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Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

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Computer Systems Science & Engineering 2023年第5期45卷 1469-1482页

作者： Doaa Sami Khafaga Amel Ali Alhussan abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mohamed Saber El-Sayed M.El-kenawy Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansouraEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Arrhythmia has been classified using a variety of *** of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more *** with cardiac arrhythmias can benefit from competent monitoring to save their *** arrhythmia classification and prediction have greatly improved in recent *** are a category of conditions in which the heart's electrical activity is abnormally rapid or *** year,it is one of the main reasons of mortality for both men and women,*** the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)*** proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia *** proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s *** purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia *** method of comprehensive arrhythmia classification outperforms earlier methods presented in the *** achieved classification accuracy using the proposed approach is 99.8%.

关键词： Feature selection electrocardiogram metaheuristics dipper throated algorithm

A Hierarchical Methodology for Performance Evaluation Based on Data Envelopment Analysis: The Case of Companies’ Competitiveness in an Economy

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American Journal of Operations Research 2011年第3期1卷 134-146页

作者： Mohamed Dia Fouad Ben abdelaziz 不详

In this research, we present a hierarchical Data Envelopment Analysis (DEA) methodology for competitiveness analysis. This methodology takes into account the heterogeneity of the decision making units (DMUs) as well as the diversity of the comparison criteria. We propose to homogenize the DMUs by grouping them hierarchically, which permits a better identification and definition of the criteria in each specific grouping. The methodology proceeds first by the determination of the performances or relative efficiencies, which are in turn aggregated into competitiveness indices in each grouping by the superiority index of [1];then, the overall competitiveness indices are determined additively along the hierarchical levels. We illustrate the methodology by a competitiveness analysis of several companies belonging to different sectors of activity in an economy, where are suggested ways of improvement for the non-competitive companies within their sectors and within the economy.

关键词： Data Envelopment Analysis Heterogeneity Hierarchies Competitiveness Analysis

Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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Computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

Qualification of Three Analytical Wake Models

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Journal of Mechanics Engineering and Automation 2016年第4期6卷 205-211页

作者： Naima Charhouni Mohammed Sallaou abdelaziz Arbaoui Mechanics and Integrated Engineering (M2I) department Moulay Ismail University ENSAM Meknes BP 4024 Morocco

The decrease of wind velocity （wake losses） in downstream area of wind turbine is generally quantified using wake models. The overall estimated power of wind farm varies according to reliability of wake model used, however it＇s unclear which model is most appropriate and able to give a high performance in predicting wind velocity deficit. In this subject, a qualification of three analytical wake models （Jensen, lshihara and Frandsen） based on three principal criteria is presented in this paper：（i） the parsimony which characterizes the inverse of model complexity, （ii） the accuracy of estimation in which wake model is compared with the experimental data and （iii） imprecision that is related to assumptions and uncertainty on the value of variables considered in each model. This qualitative analysis shows the inability of wake models to predict wind velocity deficit due to the big uncertainty of variables considered and it sensitivity to wind farm characteristic.

关键词： Wind farm wind turbine wake models parsimony accuracy imprecision.

Identification of an extracellular infection-induced glyceraldehyde-3-phosphate dehydrogenase of the phytopathogenic proteobacterium <i>Pseudomonas syringae</i>pv tomato DC3000

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Advances in Bioscience and Biotechnology 2014年第3期5卷 201-208页

作者： Bouchra Elkhalfi Aurelio Serrano abdelaziz Soukri Institute for Plant Biochemistry and Photosynthesis (IBVF) CSIC-Universidad de Sevilla Seville Spain Laboratory of Physiology & Genetics Molecular Faculty of Sci-ences Ain Chock Casablanca Morocco

According to molecular biology, genomic and proteomic data, the phytopathogenic gamma-proteobacterium Pseudomonas syringae pv. tomato DC3000 produces a number of proteins that may promote infection and draw nutrients from plants. Remarkably, P. syringae DC3000 strain possesses three paralogous gap genes encoding glyceraldehyde-3-phosphate dehydrogenase (GAPDH) enzymes with different predicted molecular sizes and metabolic functions. As GAPDH was shown to be a virulence factor in other microbial pathogens, in the current study, we analyzed the expression levels of each paralogous gap gene by realtime PCR to understand the actual impact of their protein products on P. syringae virulence. We found that all of them were strongly induced during the infection process. Nevertheless, proteomic analysis of culture supernatants revealed that only Class I GAPDH1 encoded by the gap1 gene was identified as an extracellular protein in infective cells. These results strongly suggest that this GAPDH should play a role in the infective process, including its well-know enzymatic function in the glycolytic metabolic pathway.

