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All Outputs (54)

Can we revitalize interventional healthcare with AI-XR surgical metaverses? (2023)
Conference Proceeding
Qayyum, A., Bilal, M., Hadi, M., Capik, P., Caputo, M., Vohra, H., …Qadir, J. (2023). Can we revitalize interventional healthcare with AI-XR surgical metaverses?. In IEEE International Conference on Metaverse Computing, Networking and Applications (IEEE MetaCom 2023) (496-503). https://doi.org/10.1109/MetaCom57706.2023.00091

Recent advancements in technology, particularly in machine learning (ML), deep learning (DL), and the metaverse, offer great potential for revolutionizing surgical science. The combination of artificial intelligence and extended reality (AI-XR) techn... Read More about Can we revitalize interventional healthcare with AI-XR surgical metaverses?.

Computer vision and IoT research landscape for health and safety management on construction sites (2023)
Journal Article
Arshad, S., Akinade, O., Bello, S., & Bilal, M. (2023). Computer vision and IoT research landscape for health and safety management on construction sites. Journal of Building Engineering, 76, Article 107049. https://doi.org/10.1016/j.jobe.2023.107049

Aims: Perform a systematic review of current literature to evaluate and summarise the health and safety hazards on construction sites. Methods: Science Direct, SCOPUS and web of science databases were searched for research articles published from 201... Read More about Computer vision and IoT research landscape for health and safety management on construction sites.

Deep learning-based multi-target regression for traffic-related air pollution forecasting (2023)
Journal Article
Akinosho, T. D., Bilal, M., Hayes, E. T., Ajayi, A., Ahmed, A., & Khan, Z. (2023). Deep learning-based multi-target regression for traffic-related air pollution forecasting. Machine Learning with Applications, 12, Article 100474. https://doi.org/10.1016/j.mlwa.2023.100474

Traffic-related air pollution (TRAP) remains one of the main contributors to urban pollution and its impact on climate change cannot be overemphasised. Experts in developed countries strive to make optimal use of traffic and air qua... Read More about Deep learning-based multi-target regression for traffic-related air pollution forecasting.

SegCrop: Segmentation-based dynamic cropping of endoscopic videos to address label leakage in surgical tool detection (2023)
Presentation / Conference
Qayyum, A., Bilal, M., Qadir, J., Caputo, M., Vohra, H., Akinosho, T., …Abioye, S. (2023, April). SegCrop: Segmentation-based dynamic cropping of endoscopic videos to address label leakage in surgical tool detection. Paper presented at IEEE International Symposium on Biomedical Imaging (ISBI), 2023, Colombia

In recent times, surgical data science has emerged as an important research discipline in interventional healthcare. There are many potential applications for analysing endoscopic surgical videos using machine learning (ML) techniques such as surgica... Read More about SegCrop: Segmentation-based dynamic cropping of endoscopic videos to address label leakage in surgical tool detection.

Privacy-preserving artificial intelligence in healthcare: Techniques and applications (2023)
Journal Article
Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023). Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Computers in Biology and Medicine, 158, Article 106848. https://doi.org/10.1016/j.compbiomed.2023.106848

There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very... Read More about Privacy-preserving artificial intelligence in healthcare: Techniques and applications.

A deep learning approach for context-aware citation recommendation using rhetorical zone classification and similarity to overcome cold-start problem (2022)
Journal Article
Abbas, M., Ajayi, S., Bilal, M., Oyegoke, A., Pasha, M., & Tauqeer Ali, H. (in press). A deep learning approach for context-aware citation recommendation using rhetorical zone classification and similarity to overcome cold-start problem. Journal of Ambient Intelligence and Humanized Computing, https://doi.org/10.1007/s12652-022-03899-6

In the recent decade, the citation recommendation has emerged as an important research topic due to its need for the huge size of published scientific work. Among other citation recommendation techniques, the widely used content-based filtering (CBF)... Read More about A deep learning approach for context-aware citation recommendation using rhetorical zone classification and similarity to overcome cold-start problem.

