Deep-fit_predic:一种用于适应度预测系统的新型集成金字塔膨胀高效Net-B3方案。

IF 1.7 4区 医学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2024-11-01 Epub Date: 2023-10-22 DOI:10.1080/10255842.2023.2269287
Bhagya Rekha Sangisetti, Suresh Pabboju
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引用次数: 0

摘要

本研究介绍了一种新的深度学习(DL)技术,用于使用个人健康数据进行有效的健身预测。最初,执行预处理,其中执行数据清理、一次热编码和数据归一化。然后,将预处理的数据输入到特征选择阶段,在该阶段,使用增强型变色龙群(ECham Sw)优化技术提取有用的特征。然后,使用Minkowski集成重心聚类(Min-GCC)对每个个体的健康状况进行聚类。最后,提出了金字塔扩展效率网-B3(PyDi-EfficientNet-B3)技术来有效地预测每个个体的适应度,提高了99.8%的准确率。
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Deep fit_predic: a novel integrated pyramid dilation EfficientNet-B3 scheme for fitness prediction system.

This study introduces novel deep learning (DL) techniques for effective fitness prediction using a person's health data. Initially, pre-processing is performed in which data cleaning, one-hot encoding and data normalization are performed. The pre-processed data are then fed into the feature selection stage, where the useful features are extracted using the enhanced chameleon swarm (ECham-Sw) optimization technique. Then, a clustering process is performed using Minkowski integrated gravity center clustering (Min-GCC) to cluster the health profiles of each individual. Finally, the Pyramid Dilated EfficientNet-B3 (PyDi-EfficientNet-B3) technique is proposed to predict the fitness of each individual efficiently with enhanced accuracy of 99.8%.

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来源期刊
CiteScore
4.10
自引率
6.20%
发文量
179
审稿时长
4-8 weeks
期刊介绍: The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.
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