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Internet of Things Enabled DDos Attack Detection Using Pigeon Inspired Optimization Algorithm with Deep Learning Approach

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Computers, Materials & Continua 2024年第9期80卷 4047-4064页

作者： Turki Ali Alghamdi s.ud s.alotaibi Department of Computer science and Artificial Intelligence College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia

Internet of Things(IoTs)provides better solutions in various fields,namely healthcare,smart transportation,home,*** Denial of service(Dos)outbreaks in IoT platforms is significant in certifying the accessibility and integrity of IoT *** learning(DL)models outperform in detecting complex,non-linear relationships,allowing them to effectually severe slight deviations fromnormal IoT activities that may designate a Dos *** uninterrupted observation and real-time detection actions of DL participate in accurate and rapid detection,permitting proactive reduction events to be executed,hence securing the IoT network’s safety and ***,this s.udy presents pigeon-inspired optimization with a DL-based attack detection and classification(PIODL-ADC)approach in an IoT *** PIODL-ADC approach implements a hyperparameter-tuned DL method for Distributed Denial-of-service(DDos)attack detection in an IoT ***,the PIODL-ADC model utilizes Z-score normalization to scale input data into a *** handling the convolutional and adaptive behaviors of IoT,the PIODL-ADCmodel employs the pigeon-inspired optimization(PIO)method for feature selection to detect the related features,considerably enhancing the recognition’s ***,the Elman Recurrent Neural Network(ERNN)model is utilized to recognize and classify DDos ***,reptile search algorithm(RsA)based hyperparameter tuning is employed to improve the precision and robustness of the ERNN method.A series of investigational validations is made to ensure the accomplishment of the PIODL-ADC *** experimental outcome exhibited that the PIODL-ADC method shows greater accomplishment when related to existing models,with a maximum accuracy of 99.81%.

关键词： Internet of things denial of service deep learning reptile search algorithm feature selection

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Efficient Object segmentation and Recognition Using Multi-Layer Perceptron Networks

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Computers, Materials & Continua 2024年第1期78卷 1381-1398页

作者： Aysha Naseer Nouf Abdullah Almujally s.ud s.alotaibi Abdulwahab Alazeb Jeongmin Park Department of Computer science Air UniversityIslamabad44000Pakistan Department of Information systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information system Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their *** proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their ***,two distinct kinds of features are obtained from the segmented images to help identify the objects of *** Artificial Neural Network(ANN)is then used to recognize the objects based on their *** were carried out with three standard datasets,MsRC,Ms COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested *** findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MsRC,Ms COCO,and Caltech 101 datasets,respectively.

关键词： K-region fusion segmentation recognition feature extraction artificial neural network computer vision

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Multiple scales method for analyzing a forced damped rotational pendulum oscillatorwithgallows

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Communications in Theoretical Physics 2024年第5期76卷 58-67页

作者： Haifa A Alyousef Alvaro H salas B M alotaibi s A El-Tantawy Department of Physics College of SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Mathematics and statistics Universidad Nacional de ColombiaFIZMAKO Research GroupColombia Department of Physics Faculty of SciencePort Said UniversityPort Said 42521Egypt Research Center for Physics(RCP) Department of PhysicsFaculty of Science and ArtsAl-MikhwahAl-Baha UniversityAl-Baha 1988Saudi Arabia

This s.udy reports the analytical solution for a generalized rotational pendulum system with gallows and periodic excited *** multiple scales method(MsM)is applied to solve the proposed *** types of rotational pendulum oscillators are s.udied and talked about in *** include the forced damped rotating pendulum oscillator with gallows,the damped standard simple pendulum oscillator,and the damped rotating pendulum oscillator without *** MsM first-order approximations for all the cases mentioned are derived in *** obtained results are illustrated with concrete numerical *** first-order MsM approximations are compared to the fourth-order Runge-Kutta(RK4)numerical ***,the maximum error is estimated for the first-order approximations obtained through the MsM,compared to the numerical approximations obtained by the RK4 ***,we conducted a comparative analysis of the outcomes obtained by the used method(MsM)and He-MsM to ascertain their respective levels of *** proposed method can be applied to analyze many strong nonlinear oscillatory equations.

