A novel cluster detection of COVID-19 patients and medical disease conditions using improved evolutionary clustering algorithm star

Hassan, Bryar A.; Rashid, Tarik A.; Hamarashid, Hozan K.

doi:10.1016/j.compbiomed.2021.104866

Cited by 37 publications

(18 citation statements)

References 38 publications

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“…, the variants of evolutionary clustering algorithm star[72][73][74][75], chaotic sine cosine firefly algorithm[76], and hybrid artificial intelligence algorithms[77]. Furthermore, HS can be applied to more complex and real-world applications to explore more deeply the advantages and drawbacks of the algorithm or improve its efficiencies, such as engineering application problems[76], laboratory management [78], e-organization and e-government services [79], online analytical processing [80], web science [81], the Semantic Web ontology learning [82], chronic wound image processing [83], signal detection processing [84], and concept drift detection in big social data [85].…”

mentioning

confidence: 99%

Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review

Maaroof

Rashid

Abdulla

et al. 2022

Arch Computat Methods Eng

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Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread algorithms. It was developed by Eusuff and Lansey in 2006. SFLA is a population-based metaheuristic algorithm that combines the benefits of memetics with particle swarm optimization. It has been used in various areas, especially in engineering problems due to its implementation easiness and limited variables. Many improvements have been made to the algorithm to alleviate its drawbacks, whether they were achieved through modifications or hybridizations with other well-known algorithms. This paper reviews the most relevant works on this algorithm. An overview of the SFLA is first conducted, followed by the algorithm's most recent modifications and hybridizations. Next, recent applications of the algorithm are discussed. Then, an operational framework of SLFA and its variants is proposed to analyze their uses on different cohorts of applications. Finally, future improvements to the algorithm are suggested. The main incentive to conduct this survey to provide useful information about the SFLA to researchers interested in working on the algorithm's enhancement or application.

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confidence: 99%

Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review

Maaroof

Rashid

Abdulla

et al. 2022

Arch Computat Methods Eng

Self Cite

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“…A cluster is regarded as a complete event group, and each category is regarded as an event in the complete event group with a probability of p i . When the probabilities of all categories are equal, that is, when the sample sizes in all categories are equal, the information entropy of this subcluster has the maximum value [16]. The probability is if p i = 1/k, the entropy of clustering is the largest, as shown in…”

Section: Methodsmentioning

confidence: 97%

“…In Equation (16), T represents the time, n is the number, m is the group, and m′ is the corresponding group of the group. The average waiting time of the intelligent sensor can be obtained according to Equations ( 16) and (17).…”

Section: Systemmentioning

confidence: 99%

Intelligent Online Monitoring and Remote Verification of Gateway Meters under the Embedded Sensor and Clustering Algorithm

Wang

et al. 2022

Wireless Communications and Mobile Computing

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Since gateway meters cannot simultaneously realize online monitoring and remote verification, the study is aimed at exploring the intelligent online monitoring and remote verification of the gateway meter. First, the similarity and related evaluation indexes of embedded sensors are analyzed based on the relevant theories, like the theories of the embedded sensor and clustering algorithm. Second, the gateway meter is tested on the standard vibration test bed, and the accuracy, timeliness, and environmental adaptability of its intelligent online system are tested. Finally, the remote verification of the gateway meter is carried out from three aspects: the error/load value, secondary voltage drop, and the admittance test. The results show that when the intelligent online monitoring ability of the gateway meter is tested on the standard vibration test bed, the error of the same pilot is controlled between 0.1 mm after multiple peak tests, and the error is within the allowable range. In the intelligent online monitoring system based on the embedded sensor and clustering algorithm, the vibration acceleration is -0.6 cm/s~0.3 cm/s, the speed is -1 cm/s~1 cm/s, and the displacement fluctuates between -0.8 cm/s~0.8 cm/s. This shows that the intelligent online monitoring system can meet the performance requirements of online monitoring. In the process of remote verification of gateway meters, the active error and reactive error are 0.2% mm. The results of the secondary voltage drop and the admittance test show that the relevant technical indexes of the system meet the expected requirements. Therefore, the intelligent online monitoring and remote verification of gateway meters are discussed based on the embedded sensor and clustering algorithm, which provides a reference for the rapid development of gateway meters.

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“…For additional research in the future, HS can be hybridized with several algorithms for healthcare problems to further validate its efficiency, such as the backtracking search optimization algorithm [94][95][96], the variants of evolutionary clustering algorithm star [97][98][99][100], chaotic sine cosine firefly algorithm [101], shuffled frog leaping algorithm [102] and hybrid artificial intelligence algorithms [103]. Furthermore, HS can be applied to more complex and real-world applications to explore more deeply the advantages and drawbacks of the algorithm or improve its efficiencies, such as engineering application problems [101], wind speed prediction [104][105][106][107][108][109][110], traffic flow prediction [111],…”

Section: Conclusion and Future Trendsmentioning

confidence: 99%