Challenges, Opportunities and Future Trends in Smart Health

Vyas, Sonali; Bhargava, Deepshikha

doi:10.1007/978-981-16-4201-2_10

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…It has been shown to be advantageous to use ML and DL for diagnosing acute illnesses, image scrutiny, drug breakthrough, drug shipment, and smart health surveillance. The effectiveness of previously unavailable treatment options will increase as ML-DL based applications are soon widely implemented and loaded with real-time patient data from a variety of healthcare systems in a variety of nations [99] , [100] . Figure 9 illustrates how the ML-DL method has been integrated into the healthcare system.…”

Section: Integration On Ml-dl Ml-healthcare Dl-healthcare and Ml-dl-h...mentioning

confidence: 99%

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

Rahman,

Debnath,

Kundu

et al. 2024

AIMSPH

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<abstract> <p>In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.</p> </abstract>

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Section: Integration On Ml-dl Ml-healthcare Dl-healthcare and Ml-dl-h...mentioning

confidence: 99%

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

Rahman,

Debnath,

Kundu

et al. 2024

AIMSPH

View full text Add to dashboard Cite

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Fortifying Critical Infrastructures: Secure Data Management with Edge Computing

Sahil Arora,

Apoorva Tewari

2023

IJARSCT

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Critical infrastructures (CIs), including energy, healthcare, and transportation, are vital to societal functions, making their security paramount. The emergence of edge computing as a means of safe data management is a direct result of the growing dependence of these infrastructures on real-time data and networked devices. Computing at the edge, or near the source of data, improves efficiency, simplifies data processing, and enables better real-time judgements by decentralising data processing. However, this distributed architecture introduces new security challenges, such as managing a broader attack surface and ensuring data integrity. This paper reviews the role of edge computing in securing critical infrastructures and discusses advanced security measures like encryption, access control, AI-driven anomaly detection, and blockchain. It also outlines future research directions, emphasizing the need for scalable, interoperable edge systems, AI-enhanced security models, quantum-safe encryption, and privacy-preserving techniques. Global standardization is highlighted as essential for consistent, reliable integration. Ultimately, edge computing offers a promising pathway to fortify critical infrastructures against evolving cyber threats, ensuring their continued, resilient operation in an increasingly connected and digital world

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Challenges, Opportunities and Future Trends in Smart Health

Cited by 2 publications

References 16 publications

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

Fortifying Critical Infrastructures: Secure Data Management with Edge Computing

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