Research on an intelligent assessment method for the physical health of preschool children based on the Artificial Neural Network (ANN) model
Vol 9, Issue 5, 2024, Article identifier:
VIEWS - 236 (Abstract) 111 (PDF)
Abstract
This study aims to use the BP neural network model to predict the monthly total number of reported symptoms in preschool children, thereby achieving early warning for the total number of cases in the current month. By analyzing the training results of the neural network and the fitting of the model, the feasibility and effectiveness of the BP neural network in monitoring the physical health of preschool children are validated. The predictive model is of great significance for the prevention of infectious diseases in a border region of a certain province, providing important information for the local incidence situation. The research results indicate that the established BP neural network model demonstrates good accuracy and practicality in predicting the monthly total number of reported symptoms in preschool children’s physical health.
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1. Sgueglia G, Vrettas MD, Chino M, et al. MetalHawk: Enhanced Classification of Metal Coordination Geometries by Artificial Neural Networks. Journal of chemical information and modeling, 2023.
2. Ahmed T, Wilson D. Phase-Amplitude Coordinate-Based Neural Networks for Inferring Oscillatory Dynamics. Journal of Nonlinear Science. 2023, 34(1). doi: 10.1007/s00332-023-09994-y
3. Zamani MR, Mirzadeh H, Malekan M. Artificial neural network applicability in studying hot deformation behaviour of high-entropy alloys. Materials Science and Technology. 2023, 39(18): 3351-3359. doi: 10.1080/02670836.2023.2231767
4. Avdic S, Dykin V, Croft S, et al. Item identification with a space-dependent model of neutron multiplicities and artificial neural networks. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2023, 1057: 168800. doi: 10.1016/j.nima.2023.168800
5. Wang H, Zhao L, Zhang H, et al. Carbon emission analysis of precast concrete building Construction: A study on component transportation phase using Artificial Neural Network. Energy and Buildings. 2023, 301: 113708. doi: 10.1016/j.enbuild.2023.113708
6. Hai T, Zhang G, Kumar Singh P, et al. Unleashing wastewater heat Recovery’s potential in smart building systems: Grey wolf-assisted optimization aided by artificial neural networks. Energy. 2023, 285: 129307. doi: 10.1016/j.energy.2023.129307
7. He J, Shi L, Tian H, et al. Applying artificial neural network to approximate and predict the transient dynamic behavior of CO2 combined cooling and power cycle. Energy. 2023, 285: 129451. doi: 10.1016/j.energy.2023.129451
8. Chandra P, Das R. A hybrid RSA-IPA optimizer for designing an artificial neural network to study the Jeffery-Hamel blood flow with copper nanoparticles: Application to stenotic tapering artery. Results in Engineering. 2023, 20: 101542. doi: 10.1016/j.rineng.2023.101542
9. Wang P, Li B, Luo Y, et al. Predicting vancomycin trough serum concentration in augmented renal clearance patients through an artificial neural network model. Intelligent Pharmacy. 2023, 1(4): 244-250. doi: 10.1016/j.ipha.2023.08.004
10. Hoyos JD, Noriega MA, Riascos CAM. Modeling and simulation of the enzymatic kinetics for the production of Galactooligosaccharides (GOS) using an Artificial Neural Network hybrid model. Digital Chemical Engineering. 2023, 9: 100132. doi: 10.1016/j.dche.2023.100132
11. Fonseca JH, Jang W, Han D, et al. Strength and manufacturability enhancement of a composite automotive component via an integrated finite element/artificial neural network multi-objective optimization approach. Composite Structures. 2024, 327: 117694. doi: 10.1016/j.compstruct.2023.117694
12. Rahmani SR, Libohova Z, Ackerson JP, et al. Estimating natural soil drainage classes in the Wisconsin till plain of the Midwestern U.S.A. based on lidar derived terrain indices: Evaluating prediction accuracy of multinomial logistic regression and machine learning algorithms. Geoderma Regional. 2023, 35: e00728. doi: 10.1016/j.geodrs.2023.e00728
13. Gong P, Cai Y, Chen B, et al. An Artificial Neural Network-based model that can predict inpatients’ personal thermal sensation in rehabilitation wards. Journal of Building Engineering. 2023, 80: 108033. doi: 10.1016/j.jobe.2023.108033
