A coupled Galerkin and Newmark techniques for resonance simulation of the electrically single-curved system under low-velocity impact

Zhang, Chunliang; Long, Shangbin; Mohammadi, Alireza; Bagh, Farzaneh Sharifi; Selmi, Abdellatif; Khan, Afrasyab

doi:10.1007/s00366-021-01450-3

Cited by 2 publications

(1 citation statement)

References 140 publications

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“…Comprehensive Harmony Search (HS) processing for an unattended clustering of the information regarded was initiated and expounded by Chidambaranathan et al (Zhang et al, 2021). The investigators have also examined the results obtained to determine the implementation of this device.…”

Section: Background Ovarian Cancer Area Using Internet Of Medical Thingsmentioning

confidence: 99%

Feature optimization and identification of ovarian cancer using internet of medical things

Ghazal

Taleb

2022

Expert Systems

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Ovarian cancer (OC) is one kind of tumour that impacts women's ovaries and is hard to diagnose in the initial phase as a primary cause of cancer death. The ovarian cancer information generated by the Clinical Network has been used, and the Self Organizing Map (SOM) and Optimized Neural Networks have suggested a new method for the distinction between ovarian cancer and remaining cancer. Feature optimization and identification of the ovarian cancer (FOI-OV) framework are proposed in this research. The SOM algorithm has also been used separately to improve the functional subset, with understandable and intriguing information from participants' health information steps. The SOM-based collection appears to be tolerable in guided learning strategies due to the lack of different classifiers, which would direct the quest for knowledge specific to the classification algorithm. The classification technique will classify data from ovarian cancer as benign/malignant. By optimizing Neural Network configuration, Advanced Harmony Searching Optimization (AHSO) can enhance the ovarian cancer detection method compared with other methods. This research's suggested model can also diagnose cancer with high precision, and low root means square error (RMSE) early. With 94% precision and 0.029%, RMSE, SOM, and NN techniques have shown identification and precision in ovarian cancer. Optimization (AHSO) has provided an efficient classification approach with a better failure rate.

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Section: Background Ovarian Cancer Area Using Internet Of Medical Thingsmentioning

confidence: 99%

Feature optimization and identification of ovarian cancer using internet of medical things

Ghazal

Taleb

2022

Expert Systems

View full text Add to dashboard Cite

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An algorithm based on B-differentiable equations method for solving problems involving frictional contact coupled with concrete plastic damage

Gao,

Lin,

Yin

et al. 2024

Engineering Structures

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A coupled Galerkin and Newmark techniques for resonance simulation of the electrically single-curved system under low-velocity impact

Cited by 2 publications

References 140 publications

Feature optimization and identification of ovarian cancer using internet of medical things

Feature optimization and identification of ovarian cancer using internet of medical things

An algorithm based on B-differentiable equations method for solving problems involving frictional contact coupled with concrete plastic damage

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