Outputs (61)

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.

Short-term motion prediction of autonomous vehicles in complex environments: A Deep Learning approach (2024)
Thesis
Dulian, . A. Short-term motion prediction of autonomous vehicles in complex environments: A Deep Learning approach. (Thesis). University of Hull. https://hull-repository.worktribe.com/output/4625808

Complex environments manifest a high level of complexity and it is of critical importance that the safety systems embedded within autonomous vehicles (AVs) are able to accurately anticipate short-term future motion of agents in close proximity. This... Read More about Short-term motion prediction of autonomous vehicles in complex environments: A Deep Learning approach.

Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems (2024)
Journal Article
Li, X., Ma, Z., Mian, Z., Liu, Z., Huang, R., & He, N. (2024). Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems. Computers, Materials & Continua, 78(3), 4129-4152. https://doi.org/10.32604/cmc.2024.047168

Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property specifications. Although there is an extensive literature on qualitative properties such as safety and liveness, there is sti... Read More about Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems.

Minimum Distance and Minimum Time Optimal Path Planning With Bioinspired Machine Learning Algorithms for Faulty Unmanned Air Vehicles (2024)
Journal Article
Tutsoy, O., Asadi, D., Ahmadi, K., Nabavi-Chashmi, S. Y., & Iqbal, J. (2024). Minimum Distance and Minimum Time Optimal Path Planning With Bioinspired Machine Learning Algorithms for Faulty Unmanned Air Vehicles. IEEE Transactions on Intelligent Transportation Systems, https://doi.org/10.1109/TITS.2024.3367769

Unmanned air vehicles operate in highly dynamic and unknown environments where they can encounter unexpected and unseen failures. In the presence of emergencies, autonomous unmanned air vehicles should be able to land at a minimum distance or minimum... Read More about Minimum Distance and Minimum Time Optimal Path Planning With Bioinspired Machine Learning Algorithms for Faulty Unmanned Air Vehicles.

Adaptive-optimal MIMO nonsingular terminal sliding mode control of twin-rotor helicopter system: meta-heuristics and super-twisting based control approach (2024)
Journal Article
Rezoug, A., Messah, A., Messaoud, W. A., Baizid, K., & Iqbal, J. (2024). Adaptive-optimal MIMO nonsingular terminal sliding mode control of twin-rotor helicopter system: meta-heuristics and super-twisting based control approach. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46(3), Article 162. https://doi.org/10.1007/s40430-024-04714-3

This research proposes a novel hybrid control technique based on nonsingular terminal sliding mode (NTSM) control, metaheuristic optimization algorithms and adaptive super-twisting based on Lyapunov stability analysis for controlling Quanser aero sim... Read More about Adaptive-optimal MIMO nonsingular terminal sliding mode control of twin-rotor helicopter system: meta-heuristics and super-twisting based control approach.

AI-Based Hand Gesture Recognition Through Camera on Robot (2024)
Presentation / Conference Contribution
Csonka, G., Khalid, M., Rafiq, H., & Ali, Y. (2023, December). AI-Based Hand Gesture Recognition Through Camera on Robot. Presented at 2023 International Conference on Frontiers of Information Technology (FIT), Islamabad

This paper presents an innovative approach to real-time hand gesture recognition for robot control using Artificial Intelligence (AI). The core of this project is a machine learning model trained on a custom data set of hand gestures, which was metic... Read More about AI-Based Hand Gesture Recognition Through Camera on Robot.

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.

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.

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.