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Natural science 2015年第5期7卷 238-247页

作者： K. s. Al Mugren Physics Department Faculty of Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

This study aims to determine the activity concentrations of naturally occurring, technically-enhanced levels of radiation and the gamma absorbed dose rates in soil samples collected across the land scape of historical area which discovered in east of saudi Arabia at 2009 G, Called AL- RAKKAH. By using an HPGe detector gamma-ray spectrometer, the activity concentrations of 226Ra 232Th and 40K were found in surface soil samples ranged from 17. 4 ± 1.2 Bq/kg to 28.3 ± 2.3 Bq/kg with an average value of 23 ± 1.6 Bq/kg, ranging from 1.1 ± 1.8 Bq/kg to 81.0 ± 1.7 Bq/kg with the average value 20 ± 1.4 Bq/kg and from 218 ± 11 Bq/kg to 255 ± 18 Bq/kg, with the mean value of 233 ± 12 Bq/kg respectively. The mean radium equivalent (Raeq) and outdoor radiation hazard index (Hex) for the area under study were determined as 69.52 Bq/kg and 0.16 respectively. The total absorbed dose rate due to three primordial radionuclides lies in the range of 17.74 - 72.24 nGy·h-1 with a mean of 32.69 nGy·h-1, which yields total annual effective dose of 0.37 msv·y-1. The measured values are comparable with other global radioactivity measurements and are found to be safe for public and environment. The baseline data of this type will almost certainly be of importance in making estimations of population exposure.

关键词： soil Gamma Ray spectroscopy Radioactivity Dose Rate Alrakkah saudi Arabia

Feature selection with stacked Autoencoder Based Intrusion Detection in Drones Environment

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Computers, Materials & Continua 2022年第12期73卷 5441-5458页

作者： Heba G.Mohamed saud s.Alotaibi Majdy M.Eltahir Heba Mohsen Manar Ahmed Hamza Abu sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

The Internet of Drones(IoD)offers synchronized access to organized airspace for Unmanned Aerial Vehicles(known as drones).The availability of inexpensive sensors,processors,and wireless communication makes it possible in real time *** several applications comprise IoD in real time environment,significant interest has been received by research *** IoD operates in wireless environment,it is needed to design effective intrusion detection system(IDs)to resolve security issues in the IoD *** article introduces ametaheuristics feature selection with optimal stacked autoencoder based intrusion detection(MFsOsAEID)in the IoD *** major intention of the MFsOsAE-ID technique is to identify the occurrence of intrusions in the IoD *** do so,the proposed MFsOsAE-ID technique firstly pre-processes the input data into a compatible *** addition,the presented MFsOsAEID technique designs a moth flame optimization based feature selection(MFOFs)technique to elect appropriate ***,firefly algorithm(FFA)with stacked autoencoder(sAE)model is employed for the recognition and classification of intrusions in which the sAE parameters are optimally tuned with utilize of *** performance validation of the MFsOsAE-ID model was tested utilizing benchmark dataset and the outcomes implied the promising performance of the MFsOsAE-ID model over other techniques with maximum accuracy of 99.72%.

关键词： Internet of drones unmanned aerial vehicles security intrusion detection machine learning

Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote sensing Images

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Computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： saeed Masoud Alshahrani saud s.Alotaibi shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki Department of Computer science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information system College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RsI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIs),and search and rescue *** recognition from RsIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(ssD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

Analytical approximations to a generalized forced damped complex Duffing oscillator:multiple scales method and KBM approach

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Communications in Theoretical Physics 2023年第2期75卷 18-27页

作者： Weaam Alhejaili Alvaro H salas s A El-Tantawy Department of Mathematical sciences 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 UniversitySaudi Arabia

In this investigation,some different approaches are implemented for analyzing a generalized forced damped complex Duffing oscillator,including the hybrid homotopy perturbation method(H-HPM),which is sometimes called the Krylov-Bogoliubov-Mitropolsky(KBM)method and the multiple scales method(MsM).All mentioned methods are applied to obtain some accurate and stable approximations to the proposed problem without decoupling the original *** obtained approximations are discussed graphically using different numerical values to the relevant ***,all obtained approximate solutions are compared with the 4thorder Runge-Kutta(RK4)numerical *** maximum residual distance error(MRDE)is also estimated,in order to verify the high accuracy of the obtained analytic approximations.

