FOFS: Firefly Optimization for Feature Selection to Predict Fault-Prone Software Modules

2021 ◽  
pp. 479-487
Author(s):  
Somya Goyal
Keyword(s):  
Feature Selection ◽  
Software Modules ◽  
Firefly Optimization

scholarly journals An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Ahmed I. Sharaf ◽  
Mohamed Abu El-Soud ◽  
Ibrahim M. El-Henawy
Keyword(s):  
Feature Selection ◽  
Correlation Energy ◽  
Epileptic Seizures ◽  
Compact Set ◽  
Eeg Signal ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Eeg Signals ◽  
Computer Aided ◽  
Firefly Optimization

Detection of epileptic seizures using an electroencephalogram (EEG) signals is a challenging task that requires a high level of skilled neurophysiologists. Therefore, computer-aided detection provides an asset to the neurophysiologist in interpreting the EEG. This paper introduces a novel approach to recognize and classify the epileptic seizure and seizure-free EEG signals automatically by an intelligent computer-aided method. Moreover, the prediction of the preictal phase of the epilepsy is proposed to assist the neurophysiologist in the clinic. The proposed method presents two perspectives for the EEG signal processing to detect and classify the seizures and seizure-free signals. The first perspectives consider the EEG signal as a nonlinear time series. A tunable Q-wavelet is applied to decompose the signal into smaller segments called subbands. Then a chaotic, statistical, and power spectrum features sets are extracted from each subband. The second perspectives process the EEG signal as an image; hence the gray-level co-occurrence matrix is determined from the image to obtain the textures of contrast, correlation, energy, and homogeneity. Due to a large number of features obtained, a feature selection algorithm based on firefly optimization was applied. The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. Afterward, a dataset from the University of Bonn, Germany, is used for benchmarking and evaluation. The proposed approach provided a significant result compared with other recent work regarding accuracy, recall, specificity, F-measure, and Matthew’s correlation coefficient.

scholarly journals Feature selection using firefly optimization for classification and regression models

2018 ◽  
Vol 106 ◽  
pp. 64-85 ◽  
Author(s):  
Li Zhang ◽  
Kamlesh Mistry ◽  
Chee Peng Lim ◽  
Siew Chin Neoh
Keyword(s):  
Feature Selection ◽  
Regression Models ◽  
Firefly Optimization ◽  
Classification And Regression

Feature Selection Strategies and Perceptual Expertise in Configuration Search Tasks

2011 ◽  
Author(s):  
Lindsey M. Kitchell ◽  
Francisco J. Parada ◽  
Brandi L. Emerick ◽  
Tom A. Busey
Keyword(s):  
Feature Selection ◽  
Perceptual Expertise ◽  
Search Tasks ◽  
Selection Strategies

Array Design for the Synthesis of Stair Step Patterns Using Firefly Optimization Technique

2019 ◽  
Vol 9 (4) ◽  
pp. 285
Author(s):  
Krishna Rudraraju Chaitanya ◽  
P. Mallikarjuna Rao ◽  
K. V. S. N. Raju ◽  
G. S. N. Raju
Keyword(s):  
Optimization Technique ◽  
Array Design ◽  
Firefly Optimization

BLIND FEATURE SELECTION AND EXTRACTION IN A 3D IMAGE CUBE

2012 ◽  
Vol 19 (2) ◽  
pp. 97-111 ◽  
Author(s):  
Muhammad Ahmad ◽  
Syungyoung Lee ◽  
Ihsan Ul Haq ◽  
Qaisar Mushtaq
Keyword(s):  
Feature Selection ◽  
3D Image ◽  
Image Cube

A SVD and Modified Firefly Optimization Based Robust Digital Image Watermarking Technique

2017 ◽  
Vol 7 (7) ◽  
pp. 268
Author(s):  
Chauhan Usha ◽  
Singh Rajeev Kumar
Keyword(s):  
Digital Media ◽  
Firefly Algorithm ◽  
Image Watermarking ◽  
Singular Values ◽  
Robust Watermarking ◽  
Scaling Factors ◽  
Geometric Attacks ◽  
Host Image ◽  
Firefly Optimization ◽  
Value Decomposition

Digital Watermarking is a technology, to facilitate the authentication, copyright protection and Security of digital media. The objective of developing a robust watermarking technique is to incorporate the maximum possible robustness without compromising with the transparency. Singular Value Decomposition (SVD) using Firefly Algorithm provides this objective of an optimal robust watermarking technique. Multiple scaling factors are used to embed the watermark image into the host by multiplying these scaling factors with the Singular Values (SV) of the host image. Firefly Algorithm is used to optimize the modified host image to achieve the highest possible robustness and transparency. This approach can significantly increase the quality of watermarked image and provide more robustness to the embedded watermark against various attacks such as noise, geometric attacks, filtering attacks etc.

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