Dynamic Group-Based Cooperative Optimization Algorithm

Fouad, Mohamad M.; El-Desouky, Ali I.; Al-Hajj, Rami; El-kenawy, El-Sayed M.

doi:10.1109/access.2020.3015892

Cited by 67 publications

(44 citation statements)

References 61 publications

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“…The third and last phase is the classification of infected patients. Figure 5 shows the third phase of the proposed framework for COVID- 19 IV-B1 is employed in the algorithm development. After the initialization of the WOA algorithm and find the first best solution − → G * (Lines from 1 to 6), the iteration number t starts to divide the calculation of the fitness function from the guided WOA or from the PSO algorithm.…”

Section: Third Phasementioning

confidence: 99%

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

et al. 2020

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Diagnosis is a critical preventive step in Coronavirus research which has similar manifestations with other types of pneumonia. CT scans and X-rays play an important role in that direction. However, processing chest CT images and using them to accurately diagnose COVID-19 is a computationally expensive task. Machine Learning techniques have the potential to overcome this challenge. This paper proposes two optimization algorithms for feature selection and classification of COVID-19. The proposed framework has three cascaded phases. Firstly, the features are extracted from the CT scans using a Convolutional Neural Network (CNN) named AlexNet. Secondly, a proposed features selection algorithm, Guided Whale Optimization Algorithm (Guided WOA) based on Stochastic Fractal Search (SFS), is then applied followed by balancing the selected features. Finally, a proposed voting classifier, Guided WOA based on Particle Swarm Optimization (PSO), aggregates different classifiers' predictions to choose the most voted class. This increases the chance that individual classifiers, e.g. Support Vector Machine (SVM), Neural Networks (NN), k-Nearest Neighbor (KNN), and Decision Trees (DT), to show significant discrepancies. Two datasets are used to test the proposed model: CT images containing clinical findings of positive COVID-19 and CT images negative COVID-19. The proposed feature selection algorithm (SFS-Guided WOA) is compared with other optimization algorithms widely used in recent literature to validate its efficiency. The proposed voting classifier (PSO-Guided-WOA) achieved AUC (area under the curve) of 0.995 that is superior to other voting classifiers in terms of performance metrics. Wilcoxon rank-sum, ANOVA, and T-test statistical tests are applied to statistically assess the quality of the proposed algorithms as well.

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Section: Third Phasementioning

confidence: 99%

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

et al. 2020

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“…This problem is related to the decision-making problem and achieves total benefit maximization [37][38][39][40][41]. Recently, many meta-heuristic optimization techniques have been extensively reported in the literature, but the chaotic encryption and communication technique is emphasized in this study [42]. In accordance with the objectives and Turkey ETSO integration system of the electrical system for providing accurate and sufficient ETSO standards it is required to provide ancillary services.…”

Section: Ancillary Services and Secondary Reserve Control Instrucmentioning

confidence: 99%

Transmission of Frequency Balance Instructions and Secure Data Sharing Based on Chaos Encryption in Smart Grid-Based Energy Systems Applications

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2021

IEEE Access

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Data communication security between power systems, which has become a chronic problem in the classical network, is characterized by the application of information communication technology (ICT) in modern network models. Nowadays, the electricity network is spread over very large areas, and the load taking, and load shedding instructions are transmitted one-way and unsafe in the current structure. However, utilities in the smart grid are developing new methodologies to secure the communication infrastructure. This article presents a design that will make the current grid model more reliable and provide a secure data communication in accordance with the modern grid infrastructure. This design provides auxiliary communication codes such as chaotic codes embedded in the communication instructions between power systems, which are generation balance instructions sent to the power plants by the load dispatch center to achieve frequency balance. Used as a case study, one-day instructions are transmitted and encoded four times a day, which is the amount of capacity that only the receiver and transmitter can understand with two-sided encryption. The necessary reserve capacity, chaotic encryption and suitability for real-time applications were evaluated using experimentally obtained power system data for frequency control, which is very important in terms of sustainability in energy and economy against cyber-attacks, and very accurate results were obtained.

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“…For a specific problem, optimization can be the most reliable solution between all available solutions, especially towards multi-dimensional space [24] . Practically, this involves maximization or minimization of an objective function.…”

Section: Introductionmentioning

confidence: 99%

Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification

et al. 2021

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The chest X-ray is considered a significant clinical utility for basic examination and diagnosis. The human lung area can be affected by various infections, such as bacteria and viruses, leading to pneumonia. Efficient and reliable classification method facilities the diagnosis of such infections. Deep transfer learning has been introduced for pneumonia detection from chest X-rays in different models. However, there is still a need for further improvements in the feature extraction and advanced classification stages. This paper proposes a classification method with two stages to classify different cases from the chest X-ray images based on a proposed Advanced Squirrel Search Optimization Algorithm (ASSOA). The first stage is the feature learning and extraction processes based on a Convolutional Neural Network (CNN) model named ResNet-50 with image augmentation and dropout processes. The ASSOA algorithm is then applied to the extracted features for the feature selection process. Finally, the Multi-layer Perceptron (MLP) Neural Network’s connection weights are optimized by the proposed ASSOA algorithm (using the selected features) to classify input cases. A Kaggle chest X-ray images (Pneumonia) dataset consists of 5,863 X-rays is employed in the experiments. The proposed ASSOA algorithm is compared with the basic Squirrel Search (SS) optimization algorithm, Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) for feature selection to validate its efficiency. The proposed (ASSOA + MLP) is also compared with other classifiers, based on (SS + MLP), (GWO + MLP), and (GA + MLP), in performance metrics. The proposed (ASSOA + MLP) algorithm achieved a classification mean accuracy of (99.26%). The ASSOA + MLP algorithm also achieved a classification mean accuracy of (99.7%) for a chest X-ray COVID-19 dataset tested from GitHub. The results and statistical tests demonstrate the high effectiveness of the proposed method in determining the infected cases.

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Dynamic Group-Based Cooperative Optimization Algorithm

Cited by 67 publications

References 61 publications

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

Transmission of Frequency Balance Instructions and Secure Data Sharing Based on Chaos Encryption in Smart Grid-Based Energy Systems Applications

Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification

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