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Machine Learning Method Based Industrial Risk Analysis and Prediction

Published: 11 August 2022 Publication History

Abstract

IoT-based technologies growing all over the world. After the industrial revolution, machines and robots gradually replaced human effort. In the absence of the human brain-machine and robots makes an error. In this paper, a plan was developed to get out of this situation that works not only efficiently but also thinks like humans. In this system, the machine will learn based on the situation that has been made by any occurrence. In this work Raspberry Pi-based system helps to make a proper analysis of the machines. Voltage, current, gas value, and temperate values are taken as input parameters. Machine learning matches/compares these real-time sensor data with training data (which is used to train the system). As a result, The machine learning module provides some statistics graphs of sensor data. Machine performance can analyze by observing these graphs. Also, determine the efficiency and predict the possibility of upcoming threats or risks.

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References

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cover image ACM Other conferences
ICCA '22: Proceedings of the 2nd International Conference on Computing Advancements
March 2022
543 pages
ISBN:9781450397346
DOI:10.1145/3542954
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 11 August 2022

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Author Tags

  1. Industrial revolution
  2. IoT
  3. Machine Learning
  4. Predicts
  5. Raspberry pi
  6. Statistics graph

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