Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Samaneh Madanian"'
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-21 (2024)
Abstract Background The increased application of Internet of Things (IoT) in healthcare, has fueled concerns regarding the security and privacy of patient data. Lightweight Cryptography (LWC) algorithms can be seen as a potential solution to address
Externí odkaz:
https://doaj.org/article/eb86c0fb6fae4169899fd3b8240c044f
Publikováno v:
Energies, Vol 17, Iss 19, p 4806 (2024)
Due to the increasing demand for electricity, competitive electricity markets, and economic concerns, power systems are operating near their stability margins. As a result, power systems become more vulnerable following disturbances, particularly fro
Externí odkaz:
https://doaj.org/article/e911a965b5934f4eaa13e5f3a26335fb
Publikováno v:
JMIR Medical Informatics, Vol 12, p e48273 (2024)
BackgroundThe phenomenon of patients missing booked appointments without canceling them—known as Did Not Show (DNS), Did Not Attend (DNA), or Failed To Attend (FTA)—has a detrimental effect on patients’ health and results in massive health care
Externí odkaz:
https://doaj.org/article/fea3f575a9dd442c8fa6863d2cf22e90
Publikováno v:
PEC Innovation, Vol 2, Iss , Pp 100171- (2023)
Objective: Digital technology has changed the way healthcare is delivered and accessed. However, the focus is mostly on technology and clinical aspects. This review aimed to integrate and critically analyse the available knowledge regarding patients'
Externí odkaz:
https://doaj.org/article/45499862849747a48ec7e27b0cf4e959
Autor:
Samaneh Madanian, Talen Chen, Olayinka Adeleye, John Michael Templeton, Christian Poellabauer, Dave Parry, Sandra L. Schneider
Publikováno v:
Intelligent Systems with Applications, Vol 20, Iss , Pp 200266- (2023)
Speech emotion recognition (SER) as a Machine Learning (ML) problem continues to garner a significant amount of research interest, especially in the affective computing domain. This is due to its increasing potential, algorithmic advancements, and ap
Externí odkaz:
https://doaj.org/article/5215850c4afd4e83bab6362df8dab9fe
Publikováno v:
TESEA, Transactions on Energy Systems and Engineering Applications, Vol 3, Iss 2 (2022)
Data science-based digital twin models of renewable energy system technologies developed in a real-time data-rich environment help develop better decisions and predictions than those in the present environment. Based on this real-time analysis of cou
Externí odkaz:
https://doaj.org/article/da2c1a0c13c14461b59474a3b793c754
Publikováno v:
Energies, Vol 16, Iss 12, p 4540 (2023)
Globally, renewable energy-based power generation is experiencing exponential growth due to concerns over the environmental impacts of traditional power generation methods. Microgrids (MGs) are commonly employed to integrate renewable sources due to
Externí odkaz:
https://doaj.org/article/0022d288eae64f29a28bead38025075c
Publikováno v:
Sensors, Vol 22, Iss 24, p 9971 (2022)
The need to overcome the challenges of visual inspections conducted by domain experts drives the recent surge in visual inspection research. Typical manual industrial data analysis and inspection for defects conducted by trained personnel are expensi
Externí odkaz:
https://doaj.org/article/ea418af3870c4be382d434931b469216
Publikováno v:
Sensors, Vol 22, Iss 20, p 7846 (2022)
Manual or traditional industrial product inspection and defect-recognition models have some limitations, including process complexity, time-consuming, error-prone, and expensiveness. These issues negatively impact the quality control processes. There
Externí odkaz:
https://doaj.org/article/9fa3a6e88dc547af83c048df6c5ba4eb
Publikováno v:
BMJ Health & Care Informatics, Vol 26, Iss 1 (2019)
BackgroundThe use of mobile devices in health (mobile health/mHealth) coupled with related technologies promises to transform global health delivery by creating new delivery models that can be integrated with existing health services. These delivery
Externí odkaz:
https://doaj.org/article/77269fe82c0a46f78baf38f250bc5cdc