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Assessment of groundwater suitability for different activities in Toshka district,south Egypt

Journal of Groundwater Science and Engineering 2024年第1期12卷 34-48页

作者： marwa m Aly Shymaa AK Fayad ahmed mI Abd elhamid Faculty of Engineering mataria Helwan UniversityCairo 11718Egypt Division of Engineering International Academy for Engineering and Media Science(IAEMS)6th of October City 15515Egypt Hydraulics Research Institute National Water Research CenterMWRICairo 13621Egypt

Globally,groundwater has globally emerged as a crucial freshwater source for domestic,irrigation,and industrial *** evaluation of groundwater quality in the Toshka region is imperative to ensure its suitability for the extensive agricultural and industrial activities underway in this promising,groundwater-dependent development *** is particularly significant as Egypt increasingly relies on groundwater reserves to address freshwater deficits and to implement mega-development projects in barren *** this study,fifty-two samples were collected from the recently drilled wells tapping into the Nubian Sandstone Aquifer(NSA)in the Toshka *** quality was assessed through hydrochemical analysis,Piper diagram,and various indicators such as Na%,SAR,RSC,KR,mH and *** hydrochemical analysis revealed improved groundwater quality characteristics,attributed to continuous recharge from Lake *** Piper diagram categorised most of the water samples as"secondary salinity"water *** all wells proved suitable for irrigation with only two wells unsuitable based on mH values and six wells based on KR *** Total Hardness(TH)values,all samples were classified as"Soft",indicating their suitability for domestic and industrial *** Quality Index(WQI)results concluded that all samples met WHO and FAO guidelines for drinking and irrigation,*** distribution maps,constructed using GIS,facilitate the interpretation of the *** monitoring of quality parameters is essential to detect any deviation from permissible limits.

关键词： Nubian Sandstone Aquifer Water Quality Assessment Hydro-chemical Analysis Irrigation Quality Indicators Domestic Use Water Quality Index

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microstructure and mechanical properties of hot extruded 6016 aluminum alloy/graphite composites

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Journal of materials Science & Technology 2018年第9期34卷 1580-1591页

作者： mohamed m.el-Sayed Seleman mohamed m.Z.ahmed Sabbah Ataya Department of metallurgical and materials Engineering Faculty of Petroleum and Mining Engineering Suez University 43518 Suez Egypt Suez and Sinai metallurgical and materials Research Center of Scientific Excellence (SSmmR-CSE) 43518 Suez Egypt The British University in Egypt Mechanical Engineering Deportment El-Sherouk City 11837 Cairo Egypt Department of mechanical Engineering College of Engineering AI Imam Mohammad Ibn Saud Islamic University Riyadh 11432 saudi Arabia

The incorporation of graphite particles into AA6016 aluminum alloy matrix to fabricate metal/ceramic composites is still a great challenge and various parameters should be considered. In this study, dense AA6016 aluminum alloy/（0-20 wt%） graphite composites have successfully been fabricated by powder metallurgy process. At first, the mixed aluminum and graphite powders were cold compacted at 200 mPa and then sintered at 500 ℃ for 1 h followed by hot extrusion at 450 ℃. The influence of ceramic phases（free graphite and in-situ formed carbides） on microstructure, physical and mechanical properties of the produced composites were finally investigated. The results show that the fabricated composites have a relative density of over 98%. SEm observations indicate that the graphite has a good dispersion in the alloy matrix even at high graphite content. Hardness of all the produced composites was higher than that of aluminum alloy matrix. No cracks were observed at strain less than 23% for all hot extruded *** strength, reduction in height, ultimate tensile stress, fracture stress, yield stress, and fracture strain of all Al/graphite composites were determined by high precision second order equations. Both compressive and ultimate tensile strengths have been correlated to microstructure constituents with focusing on the in-situ formed ceramic phases, silicon carbide（SiC） and aluminum carbide（Al4 C3）. The ductile fracture mode of the produced composites became less dominant with increasing free graphite content and in-situ formed carbides. Wear resistance of Al/graphite composites was increased with increasing graphite content. Aluminum/20 wt% graphite composite exhibited superior wear resistance over that of AA6016 aluminum alloy.

