Kareem Kamal A. Ghany
Other affiliations: Saudi Electronic University
Bio: Kareem Kamal A. Ghany is an academic researcher from Beni-Suef University. The author has contributed to research in topics: Swarm intelligence & Biometrics. The author has an hindex of 8, co-authored 23 publications receiving 215 citations. Previous affiliations of Kareem Kamal A. Ghany include Saudi Electronic University.
••02 Sep 2015
TL;DR: The proposed system for feature selection based on firefly algorithm (FFA) optimization proves advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.
Abstract: In this paper, a system for feature selection based on firefly algorithm (FFA) optimization is proposed. Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attributes. Redundant and irrelevant attributes might reduce the classification accuracy because of the large search space. The main goal of attribute reduction is to choose a subset of relevant attributes from a huge number of available attributes to obtain comparable or even better classification accuracy from using all attributes. A system for feature selection is proposed in this paper using a modified version of the firefly algorithm (FFA) optimization. The modified FFA algorithm adaptively balance the exploration and exploitation to quickly find the optimal solution. FFA is a new evolutionary computation technique, inspired by the flash lighting process of fireflies. The FFA can quickly search the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporate both classification accuracy and feature reduction size. The proposed system was tested on eighteen data sets and proves advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.
TL;DR: A new algorithm for ear recognition based on geometrical features extraction like (shape, mean, centroid and Euclidean distance between pixels) is presented, which is invariant to scaling, translation and rotation.
Abstract: The biometrics recognition has been paid more attention by people with the advancement of technology nowadays. The human ear is a perfect source of data for passive person identification. Ear seems to be a good candidate solution since ear is visible, their images are easy to take and structure of ear does not change radically over time. Ear satisfies biometric characteristic (universality, distinctiveness, permanence and collectability). In this paper we presented a new algorithm for ear recognition based on geometrical features extraction like (shape, mean, centroid and Euclidean distance between pixels). Firstly, we made a pre-processing phase by making all images have the same size. Then we used the snake model to detect the ear, and we applied median filter to remove noise, also we converted the images to binary format. After that we used canny edge and made some enhancement on the image, largest boundary is calculated and distance matrix is created then we extracted the image features. Finally, the extracted features were classified by using nearest neighbor with absolute error distance. This method is invariant to scaling, translation and rotation. The experimental results showed that the proposed approach gives better results and obtained over all accuracy almost 98%.
••01 Aug 2016
TL;DR: The proposed hybrid dragonfly algorithm (DA) with extreme learning machine (ELM) system for prediction problem is presented and proves the capability of the proposed DA-ELM model in searching for optimal feature combinations in feature space to enhance ELM generalization ability and prediction accuracy.
Abstract: In this work, a proposed hybrid dragonfly algorithm (DA) with extreme learning machine (ELM) system for prediction problem is presented. ELM model is considered a promising method for data regression and classification problems. It has fast training advantage, but it always requires a huge number of nodes in the hidden layer. The usage of a large number of nodes in the hidden layer increases the test/evaluation time of ELM. Also, there is no guarantee of optimality of weights and biases settings on the hidden layer. DA is a recently promising optimization algorithm that mimics the moving behavior of moths. DA is exploited here to select less number of nodes in the hidden layer to speed up the performance of the ELM. It also is used to choose the optimal hidden layer weights and biases. A set of assessment indicators is used to evaluate the proposed and compared methods over ten regression data sets from the UCI repository. Results prove the capability of the proposed DA-ELM model in searching for optimal feature combinations in feature space to enhance ELM generalization ability and prediction accuracy. The proposed model was compared against the set of commonly used optimizers and regression systems. These optimizers are namely, particle swarm optimization (PSO) and genetic algorithm (GA). The proposed DA-ELM model proved an advance overall compared methods in both accuracy and generalization ability.
TL;DR: In this article, a model based on Long Short-Term Memory (LSTM) with ten hidden units (neurons) was proposed to predict COVID-19 confirmed and death cases.
Abstract: Coronavirus-19 (COVID-19) is the black swan of 2020. Still, the human response to restrain the virus is also creating massive ripples through different systems, such as health, economy, education, and tourism. This paper focuses on research and applying Artificial Intelligence (AI) algorithms to predict COVID-19 propagation using the available time-series data and study the effect of the quality of life, the number of tests performed, and the awareness of citizens on the virus in the Gulf Cooperation Council (GCC) countries at the Gulf area. So we focused on cases in the Kingdom of Saudi Arabia (KSA), United Arab of Emirates (UAE), Kuwait, Bahrain, Oman, and Qatar. For this aim, we accessed the time-series real-datasets collected from Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). The timeline of our data is from January 22, 2020 to January 25, 2021. We have implemented the proposed model based on Long Short-Term Memory (LSTM) with ten hidden units (neurons) to predict COVID-19 confirmed and death cases. From the experimental results, we confirmed that KSA and Qatar would take the most extended period to recover from the COVID-19 virus, and the situation will be controllable in the second half of March 2021 in UAE, Kuwait, Oman, and Bahrain. Also, we calculated the root mean square error (RMSE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and death cases are 320.79 and 1.84, respectively, and both are related to Bahrain. While the worst values are 1768.35 and 21.78, respectively, and both are related to KSA. On the other hand, we also calculated the mean absolute relative errors (MARE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and deaths cases are 37.76 and 0.30, and these are related to Kuwait and Qatar respectively. While the worst values are 71.45 and 1.33, respectively, and both are related to KSA.
06 Jul 2012
TL;DR: In this article, an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019) is presented, and a categorical list of more than a hundred metaheuristics algorithms is presented.
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.
01 Jan 2016
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01 Jan 2019
TL;DR: A novel binary version of whale optimization algorithm (bWOA) is proposed to select the optimal feature subset for dimensionality reduction and classifications problem based on a sigmoid transfer function (S-shape).
Abstract: Whale optimization algorithm is one of the recent nature-inspired optimization technique based on the behavior of bubble-net hunting strategy. In this paper, a novel binary version of whale optimization algorithm (bWOA) is proposed to select the optimal feature subset for dimensionality reduction and classifications problem. The new approach is based on a sigmoid transfer function (S-shape). By dealing with the feature selection problem, a free position of the whale must be transformed to their corresponding binary solutions. This transformation is performed by applying an S-shaped transfer function in every dimension that defines the probability of transforming the position vectors’ elements from 0 to 1 and vice versa and hence force the search agents to move in a binary space. K-NN classifier is applied to ensure that the selected features are the relevant ones. A set of criteria are used to evaluate and compare the proposed bWOA-S with the native one over eleven different datasets. The results proved that the new algorithm has a significant performance in finding the optimal feature.
TL;DR: In this article, a wavelet-hybrid artificial neural network (ANN) model integrated with iterative input selection algorithm (IIS-W-ANN) is evaluated for its statistical preciseness in forecasting monthly streamflow, and it is then benchmarked against M5 Tree model.
Abstract: Forecasting streamflow is vital for strategically planning, utilizing and redistributing water resources. In this paper, a wavelet-hybrid artificial neural network (ANN) model integrated with iterative input selection (IIS) algorithm (IIS-W-ANN) is evaluated for its statistical preciseness in forecasting monthly streamflow, and it is then benchmarked against M5 Tree model. To develop hybrid IIS-W-ANN model, a global predictor matrix is constructed for three local hydrological sites (Richmond, Gwydir, and Darling River) in Australia's agricultural (Murray-Darling) Basin. Model inputs comprised of statistically significant lagged combination of streamflow water level, are supplemented by meteorological data (i.e., precipitation, maximum and minimum temperature, mean solar radiation, vapor pressure and evaporation) as the potential model inputs. To establish robust forecasting models, iterative input selection (IIS) algorithm is applied to screen the best data from the predictor matrix and is integrated with the non-decimated maximum overlap discrete wavelet transform (MODWT) applied on the IIS-selected variables. This resolved the frequencies contained in predictor data while constructing a wavelet-hybrid (i.e., IIS-W-ANN and IIS-W-M5 Tree) model. Forecasting ability of IIS-W-ANN is evaluated via correlation coefficient (r), Willmott's Index (WI), Nash–Sutcliffe Efficiency (ENS), root-mean-square-error (RMSE), and mean absolute error (MAE), including the percentage RMSE and MAE. While ANN models are seen to outperform M5 Tree executed for all hydrological sites, the IIS variable selector was efficient in determining the appropriate predictors, as stipulated by the better performance of the IIS coupled (ANN and M5 Tree) models relative to the models without IIS. When IIS-coupled models are integrated with MODWT, the wavelet-hybrid IIS-W-ANN and IIS-W-M5 Tree are seen to attain significantly accurate performance relative to their standalone counterparts. Importantly, IIS-W-ANN model accuracy outweighs IIS-ANN, as evidenced by a larger r and WI (by 7.5% and 3.8%, respectively) and a lower RMSE (by 21.3%). In comparison to the IIS-W-M5 Tree model, IIS-W-ANN model yielded larger values of WI = 0.936–0.979 and ENS = 0.770–0.920. Correspondingly, the errors (RMSE and MAE) ranged from 0.162–0.487 m and 0.139–0.390 m, respectively, with relative errors, RRMSE = (15.65–21.00) % and MAPE = (14.79–20.78) %. Distinct geographic signature is evident where the most and least accurately forecasted streamflow data is attained for the Gwydir and Darling River, respectively. Conclusively, this study advocates the efficacy of iterative input selection, allowing the proper screening of model predictors, and subsequently, its integration with MODWT resulting in enhanced performance of the models applied in streamflow forecasting.
TL;DR: In this paper, a hybrid Support Vector Machine (SVM) combined with Firefly Algorithm (FFA) techniques was used to predict the soil capacity and permanent wilting point (PWP) using easily available soil properties.
Abstract: Soil field capacity (FC) and permanent wilting point (PWP) are significant parameters in numerous biophysical models and agricultural activities. Although these parameters can be measured directly, their measurements are quite expensive. The purpose of this study was to develop a hybrid Support Vector Machine (SVM) combined with Firefly Algorithm (FFA) techniques (SVM-FFA) to predict the FC and PWP using some easily available soil properties. The data consist of 215 soil samples collected from different horizons of soil profiles located in the East Azerbaijan provinces, North-west of Iran. Several important parameters, including the sand,silt, clay, bulk density, and organic matter content were used as inputs, while the soil FC and PWP were the output parameters. The predictions from the SVM-FFA model were compared with SVM and artificial neural network (ANN) models. The model results were compared with regard to root mean square error (RMSE), correlation coefficient (CC) and relative root mean square error (RRMSE). A comparison of models indicated that the SVM-FFA model predicted better than SVM and ANN models with RMSE = 2.402%, CC = 0.972, RRMSE = 7.677% for FC and RMSE = 1.720%, CC = 0.969, RRMSE = 5.512% for PWP in the training data set while RMSE = 2.873%, CC = 0.962, RRMSE = 8.745% for FC and RMSE = 1.935%, CC = 0.965, RRMSE = 10.619% for PWP were obtained in the testing data set.