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Mobile Phone Usage Detection by ANN Trained with a Metaheuristic Algorithm †.

Efrain Mendez1, Alexandro Ortiz2, Pedro Ponce1

  • 1School of Engineering and Sciences, Tecnologico de Monterrey, Mexico city 14380, Mexico.

Sensors (Basel, Switzerland)
|July 25, 2019
PubMed
Summary

This study introduces a novel Earthquake Algorithm (EA) for training artificial neural networks (ANNs), offering faster convergence and optimal solutions. This method efficiently detects mobile phone use while driving and has broad applications in areas like nano-sensors.

Keywords:
artificial neural networknanotechnologyoptimizationsensors

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Area of Science:

  • * Computer Science
  • * Artificial Intelligence
  • * Optimization Algorithms

Background:

  • * Artificial neural networks (ANNs) are crucial for classifying non-linear systems using input/output data.
  • * Traditional gradient optimization methods for ANN training can be slow and may not reach optimal solutions.
  • * Metaheuristic optimization offers a broader search space, potentially reducing computational effort and ensuring better solutions.

Purpose of the Study:

  • * To present a novel algorithm for training ANNs using an earthquake optimization method.
  • * To demonstrate an efficient ANN training process suitable for real-world applications like mobile phone usage detection while driving.
  • * To highlight the versatility and convergence capabilities of the proposed Earthquake Algorithm (EA).

Main Methods:

  • * Development and application of a novel Earthquake Algorithm (EA) for training artificial neural networks (ANNs).
  • * Comparison with traditional gradient optimization methods, focusing on convergence speed and solution optimality.
  • * Illustration of linear classification using EA for logic gate emulation and potential nano-sensor applications.

Main Results:

  • * The Earthquake Algorithm (EA) provides an efficient training process for ANNs, reducing computational effort.
  • * EA demonstrates versatility in searching optimization spaces, enabling both fine and aggressive search strategies.
  • * Experimental results validate the proposed method for smart mobile phone applications and general optimization tasks.

Conclusions:

  • * The Earthquake Algorithm (EA) offers a superior alternative to gradient-based methods for training ANNs.
  • * The EA's efficiency and versatility make it suitable for diverse applications, including mobile sensing and nano-sensor development.
  • * The proposed method ensures optimal solutions and faster convergence, validating its practical utility.