All Outputs (481)

Experimental Development of Lightweight Manipulators with Improved Design Cycle Time that leverages off-the-shelf Robotic Arm Components (2024)
Journal Article
Rzi Abbas, M., 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.

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., Tian, Y., Tang, M., & Mian, Z. (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.

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.

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.

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.

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.

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.

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.

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.

A literature review of fault diagnosis based on ensemble learning (2023)
Journal Article
Mian, Z., Deng, X., Dong, X., Tian, Y., Cao, T., Chen, K., & Jaber, T. A. (2024). A literature review of fault diagnosis based on ensemble learning. Engineering applications of artificial intelligence, 127, Article 107357. https://doi.org/10.1016/j.engappai.2023.107357

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of... Read More about A literature review of fault diagnosis based on ensemble learning.

LViT: Language meets Vision Transformer in Medical Image Segmentation (2023)
Journal Article
Li, Z., Li, Y., Li, Q., Wang, P., Guo, D., Lu, L., …Hong, Q. (2024). LViT: Language meets Vision Transformer in Medical Image Segmentation. IEEE Transactions on Medical Imaging, 43(1), 96-107. https://doi.org/10.1109/TMI.2023.3291719

Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the... Read More about LViT: Language meets Vision Transformer in Medical Image Segmentation.