All Outputs (830)

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.

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.

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.

NEAT Activity Detection using Smartwatch (2024)
Journal Article
Dewan, A., Gunturi, V., & Naik, V. (2024). NEAT Activity Detection using Smartwatch. International Journal of Ad Hoc and Ubiquitous Computing, 45(1), 36-51. https://doi.org/10.1504/IJAHUC.2024.136141

This paper presents a system for distinguishing non-exercise activity thermogenesis (NEAT) and non-NEAT activities at home. NEAT includes energy expended on activities apart from sleep, eating, or traditional exercise. Our study focuses on specific N... Read More about NEAT Activity Detection using Smartwatch.

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.

Phase-Based Adaptive Fractional LQR for Inverted-Pendulum-Type Robots: Formulation and Verification (2024)
Journal Article
Saleem, O., & Iqbal, J. (2024). Phase-Based Adaptive Fractional LQR for Inverted-Pendulum-Type Robots: Formulation and Verification. IEEE Access, 12, 93185-93196. https://doi.org/10.1109/ACCESS.2024.3415494

The underlying principles of inverted pendulums are widely applied to develop stabilization control strategies for under-actuated robotic systems in various applications. This article methodically designs an adaptive fractional-order linear quadratic... Read More about Phase-Based Adaptive Fractional LQR for Inverted-Pendulum-Type Robots: Formulation and Verification.

Experimental development of lightweight manipulators with improved design cycle time that leverages off-the-shelf robotic arm components (2024)
Journal Article
Abbas, M. R., Ahsan, M., & Iqbal, J. (2024). Experimental development of lightweight manipulators with improved design cycle time that leverages off-the-shelf robotic arm components. PLoS ONE, 19(7), Article e0305379. https://doi.org/10.1371/journal.pone.0305379

The growing market for lightweight robots inspires new use-cases, such as collaborative manipulators for human-centered automation. However, widespread adoption faces obstacles due to high R&D costs and longer design cycles, although rapid advances i... Read More about Experimental development of lightweight manipulators with improved design cycle time that leverages off-the-shelf robotic arm components.

Robust GDI-based adaptive recursive sliding mode control (RGDI-ARSMC) for a highly nonlinear MIMO system with varying dynamics of UAV (2024)
Journal Article
Abbas, N., Liu, X., & Iqbal, J. (2024). Robust GDI-based adaptive recursive sliding mode control (RGDI-ARSMC) for a highly nonlinear MIMO system with varying dynamics of UAV. Journal of mechanical science and technology, 38(3), https://doi.org/10.1007/s12206-024-0234-6

The novelty of the proposed work lies in the control technique, referred to as the robust generalized dynamic inversion based adaptive recursive sliding mode control (RGDI-ARSMC), for addressing various challenges to control a highly coupled and pert... Read More about Robust GDI-based adaptive recursive sliding mode control (RGDI-ARSMC) for a highly nonlinear MIMO system with varying dynamics of UAV.

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.

Pyramid Hierarchical Spatial-Spectral Transformer for Hyperspectral Image Classification (2024)
Journal Article
Ahmad, M., Butt, M. H. F., Mazzara, M., Distefano, S., Khan, A. M., & Altuwaijri, H. A. (2024). Pyramid Hierarchical Spatial-Spectral Transformer for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 17681-17689. https://doi.org/10.1109/jstars.2024.3461851

The Transformer model encounters challenges with variable-length input sequences, leading to efficiency and scalability concerns. To overcome this, we propose a pyramid-based hierarchical Spatial-Spectral Transformer (PyFormer). This innovative appro... Read More about Pyramid Hierarchical Spatial-Spectral Transformer for Hyperspectral Image Classification.