关键词： complex Duffing oscillators forced damped complex oscillator multiple scales method KBM method

Metaheuristics with Machine Learning Enabled Information security on Cloud Environment

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Computers, Materials & Continua 2022年第10期73卷 1557-1570页

作者： Haya Mesfer Alshahrani Faisal s.Alsubaei Taiseer Abdalla Elfadil Eisa Mohamed K.Nour Manar Ahmed Hamza Abdelwahed Motwakel Abu sarwar Zamani Ishfaq Yaseen Department of Information systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Information systems-Girls section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The increasing quantity of sensitive and personal data being gathered by data controllers has raised the security needs in the cloud *** computing(CC)is used for storing as well as processing ***,security becomes important as the CC handles massive quantity of outsourced,and unprotected sensitive data for public *** study introduces a novel chaotic chimp optimization with machine learning enabled information security(CCOML-Is)technique on cloud *** proposed CCOML-Is technique aims to accomplish maximum security in the CC environment by the identification of intrusions or anomalies in the *** proposed CCOML-Is technique primarily normalizes the networking data by the use of data conversion and min-max *** by,the CCOML-Is technique derives a feature selection technique using chaotic chimp optimization algorithm(CCOA).In addition,kernel ridge regression(KRR)classifier is used for the detection of security issues in the *** design of CCOA technique assists in choosing optimal features and thereby boost the classification performance.A wide set of experimentations were carried out on benchmark datasets and the results are assessed under several *** comparison study reported the enhanced outcomes of the CCOML-Is technique over the recent approaches interms of several measures.

关键词： Information security cloud computing intrusion anomalies data mining feature selection classification

Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

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Computers, Materials & Continua 2023年第5期75卷 4255-4272页

作者： Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Khaled Tarmissi Amal s.Mehanna Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityMuhayil63763Saudi Arabia Department of Computer sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information system College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab *** false information is harmful to society and *** and detecting the spread of fake news in Arabic becomes *** artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake ***,fake news in Arabic is identified by utilizing AI *** article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic *** HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful ***,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LsTM-RNN)model for fake news detection and ***,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LsTM-RNN *** performance validation of the HPOHDL-FND technique is tested using two Arabic *** outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.

关键词： Arabic corpus fake news detection deep learning hunter prey optimizer classification model

Metaheuristic with Deep Learning Enabled Biomedical Bone Age Assessment and Classification Model

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Computers, Materials & Continua 2022年第12期73卷 5473-5489页

作者： Mesfer Al Duhayyim Areej A.Malibari Marwa Obayya Mohamed K.Nour Ahmed s.salama Mohamed I.Eldesouki Abu sarwar Zamani Mohammed Rizwanullah Department of Computer science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Industrial and systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Information system College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The skeletal bone age assessment(BAA)was extremely implemented in development prediction and auxiliary analysis of medicinal issues.X-ray images of hands were detected from the estimation of bone age,whereas the ossification centers of epiphysis and carpal bones are important *** typical skeletal BAA approaches remove these regions for predicting the bone age,however,few of them attain suitable efficacy or *** BAA techniques with deep learning(DL)methods are reached the leading efficiency on manual and typical ***,this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with deep learning(IsBAAC-MDL)*** presented IsBAAC-MDL technique majorly focuses on the identification of bone age prediction and classification *** attain this,the presented IsBAAC-MDL model derives a mask Region-related Convolutional Neural Network(Mask-RCNN)with MobileNet as baseline model to extract *** by,the whale optimization algorithm(WOA)is implemented for hyperparameter tuning of the MobileNet *** last,Deep Feed-Forward Module(DFFM)based age prediction and Radial Basis Function Neural Network(RBFNN)based stage classification approach is *** experimental evaluation of the IsBAAC-MDL model is tested using benchmark dataset and the outcomes are assessed over distinct *** experimental outcomes reported the better performances of the IsBAACMDL model over recent approaches with maximum accuracy of 0.9920.