关键词： rotational pendulum system multiple scales method approximate solution damped oscillations forced pendulum with gallows he-multiple scales method

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Bayesian and Non-Bayesian Analysis for the sine Generalized Linear Exponential Model under Progressively Censored Data

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Computer Modeling in Engineering & sciences 2024年第9期140卷 2795-2823页

作者： Naif alotaibi A.s.Al-Moisheer Ibrahim Elbatal Mohammed Elgarhy Ehab M.Almetwally Department of Mathematics and statistics Faculty of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Mathematics College of ScienceJouf UniversityP.O.Box 848Sakaka72351Saudi Arabia Mathematics and Computer science Department Faculty of ScienceBeni-Suef UniversityBeni-Suef62521Egypt Department of Basic sciences Higher Institute of Administrative SciencesBelbeisEgypt Faculty of Business Administration Delta University for Science and TechnologyGamasa11152Egypt

This article introduces a novel variant of the generalized linear exponential(GLE)distribution,known as the sine generalized linear exponential(sGLE)*** sGLE distribution utilizes the sine transformation to enhance its *** updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability *** suggested model incorporates a hazard rate function(HRF)that may display a rising,J-shaped,or bathtub form,depending on its unique *** model includes many well-known lifespan distributions as separate *** suggested model is accompanied with a range of statistical *** model parameters are examined using the techniques of maximum likelihood and Bayesian estimation using progressively censored *** order to evaluate the effectiveness of these techniques,we provide a set of simulated data for testing *** relevance of the newly presented model is shown via two real-world dataset applications,highlighting its superiority over other respected similar models.

关键词： sine G family generalized linear failure rate progressively censored data moments maximum likelihood estimation Bayesian estimation simulation

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Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques

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Computer Modeling in Engineering & sciences 2024年第4期139卷 759-782页

作者： Raveena selvanarayanan surendran Rajendran Youseef alotaibi Department of Computer science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India Department of Computer science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee *** deadly disease is hard to control because wind,rain,and insects carry *** researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest *** the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate *** overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate *** proposed methodology selects CBD image datasets through four different stages for training and *** to train a model on datasets of coffee berries,with each image labeled as healthy or *** themodel is trained,sHAP algorithmto figure out which features were essential formaking predictions with the proposed *** of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions *** inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of *** evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is *** involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its *** comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.

关键词： Computer vision coffee berry disease colletotrichum kahawae XG boost shapley additive explanations

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Analysis of Progressively Type-Ⅱ Inverted Generalized Gamma Censored Data and Its Engineering Application

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Computer Modeling in Engineering & sciences 2024年第10期141卷 459-489页

作者： Refah alotaibi sanku Dey Ahmed Elshahhat Department of Mathematical sciences College of SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of statistics St.Anthony’s CollegeShillong793001India Faculty of Technology and Development Zagazig UniversityZagazig44519Egypt

A novel inverted generalized gamma(IGG)distribution,proposed for data modelling with an upside-down bathtub hazard rate,is *** many real-world practical situations,when a researcher wants to conduct a comparative s.udy of the life testing of items based on cost and duration of testing,censoring strategies are frequently *** this point of view,in the presence of censored data compiled from the most well-known progressively Type-Ⅱ censoring technique,this s.udy examines different parameters of the IGG *** a classical point of view,the likelihood and product of spacing estimation methods are *** Fisher information and the deltamethod are used to obtain the approximate confidence intervals for any unknown parametric function of the *** the Bayesian paradigm,the same traditional inferential approaches are used to estimate all unknown ***-Chain with Monte-Carlo steps are considered to approximate all Bayes’*** numerical comparisons are presented to examine the performance of the proposed methodologies using various criteria of ***,using several optimality criteria,the optimumprogressive censoring design is *** highlight how the proposed estimators can be used in practice and to verify the flexibility of the proposed model,we analyze the failure times of twenty mechanical components of a diesel engine.

