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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Classification of Systems-II01:31

Classification of Systems-II

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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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Aggregates Classification01:29

Aggregates Classification

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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.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Related Experiment Video

Updated: Jan 11, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.1K

Evaluating machine learning approaches for multiple attack classification with improved computational efficiency in

Maher Alharby1,2

  • 1Department of Cybersecurity, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia. mharby@taibahu.edu.sa.

Scientific Reports
|November 14, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances Internet of Things (IoT) security by using machine learning to detect cyber-attacks like Denial of Service. The developed models achieve near-perfect accuracy and significantly reduce detection time.

Keywords:
CICIoT2023 datasetCyber attacksDoS attacksIoT securityMachine learning

Related Experiment Videos

Last Updated: Jan 11, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.1K

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Network Security

Background:

  • The proliferation of Internet of Things (IoT) devices presents significant security vulnerabilities.
  • IoT networks are increasingly susceptible to sophisticated cyber-attacks, including Denial of Service (DoS) and Distributed Denial of Service (DDoS).

Purpose of the Study:

  • To develop and evaluate a machine learning-based approach for detecting and classifying DoS, DDoS, and Mirai attacks in IoT environments.
  • To assess the performance and computational efficiency of various supervised learning algorithms for intrusion detection.

Main Methods:

  • Utilized the CICIoT2023 dataset with five supervised algorithms: Random Forest, Gradient Boosting, Naive Bayes, Decision Tree, and K-Nearest Neighbors.
  • Implemented data preprocessing, including undersampling for class imbalance, and feature selection using Chi-square, PCA, and Random Forest Regressor.
  • Evaluated models based on accuracy, precision, sensitivity, F1-score, training time, and prediction time.

Main Results:

  • Achieved a state-of-the-art accuracy of 99.99% on the CICIoT2023 dataset, outperforming existing studies.
  • The Decision Tree model demonstrated superior computational efficiency, with substantial reductions in training and prediction times while maintaining high accuracy.
  • The study provides a comprehensive performance and efficiency comparison of selected machine learning algorithms for IoT intrusion detection.

Conclusions:

  • Machine learning offers a highly effective and efficient solution for detecting and classifying cyber-attacks in IoT networks.
  • The developed approach provides practical implications for creating robust, computationally efficient intrusion detection systems for resource-constrained IoT environments.
  • Further research can build upon these findings to enhance the security posture of the rapidly expanding IoT ecosystem.