Research Article
BibTex RIS Cite

Comparative Study on Physical Education Learning Quality of Junior High School Students based on Biosensor Network

Year 2024, , 125 - 144, 30.10.2024
https://doi.org/10.28978/nesciences.1569219

Abstract

A biosensor is an innovative analytical detecting instrument utilized across many sectors due to its sensitiveness, precision, ease of use, and capability for in vivo surveillance via the internet. Biosensors provide extensive applicability in sports science, facilitating rapid physical activity tracking. This will emerge as a significant approach and technology for sports teaching and scientific study in sports. This study aims to evaluate the variations in bodily schooling mastering exceptional junior center college students underneath exceptional rearing modes and to use the specialist gadget primarily based on the synthetic neural community for analysis. The contrast index of gaining knowledge first-rate is an index gadget composed of numerous one-of-a-kind parameters. It is tough to be particular and has apparent fuzziness due to its giant variety and complicated content. There are many obstacles in fixing the contrast hassle using the skill of frequently used assessment methods. This paper proposes an assessment mannequin of junior excessive college students' bodily schooling mastering excellent primarily based on a synthetic neural community specialist system. The purpose of this method is to put processed records in a community and generate results by computation, other than by manual computing. It decreases the number of people in the comparative procedure, enhances the credibility of the assessment, and makes the comparative result more enormous and objective. However, the neural community additionally has some limitations. It can obtain international optimization by continuously editing the connection weights between neurons; however, making the community fall into neighborhood minima is convenient.

References

  • Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11).
  • Agatonovic-Kustrin, S., & Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of pharmaceutical and biomedical analysis, 22(5), 717-727.
  • Beauchamp, L., Darst, P. W., & Thompson, L. P. (1990). Academic learning time as an indication of quality high school physical education. Journal of Physical Education, Recreation & Dance, 61(1), 92-95.
  • Buchanan, B. G., & Smith, R. G. (1988). Fundamentals of expert systems. Annual review of computer science, 3(1), 23-58.
  • Chen, W., Mason, S., Hypnar, A., & Hammond-Bennett, A. (2016). Association of quality physical education teaching with students’ physical fitness. Journal of sports science & medicine, 15(2), 335-343.
  • Gan, Y., Wang, Y., Yu, F., Xiao, Q., Luo, X., Han, Z., & Ke, C. (2023). Genotype by environment interactions for productive traits of purebred and crossbred abalone strains under different rearing modes. Aquaculture, 563, 738966.
  • Goodenough, D. G., Goldberg, M., Plunkett, G., & Zelek, J. (1987). An expert system for remote sensing. IEEE Transactions on Geoscience and Remote Sensing, (3), 349-359.
  • Hendrickson, C., Zozaya-Gorostiza, C., Rehak, D., Baracco-Miller, E., & Lim, P. (1987). Expert system for construction planning. Journal of Computing in Civil Engineering, 1(4), 253-269.
  • Islam, M., Chen, G., & Jin, S. (2019). An overview of neural network. American Journal of Neural Networks and Applications, 5(1), 7-11.
  • Jerbi, M. A., Aubin, J., Garnaoui, K., Achour, L., & Kacem, A. (2012). Life cycle assessment (LCA) of two rearing techniques of sea bass (Dicentrarchus labrax). Aquacultural Engineering, 46, 1-9.
