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Improving Channel Estimation in a NOMA Modulation Environment Based on Ensemble Learning

Computer Modeling in Engineering & Sciences 2024年第8期140卷 1315-1337页

作者： Lassaad K.Smirani Leila jamel Latifah Almuqren Elearning Deanship and Distance Education Umm Al-Qura UniversityP.O.Box 715MekkahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation *** estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable *** model,called Stacked Generalization for Channel Estimation(SGCE),aims to enhance channel estimation performance by eliminating pilot insertion and improving *** SGCE model incorporates six machine learning methods:random forest(RF),gradient boosting machine(GB),light gradient boosting machine(LGBM),support vector regression(SVR),extremely randomized tree(ERT),and extreme gradient boosting(XGB).By generating meta-data from five models(RF,GB,LGBM,SVR,and ERT),we ensure accurate channel coefficient predictions using the XGB *** validate themodeling performance,we employ the leave-one-out cross-validation(LOOCV)approach,where each observation serves as the validation set while the remaining observations act as the training *** performances’results demonstrate higher mean andmedian accuracy compared to the *** achieves an average accuracy of 98.4%,precision of 98.1%,and the highest F1-score of 98.5%,accurately predicting channel ***,our proposedmethod outperforms prior traditional and intelligent techniques in terms of throughput and bit error ***’s superior performance highlights its efficacy in optimizing channel *** can effectively predict channel coefficients and contribute to enhancing the overall efficiency of radio mobile *** extensive experimentation and evaluation,we demonstrate that SGCE improved performance in channel estimation,surpassing previous ***,SGCE’s capabilities have significant implications for optimizing channel estimation in modern communication systems.

关键词： Stacked generalization ensemble learning Non-Orthogonal Multiple Access(NOMA) channel estimation 5G

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Analysis of the Manufactured Tolerances with the Three-Dimensional Method of Angular Chains of Dimensions Applied to a Cylinder Head of Car Engine

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Engineering（科研） 2010年第10期2卷 771-779页

作者： Aida Mezghani Alain Bellacicco jamel louati Alain Rivière Mohamed Haddar 不详

This paper proposes an analysis method of the manufactured tolerances applied to a cylinder head of car engine. This method allows to determine the manufacturing tolerances in the case of angular chains of dimensions and to check its correspondence with the functional tolerances. The objective of this work is to analyze two parameterized functions: the angular defect Δα and the projected length lg of the toleranced surface. The angular defects are determined from the precision of the machine tools, we consider only the geometrical defects (position and orientation of surfaces), making the assumption that the form defects are negligible. The manufactured defect is determined from these two parameterized functions. Then it will be compared with the functional condition in order to check if the selected machining range allows, at end of the manufacturing process, to give a suitable part.

关键词： Three-Dimensional Tolerancing Manufacturing Process Toleranced Surface Angular Defect

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A Chopper Negative-R Delta-Sigma ADC for Audio MEMS Sensors

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Computer Modeling in Engineering & Sciences 2022年第2期130卷 607-631页

作者： jamel Nebhen Pietro M.Ferreira College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia Universit´e Paris-Saclay Centrale SupelecCNRSLaboratory de G´enie Electrique et Electronique de ParisGif-sur-Yvette91192France Sorbonne Universite CNRSLab.de Genie Electrique et Electronique de ParisParis75252France

This paper presents a proposed low-noise and high-sensitivity Internet of Thing(IoT)system based on an M&NEMS *** IoT device consists of an M&NEMS resistive accelerometer associated with an electronic readout circuit,which is a silicon nanowire and a Continuous-Time(CT)△∑*** first integrator of the△∑ADC is based on a positive feedback DC-gain enhancement two-stage amplifier due to its high linearity and low-noise *** mitigate both the offset and 1/f noise,a suggested delay-time chopper negative-R stabilization technique is applied around the first integrator.A 65-nm CMOS process implements the CT△∑*** supply voltage of the CMOS circuit is 1.2-V while 0.96-mW is the power consumption and 0.1-mm^(2) is the silicon *** M&NEMS microphone and△∑ADC complete circuit are fabricated and *** a working frequency bandwidth of 20-kHz,the measurement results of the proposed IoT system reach a signal to noise ratio(SNR)of ***,it has a measured dynamic range(DR)of 108-dB and a measured signal to noise and distortion ratio(SNDR)of 101.3-dB.