关键词： AA6016/graphite composites Hot extrusion In-situ carbides mechanical properties Wear resistance Fracture behavior

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Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

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Computer modeling in Engineering & Sciences 2024年第8期140卷 1539-1562页

作者： Samar m.Alqhtani Toufique A.Soomro Faisal Bin Ubaid ahmed Ali muhammad Irfan Abdullah A.Asiri Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of electronic Engineering The University of LarkanoLarkanaSindh75660Pakistan Computer Science Department Sukkur IBA UniversitySukkurSindh65200Pakistan eletrical Engineering Department Sukkur IBA UniversitySukkurSindh65200Pakistan electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various *** resonance imaging(mRI)and computed tomography(CT)are utilized to capture brain *** plays a crucial role in the diagnosis of brain tumors and the examination of other brain ***,manual assessment of mRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely ***,early diagnosis of brain tumors is intricate,necessitating the use of computerized *** research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain *** proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-means(FCm)clustering technique in the third *** final step involves classification using the Support Vector machine(SVm)*** classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced magnetic Resonance Imaging(CE-mRI)*** proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of ***,this method exhibits a shorter processing time of 0.44 s compared to existing *** performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and *** enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classi

关键词： Brain tumor magnetic resonance imaging principal component analysis fuzzy c-clustering support vector machine

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Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

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Intelligent Automation & Soft Computing 2023年第8期37卷 2117-2132页

作者： Doaa Sami Khafaga el-Sayed m.el-kenawy Amel Ali Alhussan marwa m.Eid 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 TechnologyMansouraEgypt

The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and ***,the accurate prediction can be realized using the recent advances in machine learning and predictive *** research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTm)*** proposed optimization algorithm is used to optimize the parameters of the LSTm-based model to boost its forecasting *** optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic *** prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTm-based model,and the results are compared with that of the proposed *** results show that the proposed DDTOSFS+LSTm can accurately forecast the energy consumption with root mean square error RmSE of 0.00013,which is the best among the recorded results of the other *** addition,statistical experiments are conducted to prove the statistical difference of the proposed *** results of these tests confirmed the expected outcomes.

关键词： Stochastic fractal search dipper throated optimization energy consumption long short-term memory prediction models

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An Optimized Ensemble model for Prediction the Bandwidth of metamaterial Antenna

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Computers, materials & Continua 2022年第4期71卷 199-213页

作者： Abdelhameed Ibrahim Hattan F.Abutarboush Ali Wagdy mohamed mohamad Fouad el-Sayed m.el-kenawy Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt electrical Engineering Department College of EngineeringTaibah UniversityMedina42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyEgypt

metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their *** size affects the quality factor and the radiation loss of the *** antennas can overcome the limitation of bandwidth for small *** learning(mL)model is recently applied to predict antenna *** can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated *** accuracy of the prediction depends mainly on the selected *** models combine two or more base models to produce a better-enhanced *** this paper,a weighted average ensemble model is proposed to predict the bandwidth of the metamaterial *** base models are used namely:multilayer Perceptron(mLP)and Support Vector machines(SVm).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the *** Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base *** proposed model is compared with three based models and the average ensemble *** results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

关键词： metamaterial antenna machine learning ensemble model multilayer perceptron support vector machines

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Novel Optimized Feature Selection Using metaheuristics Applied to Physical Benchmark Datasets

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Computers, materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga el-Sayed m.el-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid 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 School of Computer Science University of Petroleum and Energy StudiesDehradun248001India 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

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(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 hybrid GWO and genetic algorithm 4028 CmC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

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Computer modeling in Engineering & Sciences 2024年第10期141卷 525-543页

