Classification of Remote Sensing Images Based on K-Means Clustering and Artificial Bee Colony Optimization

2020 ◽  
pp. 57-65
Author(s):  
M. Venkata Dasu ◽  
P. V. N. Reddy ◽  
S. Chandra Mohan Reddy
Keyword(s):  
Remote Sensing ◽  
Artificial Bee Colony ◽  
Remote Sensing Images ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

scholarly journals REMOTE SENSING IMAGE CLASSIFICATION USING ARTIFICIAL BEE COLONY ALGORITHM

2014 ◽  
pp. 155-160
Author(s):  
SRIDEEPA BANERJEE ◽  
AKANKSHA BHARADWAJ ◽  
DAYA GUPTA ◽  
V.K. PANCHAL
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Image Classification ◽  
Artificial Bee Colony ◽  
Spatial Information ◽  
Satellite Image ◽  
Fuzzy Classifier ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

Remote Sensing has been globally used for knowledge elicitation of earth’s surface and atmosphere. Land cover mapping, one of the widely used applications of remote sensing is a method for acquiring geo-spatial information from satellite data. We have attempted here to solve the land cover problem by image classification using one of the newest and most promising Swarm techniques of Artificial Bee Colony optimization (ABC). In this paper we propose an implementation of ABC for satellite image classification. ABC is used for optimal classification of images for mapping the land-usage efficiently. The results produced by ABC algorithm are compared with the results obtained by other techniques like BBO, MLC, MDC, Membrane computing and Fuzzy classifier to show the effectiveness of our proposed implementation.

Mining of structural motifs in proteins using artificial bee colony optimization framework for druggability

2021 ◽  
Author(s):  
L. S. Suma ◽  
S. S. Vinod Chandra
Keyword(s):  
Artificial Bee Colony ◽  
Area Under The Curve ◽  
Docking Studies ◽  
Artificial Bee Colony Optimization ◽  
Optimization Framework ◽  
Bee Colony ◽  
Novel Variant ◽  
Bee Colony Optimization ◽  
Novel Strategy ◽  
Sensitivity Specificity

In this work, we have developed an optimization framework for digging out common structural patterns inherent in DNA binding proteins. A novel variant of the artificial bee colony optimization algorithm is proposed to improve the exploitation process. Experiments on four benchmark objective functions for different dimensions proved the speedier convergence of the algorithm. Also, it has generated optimum features of Helix Turn Helix structural pattern based on the objective function defined with occurrence count on secondary structure. The proposed algorithm outperformed the compared methods in convergence speed and the quality of generated motif features. The motif locations obtained using the derived common pattern are compared with the results of two other motif detection tools. 92% of tested proteins have produced matching locations with the results of the compared methods. The performance of the approach was analyzed with various measures and observed higher sensitivity, specificity and area under the curve values. A novel strategy for druggability finding by docking studies, targeting the motif locations is also discussed.

scholarly journals Pareto front feature selection based on artificial bee colony optimization

2018 ◽  
Vol 422 ◽  
pp. 462-479 ◽  
Author(s):  
Emrah Hancer ◽  
Bing Xue ◽  
Mengjie Zhang ◽  
Dervis Karaboga ◽  
Bahriye Akay
Keyword(s):  
Feature Selection ◽  
Pareto Front ◽  
Artificial Bee Colony ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization
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