Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems.

The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author's keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community's top keywords include 'ML', 'Deep Learning', and 'Artificial Intelligence'.

Assessing cloud QoS predictions using OWA in neural network methods.

Quality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods-Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy.