El-Sayed M. El-kenawy

Bio: El-Sayed M. El-kenawy is an academic researcher from Mansoura University. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 10, co-authored 30 publications receiving 281 citations.

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

Abstract: 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 article 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.

MbGWO-SFS: Modified Binary Grey Wolf Optimizer Based on Stochastic Fractal Search for Feature Selection

Abstract: Grey Wolf Optimizer (GWO) simulates the grey wolves’ nature in leadership and hunting manners. GWO showed a good performance in the literature as a meta-heuristic algorithm for feature selection problems, however, it shows low precision and slow convergence. This paper proposes a Modified Binary GWO (MbGWO) based on Stochastic Fractal Search (SFS) to identify the main features by achieving the exploration and exploitation balance. First, the modified GWO is developed by applying an exponential form for the number of iterations of the original GWO to increase the search space accordingly exploitation and the crossover/mutation operations to increase the diversity of the population to enhance exploitation capability. Then, the diffusion procedure of SFS is applied for the best solution of the modified GWO by using the Gaussian distribution method for random walk in a growth process. The continuous values of the proposed algorithm are then converted into binary values so that it can be used for the problem of feature selection. To ensure the stability and robustness of the proposed MbGWO-SFS algorithm, nineteen datasets from the UCI machine learning repository are tested. The K-Nearest Neighbor (KNN) is used for classification tasks to measure the quality of the selected subset of features. The results, compared to binary versions of the-state-of-the-art optimization techniques such as the original GWO, SFS, Particle Swarm Optimization (PSO), hybrid of PSO and GWO, Satin Bowerbird Optimizer (SBO), Whale Optimization Algorithm (WOA), Multiverse Optimization (MVO), Firefly Algorithm (FA), and Genetic Algorithm (GA), show the superiority of the proposed algorithm. The statistical analysis by Wilcoxon’s rank-sum test is done at the 0.05 significance level to verify that the proposed algorithm can work significantly better than its competitors in a statistical way.

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

Abstract: 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.

WOA + BRNN: An imbalanced big data classification framework using Whale optimization and deep neural network

Abstract: Nowadays, big data plays a substantial part in information knowledge analysis, manipulation, and forecasting. Analyzing and extracting knowledge from such big datasets are a very challenging task due to the imbalance of data distribution, which could lead to a biased classification results and wrong decisions. The standard classifiers are not capable of handling such datasets. Hence, a new technique for dealing with such datasets is required. This paper proposes a novel classification framework for big data that consists of three developed phases. The first phase is the feature selection phase, which uses the Whale optimization algorithm (WOA) for finding the best set of features. The second phase is the preprocessing phase, which uses the SMOTE algorithm and the LSH-SMOTE algorithm for solving the class imbalance problem. Lastly, the third phase is WOA + BRNN algorithm, which is using the Whale optimization algorithm for training a deep learning approach called bidirectional recurrent neural network for the first time. Our proposed algorithm WOA-BRNN has been tested against nine highly imbalanced datasets one of them is big dataset in terms of area under curve (AUC) against four of the most common use machine learning algorithms (Naive Bayes, AdaBoostM1, decision table, random tree), in addition to GWO-MLP (training multilayer perceptron using Gray Wolf Optimizer), then we test our algorithm over four well-known datasets against GWO-MLP and particle swarm optimization (PSO-MLP), genetic algorithm (GA-MLP), ant colony optimization (ACO-MLP), evolution strategy (ES-MLP), and population-based incremental learning (PBIL-MLP) in terms of classification accuracy. Experimental results proved that our proposed algorithm WOA + BRNN has achieved promising accuracy and high local optima avoidance, and outperformed four of the most common use machine learning algorithms, and GWO-MLP in terms of AUC.

Wind Speed Ensemble Forecasting Based on Deep Learning Using Adaptive Dynamic Optimization Algorithm

Abstract: The development and deployment of an effective wind speed forecasting technology can improve the safety and stability of power systems with significant wind penetration. Due to the wind’s unpredictable and unstable qualities, accurate forecasting of wind speed and power is extremely challenging. Several algorithms were proposed for this purpose to improve the level of forecasting reliability. The Long Short-Term Memory (LSTM) network is a common method for making predictions based on time series data. This paper proposed a machine learning algorithm, called Adaptive Dynamic Particle Swarm Algorithm (AD-PSO) combined with Guided Whale Optimization Algorithm (Guided WOA), for wind speed ensemble forecasting. The AD-PSO-Guided WOA algorithm selects the optimal hyperparameters value of the LSTM deep learning model for forecasting of wind speed. In experiments, a wind power forecasting dataset is employed to predict hourly power generation up to forty-eight hours ahead at seven wind farms. This case study is taken from the Kaggle Global Energy Forecasting Competition 2012 in wind forecasting. The results demonstrated that the AD-PSO-Guided WOA algorithm provides high accuracy and outperforms several comparative optimization and deep learning algorithms. Different tests’ statistical analysis, including Wilcoxon’s rank-sum and one-way analysis of variance (ANOVA), confirms the accuracy of the presented algorithm.

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

Abstract: Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review

Abstract: In the age of industry 4.0, deep learning has attracted increasing interest for various research applications. In recent years, deep learning models have been extensively implemented in machinery fault detection and diagnosis (FDD) systems. The deep architecture’s automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis (TFDD) systems. TFDD relies on manual feature selection, which requires prior knowledge of the data and is time intensive. However, the high performance of deep learning comes with challenges and costs. This paper presents a review of deep learning challenges related to machinery fault detection and diagnosis systems. The potential for future work on deep learning implementation in FDD systems is briefly discussed.

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

Abstract: 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 article 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.

MbGWO-SFS: Modified Binary Grey Wolf Optimizer Based on Stochastic Fractal Search for Feature Selection

Abstract: Grey Wolf Optimizer (GWO) simulates the grey wolves’ nature in leadership and hunting manners. GWO showed a good performance in the literature as a meta-heuristic algorithm for feature selection problems, however, it shows low precision and slow convergence. This paper proposes a Modified Binary GWO (MbGWO) based on Stochastic Fractal Search (SFS) to identify the main features by achieving the exploration and exploitation balance. First, the modified GWO is developed by applying an exponential form for the number of iterations of the original GWO to increase the search space accordingly exploitation and the crossover/mutation operations to increase the diversity of the population to enhance exploitation capability. Then, the diffusion procedure of SFS is applied for the best solution of the modified GWO by using the Gaussian distribution method for random walk in a growth process. The continuous values of the proposed algorithm are then converted into binary values so that it can be used for the problem of feature selection. To ensure the stability and robustness of the proposed MbGWO-SFS algorithm, nineteen datasets from the UCI machine learning repository are tested. The K-Nearest Neighbor (KNN) is used for classification tasks to measure the quality of the selected subset of features. The results, compared to binary versions of the-state-of-the-art optimization techniques such as the original GWO, SFS, Particle Swarm Optimization (PSO), hybrid of PSO and GWO, Satin Bowerbird Optimizer (SBO), Whale Optimization Algorithm (WOA), Multiverse Optimization (MVO), Firefly Algorithm (FA), and Genetic Algorithm (GA), show the superiority of the proposed algorithm. The statistical analysis by Wilcoxon’s rank-sum test is done at the 0.05 significance level to verify that the proposed algorithm can work significantly better than its competitors in a statistical way.