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Review:Data center network architecture in cloud computing:review, taxonomy, and open research issues

Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第9期15卷 776-793页

作者： Han QI Muhammad SHIRAZ Jie-yao LIU Abdullah GANI Zulkanain ABDUL RAHMAN torki a.altameem Mobile Cloud Computing Research Lab Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur 50603 Malaysia Department of History Faculty of Arts and Social Sciences University of MalayaKuala Lumpur 50603 Malaysia Department of Computer Science Riyadh Community College King Saud UniversityRiyadh 11533 Saudi Arabia

The data center network(DCN), which is an important component of data centers, consists of a large number of hosted servers and switches connected with high speed communication links. A DCN enables the deployment of resources centralization and on-demand access of the information and services of data centers to users. In recent years, the scale of the DCN has constantly increased with the widespread use of cloud-based services and the unprecedented amount of data delivery in/between data centers, whereas the traditional DCN architecture lacks aggregate bandwidth, scalability, and cost effectiveness for coping with the increasing demands of tenants in accessing the services of cloud data centers. Therefore, the design of a novel DCN architecture with the features of scalability, low cost, robustness, and energy conservation is required. This paper reviews the recent research findings and technologies of DCN architectures to identify the issues in the existing DCN architectures for cloud computing. We develop a taxonomy for the classification of the current DCN architectures, and also qualitatively analyze the traditional and contemporary DCN architectures. Moreover, the DCN architectures are compared on the basis of the significant characteristics, such as bandwidth, fault tolerance, scalability, overhead, and deployment cost. Finally, we put forward open research issues in the deployment of scalable, low-cost, robust, and energy-efficient DCN architecture, for data centers in computational clouds.

关键词： Data center network Cloud computing Architecture Network topology

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Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

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Computers, Materials & Continua 2023年第11期77卷 2009-2030页

作者： Kalpesh Jadav Vishal Sorathiya Walid El-Shafai torki altameem Moustafa HAly Vipul Vekariya Kawsar Ahmed Francis MBui Department of Electronics&Communication Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt Department of Computer Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonSK S7N5A9Canada

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking *** shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and *** of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is *** real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above *** paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle *** method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground *** to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

关键词： Change illuminations ImageJ software intelligent traffic systems mixture of Gaussian model National Institute of Health vehicle tracking

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Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm

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Computers, Materials & Continua 2022年第6期71卷 4719-4736页

作者： Ayman altameem Sandeep Kumar Ramesh Chandra Poonia Abdul Khader Jilani Saudagar Department of Computer Science College of Applied StudiesKing Saud UniversityRiyadh11495Saudi Arabia Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Since the beginning of time,humans have relied on plants for food,energy,and *** are recognized by leaf,flower,or fruit and linked to their suitable *** methods are used to extract and select traits that are helpful in identifying a *** plant leaf image categorization,each plant is assigned a label according to its *** purpose of classifying plant leaf images is to enable farmers to recognize plants,leading to the management of plants in several *** study aims to present a modified whale optimization algorithm and categorizes plant leaf images into *** modified algorithm works on different sets of plant *** proposed algorithm examines several benchmark functions with adequate *** ten plant leaf images,this classification method was *** proposed model calculates precision,recall,F-measurement,and accuracy for ten different plant leaf image datasets and compares these parameters with other existing *** on experimental data,it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%.

关键词： Bag-of-features feature optimization plant leaf classification swarm intelligence nature-inspired algorithm

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IoT Devices Authentication Using Artificial Neural Network

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Computers, Materials & Continua 2022年第2期70卷 3701-3716页

作者： Syed Shabih Ul Hasan Anwar Ghani Ikram Ud Din Ahmad Almogren Ayman altameem Department of Computer Science and Software Engineering International Islamic University Islamabad45000Pakistan Department of Information Technology The University of Haripur22620HaripurPakistan Chair of Cyber Security Department of Computer ScienceCollege of Computer and Information SciencesKing Saud UniversityRiyadh11633Saudi Arabia Chair of Cyber Security Department of Natural and Engineering SciencesCollege of Applied Studies and Community ServicesKing Saud UniversityRiyadh11543Saudi Arabia

User authentication is one of the critical concerns of information *** tend to use strong textual passwords,but remembering complex passwords is hard as they often write it on a piece of paper or save it in their mobile *** passwords are slightly unprotected and are easily *** attacks include dictionary,shoulder surfing,and brute *** passwords overcome the shortcomings of textual passwords and are designed to aid memorability and ease of *** paper proposes a Process-based Pattern Authentication(PPA)system for Internet of Things(IoT)devices that does not require a server to maintain a static password of the login *** server stores user’s information,which they provide at the time of registration,i.e.,the R-code and the symbol,but the P-code,i.e.,the actual password,will change with every login attempt of *** this scheme,users may draw a pattern on the basis of calculation from the P-code and Rcode in the PPA pattern,and can authenticate themselves using their touch dynamic behaviors through Artificial Neural Network(ANN).The ANN is trained on touch behaviors of legitimate users reporting superior performance over the existing *** experimental purposes,PPA is implemented as a prototype on a computer system to carry out experiments for the evaluation in terms of memorability and *** experiments show that the system has an effect of 5.03%of the False Rejection Rate(FRR)and 4.36%of the False Acceptance Rate(FAR),respectively.

