Feature selection for image steganalysis using levy flight-based grey wolf optimization

2018 ◽  
Vol 78 (2) ◽  
pp. 1473-1494 ◽  
Author(s):  
Yadunath Pathak ◽  
K. V. Arya ◽  
Shailendra Tiwari
2019 ◽  
Vol 12 (4) ◽  
pp. 329-337 ◽  
Author(s):  
Venubabu Rachapudi ◽  
Golagani Lavanya Devi

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method. Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification. Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification. Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.


Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1360-1372
Author(s):  
Ramaprabha Jayaram ◽  
T. Senthil Kumar

Parkinson disease is a rigorous neurodegenerative disorder characterized by the cognitive behavior ending with disability problems. Especially, the elderly people should be given more care and spend more time duration to diagnose when they are at risk. It is more important to identify and diagnose Parkinson disease at an earlier stage rather than spending too much of cost later stages. Different ways of diagnosing the disease ranging from gene analysis to gait behavior, speech, writing test and olfactory models were used in the conventional testing process. In order to increase the patient’s quality of life and minimize the cost of healthcare utilization, an Onboard Cloud-Enabled Parkinson Disease Identification System (OCPDIS) is proposed. An enhanced grey wolf optimization is explored along with the differential evolution techniques to form an effective hybrid feature selection method. Using this feature selection method in the enhanced k-Nearest Neighbor (k-NN) classifier model could substantially improve the prediction time and prediction accuracy.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 4244-4249 ◽  
Author(s):  
Feng Liu ◽  
Xuehu Yan ◽  
Yuliang Lu

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