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Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

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computers, Materials & Continua 2022年第8期72卷 2783-2795页

作者： Fadwa Alrowais Ahmed S.Almasoud Radwa Marzouk Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammed Rizwanullah Abdelwahed Motwakel Ishfaq Yaseen Department of computer sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of information Systems College of Computer and Information SciencesPrince Sultan UniversityRafha StreetRiyadh11586Saudi Arabia Department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of computer Science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of information Systems Faculty of Computer and Information TechnologySana’a University61101Yemen Department of computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Mobile edge computing(MEC)provides effective cloud services and functionality at the edge device,to improve the quality of service(QoS)of end users by offloading the high computation ***,the introduction of deep learning(DL)and hardware technologies paves amethod in detecting the current traffic status,data offloading,and cyberattacks in *** study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC(AIMDO-SMEC)*** proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks(SNN)to determine the traffic status in the MEC ***,an adaptive sampling cross entropy(ASCE)technique is utilized for data offloading in MEC ***,the modified salp swarm algorithm(MSSA)with extreme gradient boosting(XGBoost)technique was implemented to identification and classification of cyberattack that exist in the MEC *** examining the enhanced outcomes of the AIMDO-SMEC technique,a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDOSMEC technique with the minimal completion time of tasks(CTT)of 0.680.

关键词： Data offloading mobile edge computing security machine learning artificial intelligence XGBoost salp swarm algorithm

Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of computer sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia faculty of computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK faculty of computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin university of Technology Poland faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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

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

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

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

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

From frequency domain to time domain:performance analysis on cyclic prefixed multi-user single-carrier transmission

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Science China(information sciences) 2016年第8期59卷 144-152页

作者： Wei PENG Qing F.ZHOU An HUANG Shaodan MA School of Electronics information and Communications Huazhong University of Science and Technology School of computer and information Hefei University of Technology faculty of Science and Technology University of Macau

This paper analyzes the performance of cyclic prefixed single-carrier transmission under multiuser interference(MUI). For multi-user receivers using single carrier frequency domain adaptive antenna array,we derive the signal to interference plus noise ratio by analyzing the time domain estimated signal after the transformation from frequency domain to time domain. By assuming the MUI follows the Gaussian distribution,we can derive the analytical expression for the bit error rate(BER) performance. The analytical result is verified by the numerical results generated by Monte Carlo method. It is concluded from the analytical results that if the number of receive antennas goes to infinity as assumed in massive multiple input multiple output(MIMO)system, the BER performance will be determined by two ratios: signal to noise ratio as well as the Nr to U ratio, where Nr is the number of receive antennas and U is the number of users.

关键词： single-carrier transmission multi-user interference frequency domain processing MIMO SCFDAAA

Prediction of COVID-19 Confirmed Cases Using Gradient Boosting Regression Method

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computers, Materials & Continua 2021年第1期66卷 315-329页

作者： Abdu Gumaei Mabrook Al-Rakhami Mohamad Mahmoud Al Rahhal Fahad Raddah H.Albogamy Eslam Al Maghayreh Hussain AlSalman College of computer and information sciences King Saud UniversityRiyadh11362Saudi Arabia computer Science Department Faculty of Applied ScienceTaiz UniversityTaizYemen College of Applied computer sciences King Saud UniversityRiyadh11362Saudi Arabia

The fast spread of coronavirus disease(COVID-19)caused by SARSCoV-2 has become a pandemic and a serious threat to the *** of May 30,2020,this disease had infected more than 6 million people globally,with hundreds of thousands of ***,there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare *** study uses gradient boosting regression(GBR)to build a trained model to predict the daily total confirmed cases of *** GBR method can minimize the loss function of the training process and create a single strong learner from weak *** are conducted on a dataset of daily confirmed COVID-19 cases from January 22,2020,to May 30,*** results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR *** results reveal that the GBR model achieves 0.00686 root mean square error,the lowest among several comparative models.

