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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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A reliable jumping-based classification methodology for environment sector.

Sepideh Etemadi1,2, Mehdi Khashei1,3, Ali Zeinal Hamadani1

  • 1Department of Industrial and Systems Engineering, Isfahan University of Technology (IUT), Isfahan, Iran.

Heliyon
|July 2, 2024
PubMed
Summary

A new reliable jumping-based intelligent classifier enhances environmental decision-making by combining accuracy and reliability. This novel approach outperforms traditional methods, offering a more effective solution for classification challenges.

Keywords:
Environment decision makingIntelligent Modeling and classificationJumping and reliable –based methodologiesMultilayer perceptrons (MLPs)

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Area of Science:

  • Environmental Science
  • Computer Science
  • Machine Learning

Background:

  • Intelligent classification models are crucial for decision-making, with a constant drive to improve accuracy.
  • Existing methodologies like reliable and jumping modeling offer distinct advantages but have not been combined.
  • A research gap exists in integrating both accuracy and reliability for enhanced classifier performance.

Purpose of the Study:

  • To introduce and evaluate a novel reliable jumping-based intelligent classifier.
  • To address the limitations of existing classification models in environmental decision-making.
  • To investigate the combined benefits of accuracy and reliability in a unified model.

Main Methods:

  • Developed a hybrid intelligent classifier integrating reliable and jumping modeling approaches.
  • Aligned the learning process with classification goals using the jumping methodology.
  • Incorporated reliability factors into the learning paradigm via the reliable methodology.
  • Evaluated the classifier on ten diverse benchmark datasets for environmental applications.

Main Results:

  • The proposed Reliable Jumping-based intelligent classifier consistently outperformed traditional intelligent classifiers.
  • The combined approach demonstrated superior performance across all tested environmental datasets.
  • Numerical results validated the effectiveness of integrating accuracy and reliability.

Conclusions:

  • The reliable jumping-based intelligent classifier is a high-performing model for environmental decision-making.
  • This approach effectively addresses classification challenges by leveraging both accuracy and reliability.
  • The proposed method offers a viable and effective alternative to existing intelligent classification techniques.