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Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

Computers, Materials & Continua 2024年第7期80卷 1163-1177页

作者： Amel Ali Alhussan doaa sami khafaga El-Sayed M.El-kenawy Marwa M.Eid Abdelhameed Ibrahim 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 for Engineering and TechnologyMansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric *** demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of *** establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this ***,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of *** the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging *** is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic *** vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering ***,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature ***,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic *** on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is *** results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algori

关键词： Electric vehicle graylag goose optimization metaheuristics optimization machine learning

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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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Computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel doaa sami khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

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Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

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Computers, Materials & Continua 2024年第6期79卷 3549-3568页

作者： Reem Ibrahim Alkanhel doaa sami khafaga Ahmed Mohamed Zaki Marwa M.Eid Abdyalaziz A.Al-Mooneam Abdelhameed Ibrahim S.K.Towfek Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science and Intelligent Systems Research Center BlacksburgVirginia24060USA Delta Higher Institute for Engineering and Technology Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Artificial Intelligence and Computational Intelligence Research Center MeridenConnecticut06450USA School of ICT Faculty of EngineeringDesign and Information&Communications Technology(EDICT)Bahrain PolytechnicP.O.Box 33349Isa TownBahrain

The networks of wireless sensors provide the ground for a range of applications,including environmental moni-toring and industrial *** the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network *** infrastructure planning should be focused on increasing performance,and it should be affected by the detailed data about node *** work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and *** using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization,our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and *** implementing adaptive protocols according to varying environments and sensor constraints,our study aspires to improve overall network *** compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization,Particle Swarm Optimization,Genetic Algorithms,and Whale Optimization about performance on real-world *** the most efficient in the optimization process,Biruni displays the lowest error rate at *** two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific *** to the multi-faceted support the comprehensive approach promotes,there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be *** delivering better performance and reliability for various in-situ applications,this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

关键词： Wireless sensor networks optimization ARIMA model BER algorithm metaheuristic algorithms

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Optical Neural Networks:Analysis and Prospects for 5G Applications

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Computers, Materials & Continua 2023年第12期77卷 3723-3740页

作者： doaa sami khafaga Zongming Lv Imran Khan Shebnam M.Sefat Amel Ali Alhussan Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia School of Information and Finance Xuancheng Vocational&Technical CollegeXuancheng242000China Department of Electrical Engineering University of Engineering and TechnologyP.O.Box 814PeshawarPakistan Islamic University Centre for Scientific Research The Islamic UniversityNajafIraq Department of Computer Science Independent UniversityDhaka1208Bangladesh

With the capacities of self-learning,acquainted capacities,high-speed looking for ideal arrangements,solid nonlin-ear fitting,and mapping self-assertively complex nonlinear relations,neural systems have made incredible advances and accomplished broad application over the final *** one of the foremost conspicuous methods for fake insights,neural systems are growing toward high computational speed and moo control *** to the inborn impediments of electronic gadgets,it may be troublesome for electronic-implemented neural systems to make the strides these two exhibitions *** neural systems can combine optoelectronic procedures and neural organization models to provide ways to break the *** paper outlines optical neural networks of feedforward repetitive and spiking models to give a clearer picture of history,wildernesses,and future optical neural *** framework demonstrates neural systems in optic communication with the serial and parallel *** graphene-based laser structure for fiber optic communication is *** comparison of different balance plans for photonic neural systems is made within the setting of hereditary calculation and molecule swarm *** expansion,the execution comparison of routine photonic neural,time-domain with and without extending commotion is additionally *** challenges and future patterns of optical neural systems on the growing scale and applications of in situ preparing nonlinear computing will hence be uncovered.

