Human Skin Abnormality Detection with Process Similarity Criteria Fit Machine Learning Method

Year : 2024 | Volume : 11 | Issue : 02 | Page : 17 24
    By

    Arkadiy Dantsker,

  • Aleksandr Veksler,

  1. Principal Investigator,, Detex Analytics, LLC, Kirkland WA, USA
  2. Research Scholar, Detex Analytics, LLC,, Kirkland WA, USA

Abstract

This method presents a machine learning method that satisfies the defined conditions for healthy waterside beach activities. The boundary conditions of the normal and abnormal radiation spaces were formulated. The objectives of using a Regression Polynomial with Process Similarity Criteria Fit for skin temperature prediction are justified by the analysis of the existing analytical and machine learning approaches. An algorithm for skin temperature prediction using the theories of similarity criteria fit and hypernumber is presented. A computational approach for identifying radiation abnormalities is provided. The proposed machine learning method for skin temperature prediction was compared with the minimum least-squares regression and long short-term memory (LSTM) neural network methods. The schema for monitoring skin temperature and implementing the prediction algorithm coverage includes a Raspberry PI Zero mini-computer and a sensor that satisfies accuracy and integration with Raspberry. The practical application of this approach is enabled by a monitoring system featuring a Raspberry Pi Zero mini-computer and compatible sensors. This configuration ensures precise data collection and smooth integration of the prediction algorithm, offering an affordable and efficient solution for real-time skin temperature monitoring. The proposed system was designed to improve beachgoer safety by delivering timely alerts and recommendations based on real-time data analysis.

Keywords: Similarity, criteria, regression, hypernumber, temperature

[This article belongs to Research & Reviews: Discrete Mathematical Structures ]

How to cite this article:
Arkadiy Dantsker, Aleksandr Veksler. Human Skin Abnormality Detection with Process Similarity Criteria Fit Machine Learning Method. Research & Reviews: Discrete Mathematical Structures. 2024; 11(02):17-24.
How to cite this URL:
Arkadiy Dantsker, Aleksandr Veksler. Human Skin Abnormality Detection with Process Similarity Criteria Fit Machine Learning Method. Research & Reviews: Discrete Mathematical Structures. 2024; 11(02):17-24. Available from: https://journals.stmjournals.com/rrdms/article=2024/view=206332


