agricultural and rural carbon emissions under the background of rural revitalization strategy–based on machine learning algorithm (2024)
Journal Article
Niu, X. Y., Tian, Y. Z., Tang, M. L., & Mian, Z. B. (2024). An empirical analysis of agricultural and rural carbon emissions under the background of rural revitalization strategy–based on machine learning algorithm. Air Quality, Atmosphere and Health, https://doi.org/10.1007/s11869-024-01606-2

Agricultural and rural carbon (ARC) emissions are a major source of greenhouse gas emissions in China and have profound implications for implementing the rural revitalization strategy. This study takes Shandong Province, a leading agricultural provin... Read More about An empirical analysis of agricultural and rural carbon emissions under the background of rural revitalization strategy–based on machine learning algorithm.

Air Quality Forecasting and Rating Based On Machine Learning Algorithm and Cumulative Logit Model - An Empirical Study For Lanzhou City of China (2024)
Journal Article
Xu, T., Tian, Y., Cai, X. R., Wu, C.-H., & Mian, Z. (in press). Air Quality Forecasting and Rating Based On Machine Learning Algorithm and Cumulative Logit Model - An Empirical Study For Lanzhou City of China. Environment, Development and Sustainability,

Artificial Intelligence in Education: An automatic Rule-Based Chatbot to generate guidance from lecture recordings (2024)
Journal Article
Hing, W., Gordon, N., & Al Jaber, T. (2024). Artificial Intelligence in Education: An automatic Rule-Based Chatbot to generate guidance from lecture recordings. Acta Scientific Computer Sciences, 6(7), 64-74

In a new era of educational and research-based chatbots, implementing personalised interactive learning resources is critical in enhancing students' academic experiences. Whilst general purpose chatbots are now available with a range of platforms, th... Read More about Artificial Intelligence in Education: An automatic Rule-Based Chatbot to generate guidance from lecture recordings.

AI enabled: a novel IoT-based fake currency detection using millimeter wave (mmWave) sensor (2024)
Journal Article
Niaz, F., Zhang, J., Khalid, M., Qureshi, K. N., Zheng, Y., Younas, M., & Imran, N. (in press). AI enabled: a novel IoT-based fake currency detection using millimeter wave (mmWave) sensor. Computing, https://doi.org/10.1007/s00607-024-01300-2

In recent years, the significance of millimeter wave sensors has achieved a paramount role, especially in the non-invasive and ubiquitous analysis of various materials and objects. This paper introduces a novel IoT-based fake currency detection using... Read More about AI enabled: a novel IoT-based fake currency detection using millimeter wave (mmWave) sensor.

Fuzzy-Augmented Model Reference Adaptive PID Control Law Design for Robust Voltage Regulation in DC–DC Buck Converters (2024)
Journal Article
Saleem, O., Rasheed Ahmad, K., & Iqbal, J. (2024). Fuzzy-Augmented Model Reference Adaptive PID Control Law Design for Robust Voltage Regulation in DC–DC Buck Converters. Mathematics, 12(12), Article 1893. https://doi.org/10.3390/math12121893

This paper presents a novel fuzzy-augmented model reference adaptive voltage regulation strategy for the DC–DC buck converters to enhance their resilience against random input variations and load-step transients. The ubiquitous proportional-integral-... Read More about Fuzzy-Augmented Model Reference Adaptive PID Control Law Design for Robust Voltage Regulation in DC–DC Buck Converters.

Meta-Heuristic Optimization of Sliding Mode Control—Application to Quadrotor-Based Inspection of Solar Panels (2024)
Presentation / Conference Contribution
Rezoug, A., Bouderbala, F.-Z., Baizid, K., & Iqbal, J. (2022, December). Meta-Heuristic Optimization of Sliding Mode Control—Application to Quadrotor-Based Inspection of Solar Panels. Presented at 1st International Conference on Advanced Renewable Energy Systems (ICARES 2022), Tipaza, Algeria

In this paper, inspection of solar energy system is addressed using a quadrotor unmanned aerial vehicle (UAV) system. The accurate positioning of the system on the solar panel requires a robust controller to precisely address the fault while ensuring... Read More about Meta-Heuristic Optimization of Sliding Mode Control—Application to Quadrotor-Based Inspection of Solar Panels.

Exploring Creation and Curation as Steps Towards a Gamification of the Arts Through Game Engines (2024)
Thesis
Torrao, L. C. Exploring Creation and Curation as Steps Towards a Gamification of the Arts Through Game Engines. (Thesis). University of Hull. https://hull-repository.worktribe.com/output/4736049

Game engines enable the creation of novel applications that can enhance how art is created and presented and provide new tools to artists. This thesis presents study, research, and development within the frontiers between the arts and computer scienc... Read More about Exploring Creation and Curation as Steps Towards a Gamification of the Arts Through Game Engines.

Improving stability and adaptability of automotive electric steering systems based on a novel optimal integrated algorithm (2024)
Journal Article
Nguyen, T. A., & Iqbal, J. (2024). Improving stability and adaptability of automotive electric steering systems based on a novel optimal integrated algorithm. Engineering Computations, 41(4), 991-1034. https://doi.org/10.1108/EC-10-2023-0675

Purpose: Design a novel optimal integrated control algorithm for the automotive electric steering system to improve the stability and adaptation of the system. Design/methodology/approach: Simulation and calculation. Findings: The output signals foll... Read More about Improving stability and adaptability of automotive electric steering systems based on a novel optimal integrated algorithm.

Fuzzy Fault-tolerant Controller With Guaranteed Performance for MIMO Systems Under Uncertain Initial State (2024)
Journal Article
Yin, C. W., Riaz, S., Uppal, A. A., & Iqbal, J. (2024). Fuzzy Fault-tolerant Controller With Guaranteed Performance for MIMO Systems Under Uncertain Initial State. International journal of control, automation and systems, 22(6), 2038-2054. https://doi.org/10.1007/s12555-023-0327-5

It is always problematic that the initial value of the trajectory tracking error must be inside the area included in the prescribed performance constraint function. To overcome this problem, a novel fault-tolerant control strategy is designed for a s... Read More about Fuzzy Fault-tolerant Controller With Guaranteed Performance for MIMO Systems Under Uncertain Initial State.

Not So Robust after All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks (2024)
Journal Article
Garaev, R., Rasheed, B., & Khan, A. M. (2024). Not So Robust after All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks. Algorithms, 17, Article 162. https://doi.org/10.3390/a17040162

Deep neural networks (DNNs) have gained prominence in various applications, but remain vulnerable to adversarial attacks that manipulate data to mislead a DNN. This paper aims to challenge the efficacy and transferability of two contemporary defense... Read More about Not So Robust after All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks.

Bayesian relative composite quantile regression approach of ordinal latent regression model with L1/2 regularization (2024)
Journal Article
Tian, Y.-Z., Wu, C.-H., Tai, L.-N., Mian, Z., & Tian, M.-Z. (2024). Bayesian relative composite quantile regression approach of ordinal latent regression model with L1/2 regularization. Statistical Analysis and Data Mining, 17(2), Article e11683. https://doi.org/10.1002/sam.11683

Ordinal data frequently occur in various fields such as knowledge level assessment, credit rating, clinical disease diagnosis, and psychological evaluation. The classic models including cumulative logistic regression or probit regression are often us... Read More about Bayesian relative composite quantile regression approach of ordinal latent regression model with L1/2 regularization.