An evaluation model for children’s foot &amp; ankle deformity severity using sparse multi-objective feature selection algorithm

Pan, Xiaotian; Zhang, Guodao; Lin, Aiju; Guan, Xiaochun; Chen, PingKuo; Ge, Yisu; Chen, Xin

doi:10.1016/j.compbiomed.2022.106229

Cited by 10 publications

(3 citation statements)

References 46 publications

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“…In this study, we present a set of grading rules for assessing the severity of deformities. Additionally, Author 50 utilise a 3D foot scanner to gather data on 30-foot structure indexes. In this study, we present a novel sparse multi-objective evolutionary algorithm designed for the purpose of feature selection.…”

Section: Related Workmentioning

confidence: 99%

“…In this context, shape, texture, and color analysis help separate images from massive databases based on the images queried. As a result, the current study aims to find the best method for calculating features in a 2 2021 Deep neural network 98.6% Accuracy Shayari et al 3 2021 Bayesian optimization 97.37% Accuracy Ayadi et al 4 2021 CNN network with 98% Accuracy Barzegar and Jamzad 5 2021 BRATS datasets including high-and low-grade glioma brain tumors, KNN: 86.32%, Naïve Bays: 60.46%, Bashir-Gonbadi and Khotan Lou 6 2021 IXI and BraTS 2017 datasets, used for proposed work and to classify Brain Tumor Naïve Bays: 86% Jena et al 7 2022 Classification accuracy of 94.25%, 87.88%, 89.57%, 96.99%, and 97% with SVM, KNN, BDT, RF Jiang et al 8 2021 Edge extraction algorithm for providing edge labels with Accuracy: 96.55% Kadry et al 9 2021 Modified Moth-Flame Optimization algorithm with 83.4% Kokkalla et al 10 2021 ResNet v2 with a deep, dense network and a soft-max layer with an accuracy of 99.69% Qiaosen et al 48 2022 Automated detection of gastrointestinal diseases using wireless capsule endoscopy (WCE) images with Convolutional Neural Networks (CNNs) with accuracy rate of 94.8% Jiancun et al 49 2022 Enhance the accuracy of skin disorder identification by studying the impact of color-based background selection on deep learning models' capacity to learn attributes of foreground lesions in skin disease classification with accuracy Xiaotian et al 50 2022 Develop a model for children's foot & ankle deformity using data mining and machine learning, including grading rules for deformity severity and 3D foot structure index data and achieve the accuracy of 98% Jiaze et al 51 2021…”

Section: Domain Descriptionmentioning

confidence: 99%

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Distinctive approach in brain tumor detection and feature extraction using biologically inspired DWT method and SVM

Kumar,

Pandey,

varshney

et al. 2023

Sci Rep

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Brain tumors result from uncontrolled cell growth, potentially leading to fatal consequences if left untreated. While significant efforts have been made with some promising results, the segmentation and classification of brain tumors remain challenging due to their diverse locations, shapes, and sizes. In this study, we employ a combination of Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) to enhance performance and streamline the medical image segmentation process. Proposed method using Otsu's segmentation method followed by PCA to identify the most informative features. Leveraging the grey-level co-occurrence matrix, we extract numerous valuable texture features. Subsequently, we apply a Support Vector Machine (SVM) with various kernels for classification. We evaluate the proposed method's performance using metrics such as accuracy, sensitivity, specificity, and the Dice Similarity Index coefficient. The experimental results validate the effectiveness of our approach, with recall rates of 86.9%, precision of 95.2%, F-measure of 90.9%, and overall accuracy. Simulation of the results shows improvements in both quality and accuracy compared to existing techniques. In results section, experimental Dice Similarity Index coefficient of 0.82 indicates a strong overlap between the machine-extracted tumor region and the manually delineated tumor region.

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Section: Related Workmentioning

confidence: 99%

Section: Domain Descriptionmentioning

confidence: 99%

Distinctive approach in brain tumor detection and feature extraction using biologically inspired DWT method and SVM

Kumar,

Pandey,

varshney

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…At present, evolutionary methods and MAs have been widely used in topology optimization 52 , image segmentation 53 , feature selection 54 , engineering design 55 , dispatch problem 56 , intrusion detection 57 , and many other fields. For these complex optimization problems, MAs have shown remarkable results in their applications.…”

Section: Introductionmentioning

confidence: 99%

Environment random interaction of rime optimization with Nelder-Mead simplex for parameter estimation of photovoltaic models

Shi,

Chen,

Heidari

et al. 2024

Sci Rep

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As countries attach importance to environmental protection, clean energy has become a hot topic. Among them, solar energy, as one of the efficient and easily accessible clean energy sources, has received widespread attention. An essential component in converting solar energy into electricity are solar cells. However, a major optimization difficulty remains in precisely and effectively calculating the parameters of photovoltaic (PV) models. In this regard, this study introduces an improved rime optimization algorithm (RIME), namely ERINMRIME, which integrates the Nelder-Mead simplex (NMs) with the environment random interaction (ERI) strategy. In the later phases of ERINMRIME, the ERI strategy serves as a complementary mechanism for augmenting the solution space exploration ability of the agent. By facilitating external interactions, this method improves the algorithm’s efficacy in conducting a global search by keeping it from becoming stuck in local optima. Moreover, by incorporating NMs, ERINMRIME enhances its ability to do local searches, leading to improved space exploration. To evaluate ERINMRIME's optimization performance on PV models, this study conducted experiments on four different models: the single diode model (SDM), the double diode model (DDM), the three-diode model (TDM), and the photovoltaic (PV) module model. The experimental results show that ERINMRIME reduces root mean square error for SDM, DDM, TDM, and PV module models by 46.23%, 59.32%, 61.49%, and 23.95%, respectively, compared with the original RIME. Furthermore, this study compared ERINMRIME with nine improved classical algorithms. The results show that ERINMRIME is a remarkable competitor. Ultimately, this study evaluated the performance of ERINMRIME across three distinct commercial PV models, while considering varying irradiation and temperature conditions. The performance of ERINMRIME is superior to existing similar algorithms in different irradiation and temperature conditions. Therefore, ERINMRIME is an algorithm with great potential in identifying and recognizing unknown parameters of PV models.

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Optimization of multipath cold-chain logistics network

Zhang,

Dai,

Yin

et al. 2023

Soft Comput

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An evaluation model for children’s foot & ankle deformity severity using sparse multi-objective feature selection algorithm

Cited by 10 publications

References 46 publications

Distinctive approach in brain tumor detection and feature extraction using biologically inspired DWT method and SVM

Distinctive approach in brain tumor detection and feature extraction using biologically inspired DWT method and SVM

Environment random interaction of rime optimization with Nelder-Mead simplex for parameter estimation of photovoltaic models

Optimization of multipath cold-chain logistics network

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