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Welcome to USIR

Welcome to the University of Salford repository (USIR), an Open Access showcase for the published research output of the university. Our collection contains a wide range of research across multiple formats and subject areas.

Whenever possible, outputs will be made openly available here in full digital format for download, with many under a Creative Commons license. See our Policies for further information https://salford-repository.worktribe.com/policies.



Latest Additions

The Importance of Applying Authentic Learning in Pre-registration Nursing Education (2024)
Journal Article
Sadat, A., & Pilkington, R. (2024). The Importance of Applying Authentic Learning in Pre-registration Nursing Education. Innovative practice in higher education, 5(3), Article 2

This paper explores the significance of authenticity in nursing education and the development of authentic healthcare practitioners. Identifying a current lack of authenticity in the learning materials used in the Transdisciplinary Science module at... Read More about The Importance of Applying Authentic Learning in Pre-registration Nursing Education.

Parametric Sensitivity Analyses for Perceived Impedance in Haptic Teleoperation (2019)
Journal Article
Uddin, R., Saleem, M. H., & Ryu, J. (2019). Parametric Sensitivity Analyses for Perceived Impedance in Haptic Teleoperation. International Journal of Control, Automation and Systems, 17(8), 2083-2096. https://doi.org/10.1007/s12555-018-0614-8

In this paper, sensitivity analyses (SA) are performed in order to find the effect of variations of master/slave site dynamics parameters on the perceived impedance in haptic teleoperation in the absence/presence of communication time delays. These a... Read More about Parametric Sensitivity Analyses for Perceived Impedance in Haptic Teleoperation.

Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments (2021)
Journal Article
Saleem, M. H., Potgieter, J., & Arif, K. M. (2021). Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments. Precision Agriculture, 22(6), 2053-2091. https://doi.org/10.1007/s11119-021-09806-x

Recently, agriculture has gained much attention regarding automation by artificial intelligence techniques and robotic systems. Particularly, with the advancements in machine learning (ML) concepts, significant improvements have been observed in agri... Read More about Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments.

Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach (2022)
Journal Article
Saleem, M. H., Potgieter, J., & Arif, K. M. (in press). Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach. Agronomy, 12(7), 1580. https://doi.org/10.3390/agronomy12071580

To apply weed control treatments effectively, the weeds must be accurately detected. Deep learning (DL) has been quite successful in performing the weed identification task. However, various aspects of the DL have not been explored in previous studie... Read More about Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach.