Cloud-based non-destructive characterization

Heidari, Arash; Navimipour, Nima Jafari; Otsuki, Akira

doi:10.1016/b978-0-323-91150-4.00006-9

Cited by 15 publications

(1 citation statement)

References 64 publications

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“…The efficiency of nature-inspired algorithms in IoT-based healthcare services lies in their transformative ability to address intricate challenges within the healthcare domain. 23 Drawing inspiration from natural systems, algorithms like GA, PSO, and ACO provide innovative solutions for optimizing the vast and dynamic data generated by interconnected devices in healthcare IoT networks. These algorithms, tailored for predictive modeling, resource allocation, and data analysis, enhance the precision and speed of decision-making processes.…”

Section: Application Of Nature-inspired Algorithms In Iot-based Healt...mentioning

confidence: 99%

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: Application Of Nature-inspired Algorithms In Iot-based Healt...mentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

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An Efficient Workflow Scheduling Using Genetically Modified Golden Jackal Optimization With Recurrent Autoencoder in Cloud Computing

Tripathi,

Tripathi

2024

Int J Network Mgmt

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In this paper, a novel workflow scheduling framework is proposed using genetically modified golden jackal optimization (GM‐GJO) with recurrent autoencoder. An integrated autoencoder and bidirectional gated recurrent unit (iAE‐BiGRU) are used to forecast the number of virtual machines (VMs) needed to manage the system's present workload. The following step involves assigning the tasks of several workflows to cloud VMs through the use of the GM‐GJO method for multiworkflow scheduling. GM‐GJO provides optimal workflow scheduling by considering minimal maximizing utilization rate, minimizing makespan, and minimizing the number of deadline missed workflows. The proposed approach attempts to allocate the best possible set of resources for the workflows based on objectives such as deadline, cost, and quality of service (QoS). Extensive experiments were conducted with the CloudSIM tool, and the performance is evaluated in terms of scheduling length ratio, cost, QoS, etc. The execution time of 513.45 ms is achieved with a Sipht workflow of 30 tasks. When comparing the suggested strategy to the current methodologies, the suggested approach performs better.

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WikiChain: An improved resilient, pseudo-transparent knowledge base

J.,

Selvaraj,

et al. 2024

Peer-to-Peer Netw. Appl.

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Cloud-based non-destructive characterization

Cited by 15 publications

References 64 publications

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

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

An Efficient Workflow Scheduling Using Genetically Modified Golden Jackal Optimization With Recurrent Autoencoder in Cloud Computing

WikiChain: An improved resilient, pseudo-transparent knowledge base

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