Use of artificial intelligence and virtual reality within clinical simulation for nursing pain education: A scoping review

Harmon, Joanne; Pitt, Victoria; Summons, Peter; Inder, Kerry

doi:10.1016/j.nedt.2020.104700

Cited by 35 publications

(29 citation statements)

References 61 publications

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“…The method finds key phrases on a test set of 18 Beatles' pop music that performs better than HMM. Foote constructed a frame-by-frame comparison of similarity matrix to measure the degree of similarity between segments and finally used image processing to process the subdiagonals of the matrix to find similar segments [16].…”

Section: Analysis Of Musicmentioning

confidence: 99%

The Influence of Artificial Intelligence Technology on Smart Music Teaching Driven by Mobile Internet

Wang

2022

Wireless Communications and Mobile Computing

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Under the trend of rapid development and popularization of modern computer technology and information technology, the field of music education has also penetrated and integrated advanced computer technology and information technology. As an important branch of computer science, artificial intelligence involves many cross-cutting comprehensive subject content. As an indispensable part of our life, music is an art that reflects the emotions of human real life, and it is an ideology older than language. The rapid development of technology has injected many new elements into music, gradually changing the way people write, perform, produce, and enjoy music. The Internet has injected new elements and content into music education. It has transformed the original methods of writing, performing, producing, and appreciating music education and has had a great impact on music education concepts and teaching models. The future development trend of music education makes music education reach a new height under the application of artificial intelligence technology. Based on the current research results at home and abroad, this thesis closely integrates music theory and computational methods and advocates the comprehensive application of music theory, cognitive psychology, music cognition, neuroscience, artificial intelligence, signal processing, and other theories to solve music problems. Signal analysis identifies problems. In-depth discussion of the possibility and development trend of artificial intelligence technology applied to music education.

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Section: Analysis Of Musicmentioning

confidence: 99%

The Influence of Artificial Intelligence Technology on Smart Music Teaching Driven by Mobile Internet

Wang

2022

Wireless Communications and Mobile Computing

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“…These limitations of energy performance evaluation have piqued the interest of academics, who are continually studying new approaches to better comprehend building energy efficiency, resulting in new advancements for estimating energy consumption (Mazzeo et al, 2021;Maltais and Gosselin, 2021;Alduailij et al, 2021). A vital component of such advancements is the use of machine learning for energy contemporary predictive analytics having been widely adopted across different industries such as healthcare: aiding in diagnoses of patients using genetic data (Huang et al, 2021;Malik, Khatana and Kaushik, 2021); manufacturing: use in managing workforces production process and allowing predictive maintenance (Chen et al, 2021); education: virtual lectures (Bajaj and Sharma, 2018;Harmon et al, 2021); finance: fraud detection (Iong-Zong Chen and Lai, 2021;Bao, Hilary and Ke, 2022), and transportation: self-driving autonomous cars (Manoharan, 2019;Ma et al, 2020) among many others. Machine learning is a subset of artificial intelligence that analyses historical data to provide predictions and then utilises those predictions to guide decision-making (Balogun, Alaka and Egwim, 2021b).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of machine learning algorithms for evaluating building energy efficiency using big data analytics

Egwim

Alaka

Egunjobi

et al. 2022

JEDT

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Purpose This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings. Design/methodology/approach This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics. Findings Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting. Research limitations/implications While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK. Practical implications This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system. Originality/value This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.

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“…Virtual Learning Environments (VLEs) and Virtual Patients (VPs) include a broad range of IT tools and systems that may be implemented in differing modalities, and can address differing learning areas, competencies and educational roles (Harmon et al, 2021). Virtual patients are based on artificial intelligence (AI) architectures and knowledge-model frameworks (Colloc & Sybord, 2003) and are implemented in various ways in health and medical applications, such as chatbots in disease education and prevention (Pereira & Diaz, 2019).…”

Section: Problem-based Learning and Technology In Health Educationmentioning

confidence: 99%

Development of a Framework for Problem Domain Transference in Health-Related Problem Based Learning and Assessment

Summons

Harmon

Park

et al. 2021

J Probl Based Learn

Self Cite

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Investigate the capability of a knowledge-based framework and architecture, used in a specific health domain problem that can utilise transfer learning, to speed virtual patient development for problem-based training and assessment in other health domains. Methods: Analysis of a case study, based on a virtual patient used in the training of pharmacy students, to discover the viability of using generic, ontological knowledge capable of transfer to virtual patients in other health domains. Results: Areas of the virtual pharmacy patient knowledge-base were identified, along with corresponding expected student questions, that are generic to other health domains. Using the framework from the case study to develop a new virtual patient for problem-based learning and assessment in a new health domain, these generic target questions could be utilised to speed up the development of other learning stimuli in future projects involving different health domains, such as nurse training in pain management. Conclusions: With some modification, the framework of the case-study virtual patient was found to be capable of supporting generic expected student questions capable of re-use in virtual patients with new clinical conditions.

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Use of artificial intelligence and virtual reality within clinical simulation for nursing pain education: A scoping review

Cited by 35 publications

References 61 publications

The Influence of Artificial Intelligence Technology on Smart Music Teaching Driven by Mobile Internet

The Influence of Artificial Intelligence Technology on Smart Music Teaching Driven by Mobile Internet

Comparison of machine learning algorithms for evaluating building energy efficiency using big data analytics

Development of a Framework for Problem Domain Transference in Health-Related Problem Based Learning and Assessment

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