A hybrid optimization algorithm‐based feature selection for thyroid disease classifier with rough type‐2 fuzzy support vector machine

Sureshkumar, V.; Balasubramaniam, Sathiyabhama; Ravi, Vinayakumar; Arunachalam, Ajay

doi:10.1111/exsy.12811

Cited by 22 publications

(12 citation statements)

References 37 publications

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“…Hybrid algorithms have recently been a popular topic applied in several domains [27][28][29][30]. Combining the qualities and features of two different algorithms would provide an optimization tool that is more dependable and potent for solving complex problems.…”

Section: Ref Nomentioning

confidence: 99%

A Hybrid Grey Wolf Assisted-Sparrow Search Algorithm for Frequency Control of RE Integrated System

et al. 2023

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Nowadays, renewable energy (RE) sources are heavily integrated into the power system due to the deregulation of the energy market along with environmental and economic benefits. The intermittent nature of RE and the stochastic behavior of loads create frequency aberrations in interconnected hybrid power systems (HPS). This paper attempts to develop an optimization technique to tune the controller optimally to regulate frequency. A hybrid Sparrow Search Algorithm-Grey Wolf Optimizer (SSAGWO) is proposed to optimize the gain values of the proportional integral derivative controller. The proposed algorithm helps to improve the original algorithms’ exploration and exploitation. The optimization technique is coded in MATLAB and applied for frequency regulation of a two-area HPS developed in Simulink. The efficacy of the proffered hybrid SSAGWO is first assessed on standard benchmark functions and then applied to the frequency control of the HPS model. The results obtained from the multi-area multi-source HPS demonstrate that the proposed hybrid SSAGWO optimized PID controller performs significantly by 53%, 60%, 20%, and 70% in terms of settling time, peak undershoot, control effort, and steady-state error values, respectively, than other state-of-the-art algorithms presented in the literature. The robustness of the proffered method is also evaluated under the random varying load, variation of HPS system parameters, and weather intermittency of RE resources in real-time conditions. Furthermore, the controller’s efficacy was also demonstrated by performing a sensitivity analysis of the proposed system with variations of 75% and 125% in the inertia constant and system loading, respectively, from the nominal values. The results show that the proposed technique damped out the transient oscillations with minimum settling time. Moreover, the stability of the system is analyzed in the frequency domain using Bode analysis.

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Section: Ref Nomentioning

confidence: 99%

A Hybrid Grey Wolf Assisted-Sparrow Search Algorithm for Frequency Control of RE Integrated System

et al. 2023

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“…This improved CNN model classifies the breast lesions into benign, malignant, and normal with 90.5% accuracy, 89.47% sensitivity, 90.71% specificity, and 0.901 receiver operating characteristics [24]. Vidhushavarshini S et al [25] have proposed a hybrid optimization algorithm-based feature selection design for thyroid disease classification with rough type-2 fuzzy support vector machine. This work combined the firefly algorithm (FA) and the butterfly optimization algorithm (BOA) namely hybrid firefly butterfly optimization-rough type-2 fuzzy support vector machine (HFBO-RT2FSVM) to select the top-n relevant features.…”

Section: Literature Reviewmentioning

confidence: 99%

A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images

Balasubramaniam,

Velmurugan,

Jaganathan

et al. 2023

Diagnostics

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Convolutional neural networks (CNNs) have been extensively utilized in medical image processing to automatically extract meaningful features and classify various medical conditions, enabling faster and more accurate diagnoses. In this paper, LeNet, a classic CNN architecture, has been successfully applied to breast cancer data analysis. It demonstrates its ability to extract discriminative features and classify malignant and benign tumors with high accuracy, thereby supporting early detection and diagnosis of breast cancer. LeNet with corrected Rectified Linear Unit (ReLU), a modification of the traditional ReLU activation function, has been found to improve the performance of LeNet in breast cancer data analysis tasks via addressing the “dying ReLU” problem and enhancing the discriminative power of the extracted features. This has led to more accurate, reliable breast cancer detection and diagnosis and improved patient outcomes. Batch normalization improves the performance and training stability of small and shallow CNN architecture like LeNet. It helps to mitigate the effects of internal covariate shift, which refers to the change in the distribution of network activations during training. This classifier will lessen the overfitting problem and reduce the running time. The designed classifier is evaluated against the benchmarking deep learning models, proving that this has produced a higher recognition rate. The accuracy of the breast image recognition rate is 89.91%. This model will achieve better performance in segmentation, feature extraction, classification, and breast cancer tumor detection.

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“…at is, it is necessary to find an optimal function in a function group based on n independent and identically distributed sample sequences to obtain the minimum empirical risk value [23,24].…”

Section: Cross Validationmentioning

confidence: 99%

Evaluation Model of College English Teaching Effect Based on Particle Swarm Algorithm and Support Vector Machine

Wei

Tsai

2022

Mathematical Problems in Engineering

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Based on the principle of particle swarm algorithm and support vector machine, this article aims to improve the classification performance of college English teaching effect and explores the best support vector machine parameter optimization algorithm to promote college English teaching for the theory and application research of data analysis. First, the advantages and disadvantages of common support vector machine parameter selection methods such as grid search algorithm, gradient descent method, and swarm intelligence algorithm are studied. Secondly, this article has a detailed analysis and comparison of various other algorithms. Finally, the study analyzed the advantages and disadvantages of the quantum particle swarm algorithm, introduced the dual-center idea into the quantum particle swarm algorithm, and proposed an improved quantum particle swarm algorithm. Through simulation experiments, it is proved that the improved quantum particle swarm algorithm is more superior in optimizing the parameters of support vector machine. In general, this paper uses the PSO algorithm to simultaneously solve the SVM feature selection and parameter optimization problems and has achieved good results. Within the scope of the literature that the author has, there is still a lack of work in this area. Compared with the existing algorithms, the algorithm proposed in this paper has stronger feature selection ability and higher efficiency.

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A hybrid optimization algorithm‐based feature selection for thyroid disease classifier with rough type‐2 fuzzy support vector machine

Cited by 22 publications

References 37 publications

A Hybrid Grey Wolf Assisted-Sparrow Search Algorithm for Frequency Control of RE Integrated System

A Hybrid Grey Wolf Assisted-Sparrow Search Algorithm for Frequency Control of RE Integrated System

A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images

Evaluation Model of College English Teaching Effect Based on Particle Swarm Algorithm and Support Vector Machine

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