Parallel strength Pareto evolutionary algorithm‐II based image encryption

Kaur, Manjit; Singh, Dilbag; Uppal, Raminder Singh

doi:10.1049/iet-ipr.2019.0587

Cited by 54 publications

(27 citation statements)

References 70 publications

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“…With this step, the recognition and judgment on the infected tissue are over. 40,41 Step 4: RBM learning method. Restricted Boltzmann machine (RBM) is known as an energy-based model.…”

Section: Deep Learning Modelmentioning

confidence: 99%

Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

Singh

Kumar

Yadav

et al. 2020

Int. J. Patt. Recogn. Artif. Intell.

Self Cite

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There are limited coronavirus disease 2019 (COVID-19) testing kits, therefore, development of other diagnosis approaches is desirable. The doctors generally utilize chest X-rays and Computed Tomography (CT) scans to diagnose pneumonia, lung inflammation, abscesses, and/or enlarged lymph nodes. Since COVID-19 attacks the epithelial cells that line our respiratory tract, therefore, X-ray images are utilized in this paper, to classify the patients with infected (COVID-19 [Formula: see text]ve) and uninfected (COVID-19 [Formula: see text]ve) lungs. Almost all hospitals have X-ray imaging machines, therefore, the chest X-ray images can be used to test for COVID-19 without utilizing any kind of dedicated test kits. However, the chest X-ray-based COVID-19 classification requires a radiology expert and significant time, which is precious when COVID-19 infection is increasing at a rapid rate. Therefore, the development of an automated analysis approach is desirable to save the medical professionals’ valuable time. In this paper, a deep convolutional neural network (CNN) approach is designed and implemented. Besides, the hyper-parameters of CNN are tuned using Multi-objective Adaptive Differential Evolution (MADE). Extensive experiments are performed by considering the benchmark COVID-19 dataset. Comparative analysis reveals that the proposed technique outperforms the competitive machine learning models in terms of various performance metrics.

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“…With this step, the recognition and judgment on the infected tissue are over. 40,41 Step 4: RBM learning method. Restricted Boltzmann machine (RBM) is known as an energy-based model.…”

Section: Deep Learning Modelmentioning

confidence: 99%

Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

Singh

Kumar

Yadav

et al. 2020

Int. J. Patt. Recogn. Artif. Intell.

Self Cite

View full text Add to dashboard Cite

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“…Recently many researchers have designed various machine learning models such as to classify various kind of applications [ 9 , 13 ]. However, the majority of the existing models suffer from the over-fitting issue [ 15 , 16 , 29 ]. Therefore, in this paper, a novel fusion mdoel by using DCNN and vector space model is proposed to achieve better results.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning based search engine for biomedical images using convolutional neural networks

Mishra

Tripathi

2021

Multimed Tools Appl

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The development of efficient search engine queries for biomedical images, especially in case of query-mismatch is still defined as an ill-posed problem. Vector-space model is found to be useful for handling the query-mismatch issue. However, vector-space model does not consider the relational details among the keywords and biomedical image search space is not evaluated. Therefore, in this paper, we have proposed a deep learning based fusion vectorspace based model. The proposed model enhances the biomedical image query similarity matching approach by fusing the vector space model and convolutional neural networks. Deep learning model is defined by converting the vector-space model to a classification model. Finally, deep learning model is trained to implement the search engine for biomedical images. Extensive experiments reveal that the proposed model achieves significant improvement over the existing models.

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“…e initial parameters for 5D hyperchaotic are tuned by using the dual local search based multiobjective optimization, and the encryption architecture is based on two levels of permutation and diffusion. Similarly, the authors in [16][17][18] also used the hyperchaotic maps in a novel way for encrypting the images and obtained better results.…”

Section: Introductionmentioning

confidence: 99%

Robust Secure Color Image Watermarking Using 4D Hyperchaotic System, DWT, HbD, and SVD Based on Improved FOA Algorithm

Nazir

Bajwa

Samiullah

et al. 2021

Security and Communication Networks

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In the recent past, a different set of algorithms for watermarking and securing the color images have been developed by using transformation, decomposition, and optimization techniques for watermark embedding and extraction. In this paper, we propose an optimized and robust watermarking algorithm coupled with a 4D hyperchaotic system, and its performance is analyzed by extending and differentiating the existing work. Our contribution in the presented work is watermarking and securing the color images by an optimized algorithm that uses transformation technique such as Discrete Wavelet Transformation (DWT) and decomposition techniques such as Hessenberg decomposition (HbD) and singular value decomposition (SVD) coupled with the 4D hyperchaotic system, while the optimization is carried out by improved evolution fruit fly optimization algorithm (IEFOA). The experimental results based on different types of attacks (filter attacks, noise attacks, cropping attack, JPEG compression, motion blur, sharpening, and rotation), key sensitivity, normalized correlation, peak signal-to-noise ratio, and structural similarity index measure are done for measuring the algorithm’s performance regarding invisibility and robustness. The experimental results show that the proposed scheme has excellent invisibility and keeps a good trade-off between invisibility and robustness. The experiment results show that the proposed approach outperforms the previous approaches.

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Parallel strength Pareto evolutionary algorithm‐II based image encryption

Cited by 54 publications

References 70 publications

Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

Deep learning based search engine for biomedical images using convolutional neural networks

Robust Secure Color Image Watermarking Using 4D Hyperchaotic System, DWT, HbD, and SVD Based on Improved FOA Algorithm

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