Machine Learning Techniques and Computing Technologies for IoT based Smart Healthcare (COVID-19 Case Study)

Mir, Mahmood Hussain; Jamwal, Sanjay; Islam, Shahidul; Khan, Qamar Rayees

doi:10.1109/icac3n53548.2021.9725648

Cited by 4 publications

(3 citation statements)

References 60 publications

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“…The utilization of the ACO algorithm in IoT‐based healthcare services encounters challenges and limitations that warrant consideration. ACO's sensitivity to parameter settings necessitates careful tuning for optimal performance across diverse healthcare optimization scenarios 112 . The algorithm's reliance on pheromone information may lead to suboptimal solutions, particularly in dynamic healthcare environments where rapid changes occur 109 .…”

Section: Results and Comparisonmentioning

confidence: 99%

“…ACO's sensitivity to parameter settings necessitates careful tuning for optimal performance across diverse healthcare optimization scenarios. 112 The algorithm's reliance on pheromone information may lead to suboptimal solutions, particularly in dynamic healthcare environments where rapid changes occur. 109 Scalability poses a challenge, as ACO may struggle when handling large-scale healthcare datasets, limiting its applicability to the extensive and intricate data generated by IoT devices.…”

Section: Aco Analysismentioning

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: Aco Analysismentioning

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

View full text Add to dashboard Cite

show abstract

“…One hope lies is acknowledging the role of information and communications technology (ICT). ICT already worked out for us during COVID-19 and proved to be our dependable savior [ 7 , 8 ]. From our homes to the workplaces, all the respective activities that demanded physical presence in our normal routine life were shifted to the different ICT platforms.…”

Section: Introductionmentioning

confidence: 99%

An IoT/FOG based framework for sports talent identification in COVID-19 like situations

Khan

Ahamad

Naseem

2022

Int. j. inf. tecnol.

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COVID-19 crippled all the domains of our society. The inevitable lockdowns and social distancing procedures have hit the process of traditional sports talent identification (TiD) severely. This will interrupt the career-excellency of athletes and will also affect the future talent in the years to come. We explore the effect of COVID-19 on sports talent identification and propose an IoT/Fog-based framework for theTiD process during COVID-19 and COVID–like situations. Our proposed novel six-layer model facilitates the sports talent identification remotely using the various latest Information and Communication Technologies like IoT, fog and cloud computing. All the stakeholders like experts, coaches, players, institutes etc. are taken into consideration. The framework is mobile, widely accessible, scalable, cost-effective, secure, platform/location independent and fast. A brief case study of cricket talent identification using the proposed framework is also provided.

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Review on Various Clustering Algorithm on Healthcare based Cloud Analytics

Pushpalatha

Durga

2023

2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT)

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Machine Learning Techniques and Computing Technologies for IoT based Smart Healthcare (COVID-19 Case Study)

Cited by 4 publications

References 60 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 IoT/FOG based framework for sports talent identification in COVID-19 like situations

Review on Various Clustering Algorithm on Healthcare based Cloud Analytics

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