TY  - JOUR
T1  - RETRACTED ARTICLE: Optimizing Classification of Spread Spectrum Signals Based on Features Extraction
AU - S. Fouad, Haidy AU - K. Girgis, Shawkat AU - A. Elsayed, Hend 
JO  - Asian Journal of Information Technology
VL  - 19
IS  - 1
SP  - 1
EP  - 11
PY  - 2020
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2020.1.11
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2020.1.11
KW  - clustering
KW  -Binary optimization
KW  -Gray Level Co-occurrence Matrix (GLCM)
KW  -Frequency Hopped Spread Spectrum(FHSS)
KW  -sequential selection
KW  -Direct Sequence Spread Spectrum (DSSS)
KW  -Whale Optimization (WOA)
AB  - Spread Spectrum techniques (SS) attract the
attention they are widely used in wireless communications
and radar. Direct sequence spread spectrum and frequency
hopped spread spectrum are the two most used
techniques. In this research, the clustering of these signals
is performed by feature-based model. Features are
extracted by Gray Level Co-occurrence Matrix (GLCM),
gray level run-length matrix, cumulants, moments, PCA,
KPCA and Fast-ICA features. Clustering by GLCM
features gets the best result which is one of the common
textures features extraction techniques. The selection of
relevant features is the big challenge. Therefore, the main
contribution is to optimize the SS identification based on
clustering techniques by decreasing the number of
features without accuracy degradation which is based on
filters, sequential forward selection and binary
metaheuristic search strategies such as Binary Particle
Swarm Optimization (BPSO), Genetic Algorithm (GA),
bat feature selection (BBA) and hybrid Whale
Optimization Algorithm with Simulated Annealing and
Tournament mechanism (WOASAT). BPSO as a wrapper
method is proposed to the optimization as it outperforms
the other techniques in terms of accuracy and selected
features with k-means, k-medoids or HAC.
ER  -