关键词： Implicit authentication behavioral authentication artificial neural network processed pattern authentication

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P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

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Intelligent Automation & Soft Computing 2023年第1期35卷 553-566页

作者： Ayman altameem Ramesh Chandra Poonia Ankit Kumar Linesh Raja Abdul Khader Jilani Saudagar Department of Computer Science and Engineering College of Applied Studies and Community ServicesKing Saud UniversityRiyadh11533Saudi Arabia Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Engineering and Applications GLA UniversityMathuraUPIndia Department of Computer Application Manipal University JaipurRajasthan303007India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Data clustering is crucial when it comes to data processing and *** new clustering method overcomes the challenge of evaluating and extracting data from big *** or categorical data can be *** clustering methods favor numerical data clustering and ignore categorical data *** recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering ***,these algorithms could not use the concept of categorical data for *** that,suggestions for expanding traditional categorical data processing methods were *** addition to expansions,several new clustering methods and extensions have been proposed in recent *** is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster *** paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster *** parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also *** results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable.

关键词： ROCK K-means algorithm clustering approaches unsupervised learning K-histogram

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Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis

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Computers, Materials & Continua 2023年第1期74卷 897-914页

作者： Sandeep Kumar Muhammad Badruddin Khan Mozaherul Hoque Abul Hasanat Abdul Khader Jilani Saudagar Abdullah altameem Mohammed AlKhathami Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Social media,like Twitter,is a data repository,and people exchange views on global issues like the COVID-19 *** media has been shown to influence the low acceptance of *** work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual’s sensitivities and feelings that lead to *** work proposes a method to analyze the opinion of an individual’s tweet about the COVID-19 *** paper introduces a sigmoidal particle swarm optimization(SPSO)***,the performance of SPSO is measured on a set of 12 benchmark problems,and later it is deployed for selecting optimal text features and categorizing *** proposed method uses TextBlob and VADER for sentiment analysis,CountVectorizer,and term frequency-inverse document frequency(TF-IDF)vectorizer for feature extraction,followed by SPSO-based feature *** Covid-19 vaccination tweets dataset was created and used for training,validating,and *** proposed approach outperformed considered algorithms in terms of ***,we augmented the newly created dataset to make it balanced to increase performance.A classical support vector machine(SVM)gives better accuracy for the augmented dataset without a feature selection *** shows that augmentation improves the overall accuracy of tweet *** the augmentation performance of PSO and SPSO is improved by almost 7%and 5%,respectively,it is observed that simple SVMwith 10-fold cross-validation significantly improved compared to the primary dataset.

关键词： Twitter data analysis sentiment analysis social media analytics swarm intelligence COVID-19 vaccine

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Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

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Computer Systems Science & Engineering 2022年第9期42卷 1095-1107页

作者： Ayman altameem Jaideep Singh Sachdev Vijander Singh Ramesh Chandra Poonia Sandeep Kumar Abdul Khader Jilani Saudagar Department of Computer Science College of Applied StudiesKing Saud UniversityRiyadh11495Saudi Arabia Department of Computer Science and Engineering Manipal University JaipurJaipur303007India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which can beused to control other IoT devices. Classification of hand movements will beone step closer to applying these algorithms in real-life situations using EEGheadsets. This paper uses different feature extraction techniques and sophisticatedmachine learning algorithms to classify hand movements from EEG brain signalsto control prosthetic hands for amputated persons. To achieve good classificationaccuracy, denoising and feature extraction of EEG signals is a significant step. Wesaw a considerable increase in all the machine learning models when the movingaverage filter was applied to the raw EEG data. Feature extraction techniques likea fast fourier transform (FFT) and continuous wave transform (CWT) were usedin this study;three types of features were extracted, i.e., FFT Features, CWTCoefficients and CWT scalogram images. We trained and compared differentmachine learning (ML) models like logistic regression, random forest, k-nearestneighbors (KNN), light gradient boosting machine (GBM) and XG boost onFFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFTfeatures gave the maximum accuracy of 88%.

关键词： Machine learning brain signal hand motion recognition braincomputer interface convolutional neural networks

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Convergence Track Based Adaptive Differential Evolution Algorithm(CTbADE)

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Computers, Materials & Continua 2022年第7期72卷 1229-1250页

作者： Qamar Abbas Khalid Mahmood Malik Abdul Khader Jilani Saudagar Muhammad Badruddin Khan Mozaherul Hoque Abul Hasanat Abdullah altameem Mohammed AlKhathami Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Engineering Oakland UniversityRochesterMIUSA Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia

One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima.A novel convergence track based adaptive differential evolution(CTbADE)algorithm is presented in this research *** crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima.A more diverse population improves the global searching capability and helps to escape from the local optima *** the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying *** adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this *** proposed algorithm will be helpful in maintaining the equilibrium between an algorithm’s exploration and exploitation capability.A comprehensive test suite of standard benchmark problems with different natures,i.e.,unimodal/multimodal and separable/non-separable,was used to test the convergence power of the proposed CTbADE *** results show the significant performance of the CTbADE algorithm in terms of average fitness,solution quality,and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms,such as jDE,CoDE,and EPSDE *** algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computationalmodels with a high number of parameters to adjust during the problem-solving process.

关键词： Differential evolution function optimization convergence track parameter sequence adaptive control parameters

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