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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

Computer Modeling in Engineering & Sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong ali wagdy mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad ali wagdy mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema Kavitha L.Francis Raj Sandeep Kautish Abdulaziz S.Almazyad Karam M.Sallam ali wagdy mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine *** enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images *** image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye *** degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal *** proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between *** methodology was used to clarify the input images and make them adequate for the process of glaucoma *** objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were *** the peak regions were identified,the recurrence relationships among those peaks were then *** partitioning was done due to varying degrees of similar and dissimilar concentrations in the *** and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and *** distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural Network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 725-739页

作者： Puja S.Prasad Adepu Sree Lakshmi Sandeep Kautish Simar Preet Singh Rajesh Kumar Shrivastava Abdulaziz S.Almazyad Hossam M.Zawbaa ali wagdy mohamed CSE Department Geethanjali College of EngineeringHyderabadTelangana501301India Department of Computer Science LBEF CampusKathmandu44600Nepal School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia CeADAR Ireland’s Centre for AI Technological University DublinDublinD07 EWV4Ireland Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

Pupil dynamics are the important characteristics of face spoofing *** face recognition system is one of the most used biometrics for authenticating individual *** main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,*** proposed model uses pupil characteristics for liveness detection during the authentication *** pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light *** proposed framework consists of two-phase *** the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video *** above measurement is converted into feature space using Kohn and Clynes model-defined *** Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the *** the second phase,we perform the facial recognition ***-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional *** features are scale,rotation,and orientation invariant and are used for recognizing facial *** brute force matching algorithm is used for matching features of two different *** threshold value we considered is 0.08 for good *** analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Err

关键词： SIFT pupil CASIA-SURF pupillary light reflex replay attack dataset brute force

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong ali wagdy mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

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Computers, Materials & Continua 2022年第5期71卷 2847-2868页

作者： Prachi Agrawal Khalid Alnowibet ali wagdy mohamed Yogananda School of Artificial Intelligence Computers&Data ScienceShoolini UniversitySolan173229India Statistics and Operations Research Department College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Science and EngineeringThe American University in CairoEgypt

This paper presents a novel application of metaheuristic algorithmsfor solving stochastic programming problems using a recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithmis based on human behavior in which people gain and share their knowledgewith others. Different types of stochastic fractional programming problemsare considered in this study. The augmented Lagrangian method (ALM)is used to handle these constrained optimization problems by convertingthem into unconstrained optimization problems. Three examples from theliterature are considered and transformed into their deterministic form usingthe chance-constrained technique. The transformed problems are solved usingGSK algorithm and the results are compared with eight other state-of-the-artmetaheuristic algorithms. The obtained results are also compared with theoptimal global solution and the results quoted in the literature. To investigatethe performance of the GSK algorithm on a real-world problem, a solidstochastic fixed charge transportation problem is examined, in which theparameters of the problem are considered as random variables. The obtainedresults show that the GSK algorithm outperforms other algorithms in termsof convergence, robustness, computational time, and quality of obtainedsolutions.

关键词： Gaining-sharing knowledge based algorithm metaheuristic algorithms stochastic programming stochastic transportation problem

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Optimum Location of Field Hospitals for COVID-19: A Nonlinear Binary Metaheuristic Algorithm

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Computers, Materials & Continua 2021年第7期68卷 1183-1202页

作者： Said ali Hassan Khalid Alnowibet Prachi Agrawal ali wagdy mohamed Department of Operations Research and Decision Support Faculty of Computers and Artificial IntelligenceCairo UniversityGiza12613Egypt College of Science Department of Statistics and Operations ResearchKing Saud UniversityRiyadhSaudi Arabia Department of Mathematics and Scientific Computing National Institute of Technology HamirpurHimachal Pradesh177005India Department of Operations Research Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt

Determining the optimum location of facilities is critical in many fields,particularly in *** study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)*** used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear *** model is used to determine the optimum location of field hospitals for COVID-19 risk *** designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper *** this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is *** DBGSK algorithm is based on how humans acquire and share knowledge throughout their *** DBGSK algorithm mainly depends on two junior and senior binary *** two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space.

关键词： Facility location nonlinear binary model field hospitals for COVID-19 gaining-sharing knowledge-based metaheuristic algorithm

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Managing Delivery of Safeguarding Substances as a Mitigation Against Outbreaks of Pandemics

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Computers, Materials & Continua 2021年第7期68卷 1161-1181页

作者： Said ali Hassan Khalid Alnowibet Prachi Agrawal ali wagdy mohamed Department of Operations Research and Decision Support Faculty of Computers and Articial IntelligenceCairo UniversityGiza12613Egypt Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadhSaudi Arabia Trafc Safety Technologies Chair Vice Deanship of Scientic ResearchKing Saud UniversityRiyadhSaudi Arabia Department of Mathematics and Scientic Computing National Institute of TechnologyHamirpur177005India Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt School of Engineering and Applied Sciences Wireless Intelligent Networks Center(WINC)Nile UniversityGizaEgypt