关键词： Inverted generalized gamma censoring spacing function likelihood Bayesian optimal plan

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Efficient Intelligent E-Learning Behavior-Based Analytics of s.udent’s Performance Using Deep Forest Model

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Computer systems science & Engineering 2024年第5期48卷 1133-1147页

作者： Raed alotaibi Omar Reyad Mohamed Esmail Karar Applied College Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Faculty of Computers and Artificial Intelligence Sohag UniversitySohag82524Egypt Faculty of Electronic Engineering Menoufia UniversityMenouf32952Egypt

E-learning behavior data indicates several s.udents’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in *** article proposes a new analytics systemto support academic evaluation for s.udents via e-learning activities to overcome the challenges faced by traditional learning *** proposed e-learning analytics system includes a new deep forest *** consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural *** developed forest model can analyze each s.udent’s activities during the use of an e-learning platform to give accurate expectations of the s.udent’s performance before ending the semester and/or the final *** have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 *** proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long short-TermMemory(LsTM)in previous *** allows academic advisors to support expected failed s.udents significantly and improve their academic level at the right ***,the proposed analytics system can enhance the quality of educational services for s.udents in an innovative e-learning framework.

关键词： E-learning behavior data s.udent evaluation artificial intelligence machine learning

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Germination Quality Prognosis: Classifying spectroscopic Images of the seed samples

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Intelligent Automation & soft Computing 2023年第2期35卷 1815-1829页

作者： s.ud s.alotaibi Department of Information systems College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

One of the most critical objectives of precision farming is to assess the germination quality of *** models contribute to thisﬁeld primarily through the use of artiﬁcial intelligence techniques such as machine learning,which present difﬁculties in feature extraction and optimization,which are critical factors in predicting accuracy with few false alarms,and another signiﬁcant dif-ﬁculty is assessing germination ***,the majority of these contri-butions make use of benchmark classiﬁcation methods that are either inept or too complex to train with the supplied *** manuscript addressed these issues by introducing a novel ensemble classiﬁcation strategy dubbed“Assessing Germination Quality of seed samples(AGQss)by Adaptive Boosting Ensemble Classiﬁcation”that learns from quantitative phase features as well as universal features in greyscale spectroscopic *** experimental inquiry illustrates the signiﬁcance of the proposed model,which outperformed the currently avail-able models when performance analysis was performed.

关键词： Precision farming ensemble classiﬁcation germination quality machine learning predictive analytics

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smart Contract:security and Privacy

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Computer systems science & Engineering 2021年第7期38卷 93-101页

作者： Leena s.alotaibi sultan s.Alshamrani Department of Information Technology College of Computer and Information TechnologyTaif UniversityP.O.Box 11099TaifSaudi Arabia

smart contracts are simply self-activated contracts between two *** idea behind their implementation relies on the concept of blockchain,wherein the details and execution of the contract are turned into code and distributed among users of a *** process controls counterfeiting and money laundering by its ability to trace who owes *** also boosts the general *** research paper shows how smart contracts in modern-day systems have changed the approach to money *** present case s.udies about the uses of smart contracts with high levels of security and *** a building block of smart contracts,a brief description of blockchain is provided in an *** other cryptography methods and techniques,the usage of hashing and hash functions in blockchain security are also *** also explore the real-time applications of blockchain and smart contract techniques in real *** main advantage of this research paper is that it discusses a state-of-the-art subject,as most of the articles referenced in this paper are from 2018 and onward.

关键词： Blockchain cryptography smart contract security

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Effect of Eu3+,Bi3+,and Li+ doping on luminescent property of GdNbO4

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Journal of Rare Earths 2024年第1期42卷 66-75页

作者： s.W.Wi Y.s.Lee Department of Physics and Integrative Institute of Basic sciences Soongsil University

We investigated the emission properties of Eu3+-doped GdNbO4-based oxide phosphors,aiming at improving the Eu3+emission by codoping of Bi3+and Li+.Eu3+singly doped,Eu3+and Bi3+doubly doped,and Eu3+,Bi3+,and Li+triply doped GdNbO4were prepared using a high-temperature solid-state reaction *** red-orange emissions from Eu3+in the singly doped samples are significantly improved by the codoping of Bi3+,mainly due to the energy transfer from Bi3+to Eu3+.The additional codoping of Li+is found to increase the Eu3+emission *** improvement might be attributed to the increase in the photoluminescent quantum yield originating from larger grain sizes and better crystallinity.A detailed analysis of the X-ray diffraction pattern and the asymmetric ratio estimated from the photoluminescence spectra show that the local lattice environment around Eu3+in GdNbO4does not change significantly with the codoping of Bi3+and Li+.Our results indicate that Li+doping is a promising way to improve the emission properties of rare-earth ion-doped GdNbO4phosphors.

关键词： Eu 3+ GdNbO 4 Codoping of Li + Photoluminescence sensitizer Rare earths

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