关键词： Optoelectronics 5G networks free-space optical transducer neural networks

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Deep Learning for Depression Detection Using Twitter Data

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Intelligent Automation & Soft Computing 2023年第5期36卷 1301-1313页

作者： doaa sami khafaga Maheshwari Auvdaiappan KDeepa Mohamed Abouhawwash Faten Khalid Karim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering M.Kumarasamy College of EngineeringKarur639113India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI48824USA

Today social media became a communication line among people to share their happiness,sadness,and anger with their *** is necessary to know people’s emotions are very important to identify depressed people from their *** depression detection helps to save people’s lives and other dangerous mental *** are many intelligent algorithms for predicting depression with high accuracy,but they lack the deﬁnition of such *** machine learning methods help to identify depressed *** the accuracy of existing methods was not *** overcome this issue,the deep learning method is used in the proposed method for depression *** this paper,a novel Deep Learning Multi-Aspect Depression Detection with Hierarchical Atten-tion Network(MDHAN)is used for classifying the depression ***,the Twitter data was preprocessed by tokenization,punctuation mark removal,stop word removal,stemming,and *** Adaptive Particle and grey Wolf optimization methods are used for feature *** MDHAN classiﬁes the Twitter data and predicts the depressed and non-depressed ***,the proposed method is compared with existing methods such as Convolutional Neur-al Network(CNN),Support Vector Machine(SVM),Minimum Description Length(MDL),and *** suggested MDH-PWO architecture gains 99.86%accuracy,more signiﬁcant than frequency-based deep learning models,with a lower false-positive *** experimental result shows that the proposed method achieves better accuracy,precision,recall,and *** also mini-mizes the execution time.

关键词： Depression detection twitter data tweets deep learning swarm intelligence multi-aspect depression detection prediction

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Space Division Multiple Access for Cellular V2X Communications

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Computers, Materials & Continua 2022年第10期73卷 1195-1206页

作者： doaa sami khafaga Mohammad Zubair Khan Muhammad Awais Javed Amel Ali Alhussan Wael Said Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Information Taibah University Madinah Saudi Arabia Department of Electrical and Computer Engineering COMSATS University Islamabad45550Pakistan Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazig44511Egypt

Vehicular communication is the backbone of future Intelligent Transportation Systems(ITS).It offers a network-based solution for vehicle safety,cooperative awareness,and traffic management *** safety applications,Basic Safety Messages(BSM)containing mobility information is shared by the vehicles in their neighborhood to continuously monitor other nearby vehicles and prepare a local traffic *** are shared using mode 4 of Cellular V2X(C-V2X)communications in which resources are allocated in an ad hoc ***,the strict packet transmission requirements of BSM and hidden node problem causes packet collisions in a vehicular network,thus reducing the reliability of safety ***,as vehicles choose the transmission resources in a distributed manner in mode 4 of CV2X,the packet collision problem is further *** paper presents a novel solution in the form of a Space Division Multiple Access(SDMA)protocol that intelligently schedules BSM transmissions using vehicle position data to reduce concurrent transmissions from hidden node *** proposed protocol works by dividing road segments into clusters and *** sub-frames are allocated to a cluster and these sub-frames are reused after a certain *** a cluster,sub-channels are allocated to *** implement the proposed SDMA protocol and evaluate its performance in a highway vehicular *** results show that the proposed SDMA protocol outperforms standard Sensing-Based Semi Persistent Scheduling(SB-SPS)in terms of safety range and packet delay.

关键词： ITS vehicular network cellular V2X SDMA

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An Improved Deep Structure for Accurately Brain Tumor Recognition

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Computer Systems Science & Engineering 2023年第8期46卷 1597-1616页

