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Research & Reviews: Discrete Mathematical Structures Original Research

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

  1. Arkadiy Dantsker Detex Analytics, LLC
  2. Aleksandr Veksler Detex Analytics, LLC,

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

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