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Epidemiology and outcome of Crohn's disease in a teaching hospital in Riyadh

World Journal of Gastroenterology 2004年第9期10卷 1341-1344页

作者： abdullah s.ai-ghamdi Ibrahim A.ai-Mofleh Rashed s.aiRashed saleh M.ai-Amri Abdulrahman M.Aljebreen Reda EI-Badawi Arthur C.Isnani Gastroenterology Division Department of Medicine (38)King Khalid University HospitalRiyadhKingdom of Saudi Arabia King Khalid University Hospital College of Medicine and Research Center (74)PO Box 2925RiyadhKingdom of Saudi Arabia

aiM: To know the epidemiology and outcome of Crohn's disease at King Khalid University Hospital, Riyadh, saudi Arabia and to compare the results from other world ***: A retrospective analysis of patients seen for 20 years (between 1983 and 2002). Individual case records were reviewed with regard to history, clinical, findings from colonoscopy, biopsies, small bowel enema, computerized tomography scan, treatment and ***: seventy-seven patients with Crohn's disease were revisited, 13% presented the disease in the first 10 years and 87% over the last 10 years. Thirty-three patients (42.9%) were males and 44 (57.1%) were females. Age ranged from 11-70 years (mean of 25.3+11.3 years). Ninety-two (92%) were saudi. The mean duration of symptoms was 26+34.7 mo. The mean annual incidence of the disease over the first 10 years was 0.32:100 000 and 1.66:100 000 over the last 10 years with a total mean annual incidence of 0.94:100 000 over the last 20 years. The chief clinical features included abdominal pain, diarrhea, weight loss,anorexia, rectal bleeding and palpable mass. Colonoscopic findings were abnormal in 58 patients (76%) showing mostly ulcerations and inflammation of the colon. Eighty nine percent of patients showed nonspecific inflammation with chronic inflammatory cells and half of these patients revealed the presence of granulomas and granulations on bowel biopsies. similarly, 69 (89%) of small bowel enema results revealed ulcerations (49%), narrowing of the bowel lumen (42%), mucosal thickening (35%) and cobblestone appearance (35%). CT scan showed abnormality in 68 (88%) of patients with features of thickened loops (66%) and lymphadenopathy (37%). seventy-eight percent of patients had small and large bowel disease, 16% had small bowel involvement and only 6% had colitis alone. Of the total 55 (71%) patients treated with steroids at some point in their disease history, a satisfactory response to therapy was seen in 28 patients (51%) w

关键词： Hospitals, Teaching Adolescent Adult Aged Child Comparative study Crohn Disease Female Humans Male Middle Aged Retrospective studies saudi Arabia Treatment Outcome

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Predicting Energy Consumption Using stacked LsTM snapshot Ensemble

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Big Data Mining and Analytics 2024年第2期7卷 247-270页

作者： Mona Ahamd Alghamdi abdullah s.A.L-Malaise AL-ghamdi Mahmoud Ragab Information systems Department Faculty of Computing and Information Technology(FCIT)King Abdulaziz University(KAU)Jeddah 21589Kingdom of Saudi Arabia Information systems Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah 22246Kingdom of Saudi Arabia Information Technology Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with Mathematics DepartmentFaculty of ScienceAl-Azhar UniversityNaser City 11884Egypt

The ability to make accurate energy predictions while considering all related energy factors allows production plants,regulatory bodies,and governments to meet energy demand and assess the effects of energy-saving *** energy consumption falls within normal parameters,it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy *** objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily *** proposed model is an implementation of the stacked Long short-Term Memory(LsTM)snapshot ensemble combined with the Fast Fourier Transform(FFT)and *** and Berard’s Individual Household Electric-Power Consumption(IHEPC)dataset incorporated with weather data are used to analyse the model’s accuracy with predicting energy *** model is trained,and the results measured using Root Mean square Error(RMsE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and coefficient of determination(R^(2))metrics are 0.020,0.013,0.017,and 0.999,*** stacked LsTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized *** results of this study show that prediction accuracy is high,and the model’s stability is high as *** model shows that high levels of accuracy prove accurate predictive ability,and together with high levels of stability,the model has good interpretability,which is not typically accounted for in ***,this study shows that it can be inferred.

