Modified Bird swarm algorithm for edge detection in noisy images using fuzzy reasoning

Pruthi, Jyotika; Arora, Shaveta; Khanna, Kavita

doi:10.1080/21681163.2018.1523751

Cited by 4 publications

(4 citation statements)

References 45 publications

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“…As an evolutionary computational method, the bird swarm algorithm is also based on swarm intelligence approach 32 . Similar to other evolutionary optimization algorithms, it is operated based on the fitness value of individuals and fitness value of global 33 . The bird swarm algorithm treats each bird as a particle without weight and volume in an M ‐dimensional search space.…”

Section: Image Reconstruction Based On the Proposed Methodsmentioning

confidence: 99%

“…32 Similar to other evolutionary optimization algorithms, it is operated based on the fitness value of individuals and fitness value of global. 33 The bird swarm algorithm treats each bird as a particle without weight and volume in an M-dimensional search space. According to the search space, the number of finite elements in the measured region is M for the EIT problem.…”

Section: Image Reconstruction Based On the Proposed Methodsmentioning

confidence: 99%

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Image reconstruction based on a modified bird swarm optimization algorithm for electrical impedance tomography

Shi

Yuhang

Wang

et al. 2023

Int J Imaging Syst Tech

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Electrical impedance tomography (EIT) is a potential and promising tomographic technique. It is known that image reconstruction of EIT is mathematically an ill-posed inverse problem that leads to poor reconstruction quality. To improve the quality of reconstructed images, a novel image reconstruction method based on modified bird swarm optimization algorithm is proposed in this paper. An image of conductivity distribution acquired by Newton's onestep error reconstruction algorithm performs as initial population. Then a modified bird swarm optimization algorithm is proposed to optimize the initial conductivity distribution. By introducing a dynamic inertia weight parameter, flight interval of the bird swarm varies with the number of iterations. With the proposed method, a strong global search capability in the initial stage and a strong local search capability in the later stage are ensured during the optimization. To verify the effectiveness of the proposed method, simulation work and experimental validation are carried out. Also, reconstructed images are compared with the results obtained by iterative Landweber method and Newton-Raphson method. Meanwhile, the impact of noise on the reconstruction is investigated. The simulation and experimental results have demonstrated the superior performance of the proposed method in visualizing conductivity distribution.

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Section: Image Reconstruction Based On the Proposed Methodsmentioning

confidence: 99%

Section: Image Reconstruction Based On the Proposed Methodsmentioning

confidence: 99%

Image reconstruction based on a modified bird swarm optimization algorithm for electrical impedance tomography

Shi

Yuhang

Wang

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

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“…The BSA is a famous metaheuristic algorithm created by Meng et al [ 31 ], and it is inspired by the social behaviors and interactions of bird swarms. In recent years, it has been widely used in various applications, including time-series forecasting [ 34 ], regression and clustering [ 35 , 36 , 37 ], global optimization [ 38 , 39 , 40 , 41 ], image processing [ 42 , 43 , 44 ], and cloud-computing scheduling [ 45 ].…”

Section: Introductionmentioning

confidence: 99%

An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection

Kareem

Mostafa

Hashim

et al. 2022

Sensors

108

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The increasing use of Internet of Things (IoT) applications in various aspects of our lives has created a huge amount of data. IoT applications often require the presence of many technologies such as cloud computing and fog computing, which have led to serious challenges to security. As a result of the use of these technologies, cyberattacks are also on the rise because current security methods are ineffective. Several artificial intelligence (AI)-based security solutions have been presented in recent years, including intrusion detection systems (IDS). Feature selection (FS) approaches are required for the development of intelligent analytic tools that need data pretreatment and machine-learning algorithm-performance enhancement. By reducing the number of selected features, FS aims to improve classification accuracy. This article presents a new FS method through boosting the performance of Gorilla Troops Optimizer (GTO) based on the algorithm for bird swarms (BSA). This BSA is used to boost performance exploitation of GTO in the newly developed GTO-BSA because it has a strong ability to find feasible regions with optimal solutions. As a result, the quality of the final output will increase, improving convergence. GTO-BSA’s performance was evaluated using a variety of performance measures on four IoT-IDS datasets: NSL-KDD, CICIDS-2017, UNSW-NB15 and BoT-IoT. The results were compared to those of the original GTO, BSA, and several state-of-the-art techniques in the literature. According to the findings of the experiments, GTO-BSA had a better convergence rate and higher-quality solutions.

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“…The result of image edge detection directly affects the effect of image analysis, understanding and recognition. Edge detection algorithms are relatively rich, which can be roughly divided into gradient-based [4], mathematical morphology-based [5], neural network-based [6], bio-inspired algorithm-based [7], etc,. In this paper, we mainly study the edge detection algorithm based on neural networks.…”

Section: Introductionmentioning

confidence: 99%

GPNRBNN: A Robot Image Edge Detection Method Based on Gaussian Positive-Negative Radial Basis Neural Network

Zheng

2021

Sens Imaging

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In the traditional edge detection models, due to the influence of artifact, occlusion and other factors, the detection efficiency is poor and the error is large, which affects the subsequent image processing. Therefore, this paper proposes a new edge detection method based on Gaussian positive-negative radial basis neural network (GPN-RBNN). Firstly, a novel GPNRBNN is constructed in this paper. Each pixel preprocessed by Gaussian filter in the image is taken as the central point of the GPNRBNN and input into the neural network. Then, the weight sharing and sparse connection in the convolutional neural network are used for processing the pixels. The results are output after the calculation in the extended layer and the hidden layer. Thirdly, the edge is extracted by contour tracking according to the output results. Finally, experiments are conducted on the composite image, part of the uneven gray image and medical images etc. Compared with other models, the efficiency of the proposed model is improved, and the edge connectivity is better.

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Modified Bird swarm algorithm for edge detection in noisy images using fuzzy reasoning

Cited by 4 publications

References 45 publications

Image reconstruction based on a modified bird swarm optimization algorithm for electrical impedance tomography

Image reconstruction based on a modified bird swarm optimization algorithm for electrical impedance tomography

An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection

GPNRBNN: A Robot Image Edge Detection Method Based on Gaussian Positive-Negative Radial Basis Neural Network

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