A scalable deep learning system for monitoring and forecasting pollutant concentration levels on UK highways (2022)
Journal Article
Akinosho, T. D., Oyedele, L. O., Bilal, M., Barrera-Animas, A. Y., Gbadamosi, A. Q., & Olawale, O. A. (2022). A scalable deep learning system for monitoring and forecasting pollutant concentration levels on UK highways. Ecological Informatics, 69, Article 101609. https://doi.org/10.1016/j.ecoinf.2022.101609

The construction of intercity highways by the government has resulted in a progressive increase in vehicle emissions and pollution from noise, dust, and vibrations despite its recognition of the air pollution menace. Efforts that have targeted roadsi... Read More about A scalable deep learning system for monitoring and forecasting pollutant concentration levels on UK highways.

Integrating single-shot fast gradient sign method (FGSM) with classical image processing techniques for generating adversarial attacks on deep learning classifiers (2022)
Conference Proceeding
Hassan, M., Younis, S., Rasheed, A., & Bilal, M. (2022). Integrating single-shot fast gradient sign method (FGSM) with classical image processing techniques for generating adversarial attacks on deep learning classifiers. In Proceedings Volume 12084, Fourteenth International Conference on Machine Vision (ICMV 2021). https://doi.org/10.1117/12.2623585

Deep learning architectures have emerged as powerful function approximators in a broad spectrum of complex representation learning tasks, such as, computer vision, natural language processing and collaborative filtering. These architectures bear a hi... Read More about Integrating single-shot fast gradient sign method (FGSM) with classical image processing techniques for generating adversarial attacks on deep learning classifiers.

Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting (2021)
Journal Article
Barrera Animas, A., Oladayo Oyedele, L., Bilal, M., Dolapo Akinosho, T., Davila Delgado, J. M., & Adewale Akanbi, L. (2022). Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting. Machine Learning with Applications, 7, Article 100204. https://doi.org/10.1016/j.mlwa.2021.100204

Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statis... Read More about Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting.

Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges (2021)
Journal Article
Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Davila Delgado, J. M., Bilal, M., …Ahmed, A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44, Article 103299. https://doi.org/10.1016/j.jobe.2021.103299

The growth of the construction industry is severely limited by the myriad complex challenges it faces such as cost and time overruns, health and safety, productivity and labour shortages. Also, construction industry is one the least digitized industr... Read More about Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges.

Deep learning and boosted trees for injuries prediction in power infrastructure projects (2021)
Journal Article
Oyedele, A., Ajayi, A., Oyedele, L. O., Delgado, J. M. D., Akanbi, L., Akinade, O., …Bilal, M. (2021). Deep learning and boosted trees for injuries prediction in power infrastructure projects. Applied Soft Computing, 110(107587), 1 - 14. https://doi.org/10.1016/j.asoc.2021.107587

Electrical injury impacts are substantial and massive. Investments in electricity will continue to increase, leading to construction project complexities, which undoubtedly contribute to injuries and associated effects. Machine learning (ML) algorith... Read More about Deep learning and boosted trees for injuries prediction in power infrastructure projects.

Cloud computing in construction industry: Use cases, benefits and challenges (2020)
Journal Article
Bello, S. A., Oyedele, L. O., Akinade, O. O., Bilal, M., Davila Delgado, J. M., Akanbi, L. A., …Owolabi, H. A. (2021). Cloud computing in construction industry: Use cases, benefits and challenges. Automation in Construction, 122, Article 103441. https://doi.org/10.1016/j.autcon.2020.103441

Cloud computing technologies have revolutionised several industries (such as aerospace, manufacturing, automobile, retail, etc.) for several years. Although the construction industry is well placed to also leverage these technologies for competitive... Read More about Cloud computing in construction industry: Use cases, benefits and challenges.

Big data for design options repository: Towards a DFMA approach for offsite construction (2020)
Journal Article
Gbadamosi, A., Oyedele, L., Mahamadu, A., Kusimo, H., Bilal, M., Davila Delgado, J. M., & Muhammed-Yakubu, N. (2020). Big data for design options repository: Towards a DFMA approach for offsite construction. Automation in Construction, 120, Article 103388. https://doi.org/10.1016/j.autcon.2020.103388

A persistent barrier to the adoption of offsite construction is the lack of information for assessing prefabrication alternatives and the choices of suppliers. This study integrates three aspects of offsite construction, including BIM, DFMA and big d... Read More about Big data for design options repository: Towards a DFMA approach for offsite construction.