关键词： energy consumption prediction Artificial Intelligence(ai) Deep Learning(DL) snapshot ensemble

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Nonlinear optical response of niobium telluride and its application for demonstrating pulsed fiber lasers

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Journal of Materiomics 2024年第2期10卷 355-365页

作者： Xinxin shang Yule Zhang Tuo Li Huanian Zhang Xiaofeng Zou s.Wageh Ahmed A.Al-ghamdi Han Zhang shuhao si Dengwang Li shandong Province Key Laboratory of Medical Physics and Image Processing Technology Shandong Provincial Key Laboratory of Optics and Photonic DeviceSchool of Physics and ElectronicsShandong Normal UniversityJinan250014China Institute of Microscale Optoelectronics College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen518060China shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Co. Ltd.JinanShandongChina school of Physics and Optoelectronic Engineering Shandong University of TechnologyZibo255049China Department of Physics Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Niobium telluride(NbTe_(2)),a kind of few-layer two-dimensional(2D)transition metal dichalcogenides(TMDs)material,has been theoretically predicted with nonlinear absorption properties and excellent optical ***,we experimentally demonstrated an Er-doped fiber(EDF)laser based NbTe_(2)as saturable absorber(sA).Few-layer NbTe_(2)nanosheets were successfully prepared by adopting the commonly used liquid-phase exfoliation(LPE)*** nonlinear optical response of highly stable few-layer NbTe_(2)was investigated through an open-aperture Z-scan laser measurement,the nonlinear absorption coefficient was 2.45×10^(-11)m/*** Q-switched and mode-locked operation centered at 1559 nm were recorded based on NbTe_(2)*** pulse duration was varied from 4.88 ms to 1.75 ms,and the adjustable range of repetition frequency is changed from 44.01 kHz to 64.12 kHz in passively Q-switched ***,a constant repetition rate of 5.33 MHz and pulse width of 2.67 ps were observed in mode-locked *** experimental results fully reveal the nonlinear optical prop-erties of NbTe_(2)used in pulsed fiber lasers and broaden its ultrafast applications in the optics field.

关键词： Niobium telluride saturable absorber Z-scan Mode-locked pulse Fiber laser

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Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic

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Computer systems science & Engineering 2023年第8期46卷 1863-1877页

作者： abdullah s AL-Malaise AL-ghamdi sultanah MAlshammari Mahmoud Ragab Information systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia Center of Excellence in smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Notwithstanding the religious intention of billions of devotees,the religious mass gathering increased major public health concerns since it likely became a huge super spreading event for the severe acute respiratory syndrome coronavirus 2(sARs-CoV-2).Most attendees ignored preventive measures,namely maintaining physical distance,practising hand hygiene,and wearing *** a face mask in public areas protects people from spreading *** intelligence(ai)based on deep learning(DL)and machine learning(ML)could assist in fighting covid-19 in several *** study introduces a new deep learning-based Face Mask Detection in Religious Mass Gathering(DLFMD-RMG)technique during the COVID-19 *** DLFMD-RMG technique focuses mainly on detecting face masks in a religious mass *** accomplish this,the presented DLFMD-RMG technique undergoes two pre-processing levels:Bilateral Filtering(BF)and Contrast *** face detection,the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 *** addition,the face detection performance can be improved by the seeker optimization algorithm(sOA)for tuning the hyperparameter of the ResNet-50 module,showing the novelty of the *** last,the faces with and without masks are classified using the Fuzzy Neural Network(FNN)*** stimulation study of the DLFMD-RMG algorithm is examined on a benchmark *** results highlighted the remarkable performance of the DLFMD-RMG model algorithm in other recent approaches.

关键词： Religious mass gathering Hajj and Umrah covid-19 pandemic face mask computer vision deep learning

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Optimal Machine Learning Driven sentiment Analysis on COVID-19 Twitter Data

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Computers, Materials & Continua 2023年第4期75卷 81-97页

作者： Bahjat Fakieh abdullah s.AL-Malaise AL-ghamdi Farrukh saleem Mahmoud Ragab Information systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Information systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia

The outbreak of the pandemic,caused by Coronavirus Disease 2019(COVID-19),has affected the daily activities of people across the *** COVID-19 outbreak and the successive lockdowns,Twitter was heavily used and the number of tweets regarding COVID-19 increased *** studies used sentiment Analysis(sA)to analyze the emotions expressed through tweets upon ***,in current study,a new Artificial Bee Colony(ABC)with Machine Learning-driven sA(ABCMLsA)model is developed for conducting sentiment Analysis of COVID-19 Twitter *** prime focus of the presented ABCML-sA model is to recognize the sentiments expressed in tweets made *** involves data pre-processing at the initial stage followed by n-gram based feature extraction to derive the feature *** identification and classification of the sentiments,the support Vector Machine(sVM)model is *** last,the ABC algorithm is applied to fine tune the parameters involved in *** demonstrate the improved performance of the proposed ABCML-sA model,a sequence of simulations was *** comparative assessment results confirmed the effectual performance of the proposed ABCML-sA model over other approaches.

