Physical Health Data Analysis of Youth Sports Based on Cloud Computing and Gait Perception

[1] Xiaoyun, Z. (2022). Athlete health status assessment based on edge computing model and large-scale multimedia health data analysis. Computational Intelligence and Neuroscience, 20(22), 1079663-1079663. XiaoyunZ. (2022). Athlete health status assessment based on edge computing model and large-scale multimedia health data analysis.Computational Intelligence and Neuroscience,20(22),1079663-1079663. Search in Google Scholar

[2] Steven, T. L. D., Donna, S., & J. A. F. (2021). Associations among wearable activity tracker use, exercise motivation, and physical activity in a cohort of cancer survivors: Secondary data analysis of the health information national trends survey. JMIR Cancer, 7(2), e24828-e24828. StevenT. L. D.DonnaS.J. A.F.(2021).Associations among wearable activity tracker use, exercise motivation, and physical activity in a cohort of cancer survivors: Secondary data analysis of the health information national trends survey.JMIR Cancer,7(2),e24828-e24828. Search in Google Scholar

[3] Zhang, L., & Cai, X. (2019). Research on human health status assessment system of pulse data collection and analysis before and after exercise. Journal of Infection and Public Health, 13(10), 1495-1500. ZhangL. & CaiX. (2019). Research on human health status assessment system of pulse data collection and analysis before and after exercise.Journal of Infection and Public Health,13(10),1495-1500. Search in Google Scholar

[4] Xinyuan, F., & Zhen, Z. (2024). Hotspots and trends in health-oriented physical literacy research: A visual analysis based on the WOS database. BMC Public Health, 24(1), 1480-1480. XinyuanF. & ZhenZ. (2024). Hotspots and trends in health-oriented physical literacy research: A visual analysis based on the WOS database.BMC Public Health,24(1),1480-1480. Search in Google Scholar

[5] Jeanette, H., Henrik, H., & Charlotte, N., et al. (2023). Association between educational level and self-reported musculoskeletal pain and physical functioning in Danes 60-70 years old from 2010 to 2017: A longitudinal analysis of trends over time on data from the Danish health and morbidity survey. BMJ Open, 13(11), e073523-e073523. JeanetteH.HenrikH. & CharlotteN. et al. (2023). Association between educational level and self-reported musculoskeletal pain and physical functioning in Danes 60-70 years old from 2010 to 2017: A longitudinal analysis of trends over time on data from the Danish health and morbidity survey.BMJ Open,13(11),e073523-e073523. Search in Google Scholar

[6] Boya, L., Lili, W., & Lei, Z., et al. (2023). Analysis of severe ozone-related human health and weather influence over China in 2019 based on a high-resolution dataset. Environmental Science and Pollution Research International, 30(51), 111536-111551. BoyaL.LiliW. & LeiZ. et al. (2023). Analysis of severe ozone-related human health and weather influence over China in 2019 based on a high-resolution dataset.Environmental Science and Pollution Research International,30(51),111536-111551. Search in Google Scholar

[7] Wei, H. (2024). Research and optimization of encryption technology for data transmission in cloud computing environment. Computer Informatization and Mechanical System, 7(4), 67-71. WeiH. (2024). Research and optimization of encryption technology for data transmission in cloud computing environment.Computer Informatization and Mechanical System,7(4),67-71. Search in Google Scholar

[8] Zhang, Z., Zhou, F., & Wang, Y. (2024). Multi-user privacy-preserving image search in distributed cloud computing. Computers and Electrical Engineering, 118(1), 109434-109434. ZhangZ.ZhouF. & WangY. (2024). Multi-user privacy-preserving image search in distributed cloud computing.Computers and Electrical Engineering,118(1),109434-109434. Search in Google Scholar

[9] Abualigah, L., Hussein, M. A., & Almomani, H. M., et al. (2024). Improved synergistic swarm optimization algorithm to optimize task scheduling problems in cloud computing. Sustainable Computing: Informatics and Systems, 4(3), 101012-101012. AbualigahL.HusseinM. A. & AlmomaniH. M. et al. (2024). Improved synergistic swarm optimization algorithm to optimize task scheduling problems in cloud computing.Sustainable Computing: Informatics and Systems,4(3),101012-101012. Search in Google Scholar

[10] Carrascosa, M. J. F., Galán, G. S., & Ibáñez, V. M., et al. (2024). Game theory-based virtual machine migration for energy sustainability in cloud data centers. Applied Energy, 37(2), 123798-123798. CarrascosaM. J. F.GalánG. S. & IbáñezV. M. et al. (2024). Game theory-based virtual machine migration for energy sustainability in cloud data centers.Applied Energy,37(2),123798-123798. Search in Google Scholar