作者： mohamed Abdel-mongy mudassir Iqbal m.Farag ahmed.m.Yosri Fahad Alsharari Saif eldeen A.S.Yousef Department of Civil Engineering College of EngineeringJouf UniversitySakaka72388Saudi Arabia Department of Civil Engineering University of Engineering and TechnologyPeshawar54890Pakistan Civil Engineering Department Faculty of EngineeringAl-Azhar UniversityCairo11884Egypt Civil Engineering Department Faculty of EngineeringAswan UniversityAswan81528Egypt

Alkali-activated materials/geopolymer(AAms),due to their low carbon emission content,have been the focus of recent studies on ecological *** terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part ***,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely ***,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingmATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part *** database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing *** performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(mAE),and rootmean square error(RmSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed *** and parametric analysis were also performed to identify the significant contributor to *** to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 *** water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.

关键词： Artificial intelligence techniques one-part geopolymer artificial neural network gene expression modelling sustainable construction polymers

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Optimized Weighted Ensemble Using Dipper Throated Optimization Algorithm in metamaterial Antenna

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Computers, materials & Continua 2022年第12期73卷 5771-5788页

作者： Doaa Sami Khafaga el-Sayed m.el-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz AAbdelhamid 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

metamaterial Antennas are a type of antenna that uses metamaterial to enhance *** bandwidth restriction associated with small antennas can be solved using metamaterial *** learning is gaining popularity as a way to improve solutions in a range of *** learning approaches are currently a big part of current research,and they’re likely to be huge in the *** model utilized determines the accuracy of the prediction in large *** goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and *** basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),multi-Layer Perceptron(mLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)*** choose the best features from the dataset,the binary(bDTO)algorithm is *** proposed ensemble model is compared to the base models and results are recorded and analyzed *** addition,two other ensembles are incorporated in the conducted experiments for *** ensembles are average ensemble and K-nearest neighbors(KNN)-based *** comparison is performed in terms of eleven evaluation *** evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.

关键词： metamaterial antenna dipper throated optimization feature selection parameters prediction

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Dipper Throated Optimization for Detecting Black-Hole Attacks inmANETs

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Computers, materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel el-Sayed m.el-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology 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 Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(mANET)continues to be in demand for additional debate and *** more mANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,mANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost mANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-mLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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machine Learning Techniques for Detecting Phishing URL Attacks

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Computers, materials & Continua 2023年第4期75卷 1271-1290页

作者： Diana T.mosa mahmoud Y.Shams Amr AAbohany el-Sayed m.el-kenawy m.Thabet Department of Cyber Security College of Engineering and Information TechnologyBuraydah Private CollegesBuraydah51418Kingdom of Saudi Arabia Faculty of Computers and Information Kafrelsheikh UniversityKafrelsheikh33516Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33516Egypt Delta Higher Institute of Engineering and Technology Mansoura35111Egypt Faculty of Computers and Information Fayoum UniversityFayoumEgypt

Cyber Attacks are critical and destructive to all industry *** affect social engineering by allowing unapproved access to a Personal Computer(PC)that breaks the corrupted system and threatens *** defense of security requires understanding the nature of Cyber Attacks,so prevention becomes easy and accurate by acquiring sufficient knowledge about various features of Cyber ***-Security proposes appropriate actions that can handle and block attacks.A phishing attack is one of the cybercrimes in which users follow a link to illegal websites that will persuade them to divulge their private *** of the online security challenges is the enormous number of daily transactions done via phishing *** Cyber-Security have a priority for all organizations,Cyber-Security risks are considered part of an organization’s risk management *** paper presents a survey of different modern machine-learning approaches that handle phishing problems and detect with high-quality accuracy different phishing attacks.A dataset consisting of more than 11000 websites from the Kaggle dataset was utilized and studying the effect of 30 website features and the resulting class label indicating whether or not it is a phishing website(1 or−1).Furthermore,we determined the confusion matrices of machine Learning models:Neural Networks(NN),Na飗e Bayes,and Adaboost,and the results indicated that the accuracies achieved were 90.23%,92.97%,and 95.43%,respectively.

关键词： Cyber security phishing attack URL phishing online social networks machine learning

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