关键词： sentiment analysis twitter data data mining COVID-19 machine learning artificial bee colony

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Enhanced Artificial Intelligence-based Cybersecurity Intrusion Detection for Higher Education Institutions

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Computers, Materials & Continua 2022年第8期72卷 2895-2907页

作者： abdullah s.AL-Malaise AL-ghamdi Mahmoud Ragab Maha Farouk s.sabir Information systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt

As higher education institutions(HEIs)go online,several benefits are attained,and also it is vulnerable to several kinds of *** accomplish security,this paper presents artificial intelligence based cybersecurity intrusion detection models to accomplish *** incorporation of the strategies into business is a tendency among several distinct industries,comprising education,have recognized as game ***,the HEIs are highly related to the requirement and knowledge of the learner,making the education procedure highly ***,artificial intelligence(ai)and machine learning(ML)models have shown significant interest in *** study designs a novel Artificial Intelligence based Cybersecurity Intrusion Detection Model for Higher Education Institutions named aiCIDHEI *** goal of the aiCID-HEI technique is to determine the occurrence of distinct kinds of intrusions in higher education *** aiCID-HEI technique encompassesmin-max normalization approach to preprocess the ***,the aiCID-HEI technique involves the design of improved differential evolution algorithm based feature selection(IDEA-Fs)technique is applied to choose the feature ***,the bidirectional long short-term memory(BiLsTM)model is utilized for the detection and classification of intrusions in the ***,the Adam optimizer is applied for hyperparameter tuning to properly adjust the hyperparameters in higher educational *** order to validate the experimental results of the proposed aiCID-HEI technique,the simulation results of the aiCIDHEI technique take place by the use of benchmark *** experimental results reported the betterment of the aiCID-HEI technique over the other methods interms of different measures.

关键词： Higher education institutions deep learning machine learning cybersecurity intrusion detection

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Optimal Logistics Activities Based Deep Learning Enabled Traffic Flow Prediction Model

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Computers, Materials & Continua 2022年第12期73卷 5269-5282页

作者： Basim Aljabhan Mahmoud Ragab sultanah M.Alshammari abdullah s.Al-Malaise Al-ghamdi Ports and Maritime Transportation Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Excellence in smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia s 7Information systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia

Traffic flow prediction becomes an essential process for intelligent transportation systems(ITs).Though traffic sensor devices are manually controllable,traffic flow data with distinct length,uneven sampling,and missing data finds challenging for effective *** traffic data has been considerably increased in recent times which cannot be handled by traditional mathematical *** recent developments of statistic and deep learning(DL)models pave a way for the effectual design of traffic flow prediction(TFP)*** this view,this study designs optimal attentionbased deep learning with statistical analysis for TFP(OADLsA-TFP)*** presentedOADLsA-TFP model intends to effectually forecast the level of traffic in the *** attain this,the OADLsA-TFP model employs attention-based bidirectional long short-term memory(ABLsTM)model for predicting traffic *** order to enhance the performance of the ABLsTM model,the hyperparameter optimization process is performed using artificial fish swarm algorithm(AFsA).A wide-ranging experimental analysis is carried out on benchmark dataset and the obtained values reported the enhancements of the OADLsA-TFP model over the recent approaches mean square error(MsE),root mean square error(RMsE),and mean absolute percentage error(MAPE)of 120.342%,10.970%,and 8.146%respectively.

关键词： Traffic flow prediction deep learning artificial fish swarm algorithm mass gatherings statistical analysis logistics

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Intelligent Forensic Investigation Using Optimal stacked Autoencoder for Critical Industrial Infrastructures

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Computers, Materials & Continua 2022年第8期72卷 2275-2289页

作者： abdullah s.AL-Malaise AL-ghamdi Mahmoud Ragab F.J.Alsolami Hani Choudhry Ibrahim Rizqallah Alzahrani Information systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Computer science Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer science and Engineering Department College of Computer Science and EngineeringUniversity of Hafr Al BatinAl JamiahHafar Al Batin39524Saudi Arabia

