Breast Cancer Diagnosis Using Support Vector Machines Optimized by Whale Optimization and Dragonfly Algorithms

Elkorany, Ahmed S.; Marey, Mohamed; Mellouli, Khaled; Elsharkawy, Zeinab F.

doi:10.1109/access.2022.3186021

Cited by 37 publications

(13 citation statements)

References 57 publications

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“…Currently, the numerical programs that offer the significant prediction performance are those based SVM. It is known that SVM models are designed to maximize the margin to find out an ideal isolating hyperplane between the classes (Elkorany et al, 2022). In this research, a novel hybrid machine-learning model DA-SVM was constructed to predict the MR of pea pods for different drying conditions.…”

Section: Dragonfly Support Vector Regression Resultsmentioning

confidence: 99%

Study of microwave and convective drying kinetics of pea pods (Pisum sativum L.): A new modeling approach using support vector regression methods optimized by dragonfly algorithm techniques

Hadjout‐Krimat

Belbahi

Dahmoune

et al. 2022

J Food Process Engineering

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Machine learning and mathematical modeling techniques have been conducted to model the thin layer drying kinetics of pea pods, under either microwave or conventional air drying,. The effect of nine different microwave output powers (200-1000 W) and five different ventilated oven temperatures (40, 60, 80, 100, and 120 C) on drying kinetics was studied. The experimental drying rates were fitted to 11 literature semi-empirical models to determine the kinetic parameters, finding the higher goodness-of-fit for the Midilli et al. model (average R 2 = 0.999 for both drying methods). Moreover, the data were modeled using support vector machine (SVM) for regression which was optimized with dragonfly algorithm (DA) technique. The best result was obtained by Gaussian kernel with the optimal parameters σ, C, and ε values estimated as 0.2871, 78.45, and 0, respectively. The small root mean square error (RMSE = 0.0132) and the high determination coefficient (R 2 = 0.9983) values proved how robust the SVM model is. DA-SVM techniques can reliably be utilized to describe the thin layer drying kinetics of pea pods. It is useful to provide models that can assist in the development of food process control algorithms, and provided insights into complex processes, for the technological design of microwave or convective drying for pea pods preservation. Practical applicationsDrying of by-products from pea processing industry was investigated as a critical step prior to their valorization. The drying of pea pods has never been investigated before which is the case of the present study whose objective was to study and model the

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Section: Dragonfly Support Vector Regression Resultsmentioning

confidence: 99%

Study of microwave and convective drying kinetics of pea pods (Pisum sativum L.): A new modeling approach using support vector regression methods optimized by dragonfly algorithm techniques

Hadjout‐Krimat

Belbahi

Dahmoune

et al. 2022

J Food Process Engineering

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show abstract

“…DAs main benefits lie in its relatively few control parameters and its simple, adaptable, and easily applied structure [40]. Many optimization problems in medical image analysis have been solved by employing DA [59][60][61][62][63][64]. Convergence is slow, and the algorithm gets stuck in local optima because the DAs constrained design hinders monitoring the best searching experience of dragonflies in earlier generations throughout the local searching phase [65].…”

Section: Step 2 Quantum Dragonfly-based Clustering Phasementioning

confidence: 99%

A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique

Darwish,

Abu Shaheen,

Elzoghabi

2023

Bioengineering

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Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that has greatly aided in the advancement of the geometric active contour model. An important factor in reducing segmentation error and the number of required iterations when using the level set technique is the choice of the initial contour points, both of which are important when dealing with the wide range of sizes, shapes, and structures that brain tumors may take. To define the velocity function, conventional methods simply use the image gradient, edge strength, and region intensity. This article suggests a clustering method influenced by the Quantum Inspired Dragonfly Algorithm (QDA), a metaheuristic optimizer inspired by the swarming behaviors of dragonflies, to accurately extract initial contour points. The proposed model employs a quantum-inspired computing paradigm to stabilize the trade-off between exploitation and exploration, thereby compensating for any shortcomings of the conventional DA-based clustering method, such as slow convergence or falling into a local optimum. To begin, the quantum rotation gate concept can be used to relocate a colony of agents to a location where they can better achieve the optimum value. The main technique is then given a robust local search capacity by adopting a mutation procedure to enhance the swarm’s mutation and realize its variety. After a preliminary phase in which the cranium is disembodied from the brain, tumor contours (edges) are determined with the help of QDA. An initial contour for the MRI series will be derived from these extracted edges. The final step is to use a level set segmentation technique to isolate the tumor area across all volume segments. When applied to 3D-MRI images from the BraTS’ 2019 dataset, the proposed technique outperformed state-of-the-art approaches to brain tumor segmentation, as shown by the obtained results.

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“…Support vector machines can be applied in different fields such as chemistry to identify the polar liquids [ 38 ], energy storage to self-discharge prediction in batteries of lithium-ion [ 39 ], or in medicine to diagnose breast cancer [ 40 ], inter alia.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning to Predict the Adsorption Capacity of Microplastics

et al. 2023

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Nowadays, there is an extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through their own degradation processes, can contaminate ecosystems with micro- and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) were developed to predict different microplastic/water partition coefficients (log Kd) using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients above 0.92 in the query phase, which indicates that these types of models could be used for the rapid estimation of the absorption of organic contaminants on microplastics.

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Breast Cancer Diagnosis Using Support Vector Machines Optimized by Whale Optimization and Dragonfly Algorithms

Cited by 37 publications

References 57 publications

Study of microwave and convective drying kinetics of pea pods (Pisum sativum L.): A new modeling approach using support vector regression methods optimized by dragonfly algorithm techniques

Study of microwave and convective drying kinetics of pea pods (Pisum sativum L.): A new modeling approach using support vector regression methods optimized by dragonfly algorithm techniques

A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique

Machine Learning to Predict the Adsorption Capacity of Microplastics

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