A clustering based Swarm Intelligence optimization technique for the Internet of Medical Things

El-Shafeiy, Engy; Sallam, Karam M.; Chakrabortty, Ripon K.; Abohany, Amr A.

doi:10.1016/j.eswa.2021.114648

Cited by 39 publications

(15 citation statements)

References 79 publications

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“…It is necessary to monitor patients’ conditions to respond to their medical needs and manage their conditions, which can be realized using swarm optimization in IoMT [ 87 ]. In this method, clustering is done based on features and the distance between objects (i.e., devices) or groups.…”

Section: Application Of Si In Iot/iomtmentioning

confidence: 99%

Swarm Intelligence in Internet of Medical Things: A Review

Alizadehsani

Roshanzamir

Izadi

et al. 2023

Sensors

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Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

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Section: Application Of Si In Iot/iomtmentioning

confidence: 99%

Swarm Intelligence in Internet of Medical Things: A Review

Alizadehsani

Roshanzamir

Izadi

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Many meta-heuristic strategies have been developed to help schedule tasks in the IoMT [ 41 ]. Some of the existing FS methods suffer from premature convergence and local minima, especially when faced with a large solution space [ 42 ]. Often, this limit results in inefficient task scheduling solutions, which has a negative impact on system performance.…”

Section: Related Workmentioning

confidence: 99%

Medical Image Classification Utilizing Ensemble Learning and Levy Flight-Based Honey Badger Algorithm on 6G-Enabled Internet of Things

Elaziz

Mabrouk

Dahou

et al. 2022

Computational Intelligence and Neuroscience

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Recently, the 6G-enabled Internet of Medical Things (IoMT) has played a key role in the development of functional health systems due to the massive data generated daily from the hospitals. Therefore, the automatic detection and prediction of future risks such as pneumonia and retinal diseases are still under research and study. However, traditional approaches did not yield good results for accurate diagnosis. In this paper, a robust 6G-enabled IoMT framework is proposed for medical image classification with an ensemble learning (EL)-based model. EL is achieved using MobileNet and DenseNet architecture as a feature extraction backbone. In addition, the developed framework uses a modified honey badger algorithm (HBA) based on Levy flight (LFHBA) as a feature selection method that aims to remove the irrelevant features from those extracted features using the EL model. For evaluation of the performance of the proposed framework, the chest X-ray (CXR) dataset and the optical coherence tomography (OCT) dataset were employed. The accuracy of our technique was 87.10% on the CXR dataset and 94.32% on OCT dataset—both very good results. Compared to other current methods, the proposed method is more accurate and efficient than other well-known and popular algorithms.

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“…Two datasets were analyzed using the developed structure, and the accuracy was 87% on both datasets. Some of them suffer from premature convergence and local minima, especially when faced with a large solution space [ 42 ]. Often, this limit results in inefficient task scheduling solutions, which hurts system performance.…”

Section: Related Workmentioning

confidence: 99%

Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things

Mabrouk

Dahou

Elaziz

et al. 2022

Computational Intelligence and Neuroscience

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The Internet of Medical Things (IoMT) has dramatically benefited medical professionals that patients and physicians can access from all regions. Although the automatic detection and prediction of diseases such as melanoma and leukemia is still being investigated and studied in IoMT, existing approaches are not able to achieve a high degree of efficiency. Thus, with a new approach that provides better results, patients would access the adequate treatments earlier and the death rate would be reduced. Therefore, this paper introduces an IoMT proposal for medical images’ classification that may be used anywhere, i.e., it is an ubiquitous approach. It was designed in two stages: first, we employ a transfer learning (TL)-based method for feature extraction, which is carried out using MobileNetV3; second, we use the chaos game optimization (CGO) for feature selection, with the aim of excluding unnecessary features and improving the performance, which is key in IoMT. Our methodology was evaluated using ISIC-2016, PH2, and Blood-Cell datasets. The experimental results indicated that the proposed approach obtained an accuracy of 88.39% on ISIC-2016, 97.52% on PH2, and 88.79% on Blood-cell datsets. Moreover, our approach had successful performances for the metrics employed compared to other existing methods.

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A clustering based Swarm Intelligence optimization technique for the Internet of Medical Things

Cited by 39 publications

References 79 publications

Swarm Intelligence in Internet of Medical Things: A Review

Swarm Intelligence in Internet of Medical Things: A Review

Medical Image Classification Utilizing Ensemble Learning and Levy Flight-Based Honey Badger Algorithm on 6G-Enabled Internet of Things

Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things

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