Deep learning in the construction industry: A review of present status and future innovations (2020)
Journal Article
Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, Article 101827. https://doi.org/10.1016/j.jobe.2020.101827

The construction industry is known to be overwhelmed with resource planning, risk management and logistic challenges which often result in design defects, project delivery delays, cost overruns and contractual disputes. These challenges have instigat... Read More about Deep learning in the construction industry: A review of present status and future innovations.

Life cycle assessment approach for renewable multi-energy system: A comprehensive analysis (2020)
Journal Article
Luo, X., Oyedele, L. O., Owolabi, H. A., Bilal, M., Ajayi, A. O., & Akinade, O. O. (2020). Life cycle assessment approach for renewable multi-energy system: A comprehensive analysis. Energy Conversion and Management, 224, Article 113354. https://doi.org/10.1016/j.enconman.2020.113354

In response to the gradual degradation of natural sources, there is a growing interest in adopting renewable resources for various building energy supply. In this study, a comprehensive life cycle assessment approach is proposed for a renewable multi... Read More about Life cycle assessment approach for renewable multi-energy system: A comprehensive analysis.

Secure and robust machine learning for healthcare: A survey (2020)
Journal Article
Qayyum, A., Qadir, J., Bilal, M., & Al Fuqaha, A. (2020). Secure and robust machine learning for healthcare: A survey. IEEE Reviews in Biomedical Engineering, 14, 156-180. https://doi.org/10.1109/rbme.2020.3013489

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart... Read More about Secure and robust machine learning for healthcare: A survey.

Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations (2020)
Journal Article
Ajayi, A., Oyedele, L., Akinade, O., Bilal, M., Owolabi, H., Akanbi, L., & Delgado, J. M. D. (2020). Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations. Safety Science, 125, Article 104656. https://doi.org/10.1016/j.ssci.2020.104656

© 2020 Elsevier Ltd Forecasting imminent accidents in power infrastructure projects require a robust and accurate prediction model to trigger a proactive strategy for risk mitigation. Unfortunately, getting ready-made machine learning algorithms to e... Read More about Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations.

Big Data with deep learning for benchmarking profitability performance in project tendering (2020)
Journal Article
Bilal, M., & Oyedele, L. O. (2020). Big Data with deep learning for benchmarking profitability performance in project tendering. Expert Systems with Applications, 147, Article 113194. https://doi.org/10.1016/j.eswa.2020.113194

© 2020 A reliable benchmarking system is crucial for the contractors to evaluate the profitability performance of project tenders. Existing benchmarks are ineffective in the tender evaluation task for three reasons. Firstly, these benchmarks are most... Read More about Big Data with deep learning for benchmarking profitability performance in project tendering.

Guidelines for applied machine learning in construction industry—A case of profit margins estimation (2019)
Journal Article
Bilal, M., & Oyedele, L. (2020). Guidelines for applied machine learning in construction industry—A case of profit margins estimation. Advanced Engineering Informatics, 43, 101013. https://doi.org/10.1016/j.aei.2019.101013

© 2019 Elsevier Ltd The progress in the field of Machine Learning (ML) has enabled the automation of tasks that were considered impossible to program until recently. These advancements today have incited firms to seek intelligent solutions as part of... Read More about Guidelines for applied machine learning in construction industry—A case of profit margins estimation.

Risk mitigation in PFI/PPP project finance: A framework model for financiers’ bankability criteria (2019)
Journal Article
Owolabi, H., Oyedele, L., Alaka, H., Ajayi, S., Bilal, M., & Akinade, O. (2020). Risk mitigation in PFI/PPP project finance: A framework model for financiers’ bankability criteria. Built Environment Project and Asset Management, 10(1), 28-49. https://doi.org/10.1108/BEPAM-09-2018-0120

Purpose: Earlier studies on risk evaluation in private finance initiative and public private partnerships (PFI/PPP) projects have focussed more on quantitative approaches despite increasing call for contextual understanding of the bankability of risk... Read More about Risk mitigation in PFI/PPP project finance: A framework model for financiers’ bankability criteria.