关键词： M&NEMS sensor △∑ ADC chopper negative-R IoT 1/f noise SNR

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Demonstration of synaptic and resistive switching characteristics in W/TiO_(2)/HfO_(2)/TaN memristor crossbar array for bioinspired neuromorphic computing

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Journal of Materials Science & Technology 2022年第1期96卷 94-102页

作者： Muhammad Ismail Umesh Chand Chandreswar Mahata jamel Nebhen Sungjun Kim Division of Electronics and Electrical Engineering Dongguk UniversitySeoul 04620South Korea Department of Electrical and Computer Engineering National University SingaporeSingapore 117583Singapore College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia

In this study,resistive random-access memory(RRAM)-based crossbar arrays with a memristor W/TiO_(2)/HfO_(2)/TaN structure were fabricated through atomic layer deposition(ALD)to investigate synaptic plasticity and resistive switching(RS)characteristics for bioinspired neuromorphic computing.X-ray photoelectron spectroscopy(XPS)was employed to explore oxygen vacancy concentrations in bilayer TiO_(2)/HfO_(2)*** fitting for O1s peaks confirmed that the HfO_(2)layer contained a larger number of oxygen vacancies than the TiO_(2)*** addition,HfO_(2)had lower Gibbs free energy(ΔG°=-1010.8 kJ/mol)than the TiO_(2)layer(ΔG°=-924.0 kJ/mol),resulting in more oxygen vacancies in the HfO_(2)*** results andΔG°magnitudes confirmed that formation/disruption of oxygen-based conductive filaments took place in the TiO_(2)*** W/TiO_(2)/HfO_(2)/TaN memristive device exhibited excellent and repeatable RS characteristics,including superb 10^(3) dc switching cycles,outstanding 107 pulse endurance,and high-thermal stability(10^(4) s at 125℃)important for digital computing ***,some essential biological synaptic characteristics such as potentiation-depression plasticity,paired-pulse facilitation(PPF),and spike-timing-dependent plasticity(STDP,asymmetric Hebbian and asymmetric anti-Hebbian)were successfully mimicked herein using the crossbar-array memristive *** on experimental results,a migration and diffusion of oxygen vacancy based physical model is proposed to describe the synaptic plasticity and RS *** study demonstrates that the proposed W/TiO_(2)/HfO_(2)/TaN memristor crossbar-array has a significant potential for applications in non-volatile memory(NVM)and bioinspired neuromorphic systems.

关键词： Resistive switching Crossbar-array memristive device Synaptic plasticity TiO_(2)/HfO_(2)film Oxygen vacancy

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A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

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Computers, Materials & Continua 2022年第11期73卷 4291-4309页

作者： Kais Tissaoui Sahbi Boubaker Waleed Saud Alghassab Taha Zaghdoudi jamel Azibi University of Ha’il Applied CollegeHail CitySaudi Arabia University of Tunis El Manar Faculty of Economic Sciences and Management of Tunisthe International Finance GroupTunisTunisia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Research Unit on Study of Systems and Renewable Energy National College of Engineering of MonastirUniversity of MonastirMonastirTunisia LAREQUAD&FSEGT University of Tunis El ManarTunisTunisia University of Jendouba Law FacultyManagement and Economic Sciences of JendoubaTunisia

The application of optimization methods to prediction issues is a continually exploring *** line with this,this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting *** complex characteristics of implied volatility risk index such as non-linearity structure,time-varying and nonstationarity motivate us to apply a nonlinear polynomial Hammerstein model with known structure and unknown *** use the Hybrid Particle Swarm Optimization(HPSO)tool to identify the model parameters of nonlinear polynomial Hammerstein *** indicate that,following a nonlinear polynomial behaviour cascaded to an autoregressive with exogenous input(ARX)behaviour,the fear index in US financial market is significantly affected by COVID-19-infected cases in the US,COVID-19-infected cases in the world and COVID-19-infected cases in China,*** performance indicators provided by the developed models show that COVID-19-infected cases in the US are particularly powerful in predicting the Cboe volatility index compared to COVID-19-infected cases in the world and China(MAPE(2.1013%);R2(91.78%)and RMSE(0.6363 percentage points)).The proposed approaches have also shown good convergence characteristics and accurate fits of the data.

关键词： Forecasting Cboe’s volatility index COVID-19 pandemic nonlinear polynomial hammerstein model hybrid particle swarm optimization

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Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

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Computers, Materials & Continua 2022年第2期70卷 4199-4215页

作者： Nebojsa Bacanin Khaled Alhazmi Miodrag Zivkovic K.Venkatachalam Timea Bezdan jamel Nebhen Singidunum University Danijelova11000BelgradeSerbia National Center for Robotics and IoT Communication and Information Technology Research InstituteKing Abdulaziz City for Science and Technology(KACST)Riyadh12371Saudi Arabia Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Prince Sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia

In the domain of artificial neural networks,the learning process represents one of the most challenging *** the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at ***,to a very large search space,it is very difficult to find the proper values of connection weights and *** traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local *** commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient *** an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network *** thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural *** the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization *** results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local *** proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on *** mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.

