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Outputs (25)

A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring (2024)
Journal Article
Albaroudi, E., Mansouri, T., & Alameer, A. (2024). A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring. AI and Ethics, 5(1), 383-404. https://doi.org/10.3390/ai5010019

The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, i... Read More about A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring.

SkinLesNet: Classification of Skin Lesions and Detection of Melanoma Cancer Using a Novel Multi-Layer Deep Convolutional Neural Network (2023)
Journal Article
Azeem, M., Kiani, K., Mansouri, T., & Topping, N. (2023). SkinLesNet: Classification of Skin Lesions and Detection of Melanoma Cancer Using a Novel Multi-Layer Deep Convolutional Neural Network. Cancers, 16(1), 108. https://doi.org/10.3390/cancers16010108

Skin cancer is a widespread disease that typically develops on the skin due to frequent exposure to sunlight. Although cancer can appear on any part of the human body, skin cancer accounts for a significant proportion of all new cancer diagnoses worl... Read More about SkinLesNet: Classification of Skin Lesions and Detection of Melanoma Cancer Using a Novel Multi-Layer Deep Convolutional Neural Network.

Startup’s critical failure factors dynamic modeling using FCM (2023)
Journal Article
Salmeron, J. L., Mansouri, T., Sadeghi Moghaddam, R., Yousefi, N., & Tayebi, A. (2023). Startup’s critical failure factors dynamic modeling using FCM. Journal of Global Entrepreneurship Research, 13(1), https://doi.org/10.1007/s40497-023-00352-6

The emergence of startups and their influence on a country's economic growth has become a significant concern for governments. The failure of these ventures leads to substantial depletion of financial resources and workforce, resulting in detrimental... Read More about Startup’s critical failure factors dynamic modeling using FCM.

Explainable fault prediction using learning fuzzy cognitive maps (2023)
Journal Article
Mansouri, T., & Vadera, S. (2023). Explainable fault prediction using learning fuzzy cognitive maps. Expert Systems, 40(8), https://doi.org/10.1111/exsy.13316

IoT sensors capture different aspects of the environment and generate high throughput data streams. Besides capturing these data streams and reporting the monitoring information, there is significant potential for adopting deep learning to identify v... Read More about Explainable fault prediction using learning fuzzy cognitive maps.

Developing an industry 4.0 readiness model using fuzzy cognitive maps approach (2022)
Journal Article
Monshizadeh, F., Moghadam, M., Mansouri, T., & Kumar, M. (2022). Developing an industry 4.0 readiness model using fuzzy cognitive maps approach. International Journal of Production Economics, 255, https://doi.org/10.1016/j.ijpe.2022.108658

Industry 4.0, or the fourth industrial revolution, is a new paradigm in manufacturing digitalization, which provides various opportunities for enterprises. Industry 4.0 readiness models are worthy methods to aid manufacturing organizations in trackin... Read More about Developing an industry 4.0 readiness model using fuzzy cognitive maps approach.

Markowitz-based cardinality constrained portfolio selection using Asexual Reproduction Optimization (ARO) (2022)
Journal Article
Mansouri, T., Sadeghi Moghadam, M. R., & Sheykhizadeh, M. (2022). Markowitz-based cardinality constrained portfolio selection using Asexual Reproduction Optimization (ARO). https://doi.org/10.22059/IJMS.2021.313393.674293

The Markowitz-based portfolio selection turns to an NP-hard problem when considering cardinality constraints. In this case, existing exact solutions like quadratic programming may not be efficient to solve the problem. Many researchers, therefore, us... Read More about Markowitz-based cardinality constrained portfolio selection using Asexual Reproduction Optimization (ARO).

A deep explainable model for fault prediction using IoT sensors (2022)
Journal Article
Mansouri, T., & Vadera, S. (2022). A deep explainable model for fault prediction using IoT sensors. IEEE Access, https://doi.org/10.1109/ACCESS.2022.3184693

IoT sensors and deep learning models can widely be applied for fault prediction. Although deep learning models are considerably more potent than many conventional machine learning models, they are not transparent. This paper first examines differen... Read More about A deep explainable model for fault prediction using IoT sensors.

IoT data quality issues and potential solutions: a literature review (2021)
Journal Article
Mansouri, T., Moghadam, M., Monshizadeh, F., & Zareravasan, A. (2021). IoT data quality issues and potential solutions: a literature review. Computer Journal, https://doi.org/10.1093/comjnl/bxab183

In the Internet of Things (IoT), data gathered from dozens of devices are the base for creating business value and developing new products and services. If data are of poor quality, decisions are likely to be non-sense. Data quality is crucial to gai... Read More about IoT data quality issues and potential solutions: a literature review.

IoT data quality issues and potential solutions : a literature review (2021)
Journal Article
Mansouri, T., Sadeghi Moghadam, M., Monshizadeh, F., & Zareravasan, A. (2021). IoT data quality issues and potential solutions : a literature review. Computer Journal, https://doi.org/10.1093/comjnl/bxab183

In the Internet of Things (IoT), data gathered from dozens of devices are the base for creating business value and developing new products and services. If data are of poor quality, decisions are likely to be non-sense. Data quality is crucial to gai... Read More about IoT data quality issues and potential solutions : a literature review.

IoT data quality issues and potential solutions: a literature review (2021)
Journal Article
Mansouri, T., Moghadam, M., Monshizadeh, F., & Zareravasan, A. (2021). IoT data quality issues and potential solutions: a literature review. Computer Journal, https://doi.org/10.1093/comjnl/bxab183

In the Internet of Things (IoT), data gathered from dozens of devices are the base for creating business value and developing new products and services. If data are of poor quality, decisions are likely to be non-sense. Data quality is crucial to gai... Read More about IoT data quality issues and potential solutions: a literature review.