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5G Resource Allocation Using Feature Selection and Greylag Goose Optimization Algorithm

Computers, Materials & Continua 2024年第7期80卷 1179-1201页

作者： amel ali alhussan S.K.Towfek 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 BlacksburgVA 24060USA

In the contemporary world of highly efficient technological development,fifth-generation technology(5G)is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps).As far as the current implementations are concerned,they are at the level of slightly below 1 Gbps,but this allowed a great leap forward from fourth generation technology(4G),as well as enabling significantly reduced latency,making 5G an absolute necessity for applications such as gaming,virtual conferencing,and other interactive electronic *** of this change are not limited to connectivity alone;it urges operators to refine their business strategies and offers users better and improved digital *** essential factor is optimization and the application of artificial intelligence throughout the general arrangement of intricate and detailed 5G *** Binary Greylag Goose Optimization(bGGO)to achieve a significant reduction in the feature set while maintaining or improving model performance,leading to more efficient and effective 5G network management,and Greylag Goose Optimization(GGO)increases the efficiency of the machine ***,the model performs and yields more accurate *** work proposes a new method to schedule the resources in the next generation,5G,based on a feature selection using GGO and a regression model that is an ensemble of K-Nearest Neighbors(KNN),Gradient Boosting,and Extra Trees *** ensemble model shows better prediction performance with the coefficient of determination R squared value equal *** proposed framework is supported by several Statistical analyses,such as theWilcoxon signed-rank *** of the benefits of this study are the introduction of new efficient optimization algorithms,the selection of features and more reliable ensemble models which improve the efficiency of 5G technology.

关键词： Optimization ensemble learning 5G technology artificial intelligence greylag goose optimization ANOVA test

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An Improved Deep Learning Framework for Automated Optic Disc Localization and Glaucoma Detection

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1429-1457页

作者： Hela Elmannai Monia Hamdi Souham Meshoul amel ali alhussan Manel Ayadi amel Ksibi 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 Department of Information System College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Glaucoma disease causes irreversible damage to the optical nerve and it has the potential to cause permanent loss of *** ranks as the second most prevalent cause of permanent *** glaucoma diagnosis requires a highly experienced specialist,costly equipment,and a lengthy wait *** automatic glaucoma detection,state-of-the-art glaucoma detection methods include a segmentation-based method to calculate the cup-to-disc *** methods include multi-label segmentation networks and learning-based methods and rely on hand-crafted *** the optic disc(OD)is one of the key features in retinal images for detecting retinal diseases,especially for glaucoma disease *** approach presented in this study is based on deep classifiers for OD segmentation and glaucoma ***,the optic disc detection process is based on object detection using a Mask Region-Based Convolutional Neural Network(Mask-RCNN).The OD detection task was validated using the Dice score,intersection over union,and accuracy *** OD region is then fed into the second stage for glaucoma ***,considering only the OD area for glaucoma detection will reduce the number of classification artifacts by limiting the assessment to the optic disc *** this task,VGG-16(Visual Geometry Group),Resnet-18(Residual Network),and Inception-v3 were pre-trained and *** also used the Support Vector Machine *** feature-based method uses region content features obtained by Histogram of Oriented Gradients(HOG)and Gabor *** final decision is based on weighted fusion.A comparison of the obtained results from all classification approaches is *** metrics including accuracy and ROC curve are compared for each classification *** novelty of this research project is the integration of automatic OD detection and glaucoma diagnosis in a global ***,the fusion-based decision system uses the g

关键词： Optic disc glaucoma fundus image deep learning

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HybridWatermarking and Encryption Techniques for Securing Medical Images

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Computer Systems Science & Engineering 2023年第7期46卷 403-416页

作者： amel ali alhussan Hanaa A.Abdallah Sara Alsodairi Abdelhamied A.Ateya Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Engineering Zagazig UniversityZagazigSharqia44519Egypt