14. Bellagarda A, Grassi D, Aliberti A, et al. Effectiveness of neural networks and transfer learning to forecast photovoltaic power production. Applied Soft Computing. 2023, 149: 110988. doi: 10.1016/j.asoc.2023.110988
15. Ali A, Aurangzeb K, Shoaib M, et al. Second-law analysis of nanofluid-based photovoltaic/thermal system modeling and forecasting model based on artificial neural network. Engineering Analysis with Boundary Elements. 2023, 157: 342-352. doi: 10.1016/j.enganabound.2023.09.018
16. Jara Do Nascimento C, Orchard ME, Devia C. Exploring the benefits of images with frequency visual content in predicting human ocular scanpaths using Artificial Neural Networks. Expert Systems with Applications. 2024, 239: 121839. doi: 10.1016/j.eswa.2023.121839
17. Thangapandian E, Palanisamy P, Selvaraj SK, et al. Detailed experimentation and prediction of thermophysical properties in lauric acid-based nanocomposite phase change material using artificial neural network. Journal of Energy Storage. 2023, 74: 109345. doi: 10.1016/j.est.2023.109345
18. Alhasnawi MY, Mohd Said R, Mat Daud Z, et al. Enhancing managerial performance through budget participation: Insights from a two-stage A PLS-SEM and artificial neural network approach (ANN). Journal of Open Innovation: Technology, Market, and Complexity. 2023, 9(4): 100161. doi: 10.1016/j.joitmc.2023.100161
19. Fedorov A, Perechodjuk A, Linke D. Kinetics-constrained neural ordinary differential equations: Artificial neural network models tailored for small data to boost kinetic model development. Chemical Engineering Journal. 2023, 477: 146869. doi: 10.1016/j.cej.2023.146869
20. Yang S, Tian X, Zhang Q, et al. Microorganism inspired hydrogels: Optimization by response surface methodology and genetic algorithm based on artificial neural network. European Polymer Journal. 2023, 201: 112497. doi: 10.1016/j.eurpolymj.2023.112497
21. Baiocchi A, Giagu S, Napoli C, et al. Artificial neural networks exploiting point cloud data for fragmented solid objects classification. Machine Learning: Science and Technology. 2023, 4(4): 045025. doi: 10.1088/2632-2153/ad035e
22. Li K, Zhou G, Liu Y, et al. Prediction on X-ray output of free electron laser based on artificial neural networks. Nature Communications. 2023, 14(1). doi: 10.1038/s41467-023-42573-z
23. Lai W, Kuang M, Wang X, et al. Skin cancer diagnosis (SCD) using Artificial Neural Network (ANN) and Improved Gray Wolf Optimization (IGWO). Scientific Reports. 2023, 13(1). doi: 10.1038/s41598-023-45039-w
24. Iqbal M, Karuppanan S, Perumal V, et al. An Artificial Neural Network Model for the Stress Concentration Factors in KT-Joints Subjected to Axial Compressive Load. Materials Science Forum. 2023, 1103: 163-175. doi: 10.4028/p-ypo50i
25. Zhou W, Sheng Y, Alizadeh A, et al. Synthesis and characterization of Alg/Gel/n-HAP/MNPs porous nanocomposite adsorbent for efficient water conservancy and removal of methylene blue in aqueous environments: Kinetic modeling and artificial neural network predictions. Journal of Environmental Management. 2024, 349: 119446. doi: 10.1016/j.jenvman.2023.119446
26. Achouri F, Khatir A, Smahi Z, et al. Structural health monitoring of beam model based on swarm intelligence-based algorithms and neural networks employing FRF. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2023, 45(12). doi: 10.1007/s40430-023-04525-y
27. Iranmanesh R, Pourahmad A, Shabestani DS, et al. Author Correction: Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites. Scientific Reports. 2023, 13(1). doi: 10.1038/s41598-023-46411-6
28. Pais A, Alves JL, Belinha J. Predicting trabecular arrangement in the proximal femur: An artificial neural network approach for varied geometries and load cases. Journal of Biomechanics. 2023, 161: 111860. doi: 10.1016/j.jbiomech.2023.111860
29. Yao C, Zhang J, Gao L, et al. Enhancing sodium percarbonate catalytic wet peroxide oxidation with artificial intelligence-optimized swirl flow: Ni single atom sites on carbon nanotubes for improved reactivity and silicon resistance. Chemosphere. 2024, 346: 140606. doi: 10.1016/j.chemosphere.2023.140606
DOI: https://doi.org/10.54517/esp.v9i5.2185
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