关键词： GAPDH Secretome MALDI-TOF Gene Expression Infective State Bacterial Speak RNA

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

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Computers, Materials & Continua 2023年第2期74卷 2379-2395页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Electrocardiogram(ECG)signal is a measure of the heart’s electrical ***,ECG detection and classification have benefited from the use of computer-aided systems by *** goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification *** addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall *** prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing ***,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA *** results confirmed the superiority and effectiveness of the proposed *** classification accuracy achieved by the proposed approach is(99.98%).

关键词： Electrocardiogram differential evolution algorithm dipper throated optimization neural networks

Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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Computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

Optimizing wind farm layout for enhanced electricity extraction using a new hybrid PSO-ANN method

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Global Energy Interconnection 2024年第3期7卷 254-269页

作者： Mariam El Jaadi Touria Haidi abdelaziz Belfqih Mounia Farah Atar Dialmy LAGES Laboratory Hassania School of Public Works(EHTP)Casablanca 20230Morocco LESE Laboratory National School of Electricity and Mechanics(ENSEM)Casablanca 20100Morocco LAGCET Laboratory Hassania School of Public Works(EHTP)Casablanca 20230Morocco

With the growing need for renewable energy,wind farms are playing an important role in generating clean power from wind *** best wind turbine architecture in a wind farm has a major influence on the energy extraction *** paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization(PSO)and artificial neural networks(ANNs).The PSO method was used to explore the solution space and develop preliminary turbine layouts,and the ANN model was used to fine-tune the placements based on the predicted energy *** proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical *** efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches,giving exciting prospects for developing more efficient and sustainable wind *** integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both ***,this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency.A notable increase in power generation is observed across various *** percentage increase in the power generation ranged from approximately 7.7%to 11.1%.Owing to its versatility and adaptability to site-specific conditions,the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.

关键词： Layout optimization Turbine placement Wind energy Hybrid optimization Particle swarm optimization Artificial neural networks Renewable energy Energy efficiency

Phytochemical and biological activities of Crataegus sinaica growing in Egypt

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Asian Pacific Journal of Tropical Medicine 2010年第4期3卷 257-261页

作者： Alaa Tawfeek Refaat Abdelaaty abdelaziz Shahat Nermine Ahmed Ehsan Nemat Yassin Faiza Hammouda Elsayed Abou Tabl Shams Imbabi Ismail Phytochemistry Department National Research CenterEgypt National Liver Institute Monofia UniversityEgypt Pharmacology Department National Research CenterEgypt Pharmacognosy Department Faculty of PharmacyCairo UniversityEgypt

Objective:To evaluate the cardiac activity and hepatoprotection of Crataegus sinaica (***).Methods:All the isolated compounds were isolated by open-column liquid chromatography(CC) using sephadex LH-20 as stationary *** of the column was performed with EtOH or *** phytochemical investigation of the young stem of *** for the first time together with the leaves and flowers lead to the isolation and identification of quercetin,hyperoside,vitexin-2"-0-rhamnoside,epicatechin,procyanidin B2 and procyanidins ***:Rats treated with the low and high dose of *** leaves with flowers extract showed 15%and 17%reduction in the heart rate,and reduction in the STsegment by 107%and 57%;*** T-amplitude was decreased by 59%of the high dose *** the other hand,the young steins and leaves with flowers extracts of *** on primary culture of rat hepatocytes monolayer indicated a hepatoprotection for the total extract,ethyl acetate,butanol,and chlor of orm fractions at 100μg/mL,75μg/mL,50μg/mL,and 25μg/mL; ***:The results of these chemical and biological studies suggest the use of *** growing in Egypt as a preventive drug against cardiovascular and hepatic diseases. The chemical studies suggest the use of woody young stems as a newly investigated bioactive *** extraction of unsaturated fatty acids from the seeds of the plant would serve as a good health and nutritive product.

关键词： Crataegus sinaica Cardiovascular Hepatoprotection Flavonoids Proanthocyanidins High performance liquid chromatography