ORCID Profile
0000-0002-1763-8583
Current Organisations
Iran University of Science and Technology
,
Yasouj University
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Publisher: Wiley
Date: 26-01-2015
DOI: 10.1002/CPE.3454
Publisher: IEEE
Date: 02-2008
Publisher: IEEE
Date: 08-2008
Publisher: Elsevier BV
Date: 10-2015
Publisher: SAGE Publications
Date: 10-2022
DOI: 10.1177/14604582221137453
Abstract: Various studies have shown the benefits of using distributed fog computing for healthcare systems. The new pattern of fog and edge computing reduces latency for data processing compared to cloud computing. Nevertheless, the proposed fog models still have many limitations in improving system performance and patients’ response time. This paper, proposes a new performance model by integrating fog computing, priority queues and certainty theory into the Edge computing devices and validating it by analyzing heart disease patients' conditions in clinical decision support systems (CDSS). In this model, a Certainty Factor (CF) value is assigned to each symptom of heart disease. When one or more symptoms show an abnormal value, the patient’s condition will be evaluated using CF values in the fog layer. In the fog layer, requests are categorized in different priority queues before arriving into the system. The results demonstrate that network usage, latency, and response time of patients’ requests are respectively improved by 25.55%, 42.92%, and 34.28% compared to the cloud model. Prioritizing patient requests with respect to CF values in the CDSS provides higher system Quality of Service (QoS) and patients’ response time.
Publisher: Oxford University Press (OUP)
Date: 20-12-2016
Publisher: MDPI AG
Date: 22-06-2022
DOI: 10.3390/APP12136339
Abstract: Shear wave velocity (VS) is one of the most important parameters in deep and surface studies and the estimation of geotechnical design parameters. This parameter is widely utilized to determine permeability and porosity, lithology, rock mechanical parameters, and fracture assessment. However, measuring this important parameter is either impossible or difficult due to the challenges related to horizontal and deviation wells or the difficulty in reaching cores. Artificial Intelligence (AI) techniques, especially Machine Learning (ML), have emerged as efficient approaches for dealing with such challenges. Therefore, considering the advantage of the ML, the current research proposes a novel Fully-Self-Adaptive Harmony Search—Group Method of Data Handling (GMDH)-type neural network, named FSHS-GMDH, to estimate the VS parameter. In this way, the Harmony Memory Consideration Rate (HMCR) and Pitch Adjustment Rate (PAR) parameters are calculated automatically. A novel method is also introduced to adjust the value of the Bandwidth (BW) parameter based on the cosine wave and each decision variable values. In addition, a variable-size harmony memory is proposed to enhance both the ersification and intensification. Our proposed FSHS-GMDH algorithm quickly explores the problem space and exploits the best regions at the late iterations. This algorithm allows for the training of the prediction model based on the P-wave velocity (VP) and the bulk density of rock (RHOB). Applying the proposed algorithm to a carbonate petroleum reservoir in the Persian Gulf demonstrates that it is capable of accurately estimating the VS parameter better than state-of-the-art machine learning methods in terms of the coefficient of determination (R2), Mean Square Error (MSE), and Root Mean Square Error (RMSE).
Publisher: Springer Science and Business Media LLC
Date: 17-05-2021
Publisher: Wiley
Date: 16-09-2013
DOI: 10.1002/SEC.872
Publisher: Elsevier BV
Date: 10-2021
Publisher: Elsevier BV
Date: 10-2023
Publisher: IEEE
Date: 05-2009
DOI: 10.1109/CNSR.2009.72
Publisher: Springer Science and Business Media LLC
Date: 05-07-2021
Publisher: IEEE
Date: 02-2008
Publisher: Elsevier BV
Date: 2019
Publisher: IEEE
Date: 10-2015
Publisher: Springer Science and Business Media LLC
Date: 15-06-2021
Publisher: Springer Science and Business Media LLC
Date: 24-05-2021
Location: Iran (Islamic Republic of)
Location: Iran (Islamic Republic of)
No related grants have been discovered for Keyvan RahimiZadeh.