All Outputs (6)

A comparative analysis of machine learning algorithms for detecting COVID-19 using lung X-ray images (2024)
Journal Article
Hamal, S., Mishra, B. K., Baldock, R., Sayers, W., Adhikari, T. N., & Gibson, R. M. (2024). A comparative analysis of machine learning algorithms for detecting COVID-19 using lung X-ray images. Decision Analytics Journal, 11, Article 100460. https://doi.org/10.1016/j.dajour.2024.100460

Machine intelligence has the potential to play a significant role in diagnosing, managing, and guiding the treatment of disease, which supports the rising demands on healthcare to provide rapid and accurate interpretation of clinical data. The global... Read More about A comparative analysis of machine learning algorithms for detecting COVID-19 using lung X-ray images.

Tailored risk assessment and forecasting in intermittent claudication (2024)
Journal Article
Ravindhran, B., Prosser, J., Lim, A., Lathan, R., Mishra, B., Hitchman, L., …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.

A two-phase confirmatory factor analysis and structural equation modelling for customer-based brand equity framework in the smartphone industry (2023)
Journal Article
Shrestha, R., Kadel, R., & Mishra, B. K. (2023). A two-phase confirmatory factor analysis and structural equation modelling for customer-based brand equity framework in the smartphone industry. Decision Analytics Journal, 8, Article 100306. https://doi.org/10.1016/j.dajour.2023.100306

The emergence of smartphones has brought a transformative change in the smartphone industry in terms of technological innovations and business decision-making dynamics. Smartphones have appeared in the market as the standard configuration and current... Read More about A two-phase confirmatory factor analysis and structural equation modelling for customer-based brand equity framework in the smartphone industry.

From Raising Awareness to a Behavioural Change: A Case Study of Indoor Air Quality Improvement Using IoT and COM-B Model (2023)
Journal Article
Kureshi, R. R., Thakker, D., Mishra, B. K., & Barnes, J. (2023). From Raising Awareness to a Behavioural Change: A Case Study of Indoor Air Quality Improvement Using IoT and COM-B Model. Sensors, 23(7), Article 3613. https://doi.org/10.3390/s23073613

The topic of indoor air pollution has yet to receive the same level of attention as ambient pollution. We spend considerable time indoors, and poorer indoor air quality affects most of us, particularly people with respiratory and other health conditi... Read More about From Raising Awareness to a Behavioural Change: A Case Study of Indoor Air Quality Improvement Using IoT and COM-B Model.

Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment (2022)
Journal Article
Mishra, B. K., Dahal, K., & Pervez, Z. (2022). Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment. Applied Sciences, 12(16), Article 8358. https://doi.org/10.3390/app12168358

In smart cities, relief items distribution is a complex task due to the factors such as incomplete information, unpredictable exact demand, lack of resources, and causality levels, to name a few. With the development of Internet of Things (IoT) techn... Read More about Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment.

Explainable artificial intelligence for developing smart cities solutions (2020)
Journal Article
Thakker, D., Mishra, B. K., Abdullatif, A., Mazumdar, S., & Simpson, S. (2020). Explainable artificial intelligence for developing smart cities solutions. Smart Cities, 3(4), 1353-1382. https://doi.org/10.3390/smartcities3040065

Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on... Read More about Explainable artificial intelligence for developing smart cities solutions.