关键词： Artificial neural network optimization metaheuristics algorithm hybridization brain storm optimization

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Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

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Computers, Materials & Continua 2023年第11期77卷 2545-2564页

作者： Medhat A.Tawfeek Nacim Yanes Leila jamel Ghadah Aldehim Mahmood A.Mahmood Department of Computer Science College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer Science Faculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software Engineering College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI Laboratory La Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

关键词： Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation

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A Hybrid Approach for Network Intrusion Detection

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Computers, Materials & Continua 2022年第1期70卷 91-107页

作者： Mavra Mehmood Talha Javed jamel Nebhen Sidra Abbas Rabia Abid Giridhar Reddy Bojja Muhammad Rizwan Department of Computer Science Kinnaird College for WomenLahore54000Pakistan ASET Labs IslamabadPakistan Prince Sattam bin Abdulaziz University College of Computer Science and EngineeringAlkharj11942Saudi Arabia College of Business and Information Systems Dakota State UniversityMadisonUnited States of America

Due to the widespread use of the internet and smart devices,various attacks like intrusion,zero-day,Malware,and security breaches are a constant threat to any organization’s network ***,a Network Intrusion Detection System(NIDS)is required to detect attacks in network *** paper proposes a new hybrid method for intrusion detection and attack *** proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack *** the first step,the dataset is preprocessed through the data transformation technique and min-max ***,the random forest recursive feature elimination method is applied to identify optimal features that positively impact the model’s ***,we use various Support Vector Machine(SVM)types to detect intrusion and the Adaptive Neuro-Fuzzy System(ANFIS)to categorize probe,U2R,R2U,and DDOS *** validation of the proposed method is calculated through Fine Gaussian SVM(FGSVM),which is 99.3%for the binary *** Square Error(MSE)is reported as 0.084964 for training data,0.0855203 for testing,and 0.084964 to validate multiclass categorization.

关键词： Network security intrusion detection system machine learning attacks data mining classification feature selection

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Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks

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Computers, Materials & Continua 2022年第3期70卷 6057-6072页

作者： Syed Rizwan Hassan Ishtiaq Ahmad jamel Nebhen Ateeq Ur Rehman Muhammad Shafiq Jin-Ghoo Choi Department of Electrical Engineering The University of LahoreLahore54000Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Electrical Engineering Government College UniversityLahore54000Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

The modern paradigm of the Internet of Things(IoT)has led to a significant increase in demand for latency-sensitive applications in Fog-based cloud ***,such applications cannot meet strict quality of service(QoS)*** large-scale deployment of IoT requires more effective use of network infrastructure to ensure QoS when processing big ***,cloud-centric IoT application deployment involves different modules running on terminal devices and cloud *** devices with different computing capabilities must process the data generated by the end device,so deploying latency-sensitive applications in a heterogeneous fog computing environment is a difficult *** addition,when there is an inconsistent connection delay between the fog and the terminal device,the deployment of such applications becomes more *** this article,we propose an algorithm that can effectively place application modules on network nodes while considering connection delay,processing power,and sensing data *** with traditional cloud computing deployment,we conducted simulations in iFogSim to confirm the effectiveness of the *** simulation results verify the effectiveness of the proposed algorithm in terms of end-to-end delay and network ***,latency and execution time is insensitive to the number of sensors.

关键词： IoT fog-cloud computing architecture module placement latency sensitive application resource aware placement

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An Efficient Internet Traffic Classification System Using Deep Learning for IoT

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Computers, Materials & Continua 2022年第4期71卷 407-422页

作者： Muhammad Basit Umair Zeshan Iqbal Muhammad Bilal jamel Nebhen Tarik Adnan Almohamad Raja Majid Mehmood Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Division of Computer and Electronics Systems Engineering Hankuk University of Foreign StudiesYongin-siKorea Electrical-Electronics Engineering Department Faculty of EngineeringKarabük University78050KarabükTurkey Prince Sattam bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia Information and Communication Technology Department School of Electrical and Computer EngineeringXiamen University MalaysiaSepang43900Malaysia

Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central *** IoT devices are connected to a network therefore prone to *** management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic *** to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural ***,machine learning-based models incline to misclassify internet traffic due to improper feature *** this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet *** examine the performance of the proposed technique,Moore-dataset is used for training the *** proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet *** experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.

关键词： Deep learning internet traffic classification network traffic management QoS aware application classification

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