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Related Experiment Video

Updated: Apr 7, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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OpEffiRes Net: Optimization based EfficientNetB0-ResNet50 and correlation based feature selection for software

Vinay Singh1, Sanjiv Sharma2, Amar Singh2

  • 1Department of Computer Science & Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India.

Computational Biology and Chemistry
|April 5, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced hybrid Deep Learning strategy for Software Failure Prediction (SFP). The novel OpEffiResNet model achieves high accuracy in identifying software defects, enhancing system reliability.

Keywords:
Deep learningFault predictionOptimization algorithmResNet50Software failure prediction

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Software failures pose significant risks to system reliability and operational continuity.
  • Accurate Software Failure Prediction (SFP) is crucial for proactive maintenance and defect mitigation.
  • Existing prediction methods often face challenges in accuracy and efficiency.

Purpose of the Study:

  • To develop an advanced hybrid Deep Learning (DL) strategy for enhanced Software Failure Prediction (SFP).
  • To introduce a novel feature selection algorithm (DE-COA) and a hybrid prediction model (OpEffiResNet).
  • To improve the accuracy and reliability of software defect prediction.

Main Methods:

  • Data pre-processing including min-max normalization.
  • Feature selection using the proposed Double Exponential-Coati Optimization Algorithm (DE-COA).
  • Hybrid prediction model combining EfficientNetB0 and ResNet50 (OpEffiResNet).
  • Evaluation using 5-fold cross-validation.

Main Results:

  • The proposed OpEffiResNet model achieved high performance metrics.
  • Average accuracy of 98.8% ± 0.6, sensitivity of 99.4% ± 0.4, specificity of 99.0% ± 0.5, precision of 98.8% ± 0.7, and F1-score of 98.7% ± 0.6.
  • Demonstrated significant improvement in software defect prediction.

Conclusions:

  • The developed hybrid DL framework effectively enhances software defect prediction.
  • The OpEffiResNet model offers accurate, efficient, and reliable software fault predictions.
  • Future work includes validation on diverse datasets and comparison with other advanced methods.