关键词： Biomedical images bone age assessment age prediction computer vision deep learning image classification

An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

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Computers, Materials & Continua 2022年第1期70卷 1315-1334页

作者： Naglaa F.soliman Naglaa s.Ali Mahmoud I.Aly Abeer D.Algarni Walid El-shafai Fathi E.Abd El-samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt International Academy for Engineering and Media science October CityEgypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Breast cancer is themost common type of cancer,and it is the reason for cancer death toll in women in recent *** diagnosis is essential to handle breast cancer patients for treatment at the right *** with mammography is the preferred examination for breast cancer,as it is available worldwide and ***-Aided Detection(CAD)systems are used to analyze medical images to detect breast cancer,*** death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after *** of mammogram images has four main steps:pre-processing,segmentation of the region of interest,feature extraction and classification of the images into normal or abnormal *** paper presents an efficient framework for processing of mammogram images and introduces an algorithm for segmentation of the images to detect *** pre-processing step of mammogram images includes removal of digitization noise using a 2D median filter,removal of artifacts using morphological operations,and contrast enhancement using a fuzzy enhancement *** proposed fuzzy image enhancement technique is analyzed and compared with conventional techniques based on an Enhancement Measure(EME)and local contrast *** comparison shows an outstanding performance of the proposed technique from the visual and numerical *** segmentation process is performed using Otsu’smultiple thresholding *** method segments the image regions into five classes with variable intensities using four *** effectiveness is measured based on visual quality of the segmentation output,as it gives details about the image and positions of *** performance of the proposed framework is measured using Dice coefficient,Hausdorff,and Peak signal-to-Noise Ratio(PsNR)*** segmented tumor region with the proposed segmentation method is 81%of the ground truth region provided by an ***,the proposed framew

关键词： Breast cancer mammogram images CAD contrast enhancement fuzzy logic segmentation

Manta Ray Foraging Optimization with Machine Learning Based Biomedical Data Classification

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Computers, Materials & Continua 2022年第11期73卷 3275-3290页

作者： Amal Al-Rasheed Jaber s.Alzahrani Majdy M.Eltahir Abdullah Mohamed Anwer Mustafa Hilal Abdelwahed Motwakel Abu sarwar Zamani Mohamed I.Eldesouki Department of Information systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information system College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various *** developments of artificial intelligence(AI)and machine learning(ML)models assist in the effectual design of medical data classification ***,this article concentrates on the development of optimal stacked Long short Term Memory sequence-tosequence Autoencoder(OsAE-LsTM)model for biomedical data *** presented OsAE-LsTM model intends to classify the biomedical data for the existence of ***,the OsAE-LsTM model involves min-max normalization based pre-processing to scale the data into uniform *** by,the sAE-LsTM model is utilized for the detection and classification of diseases in biomedical *** last,manta ray foraging optimization(MRFO)algorithm has been employed for hyperparameter optimization *** utilization of MRFO algorithm assists in optimal selection of hypermeters involved in the sAE-LsTM *** simulation analysis of the OsAE-LsTM model has been tested using a set of benchmark medical datasets and the results reported the improvements of the OsAELsTM model over the other approaches under several dimensions.

关键词： Biomedical data classification deep learning manta ray foraging optimization healthcare machine learning artificial intelligence

Clinical Practice Guideline for the Management of Overweight and Obesity in Adults in saudi Arabia

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