Securing medical data while transmission on the network is required because it is sensitive and life-dependent *** methods are used for protection,such as Steganography,Digital Signature,Cryptography,and *** paper introduces a novel robust algorithm that combines discrete wavelet transform(DWT),discrete cosine transform(DCT),and singular value decomposition(SVD)digital image-watermarking *** host image is decomposed using a two-dimensional DWT(2D-DWT)to approximate low-frequency sub-bands in the embedding *** the sub-band low-high(LH)is decomposed using 2D-DWT to four new *** resulting sub-band low-high(LH1)is decomposed using 2D-DWT to four new *** frequency bands,high-high(HH_(2))and high-low(HL_(2)),are transformed by DCT,and then the SVD is applied to the DCT *** strongest modified singular values(SVs)vary very little for most attacks,which is an important property of SVD *** two watermark images are encrypted using two layers of encryption,circular and chaotic encryption techniques,to increase *** first encrypted watermark is embedded in the S component of the DCT components of the HL_(2)*** second encrypted watermark is embedded in the S component of the DCT components of the HH2 *** suggested technique has been tested against various attacks and proven to provide excellent stability and imperceptibility results.

关键词： Watermarking discrete wavelet transform discrete cosine transform singular value decomposition circular encryption chaotic encryption

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

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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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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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Transfer Learning for Chest X-rays Diagnosis Using Dipper Throated Algorithm

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Computers, Materials & Continua 2022年第11期73卷 2371-2387页

作者： Hussah Nasser AlEisa El-Sayed M.El-kenawy amel ali alhussan Mohamed Saber Abdelaziz A.Abdelhamid Doaa Sami Khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta 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

Most children and elderly people worldwide die from pneumonia,which is a contagious illness that causes lung *** diagnosing pneumonia from chest X-ray images,many deep learning models have been put *** goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia *** varying the deep learning approach,three pre-trained models,GoogLeNet,ResNet18,and DenseNet121,are employed in this research to extract the main features of pneumonia and normal *** addition,the binary dipper throated optimization(DTO)algorithm is utilized to select the most significant features,which are then fed to the K-nearest neighbor(KNN)classifier for getting the final classification *** guarantee the best performance of KNN,its main parameter(K)is optimized using the continuous DTO *** test the proposed approach,six evaluation metrics were employed namely,positive and negative predictive values,accuracy,specificity,sensitivity,and ***,the proposed approach is compared with other traditional approaches,and the findings confirmed the superiority of the proposed approach in terms of all the evaluation *** minimum accuracy achieved by the proposed approach is(98.5%),and the maximum accuracy is(99.8%)when different test cases are included in the evaluation experiments.

关键词： Metaheuristic pneumonia dipper throated optimization KNN

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Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

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Computer Systems Science & Engineering 2023年第5期45卷 1775-1787页

作者： Abdelhamied A.Ateya amel ali alhussan Hanaa A.Abdallah Mona A.Al duailij Abdukodir Khakimov Ammar Muthanna Department of Electronics and Communications Engineering Zagazig UniversityZagazigSharqia44519Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Applied Probability and Informatics Peoples’Friendship University of Russia(RUDN University)Moscow117198Russia Center for Telecommunication Research School of Postgraduate Studies&ResearchSri Lanka Technological CampusPadukka10500Sri Lanka

Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability,availability,and ultra-low *** requirements of such networks are the main challenges that can be handled using a range of recent technologies,including multi-access edge computing(MEC),artificial intelligence(AI),millimeterwave communications(mmWave),and software-defined *** aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service(QoS).This article considers developing a complex MEC structure for fifth and sixth-generation(5G/6G)cellular ***,we propose a seamless migration technique for complex edge computing *** developed migration scheme enables services to adapt to the required load on the radio *** proposed algorithm is analyzed for various use cases,and a test bench has been developed to emulate the operator’s *** obtained results are introduced and discussed.

关键词： 5G 6G mobile edge computing migration offloading quality of service

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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

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