Improved Nature-Inspired Algorithms in Cloud Computing for Load Balancing

Akila, D.; Bhaumik, Amiya; Duraisamy, Balaganesh; Suseendran, G.; Pal, Souvik

doi:10.1007/978-981-16-3153-5_57

Cited by 2 publications

(2 citation statements)

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“…These datasets serve as the foundation for training and validating algorithms, enabling them to learn and adapt effectively to the complex and dynamic healthcare data environment 124 . Prevalent datasets derived from diverse sources such as patient records, medical sensors, and health monitoring devices offer a representative and comprehensive view of healthcare scenarios, ensuring that algorithms are exposed to a wide range of conditions and variations 125 . Access to such datasets fosters the development of robust models that can generalize well across different healthcare domains, enhancing the algorithms' accuracy, reliability, and adaptability.…”

Section: Results and Comparisonmentioning

confidence: 99%

“…124 Prevalent datasets derived from diverse sources such as patient records, medical sensors, and health monitoring devices offer a representative and comprehensive view of healthcare scenarios, ensuring that algorithms are exposed to a wide range of conditions and variations. 125 Access to such datasets fosters the development of robust models that can generalize well across different healthcare domains, enhancing the algorithms' accuracy, reliability, and adaptability. Moreover, the use of prevalent datasets supports benchmarking and facilitates the comparison of algorithmic performance, promoting transparency and aiding in the selection of the most suitable nature-inspired algorithms for specific IoT-based healthcare applications.…”

Section: 4mentioning

confidence: 99%

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The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Amiri,

Heidari,

Zavvar

et al. 2024

Trans Emerging Tel Tech

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Nature‐inspired algorithms revolve around the intersection of nature‐inspired algorithms and the IoT within the healthcare domain. This domain addresses the emerging trends and potential synergies between nature‐inspired computational approaches and IoT technologies for advancing healthcare services. Our research aims to fill gaps in addressing algorithmic integration challenges, real‐world implementation issues, and the efficacy of nature‐inspired algorithms in IoT‐based healthcare. We provide insights into the practical aspects and limitations of such applications through a systematic literature review. Specifically, we address the need for a comprehensive understanding of the applications of nature‐inspired algorithms in IoT‐based healthcare, identifying gaps such as the lack of standardized evaluation metrics and studies on integration challenges and security considerations. By bridging these gaps, our paper offers insights and directions for future research in this domain, exploring the diverse landscape of nature‐inspired algorithms in healthcare. Our chosen methodology is a Systematic Literature Review (SLR) to investigate related papers rigorously. Categorizing these algorithms into groups such as genetic algorithms, particle swarm optimization, cuckoo algorithms, ant colony optimization, other approaches, and hybrid methods, we employ meticulous classification based on critical criteria. MATLAB emerges as the predominant programming language, constituting 37.9% of cases, showcasing a prevalent choice among researchers. Our evaluation emphasizes adaptability as the paramount parameter, accounting for 18.4% of considerations. By shedding light on attributes, limitations, and potential directions for future research and development, this review aims to contribute to a comprehensive understanding of nature‐inspired algorithms in the dynamic landscape of IoT‐based healthcare services.

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Section: Results and Comparisonmentioning

confidence: 99%

Section: 4mentioning

confidence: 99%