An efficient ECG arrhythmia classification method based on Manta ray foraging optimization

Houssein, Essam H.; Ibrahim, Ibrahim Elsayed; Neggaz, Nabil; Hassaballah, M.; Wazery, Yaser M.

doi:10.1016/j.eswa.2021.115131

Cited by 96 publications

(26 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The local exploitation is performed by the chain foraging and somersault foraging behaviors, while the global exploration is performed by the cyclone foraging behavior. This algorithm shows some optimization capabilities and is very successful in applying in some engineering domains, such as geophysics [44], energy allocation [45][46][47], image processing [48] and electric power [49]. These successful applications in the literature confirm MRFO is effective in solving different complex real-world problems.…”

Section: Introductionmentioning

confidence: 90%

Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers

2021

View full text Add to dashboard Cite

Magnetorheological (MR) dampers play a crucial role in various engineering systems, and how to identify the control parameters of MR damper models without any prior knowledge has become a burning problem. In this study, to identify the control parameters of MR damper models more accurately, an improved manta ray foraging optimization (IMRFO) is proposed. The new algorithm designs a searching control factor according to a weak exploration ability of MRFO, which can effectively increase the global exploration of the algorithm. To prevent the premature convergence of the local optima, an adaptive weight coefficient based on the Levy flight is designed. Moreover, by introducing the Morlet wavelet mutation strategy to the algorithm, the mutation space is adaptively adjusted to enhance the ability of the algorithm to step out of stagnation and the convergence rate. The performance of the IMRFO is evaluated on two sets of benchmark functions and the results confirm the competitiveness of the proposed algorithm. Additionally, the IMRFO is applied in identifying the control parameters of MR dampers, the simulation results reveal the effectiveness and practicality of the IMRFO in the engineering applications.

show abstract

Section: Introductionmentioning

confidence: 90%

Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers

2021

View full text Add to dashboard Cite

show abstract

“…Because meta-heuristic algorithms have great performance and are straightforward to implement, researchers have widely proved their experience to handle many types of challenging optimization issues in engineering, communications, industry, and social sciences [ 20 ]. In addition, they have been employed in biological information [ 21 ], chemical information [ 22 ], feature selection [ 23 ], task scheduling in cloud computing [ 24 ], image segmentation [ 25 ], global optimization [ 26 ], and as well as cost-effective emission dispatch [ 27 ]. There are various meta-heuristic algorithms, of which the marine predators algorithm (MPA) [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm

Houssein

Emam

Ali

2022

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

Breast cancer is the second leading cause of death in women; therefore, effective early detection of this cancer can reduce its mortality rate. Breast cancer detection and classification in the early phases of development may allow for optimal therapy. Convolutional neural networks (CNNs) have enhanced tumor detection and classification efficiency in medical imaging compared to traditional approaches. This paper proposes a novel classification model for breast cancer diagnosis based on a hybridized CNN and an improved optimization algorithm, along with transfer learning, to help radiologists detect abnormalities efficiently. The marine predators algorithm (MPA) is the optimization algorithm we used, and we improve it using the opposition-based learning strategy to cope with the implied weaknesses of the original MPA. The improved marine predators algorithm (IMPA) is used to find the best values for the hyperparameters of the CNN architecture. The proposed method uses a pretrained CNN model called ResNet50 (residual network). This model is hybridized with the IMPA algorithm, resulting in an architecture called IMPA-ResNet50. Our evaluation is performed on two mammographic datasets, the mammographic image analysis society (MIAS) and curated breast imaging subset of DDSM (CBIS-DDSM) datasets. The proposed model was compared with other state-of-the-art approaches. The obtained results showed that the proposed model outperforms the compared state-of-the-art approaches, which are beneficial to classification performance, achieving 98.32% accuracy, 98.56% sensitivity, and 98.68% specificity on the CBIS-DDSM dataset and 98.88% accuracy, 97.61% sensitivity, and 98.40% specificity on the MIAS dataset. To evaluate the performance of IMPA in finding the optimal values for the hyperparameters of ResNet50 architecture, it compared to four other optimization algorithms including gravitational search algorithm (GSA), Harris hawks optimization (HHO), whale optimization algorithm (WOA), and the original MPA algorithm. The counterparts algorithms are also hybrid with the ResNet50 architecture produce models named GSA-ResNet50, HHO-ResNet50, WOA-ResNet50, and MPA-ResNet50, respectively. The results indicated that the proposed IMPA-ResNet50 is achieved a better performance than other counterparts.

show abstract

“…Manta ray foraging optimization (MRFO) is a new swarm intelligence optimization algorithm proposed in 2020. With excellent searchability, fewer parameters, simple model and easily understood, it is better than particle swarm optimization (PSO) [ 11 , 12 ], genetic algorithm (GA) [ 13 , 14 ], Differential Evolution (DE) [ 15 , 16 ], Cuckoo Search (CS) [ 17 ], gravitational search algorithm (GSA) [ 18 ], and ABC [ 19 ] in some function optimization [ 20 ], and it has been successfully applied to solar energy [ 21 , 22 ], ECG [ 23 ], generator [ 24 , 25 ], power system [ 26 ], cogeneration energy system [ 27 ], geophysical inversion problem [ 28 ], directional overcurrent relay [ 29 ], feature selection [ 30 ], hybrid energy system [ 31 ], and sewage treatment [ 32 ]. Although MRFO has good optimization ability, it still has its own defects.…”

Section: Introductionmentioning

confidence: 99%

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

Zhu

Xie

Zhou

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Image segmentation plays an important role in daily life. The traditional K-means image segmentation has the shortcomings of randomness and is easy to fall into local optimum, which greatly reduces the quality of segmentation. To improve these phenomena, a K-means image segmentation method based on improved manta ray foraging optimization (IMRFO) is proposed. IMRFO uses Lévy flight to improve the flexibility of individual manta rays and then puts forward a random walk learning that prevents the algorithm from falling into the local optimal state. Finally, the learning idea of particle swarm optimization is introduced to enhance the convergence accuracy of the algorithm, which effectively improves the global and local optimization ability of the algorithm simultaneously. With the probability that K-means will fall into local optimum reducing, the optimized K-means hold stronger stability. In the 12 standard test functions, 7 basic algorithms and 4 variant algorithms are compared with IMRFO. The results of the optimization index and statistical test show that IMRFO has better optimization ability. Eight underwater images were selected for the experiment and compared with 11 algorithms. The results show that PSNR, SSIM, and FSIM of IMRFO in each image are better. Meanwhile, the optimized K-means image segmentation performance is better.

show abstract

An efficient ECG arrhythmia classification method based on Manta ray foraging optimization

Cited by 96 publications

References 60 publications

Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers

Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers

An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

Contact Info

Product

Resources

About