[11] Ferdaus, M. M., Dam, T., & Anavatti, S., et al. (2024). Digital technologies for a net-zero energy future: A comprehensive review. Renewable and Sustainable Energy Reviews, 20(2), 114681-114681. FerdausM. M.DamT. & AnavattiS. et al. (2024). Digital technologies for a net-zero energy future: A comprehensive review.Renewable and Sustainable Energy Reviews,20(2),114681-114681. Search in Google Scholar

[12] Mušić, D., Hribar, J., & Fortuna, C. (2024). Digital transformation with a lightweight on-premise PaaS. Future Generation Computer Systems, 16(1), 619-629. MušićD.HribarJ. & FortunaC. (2024). Digital transformation with a lightweight on-premise PaaS.Future Generation Computer Systems,16(1),619-629. Search in Google Scholar

[13] You, D. (2024). Application of cloud computing detection based on sensor networks in enterprise economic statistics. Measurement: Sensors, 3(4), 101254-101254. YouD. (2024). Application of cloud computing detection based on sensor networks in enterprise economic statistics.Measurement: Sensors,3(4),101254-101254. Search in Google Scholar

[14] Mohammadzadeh, M., Anisi, A., & Sheikholeslami, M. (2024). Multi-objective optimization and thermodynamic assessment of a solar unit with a novel tube shape equipped with a helical tape. Applied Thermal Engineering, 25(4), 123851-123851. MohammadzadehM.AnisiA. & SheikholeslamiM. (2024). Multi-objective optimization and thermodynamic assessment of a solar unit with a novel tube shape equipped with a helical tape.Applied Thermal Engineering,25(4),123851-123851. Search in Google Scholar

[15] Haghirad, M., Heidari, S., & Hosseini, H. (2024). Advancing personal thermal comfort prediction: A data-driven framework integrating environmental and occupant dynamics using machine learning. Building and Environment, 26(2), 111799-111799. HaghiradM.HeidariS. & HosseiniH. (2024). Advancing personal thermal comfort prediction: A data-driven framework integrating environmental and occupant dynamics using machine learning.Building and Environment,26(2),111799-111799. Search in Google Scholar

[16] Tchakoucht, A. T., Elkari, B., & Chaibi, Y., et al. (2024). Random forest with feature selection and K-fold cross-validation for predicting the electrical and thermal efficiencies of air-based photovoltaic-thermal systems. Energy Reports, 1(2), 988-999. TchakouchtA. T.ElkariB. & ChaibiY. et al. (2024). Random forest with feature selection and K-fold cross-validation for predicting the electrical and thermal efficiencies of air-based photovoltaic-thermal systems.Energy Reports,1(2),988-999. Search in Google Scholar

[17] Liu, Y., Han, X., & Zhu, Y., et al. (2024). Spatial mapping and driving factor identification for salt-affected soils at continental scale using machine learning methods. Journal of Hydrology, 63(9), 131589-131589. LiuY.HanX. & ZhuY. et al. (2024). Spatial mapping and driving factor identification for salt-affected soils at continental scale using machine learning methods.Journal of Hydrology,63(9),131589-131589. Search in Google Scholar

[18] Melo, D. W. W., Pinho, J., & Iglesias, I. (2024). A data model to forecast the morphological evolution of multiple beach profiles. Coastal Engineering, 19(2), 104574-104574. MeloD. W. W.PinhoJ. & IglesiasI. (2024). A data model to forecast the morphological evolution of multiple beach profiles.Coastal Engineering,19(2),104574-104574. Search in Google Scholar

[19] Ru, W., Zhang, L., & Liu, D., et al. (2024). New approach for regional water-energy-food nexus security assessment: Enhancing the random forest model with the aquila optimizer algorithm. Agricultural Water Management, 30(1), 108946-108946. RuW.ZhangL. & LiuD. et al. (2024). New approach for regional water-energy-food nexus security assessment: Enhancing the random forest model with the aquila optimizer algorithm.Agricultural Water Management,30(1),108946-108946. Search in Google Scholar

[20] Wu, Y., Zhang, Y., & Duan, S., et al. (2024). Survival prediction in second primary breast cancer patients with machine learning: An analysis of SEER database. Computer Methods and Programs in Biomedicine, 25(4), 108310-108310. WuY.ZhangY. & DuanS. et al. (2024). Survival prediction in second primary breast cancer patients with machine learning: An analysis of SEER database.Computer Methods and Programs in Biomedicine,25(4),108310-108310. Search in Google Scholar

