Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm

El-kenawy, El-Sayed M.; Albalawi, Fahad; Ward, Sayed A.; Ghoneim, Sherif S. M.; Eid, Marwa M.; Abdelhamid, Abdelaziz A.; Bailek, Nadjem; Ibrahim, Abdelhameed

doi:10.3390/math10173144

Cited by 63 publications

(37 citation statements)

References 52 publications

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“…The achieved results are assessed using the criteria presented in Table 3 . The criteria listed in this table are used to evaluate the performance of the proposed feature selection method [ 60 , 61 , 62 , 63 , 64 , 65 ]. In addition, the criteria listed in this table are used to assess the performance of the proposed optimized classification model.…”

Section: Experimental Resultsmentioning

confidence: 99%

Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm

et al. 2023

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According to the American Cancer Society, breast cancer is the second largest cause of mortality among women after lung cancer. Women’s death rates can be decreased if breast cancer is diagnosed and treated early. Due to the lengthy duration of manual breast cancer diagnosis, an automated approach is necessary for early cancer identification. This research proposes a novel framework integrating metaheuristic optimization with deep learning and feature selection for robustly classifying breast cancer from ultrasound images. The structure of the proposed methodology consists of five stages, namely, data augmentation to improve the learning of convolutional neural network (CNN) models, transfer learning using GoogleNet deep network for feature extraction, selection of the best set of features using a novel optimization algorithm based on a hybrid of dipper throated and particle swarm optimization algorithms, and classification of the selected features using CNN optimized using the proposed optimization algorithm. To prove the effectiveness of the proposed approach, a set of experiments were conducted on a breast cancer dataset, freely available on Kaggle, to evaluate the performance of the proposed feature selection method and the performance of the optimized CNN. In addition, statistical tests were established to study the stability and difference of the proposed approach compared to state-of-the-art approaches. The achieved results confirmed the superiority of the proposed approach with a classification accuracy of 98.1%, which is better than the other approaches considered in the conducted experiments.

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Section: Experimental Resultsmentioning

confidence: 99%

Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm

et al. 2023

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“…These three guidelines are the foundation of the fundamental fractal search (FS) procedure, which is utilized to locate a remedy for a specific issue that has been presented. The stochastic fractal search, also known as SFS, is a type of algorithm that was designed with the fractal paradigm as its foundation [ 59 , 60 ]. By utilizing three different update mechanisms—one for diffusion, one for the first update, and one for the second update—SFS is able to go around the limitations of FS [ 61 , 62 ].…”

Section: Methodsmentioning

confidence: 99%

An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease

et al. 2022

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Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals in developed countries experiencing monkeypox. Such conditions often carry less obvious but no less devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due to the low visual resolution of monkeypox disease images, medical specialists with high-level tools are typically required for a proper diagnosis. The manual diagnosis of monkeypox disease is subjective, time-consuming, and labor-intensive. Therefore, it is necessary to create a computer-aided approach for the automated diagnosis of monkeypox disease. Most research articles on monkeypox disease relied on convolutional neural networks (CNNs) and using classical loss functions, allowing them to pick up discriminative elements in monkeypox images. To enhance this, a novel framework using Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) is proposed to fine-tune the deep CNN layers for classifying monkeypox disease from images. As a first step in the proposed approach, we use deep CNN-based models to learn the embedding of input images in Euclidean space. In the second step, we use an optimized classification model based on the triplet loss function to calculate the distance between pairs of images in Euclidean space and learn features that may be used to distinguish between different cases, including monkeypox cases. The proposed approach uses images of human skin diseases obtained from an African hospital. The experimental results of the study demonstrate the proposed framework’s efficacy, as it outperforms numerous examples of prior research on skin disease problems. On the other hand, statistical experiments with Wilcoxon and analysis of variance (ANOVA) tests are conducted to evaluate the proposed approach in terms of effectiveness and stability. The recorded results confirm the superiority of the proposed method when compared with other optimization algorithms and machine learning models.

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“…After gaining knowledge from the extracted all-encompassing and complicated characteristics, the next layer incorporates LSTM layers, which are effective in the forward dependencies and receives the outputs from the bottom layer. One of the most effective methods is regularizing and preventing overfitting in neural network designs by the dropout mechanism [41,42]. A dropout occurs when a portion of the neuron units are removed, along with their associated incoming and outgoing connections, resulting in a weaker network.…”

Section: The Proposed Methodologymentioning

confidence: 99%

Deep Learning with Dipper Throated Optimization Algorithm for Energy Consumption Forecasting in Smart Households

et al. 2022

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One of the relevant factors in smart energy management is the ability to predict the consumption of energy in smart households and use the resulting data for planning and operating energy generation. For the utility to save money on energy generation, it must be able to forecast electrical demands and schedule generation resources to meet the demand. In this paper, we propose an optimized deep network model for predicting future consumption of energy in smart households based on the Dipper Throated Optimization (DTO) algorithm and Long Short-Term Memory (LSTM). The proposed deep network consists of three parts, the first part contains a single layer of bidirectional LSTM, the second part contains a set of stacked unidirectional LSTM, and the third part contains a single layer of fully connected neurons. The design of the proposed deep network targets represents the temporal dependencies of energy consumption for boosting prediction accuracy. The parameters of the proposed deep network are optimized using the DTO algorithm. The proposed model is validated using the publicly available UCI household energy dataset. In comparison to the other competing machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Sequence-to-Sequence (Seq2Seq), and standard LSTM, the performance of the proposed model shows promising effectiveness and superiority when evaluated using eight evaluation criteria including Root Mean Square Error (RMSE) and R2. Experimental results show that the proposed optimized deep model achieved an RMSE of (0.0047) and R2 of (0.998), which outperform those values achieved by the other models. In addition, a sensitivity analysis is performed to study the stability and significance of the proposed approach. The recorded results confirm the effectiveness, superiority, and stability of the proposed approach in predicting the future consumption of energy in smart households.

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Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm

Cited by 63 publications

References 52 publications

Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm

Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm

An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease

Deep Learning with Dipper Throated Optimization Algorithm for Energy Consumption Forecasting in Smart Households

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