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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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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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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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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Related Experiment Video

Updated: Oct 16, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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One- and Two-Phase Software Requirement Classification Using Ensemble Deep Learning.

Nouf Rahimi1,2, Fathy Eassa1, Lamiaa Elrefaei3

  • 1Computer Science Department, Faculty of Computing and Information Technology, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia.

Entropy (Basel, Switzerland)
|October 23, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning ensemble methods effectively classify software requirements. A two-phase approach achieved 95.7% accuracy in binary classification and 93.4% in multi-class classification, outperforming single-phase systems.

Keywords:
BiLSTMCNNGRULSTMclassificationdeep learningensemblefunctional requirementnon-functional requirementsoftware requirement

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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning (DL) shows promise in automating software engineering (SE) tasks.
  • Ensemble approaches in DL aim to enhance performance and reduce errors.
  • Classifying software requirements (SRs) is crucial for SE.

Purpose of the Study:

  • To apply and evaluate three DL ensemble methods for software requirement classification.
  • To compare a one-phase vs. a two-phase classification system for SRs.
  • To assess the performance of DL models including LSTM, BiLSTM, GRU, and CNN.

Main Methods:

  • Utilized three ensemble techniques: accuracy-weighted, mean, and accuracy-per-class weighted ensembles.
  • Combined four DL models: LSTM, BiLSTM, GRU, and CNN.
  • Developed one-phase and two-phase classification systems for SRs on the PROMISE dataset.

Main Results:

  • The two-phase classification system achieved 95.7% accuracy for binary (FR/NFR) classification and 93.4% for multi-class (17 classes) classification.
  • Both proposed systems demonstrated competitive performance against state-of-the-art methods.
  • The two-phase system showed superior robustness compared to the one-phase system.

Conclusions:

  • Deep learning ensemble methods offer a robust solution for software requirement classification.
  • The two-phase classification strategy is more effective for complex SR classification tasks.
  • The study highlights the potential of DL in advancing automated software engineering.