  • Kim, K., Yoo, H., & Lee, E. K. (2022). New opportunities for organic semiconducting polymers in biomedical applications. Polymers, 14(14), 2960. https://doi.org/10.3390/polym14142960
  • Lei, Z. L., & Guo, B. (2022). 2D material‐based optical biosensor: status and prospect. Advanced Science, 9(4), 2102924. https://doi.org/10.1002/advs.202102924
  • Liao, S. H. (2005). Expert system methodologies and applications—a decade review from 1995 to 2004. Expert systems with applications, 28(1), 93-103.
  • Liu, H., Qi, J., Yang, Q., Tang, Q., Qi, J., Li, Y., & Li, L. (2022). Effects of cage and floor rearing systems on the metabolic components of the uropygial gland in ducks. Animals, 12(2), 214.
  • Lu, T., Ji, S., Jin, W., Yang, Q., Luo, Q., & Ren, T. L. (2023). Biocompatible and long-term monitoring strategies of wearable, ingestible and implantable biosensors: reform the next generation healthcare. Sensors, 23(6), 2991.
  • Lv, W., Yuan, Q., He, D., Lv, W., & Zhou, W. (2020). Microplastic contamination caused by different rearing modes of Asian swamp eel (Monopterus albus). Aquaculture Research, 51(12), 5084-5095.
  • Mehrotra, K., Mohan, C. K., & Ranka, S. (1997). Elements of artificial neural networks. MIT press.
  • Richer, M. H. (1986). An evaluation of expert system development tools. Expert systems, 3(3), 166-183.
  • Roudjane, M., Bellemare-Rousseau, S., Drouin, E., Belanger-Huot, B., Dugas, M. A., Miled, A., & Messaddeq, Y. (2020). Smart T-shirt based on wireless communication spiral fiber sensor array for real-time breath monitoring: Validation of the technology. IEEE Sensors Journal, 20(18), 10841-10850.
  • Shanmuganathan, S. (2016). Artificial neural network modelling: An introduction, 1-14. Springer International Publishing.
  • Shortliffe, E. H., Scott, A. C., Bischoff, M. B., Campbell, A. B., Van Melle, W., & Jacobs, C. D. (1984). An expert system for oncology protocol management. Rule-Based Expert Systems, BG Buchanan and EH Shortiffe, Editors, 653-65.
  • Wang, Y., Zhang, X., Liu, Y., Jiang, F., Liu, Y., Xu, F., & Liu, Y. (2023). The influence of parental rearing style on the incidence of panic disorder, major depressive disorder and the comorbidity among Chinese college students. Journal of Affective Disorders, 338, 100-106.
  • Waterman, D. A. (1985). A guide to expert systems. Addison-Wesley Longman Publishing Co., Inc..
  • Wu, Y. C., & Feng, J. W. (2018). Development and application of artificial neural network. Wireless Personal Communications, 102, 1645-1656.
  • Yang, Y., Fang, G., Li, Z., Liao, F., & Feng, Y. (2009). Effects of different rearing modes on chicken's muscular histological traits and meat tenderness. Animal Husbandry and Feed Science, 1(3), 24-27.
  • Ye, S., Feng, S., Huang, L., & Bian, S. (2020). Recent progress in wearable biosensors: From healthcare monitoring to sports analytics. Biosensors, 10(12), 205. https://doi.org/10.3390/bios10120205
  • Zakrajsek, D., Carnes, L., & Pettigrew, F. E. (2003). Quality lesson plans for secondary physical education, 1, Human Kinetics.
  • Zhou, Y., & Wang, L. (2019). Correlates of physical activity of students in secondary school physical education: a systematic review of literature. BioMed Research International, 2019(1), 4563484. https://doi.org/10.1155/2019/4563484
  • Zou, J., Han, Y., & So, S. S. (2009). Overview of artificial neural networks. Artificial neural networks: methods and applications, 458, 14-22.
Year 2024, , 125 - 144, 30.10.2024
https://doi.org/10.28978/nesciences.1569219