[21] Olsen, M., Kuhn, R. D., & Raunak, M. (2024). Explaining the impact of parameter combinations in agent-based models. Journal of Computational Science, 8(1), 102342-102342. OlsenM.KuhnR. D. & RaunakM. (2024). Explaining the impact of parameter combinations in agent-based models.Journal of Computational Science,8(1),102342-102342. Search in Google Scholar

[22] Guo, X., Meng, X., & Han, F., et al. (2024). Assessing the strength of deep-sea surface ultrasoft sediments with T-bar penetration: A machine learning approach. Engineering Geology, 33(8), 107632-107632. GuoX.MengX. & HanF. et al. (2024). Assessing the strength of deep-sea surface ultrasoft sediments with T-bar penetration: A machine learning approach.Engineering Geology,33(8),107632-107632. Search in Google Scholar

[23] Shandookh, A. A., Ogaili, F. A. A., & Haddad, A. A. L. (2024). Failure analysis in predictive maintenance: Belt drive diagnostics with expert systems and Taguchi method for unconventional vibration features. Heliyon, 10(13), e34202-e34202. ShandookhA. A.OgailiF. A. A. & HaddadA. A. L. (2024). Failure analysis in predictive maintenance: Belt drive diagnostics with expert systems and Taguchi method for unconventional vibration features.Heliyon,10(13), e34202-e34202. Search in Google Scholar

[24] Liang, M., Huang, J., & Liu, C., et al. (2024). Predictive modeling of long-term prognosis after resection in typical pulmonary carcinoid: A machine learning perspective. Cancer Investigation, (3), 11-15. LiangM.HuangJ. & LiuC. et al. (2024). Predictive modeling of long-term prognosis after resection in typical pulmonary carcinoid: A machine learning perspective.Cancer Investigation, (3),11-15. Search in Google Scholar

[25] Bader, M., Abdelwanis, M., & Maalouf, M., et al. (2024). Detecting depression severity using weighted random forest and oxidative stress biomarkers. Scientific Reports, 14(1), 16328. BaderM.AbdelwanisM. & MaaloufM. et al. (2024). Detecting depression severity using weighted random forest and oxidative stress biomarkers.Scientific Reports,14(1),16328. Search in Google Scholar

[26] Fazekas, M., Veljanov, Z., & Oliveira, D. B. A. (2024). Predicting pharmaceutical prices. Advances based on purchase-level data and machine learning. BMC Public Health, 24(1), 1888. FazekasM.VeljanovZ. & OliveiraD. B. A. (2024). Predicting pharmaceutical prices. Advances based on purchase-level data and machine learning.BMC Public Health,24(1),1888. Search in Google Scholar

[27] Ding, C., Wei, P., & Shi, Y., et al. (2024). Sampling and active learning methods for network reliability estimation using K-terminal spanning tree. Reliability Engineering and System Safety, 25(1), 110309-110309. DingC.WeiP. & ShiY. et al. (2024). Sampling and active learning methods for network reliability estimation using K-terminal spanning tree.Reliability Engineering and System Safety,25(1),110309-110309. Search in Google Scholar

[28] Thananjayan, P., Natarajan, S., & Ooi, T. E., et al. (2024). Scaled boundary finite element based two-level learning approach for structural flaw identification. Engineering Analysis with Boundary Elements, 16(6), 105855-105855. ThananjayanP.NatarajanS. & OoiT. E. et al. (2024). Scaled boundary finite element based two-level learning approach for structural flaw identification.Engineering Analysis with Boundary Elements,16(6),105855-105855. Search in Google Scholar

[29] Kamali, A. H., Erfanian, R. M., & Pasandidehfard, M. (2024). Experimental and numerical analysis of cavitator angle effects on artificial cavitation characteristics under low ventilation coefficients, with prediction using optimized random forest and extreme gradient boosting models. Ocean Engineering, 309(P1), 118446-118446. KamaliA. H.ErfanianR. M. & PasandidehfardM. (2024). Experimental and numerical analysis of cavitator angle effects on artificial cavitation characteristics under low ventilation coefficients, with prediction using optimized random forest and extreme gradient boosting models.Ocean Engineering,309(P1),118446-118446. Search in Google Scholar

[30] Panday, P. D., & Kumar, M. (2024). Decoding uranium variation over the Indian peninsula through leveraging standardized precipitation evapotranspiration indices and groundwater level fluctuations. Groundwater for Sustainable Development, 2(6), 101241-101241. PandayP. D. & KumarM. (2024). Decoding uranium variation over the Indian peninsula through leveraging standardized precipitation evapotranspiration indices and groundwater level fluctuations.Groundwater for Sustainable Development,2(6),101241-101241. Search in Google Scholar