References

  1. Horton L, Brady J, Kincaid CM, Torres AE, Lim HW. The effects of infrared radiation on the human skin. Photodermatol Photoimmunol Photomed. 2023;39:549–55. DOI: 10.1111/phpp.12899, PubMed: 37431693.
  2. Marion JW, Lee J, Rosenblum JS, Buckley TJ. Assessment of temperature and ultraviolet radiation effects on sunburn incidence at an inland U.S. beach: A cohort study. Environ Res. 2018;161:479–84. DOI: 10.1016/j.envres.2017.11.036, PubMed: 29220801.
  3. Kricker A, Armstrong BK, Goumas C, Litchfield M, Begg CB, Hummer AJ, et al. Ambient UV, personal sun exposure and risk of multiple primary melanomas. Cancer Causes Control. 2007;18:295–304. DOI: 10.1007/s10552-006-0091-x, PubMed: 17206532.
  4. Petersen B, Philipsen PA, Wulf HC. Skin temperature during sunbathing—Relevance for skin cancer. Photochem Photobiol Sci. 2014;13:1123–5. DOI: 10.1039/c4pp00066h, PubMed: 24930491.
  5. Moore HM, Bai B, Boisvert FM, Latonen L, Rantanen V, Simpson JC, et al. Quantitative proteomics and dynamic imaging of the nucleolus reveal distinct responses to UV and ionizing radiation. Mol Cell Proteomics. 2011;10.009241. DOI: 10.1074/mcp.M111.009241, PubMed: 21778410.
  6. Cho S, Shin MH, Kim YK, Seo JE, Lee YM, Park CH, et al. Effects of infrared radiation and heat on human skin aging in vivo. J Invest Dermatol Symp Proc. 2009;14:15–9. DOI: 10.1038/jidsymp.
    7, PubMed: 19675547.
  7. Finlayson L, Barnard IRM, McMillan L, Ibbotson SH, Brown CTA, Eadie E, et al. Depth penetration of light into skin as a function of wavelength from 200 to 1000 nm. Photochem Photobiol. 2022;98:974–81. DOI: 10.1111/php.13550, PubMed: 34699624.
  8. Ash C, Dubec M, Donne K, Bashford T. Effect of wavelength and beam width on penetration in light-tissue interaction using computational methods. Lasers Med Sci. 2017;32:1909–18. DOI: 10.1007/s10103-017-2317-4, PubMed: 28900751.
  9. Burgin M, Dantsker AM. Real-time inverse modeling of control systems using hypernumbers. In: Functional Analysis and Probability. New York: Nova Science Publishers; 2015. p. 439–56.
  10. Burgin M. Semitopological vector spaces: Hypernorms, hyperseminorms, and operators. Oakville: Apple Academic Press; 2017.
  11. Dantsker A, Burgin M. Monitoring thermal conditions and finding sources of overheating. In: Proceedings of the 2021 Summit of the International Society for the Study of Information. 2022. DOI: 10.3390/proceedings2022081038.
  12. Burgin M. Hypernumbers and extrafunctions: Extending the classical calculus. New York: Springer Science+Business Media; 2012.
  13. Burgin M. Integration in bundles with a hyperspace base: Indefinite integration. In: Topics in Integration Research. New York: Nova Science Publishers; 2013.
  14. Burgin M, Dantzler A. A method of solving operator equations of mechanics with the theory of hypernumbers. Notices Natl Acad Sci Ukraine. 1995;8:27–30.
  15. Lai D, Zhou X, Chen Q. Measurements and predictions of the skin temperature of human subjects on outdoor environments. Energy Build. 2017;151:476–86. DOI: 10.1016/j.enbuild.2017.07.009.
  16. Zhou W, Yang M, Yu X, Peng Y, Fan C, Xu D, et al. Enhancing thermal comfort prediction in high-speed trains through machine learning and physiological signals integration. J Therm Biol. 2024;121:103828. DOI: 10.1016/j.jtherbio.2024.103828, PubMed: 38604115.
  17. Cheng X, Yang B, Tan K, Isaksson E, Li L, Hedman A, et al. Non-invasive measuring method of skin temperature based on skin sensitivity index and deep learning. [pre-print]. 2018;arXiv:1812.06509. DOI: 10.48550/arXiv.1812.06509.
  18. Gorczyca MT, Milan HFM, Maia ASC, Gebremedhin KG. Machine learning algorithms to predict core, skin, and hair-coat temperatures of piglets. Comput Electron Agric. 2018;151:286–94. DOI: 10.1016/j.compag.2018.06.028.
  19. Dantsker A, Brito J, Pryor W. Defining regression polynomials with process similarity criteria. Res Rev. 2023;10.
  20. Keogh E, Chu S, Hart D, Pazzani M. A survey and novel approach. Segmenting time series. Ser Mach Percept Artif Intell. 2004;1–21.
  21. Foster KR, Adair ER. Modeling thermal responses in human subjects following extended exposure to radiofrequency energy. Biomed Eng Online. 2004;3:4. DOI: 10.1186/1475-925X-3-4, PubMed: 14989757.
  22. Kurazumi Y, Fukagawa K, Sakoi T, Aruninta A, Kondo E, Yamashita K. Skin temperature and body surface section in non-uniform and asymmetric outdoor thermal environment. Health. 2018;10:1321–41. DOI: 10.4236/health.2018.1010102.
  23. Sollu TS, Alamsyah, Bachtiar M, Bontong B. Monitoring system heartbeat and body temperature using Raspberry Pi. E3S Web Conf. 2018;73:12003. DOI: 10.1051/e3sconf/20187312003.
  24. Siepert B. (2020). Adafruit TMP117 High Accuracy I2C Temperature Monitor. [online] Adafruit Learning System. Available from: https://learn.adafruit.com/adafruit-tmp117-high-accuracy-i2c-temperature-monitor/overview
  25. DiCola T. (2014). MCP9808 Temperature Sensor Python Library. [online] Adafruit Learning System. Available from: https://learn.adafruit.com/mcp9808-temperature-sensor-python-library/overview
Regular Issue Subscription Original Research
Volume 11
Issue 02
Received 12/08/2024
Accepted 27/08/2024
Published 30/08/2024
Publication Time 18 Days
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