Abstract

References

  • Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11).
  • Agatonovic-Kustrin, S., & Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of pharmaceutical and biomedical analysis, 22(5), 717-727.
  • Beauchamp, L., Darst, P. W., & Thompson, L. P. (1990). Academic learning time as an indication of quality high school physical education. Journal of Physical Education, Recreation & Dance, 61(1), 92-95.
  • Buchanan, B. G., & Smith, R. G. (1988). Fundamentals of expert systems. Annual review of computer science, 3(1), 23-58.
  • Chen, W., Mason, S., Hypnar, A., & Hammond-Bennett, A. (2016). Association of quality physical education teaching with students’ physical fitness. Journal of sports science & medicine, 15(2), 335-343.
  • Gan, Y., Wang, Y., Yu, F., Xiao, Q., Luo, X., Han, Z., & Ke, C. (2023). Genotype by environment interactions for productive traits of purebred and crossbred abalone strains under different rearing modes. Aquaculture, 563, 738966.
  • Goodenough, D. G., Goldberg, M., Plunkett, G., & Zelek, J. (1987). An expert system for remote sensing. IEEE Transactions on Geoscience and Remote Sensing, (3), 349-359.
  • Hendrickson, C., Zozaya-Gorostiza, C., Rehak, D., Baracco-Miller, E., & Lim, P. (1987). Expert system for construction planning. Journal of Computing in Civil Engineering, 1(4), 253-269.
  • Islam, M., Chen, G., & Jin, S. (2019). An overview of neural network. American Journal of Neural Networks and Applications, 5(1), 7-11.
  • Jerbi, M. A., Aubin, J., Garnaoui, K., Achour, L., & Kacem, A. (2012). Life cycle assessment (LCA) of two rearing techniques of sea bass (Dicentrarchus labrax). Aquacultural Engineering, 46, 1-9.
  • Kim, K., Yoo, H., & Lee, E. K. (2022). New opportunities for organic semiconducting polymers in biomedical applications. Polymers, 14(14), 2960. https://doi.org/10.3390/polym14142960
  • Lei, Z. L., & Guo, B. (2022). 2D material‐based optical biosensor: status and prospect. Advanced Science, 9(4), 2102924. https://doi.org/10.1002/advs.202102924
  • Liao, S. H. (2005). Expert system methodologies and applications—a decade review from 1995 to 2004. Expert systems with applications, 28(1), 93-103.
  • Liu, H., Qi, J., Yang, Q., Tang, Q., Qi, J., Li, Y., & Li, L. (2022). Effects of cage and floor rearing systems on the metabolic components of the uropygial gland in ducks. Animals, 12(2), 214.
  • Lu, T., Ji, S., Jin, W., Yang, Q., Luo, Q., & Ren, T. L. (2023). Biocompatible and long-term monitoring strategies of wearable, ingestible and implantable biosensors: reform the next generation healthcare. Sensors, 23(6), 2991.
  • Lv, W., Yuan, Q., He, D., Lv, W., & Zhou, W. (2020). Microplastic contamination caused by different rearing modes of Asian swamp eel (Monopterus albus). Aquaculture Research, 51(12), 5084-5095.
  • Mehrotra, K., Mohan, C. K., & Ranka, S. (1997). Elements of artificial neural networks. MIT press.
  • Richer, M. H. (1986). An evaluation of expert system development tools. Expert systems, 3(3), 166-183.
  • Roudjane, M., Bellemare-Rousseau, S., Drouin, E., Belanger-Huot, B., Dugas, M. A., Miled, A., & Messaddeq, Y. (2020). Smart T-shirt based on wireless communication spiral fiber sensor array for real-time breath monitoring: Validation of the technology. IEEE Sensors Journal, 20(18), 10841-10850.
  • Shanmuganathan, S. (2016). Artificial neural network modelling: An introduction, 1-14. Springer International Publishing.
  • Shortliffe, E. H., Scott, A. C., Bischoff, M. B., Campbell, A. B., Van Melle, W., & Jacobs, C. D. (1984). An expert system for oncology protocol management. Rule-Based Expert Systems, BG Buchanan and EH Shortiffe, Editors, 653-65.
  • Wang, Y., Zhang, X., Liu, Y., Jiang, F., Liu, Y., Xu, F., & Liu, Y. (2023). The influence of parental rearing style on the incidence of panic disorder, major depressive disorder and the comorbidity among Chinese college students. Journal of Affective Disorders, 338, 100-106.
  • Waterman, D. A. (1985). A guide to expert systems. Addison-Wesley Longman Publishing Co., Inc..
  • Wu, Y. C., & Feng, J. W. (2018). Development and application of artificial neural network. Wireless Personal Communications, 102, 1645-1656.
  • Yang, Y., Fang, G., Li, Z., Liao, F., & Feng, Y. (2009). Effects of different rearing modes on chicken's muscular histological traits and meat tenderness. Animal Husbandry and Feed Science, 1(3), 24-27.
  • Ye, S., Feng, S., Huang, L., & Bian, S. (2020). Recent progress in wearable biosensors: From healthcare monitoring to sports analytics. Biosensors, 10(12), 205. https://doi.org/10.3390/bios10120205
  • Zakrajsek, D., Carnes, L., & Pettigrew, F. E. (2003). Quality lesson plans for secondary physical education, 1, Human Kinetics.
  • Zhou, Y., & Wang, L. (2019). Correlates of physical activity of students in secondary school physical education: a systematic review of literature. BioMed Research International, 2019(1), 4563484. https://doi.org/10.1155/2019/4563484
  • Zou, J., Han, Y., & So, S. S. (2009). Overview of artificial neural networks. Artificial neural networks: methods and applications, 458, 14-22.
There are 29 citations in total.

Details

Primary Language English
Subjects Agricultural Biotechnology (Other)
Journal Section Articles
Authors

Yu Geng 0009-0006-9390-2653

Publication Date October 30, 2024
Submission Date October 17, 2024
Acceptance Date October 17, 2024
Published in Issue Year 2024

Cite

APA Geng, Y. (2024). Comparative Study on Physical Education Learning Quality of Junior High School Students based on Biosensor Network. Natural and Engineering Sciences, 9(2), 125-144. https://doi.org/10.28978/nesciences.1569219

                                                                                               We welcome all your submissions

                                                                                                             Warm regards,
                                                                                                      


All published work is licensed under a Creative Commons Attribution 4.0 International License Link . Creative Commons License
                                                                                         NESciences.com © 2015