The optimum delivery of safeguarding substances is a major part of supply chain management and a crucial issue in the mitigation against the outbreak of pandemics.A problem arises for a decision maker who wants to optimally choose a subset of candidate consumers to maximize the distributed quantities of the needed safeguarding substances within a specic time period.A nonlinear binary mathematical programming model for the problem is *** decision variables are binary ones that represent whether to choose a specic consumer,and design constraints are formulated to keep track of the chosen *** better illustrate the problem,objective,and problem constraints,a real application case study is *** case study involves the optimum delivery of safeguarding substances to several hospitals in the Al-Gharbia Governorate in *** hospitals are selected to represent the consumers of safeguarding substances,as they are the rst crucial frontline for mitigation against a pandemic outbreak.A distribution truck is used to distribute the substances from the main store to the hospitals in specied required quantities during a given working *** objective function is formulated in order to maximize the total amount of delivered quantities during the specied time *** case study is solved using a novel Discrete Binary Gaining Sharing Knowledge-based Optimization algorithm(DBGSK),which involves two main stages:discrete binary junior and senior gaining and sharing *** has the ability of nding the solutions of the introduced problem,and the obtained results demonstrate robustness and convergence toward the optimal solutions.

关键词： Safeguarding substances mitigation against pandemic outbreaks supply chain management metaheuristic optimization

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UAV-Aided Data Acquisition Using Gaining-Sharing Knowledge Optimization Algorithm

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Computers, Materials & Continua 2022年第9期72卷 5999-6013页

作者： Rania M Tawfik Hazem A.A.Nomer M.Saeed Darweesh ali wagdy mohamed Hassan Mostafa Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12677Egypt School of Information Technology and Computer Science(ITCS) Nile UniversityGiza12677Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Science and EngineeringThe American University in CairoEgypt Electronics and Communications Engineering Department Cairo UniversityEgypt University of Science and Technology Nanotechnology and Nanoelectronics ProgramZewal City of Science and TechnologyOctober Gardens6th of OctoberGiza12578Egypt

Unmanned Aerial Vehicles(UAVs)provide a reliable and energyefficient solution for data collection from the Narrowband Internet of Things(NB-IoT)***,the UAV’s deployment optimization,including locations of the UAV’s stop points,is a necessity to minimize the energy consumption of the UAV and the NB-IoT devices and also to conduct the data collection *** this regard,this paper proposes GainingSharing Knowledge(GSK)algorithm for optimizing the UAV’s *** GSK,the number of UAV’s stop points in the three-dimensional space is encapsulated into a single individual with a fixed length representing an entire *** superiority of using GSK in the tackled problem is verified by simulation in seven *** provides significant results in all seven scenarios compared with other four optimization algorithms used before with the same ***,the NB-IoT is proposed as the wireless communication technology between the UAV and IoT devices.

关键词： NB-IoT UAV GSK stop points optimization

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A Stochastic Flight Problem Simulation to Minimize Cost of Refuelling

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Computers, Materials & Continua 2021年第10期69卷 849-871页

作者： Said ali Hassan Khalid Alnowibet Miral H.Khodeir Prachi Agrawal Adel F.Alrasheedi ali wagdy mohamed Department of Operations Research and Decision Support Faculty of Computers and Artificial IntelligenceCairo UniversityGiza12613Egypt Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia Department of Mathematics and Scientific Computing National Institute of Technology HamirpurHimachal Pradesh177005India Department of Operations Research Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt

Commercial airline companies are continuously seeking to implement strategies for minimizing costs of fuel for their flight routes as acquiring jet fuel represents a significant part of operating and managing expenses for airline activities.A nonlinear mixed binary mathematical programming model for the airline fuel task is presented to minimize the total cost of refueling in an entire flight route *** model is enhanced to include possible discounts in fuel prices,which are performed by adding dummy variables and some restrictive constraints,or by fitting a suitable distribution function that relates prices to purchased *** obtained fuel plan explains exactly the amounts of fuel in gallons to be purchased from each airport considering tankering strategy while minimizing the pertinent cost of the whole flight *** relation between the amount of extra burnt fuel taken through tinkering strategy and the total flight time is also considered.A case study is introduced for a certain flight rotation in domestic US air transport *** mathematical model including stepped discounted fuel prices is *** problem has a stochastic nature as the total flight time is a random variable,the stochastic nature of the problem is realistic and more appropriate than the deterministic *** stochastic style of the problem is simulated by introducing a suitable probability distribution for the flight time duration and generating enough number of runs to mimic the probabilistic real *** similar real application problems are modelled as nonlinear mixed binary ones that are difficult to handle by exact ***,metaheuristic approaches are widely used in treating such different optimization *** this paper,a gaining sharing knowledge-based procedure is used to handle the mathematical *** algorithm basically based on the process of gaining and sharing knowledge throughout the human *** generated simulation runs of

关键词： Stochastic flight problem cost of refuelling ferrying strategy tankering discounted prices simulation procedure nonlinear mixed binary model metaheuristic algorithm gaining-sharing knowledge-based algorithm

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