作者： Mohamed Maher Ata Reem N.Yousef Faten Khalid Karim doaa sami khafaga Department of Communications and Electronics Engineering MISR Higher Institute for Engineering and TechnologyMansoura35516Egypt Department of Electronics and Communications Delta Higher Institute for Engineering and TechnologyMansoura35516Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Brain neoplasms are recognized with a biopsy,which is not commonly done before decisive brain *** using Convolutional Neural Networks(CNNs)and textural features,the process of diagnosing brain tumors by radiologists would be a noninvasive *** paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning *** proposed model extracts Gray Level Co-occurrence Matrix(GLCM)textural features from MRI brain tumor ***,a deep neural network(DNN)model has been proposed to select the most salient features from the ***,it manipulates the extraction of the additional high levels of salient features from a proposed CNN ***,a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition *** common datasets have been applied and tested,Br35H and FigShare *** first dataset contains binary labels,while the second one splits the brain tumor into four classes;glioma,meningioma,pituitary,and no ***,several performance metrics have been evaluated from both datasets,including,accuracy,sensitivity,specificity,F-score,and training *** results show that the proposed methodology has achieved superior performance compared with the current state of art *** proposed system has achieved about 98.22%accuracy value in the case of the Br35H dataset however,an accuracy of 98.01%has been achieved in the case of the FigShare dataset.

关键词： Brain tumor convolutional neural network gray level co-occurrence matrix noninvasive FigShare dataset Br35H dataset

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Wireless Network Security Using Load Balanced Mobile Sink Technique

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Intelligent Automation & Soft Computing 2023年第2期35卷 2135-2149页

作者： Reem Alkanhel Mohamed Abouhawwash S.N.Sangeethaa K.Venkatachalam doaa sami khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA Department of Computer Science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bengaluru560074India Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent *** the most signiﬁcant resources in the WSN are battery power and *** stra-tegies improve the power factor and secure the WSN *** takes more electricity to forward data in a *** numerous clustering methods have been developed to provide energy consumption,there is indeed a risk of unequal load balancing,resulting in a decrease in the network’s lifetime due to network inequalities and less *** possibilities arise due to the cluster head’s limited life *** cluster heads(CH)are in charge of all activities and con-trol intra-cluster and inter-cluster *** proposed method uses Lifetime centric load balancing mechanisms(LCLBM)and Cluster-based energy optimiza-tion using a mobile sink algorithm(CEOMS).LCLBM emphasizes the selection of CH,system architectures,and optimal distribution of *** addition,the LCLBM was added with an assistant cluster head(ACH)for load *** consumption,communications latency,the frequency of failing nodes,high security,and one-way delay are essential variables to consider while evaluating *** will choose a cluster leader based on the inﬂuence of the fol-lowing parameters on the energy balance of *** to simulatedﬁnd-ings,the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability,improves the network’s lifetime,decreases data latency,and bal-ances network capacity.

关键词： Wireless sensor network load balancing mechanism optimization power consumption network’s lifetime

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Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

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Intelligent Automation & Soft Computing 2023年第3期35卷 2773-2787页

作者： Amal H.Alharbi S.Karthick K.Venkatachalam Mohamed Abouhawwash doaa sami khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Electrical Electronics and Communication Engineering GITAM School of TechnologyGITAM Deemed to be UniversityBengaluru CampusKarnataka560065India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura 35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Recent security applications in mobile technologies and computer sys-tems use face recognition for high-end *** numerous security tech-niques,face recognition is considered a high-security *** fuse and carry out face identiﬁcation as an access authority into these ***,face identiﬁcation authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized *** the existing spooﬁng detection algorithm,there was some loss in the recreation of *** research proposes an unobtrusive technique to detect face spooﬁng attacks that apply a single frame of the sequenced set of frames to overcome the above-said *** research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused ***,this pro-posed method is tested with the Cross-ethnicity Face Anti-spooﬁng(CASIA),Fetal alcohol spectrum disorders(FASD)*** database has three models of attacks:distorted photographs in printed form,photographs with removed eyes portion,and video *** images are taken with three different quality cameras:low,average,and high-quality real and spoofed *** extensive experimental study was performed with CASIA-FASD,3 Diagnostic Machine Aid-Digital(DMAD)dataset that proved higher results when compared to existing algorithms.

关键词： Image processing edge detection edge net auto-encoder face authentication digital security

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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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