Utilizing Big Data for Health Care Automation: Obligations, Fitness and Challenges

Zafar, Sherin

doi:10.1007/978-3-030-04203-5_12

Cited by 1 publication

(3 citation statements)

References 28 publications

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“…Also, because of the massive data from patients' records, tests, treatment records and so forth and the urgent need to find effective connections between them, medical data mining algorithms based on DL are widely used 72,73 . However, databases containing medical records are usually big, heterogeneous, unstructured, and noisy, that the data processing has become difficult, and traditional algorithms have become inefficient 74,75 . So, DL techniques not only for data processing but also for preparing data are strong tools.…”

Section: The Proposed Taxonomy and Analysismentioning

confidence: 99%

“…72,73 In addition, the more people utilize wireless and wearable sensor networks, the more data the devices at the edge of the network collect and send to F I G U R E 6 A taxonomy of DL applications and methods for combating Covid-19 the cloud. For this reason, although there are recently real-time data stream algorithms that perform, [74][75][76] robust algorithms for medical data are essential.…”

Section: Data Gathering and Processingmentioning

confidence: 99%

“…72,73 However, databases containing medical records are usually big, heterogeneous, unstructured, and noisy, that the data processing has become difficult, and traditional algorithms have become inefficient. 74,75 So, DL techniques not only for data processing but also for preparing data are strong tools. Given the extraordinary benefits of the wearable internet of things (WIoT) in medicine, where treatment goals are achieved at a lower cost by reducing logistical needs, and due to the production of sufficient datasets by them intending to solve medical problems, the development of DL algorithms is underway.…”

Section: Data Gathering and Processingmentioning

confidence: 99%

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Deep learning: A taxonomy of modern weapons to combat Covid‐19 similar pandemics in smart cities

Saeedvand

Jafari

Aghdasi

et al. 2022

Concurrency and Computation

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Summary The Covid‐19 pandemic has affected many lives over the past year. In addition to the enormous health cost, the necessary lockdowns and government‐mandated suspension to prevent the spread of the virus had a huge economic impact. The new challenges in 2021 were combating new virus mutations and providing effective vaccines globally. Artificial intelligent (AI) and machine learning have made significant improvements in many different applications during the last decades. One of the advanced and robust technologies in machine learning is deep learning (DL), which can be employed to help prevent initial infections and detect and monitor their progress and side effects. Fast and accurate Covid‐19 infection detection and treatment of suspected patients is essential to make better decisions, ensure treatment, and even save patients' lives. Modern technologies are required to achieve these objectives and create a sustainable society. This article presents a taxonomy in DL algorithms to cover both the technical novelties and empirical results techniques for Covid‐19 in smart cities. In this regard, (i) we demonstrate possible DL algorithms capable of combating Covid‐19; (ii) we propose an up‐to‐date perspective of DL algorithms in social prevention and medical treatment; and (iii) we identify the challenges in combating Covid‐19 outbreaks.

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Section: The Proposed Taxonomy and Analysismentioning

confidence: 99%