Hybrid image embedding technique using Steganographic Signcryption and IWT-GWO methods

Ambika, .; Virupakshappa,; Lim, Se-Jung

doi:10.1016/j.micpro.2022.104688

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…The advantages of this algorithm lie in its simple structure, few parameter settings, fast convergence speed, strong global optimization capability, and applicability to solving high-dimensional problems. As a result, GWO has been widely applied in various fields such as job-shop scheduling, engineering manufacturing, image information, and energy optimization [19][20][21][22].…”

Section: Plos Onementioning

confidence: 99%

An improved gray wolf optimization to solve the multi-objective tugboat scheduling problem

Yao,

Duan,

Tang

2024

PLoS ONE

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With the continuous prosperity of maritime transportation on a global scale and the resulting escalation in port trade volume, tugboats assume a pivotal role as essential auxiliary tools influencing the ingress and egress of vessels into and out of ports. As a result, the optimization of port tug scheduling becomes of paramount importance, as it contributes to the heightened efficiency of ship movements, cost savings in port operations, and the promotion of sustainable development within the realm of maritime transportation. However, a majority of current tugboat scheduling models tend to focus solely on the maximum operational time. Alternatively, the formulated objective functions often deviate from real-world scenarios. Furthermore, prevailing scheduling methods exhibit shortcomings, including inadequate solution accuracy and incompatibility with integer programming. Consequently, this paper introduces a novel multi-objective tugboat scheduling model to align more effectively with practical considerations. We propose a novel optimization algorithm, the Improved Grey Wolf Optimization (IGWO), for solving the tugboat scheduling model. The algorithm enhances convergence performance by optimizing convergence parameters and individual updates, making it particularly suited for solving integer programming problems. The experimental session designs several scale instances according to the reality of the port, carries out simulation experiments comparing several groups of intelligent algorithms, verifies the effectiveness of IGWO, and verifies it in the comprehensive port area of Huanghua Port to get the optimal scheduling scheme of this port area, and finally gives management suggestions to reduce the cost of tugboat operation through sensitivity analysis.

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Section: Plos Onementioning

confidence: 99%

An improved gray wolf optimization to solve the multi-objective tugboat scheduling problem

Yao,

Duan,

Tang

2024

PLoS ONE

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“…Glaucoma Image Ovarian Image Tumour Normal Glaucoma Healthy Cancer Normal analysis is carried out using various performance parameters between the performance of the proposed approach with the GAN-based steganography method (SteganoGAN [28]), High_Capacity_Information_Hiding with_Generative_ Adversar-ial_Network_ (HCISNet [29]), and Compressed_sensing_based_ enhanced_embed-ding_capacity_image_steganography_(CSIS [30]). Reed_Solomon_Bits_per_pixel_ (RS_BPP), Peak_signal_to_noise_ratio_(PSNR), weighted_peak_ signal_to_noise_ratio_ (WPSNR), accuracy and structural_similarity_(SSIM) are used to compare the performance of the methods [31][32][33][34].…”

Section: Brain Imagementioning

confidence: 99%

Deep Learning-Based Coverless Image Steganography on Medical Images Shared via Cloud

Ambika,

Virupakshappa,

Uplaonkar

2024

RAiSE-2023

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“…GWO has been combined with a gated recurrent unit (GRU) neural network to compensate for the reduced calibration time and ensure the fibre optic gyroscope's (FOG) calibration accuracy [55]. The GWO algorithm combining the Integer Wavelet Transform (IWT) reduces information loss regarding image steganography [56]. Then, the inverse of IWT and unsigncryption algorithms is utilized to extract the secret image from the stego image.…”

Section: • Practical Applicationmentioning

confidence: 99%

Optimizing a Multi-Layer Perceptron Based on an Improved Gray Wolf Algorithm to Identify Plant Diseases

Tian

Chen

et al. 2023

Mathematics

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Metaheuristic optimization algorithms play a crucial role in optimization problems. However, the traditional identification methods have the following problems: (1) difficulties in nonlinear data processing; (2) high error rates caused by local stagnation; and (3) low classification rates resulting from premature convergence. This paper proposed a variant based on the gray wolf optimization algorithm (GWO) with chaotic disturbance, candidate migration, and attacking mechanisms, naming it the enhanced gray wolf optimizer (EGWO), to solve the problem of premature convergence and local stagnation. The performance of the EGWO was tested on IEEE CEC 2014 benchmark functions, and the results of the EGWO were compared with the performance of three GWO variants, five traditional and popular algorithms, and six recent algorithms. In addition, EGWO optimized the weights and biases of a multi-layer perceptron (MLP) and proposed an EGWO-MLP disease identification model; the model was tested on IEEE CEC 2014 benchmark functions, and EGWO-MLP was verified by UCI dataset including Tic-Tac-Toe, Heart, XOR, and Balloon datasets. The experimental results demonstrate that the proposed EGWO-MLP model can effectively avoid local optimization problems and premature convergence and provide a quasi-optimal solution for the optimization problem.

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Hybrid image embedding technique using Steganographic Signcryption and IWT-GWO methods

Cited by 10 publications

References 19 publications

An improved gray wolf optimization to solve the multi-objective tugboat scheduling problem

An improved gray wolf optimization to solve the multi-objective tugboat scheduling problem

Deep Learning-Based Coverless Image Steganography on Medical Images Shared via Cloud

Optimizing a Multi-Layer Perceptron Based on an Improved Gray Wolf Algorithm to Identify Plant Diseases

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