2022
DOI: 10.1007/s00521-022-07297-z
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Assessing cloud QoS predictions using OWA in neural network methods

Abstract: 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 accurac… Show more

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Cited by 18 publications
(8 citation statements)
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“…These difficulties may include the large costs associated with R&D and implementation, the business's unrealistic expectations, the shortage of specialised engineers, interpretability and the lack of agility within huge corporations (Dixon et al, 2020). However, with the exponential increase of computational power and data abundance, the shift into automated intelligent structures powered by machine learning is a crucial step that could determine the survival of many existing corporations (Hussain, Gao, et al, 2022).…”
Section: Introductionmentioning
confidence: 99%
“…These difficulties may include the large costs associated with R&D and implementation, the business's unrealistic expectations, the shortage of specialised engineers, interpretability and the lack of agility within huge corporations (Dixon et al, 2020). However, with the exponential increase of computational power and data abundance, the shift into automated intelligent structures powered by machine learning is a crucial step that could determine the survival of many existing corporations (Hussain, Gao, et al, 2022).…”
Section: Introductionmentioning
confidence: 99%
“…Yager (1988) introduced the ordered weighted averaging (OWA) in combination prediction method for aggregate the exact arguments that lie between the max and the min operators, which has substantially increased the forecast precision. Since it has appeared, the OWA operator has been studied in a wide range of applications such as decision making (Gadomer & Sosnowski, 2019; La Rad et al, 2011; Merigó & Gil‐Lafuente, 2011, 2012), fuzzy logic controllers (Yager & Filev, 1995), and neural networks (Dominguez‐Catena et al, 2021; Hussain et al, 2022; Yager, 1997). Nevertheless, all the previous combination methods assign the different weights to each individual prediction model and the weight of the individual prediction model cannot change at each time point.…”
Section: Introductionmentioning
confidence: 99%
“…While there is a huge interest in such predictions in various business domains Responsible Editor: Fethi Abderrahmane Rabhi This article is part of the Topical Collection on Explainable and responsible artificial intelligence (Ribeiro et al, 2016), one of the major problems of complex machine learning models is that they are very difficult to understand (Abedin et al, 2022;Adadi et al, 2018;Thiebes et al, 2021). Several Methods using induced ordered weighted averaging (IOWA) adaptive neuro-fuzzy inference system (ANFIS) can deal with multidimensional data to predict the quality of service and hence it help stakeholders in the decision-making process (Hussain et al, 2022a(Hussain et al, , b, 2021. As decisions often depend on a huge number of model parameters (Alvarez-Melis and Jaakkola, 2017), machine learning and deep learning techniques are like black-boxes or magic boxes to the general users (and often even for developers).…”
Section: Introductionmentioning
confidence: 99%