关键词： COVID-19 coronavirus disease SARS-CoV-2 machine learning gradient boosting regression(GBR)method

Large-Capacity Three-Party Quantum Digital Secret Sharing Using Three Particular Matrices Coding

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Communications in Theoretical Physics 2016年第11期65卷 501-508页

作者：赖红罗明星 Josef Pieprzyk 陶丽刘志明 Mehmet A.Orgun School of computer and information Science Southwest University information Security and National Computing Grid Laboratory School of Information Science and TechnologySouthwest Jiaotong University School of Electrical Engineering and computer Science Queensland University of Technology Institute of computer Science Polish Academy of Sciences Department of Computing Macquarie University faculty of information Technology Macao University of Science and Technology

In this paper, we develop a large-capacity quantum digital secret sharing(QDSS) scheme, combined the Fibonacci- and Lucas-valued orbital angular momentum(OAM) entanglement with the recursive Fibonacci and Lucas matrices. To be exact, Alice prepares pairs of photons in the Fibonacci- and Lucas-valued OAM entangled states, and then allocates them to two participants, say, Bob and Charlie, to establish the secret key. Moreover, the available Fibonacci and Lucas values from the matching entangled states are used as the seed for generating the Fibonacci and Lucas matrices. This is achieved because the entries of the Fibonacci and Lucas matrices are recursive. The secret key can only be obtained jointly by Bob and Charlie, who can further recover the secret. Its security is based on the facts that nonorthogonal states are indistinguishable, and Bob or Charlie detects a Fibonacci number, there is still a twofold uncertainty for Charlie'(Bob') detected value.

关键词： Fibonacci-and Lucas-valued orbital angular momentum(OAM) states the first kind of Chebyshev maps lower error rates longer destances

Analyzing Customer Reviews on Social Media via Applying Association Rule

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computers, Materials & Continua 2021年第8期68卷 1519-1530页

作者： Nancy Awadallah Awad Amena Mahmoud Department of computer and information Systems Sadat Academy for Management SciencesCairo11742Egypt faculty of computers and information Department of Computer ScienceKafr el Sheikh UniversityKafr El Sheikh33511Egypt

The rapid growth of the use of social media opens up new challenges and opportunities to analyze various aspects and patterns in ***-text mining,several techniques are available such as information clustering,extraction,summarization,*** this study,a text mining framework was presented which consists of 4 phases retrieving,processing,indexing,and mine association rule *** is applied by using the association rule mining technique to check the associated term with the Huawei P30 Pro *** reviews are extracted from many websites and Facebook groups,such as ***,*** and *** technology,where customers from all over the world placed their notes on cell *** this analysis,a total of 192 reviews of Huawei P30 Pro were collected to evaluate them by text mining *** findings demonstrate that Huawei P30 Pro,has strong points such as the best safety,high-quality camera,battery that lasts more than 24 hours,and the processor is very *** paper aims to prove that text mining decreases human efforts by recognizing significant *** will lead to improving the awareness of customers to choose their products and at the same time sales managers also get to know what their products were accepted by customers suspended.

关键词： Machine learning text mining social media big data association rule document clustering

COVID19 Classification Using CT Images via Ensembles of Deep Learning Models

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computers, Materials & Continua 2021年第10期69卷 319-337页

作者： Abdul Majid Muhammad Attique Khan Yunyoung Nam Usman Tariq Sudipta Roy Reham R.Mostafa Rasha H.Sakr Department of computer Science HITEC University TaxilaTaxila47080Pakistan Department of computer Science and Engineering Soonchunhyang UniversityAsanKorea College of computer Engineering and sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia PRTTL Washington University in Saint LouisSaint LouisMO 63110USA Department of information Systems Faculty of Computers and Information SciencesMansoura UniversityMansoura35516Egypt Department of computer Science Faculty of Computers and Information SciencesMansoura UniversityMansoura35516Egypt

The recent COVID-19 pandemic caused by the novel coronavirus,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),has had a significant impact on human life and the economy around the world.A reverse transcription polymerase chain reaction(RT-PCR)test is used to screen for this disease,but its low sensitivity means that it is not sufficient for early detection and *** RT-PCR is a time-consuming procedure,there is interest in the introduction of automated techniques for *** learning has a key role to play in the field of medical *** most important issue in this area is the choice of key ***,we propose a set of deep learning features based on a system for automated classification of computed tomography(CT)images to identify ***,this method was used to prepare a database of three classes:Pneumonia,COVID19,and *** dataset consisted of 6000 CT images refined by a hybrid contrast stretching *** the next step,two advanced deep learning models(ResNet50 and DarkNet53)were fine-tuned and trained through transfer *** features were extracted from the second last feature layer of both models and further optimized using a hybrid optimization *** each deep model,the Rao-1 algorithm and the PSO algorithm were combined in the hybrid ***,the selected features were merged using the new minimum parallel distance non-redundant(PMDNR)*** final fused vector was finally classified using the extreme machine *** experimental process was carried out on a set of prepared data with an overall accuracy of 95.6%.Comparing the different classification algorithms at the different levels of the features demonstrated the reliability of the proposed framework.

关键词： COVID19 preprocessing deep learning information fusion firefly algorithm extreme learning machine