Industrial Control systems(ICs)can be employed on the industrial processes in order to reduce the manual labor and handle the complicated industrial system processes as well as communicate *** of Things(IoT)integrates numerous sets of sensors and devices via a data network enabling independent *** incorporation of the IoT in the industrial sector leads to the design of Industrial Internet of Things(IIoT),which find use in water distribution system,power plants,*** the IIoT is susceptible to different kinds of attacks due to the utilization of Internet connection,an effective forensic investigation process becomes *** study offers the design of an intelligent forensic investigation using optimal stacked autoencoder for critical industrial *** proposed strategy involves the design of manta ray foraging optimization(MRFO)based feature selection with optimal stacked autoencoder(OsAE)model,named MFROFs-OsAE *** primary objective of the MFROFs-OsAE technique is to determine the presence of abnormal events in critical industrial ***-OsAE approach involves several subprocesses namely data gathering,data handling,feature selection,classification,and parameter ***,the MRFO based feature selection approach is designed for the optimal selection of feature ***,the OsAE based classifier is derived to detect abnormal events and the parameter tuning process is carried out via the coyote optimization algorithm(COA).The performance validation of the MFROFs-OsAE technique takes place using the benchmark dataset and the experimental results reported the betterment of the MFROFs-OsAE technique over the recent approaches interms of different measures.

关键词： Industrial control systems internet of things artificial intelligence feature selection deep learning

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Optimized Artificial Neural Network Techniques to Improve Cybersecurity of Higher Education Institution

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Computers, Materials & Continua 2022年第8期72卷 3385-3399页

作者： abdullah saad AL-Malaise AL-ghamdi Mahmoud Ragab Maha Farouk s.sabir Ahmed Elhassanein Ashraf A.Gouda Information systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Mathematics Department College of ScienceUniversity of BishaBishaSaudi Arabia Mathematics Department Faculty of ScienceDamanhour UniversityDamanhourEgypt

Education acts as an important part of economic growth and improvement in human *** educational sectors have transformed a lot in recent days,and Information and Communication Technology(ICT)is an effective part of the education *** every action in university and college,right from the process fromcounselling to admissions and fee deposits has been *** records,quiz,evaluation,mark,and grade submissions involved the utilization of the ***,security is essential to accomplish cybersecurity in higher security institutions(HEIs).In this view,this study develops an Automated Outlier Detection for Cybersecurity in Higher Education Institutions(AOD-CsHEI)*** AOD-CsHEI technique intends to determine the presence of intrusions or attacks in the *** AOD-CsHEI technique initially performs data pre-processing in two stages namely data conversion and class *** addition,the Adaptive synthetic(ADAsYN)technique is exploited for the removal of outliers in the ***,the sparrow search algorithm(ssA)with deep neural network(DNN)model is used for the classification of data into the existence or absence of intrusions in the HEIs ***,the ssA is utilized to effectually adjust the hyper parameters of the DNN *** order to showcase the enhanced performance of the AOD-CsHEI technique,a set of simulations take place on three benchmark datasets and the results reported the enhanced efficiency of the AOD-CsHEI technique over its compared methods with higher accuracy of 0.9997.

关键词： Higher security institutions intrusion detection system artificial intelligence deep neural network hyperparameter tuning deep learning

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Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning Model for Intrusion Detection system

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Computer systems science & Engineering 2023年第11期47卷 2497-2512页

作者： Mahmoud Ragab sultanah M.Alshammari abdullah s.Al-Malaise Al-ghamdi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The Internet of Things(IoT)system has confronted dramatic growth in high dimensionality and data *** system named intrusion detection systems(IDs)is broadly utilized for the enhancement of security posture in an IT *** IDs is a practical and suitable method for assuring network security and identifying attacks by protecting it from intrusive ***,machine learning(ML)-related techniques were used for detecting intrusion in IoTs ***,the IoT IDs mechanism faces significant challenges because of physical and functional *** IoT features use every attribute and feature for IDs self-protection unrealistic and *** study develops a Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning(MM-WMVEDL)model for *** proposed MM-WMVEDL technique aims to discriminate distinct kinds of attacks in the IoT *** attain this,the presented MM-WMVEDL technique implements min-max normalization to scale the input *** feature selection purposes,the MM-WMVEDL technique exploits the Harris hawk optimization-based elite fractional derivative mutation(HHO-EFDM)*** the presented MM-WMVEDL technique,a Bi-directional long short-term memory(BiLsTM),extreme learning machine(ELM)and an ensemble of gated recurrent unit(GRU)models take place.A wide range of simulation analyses was performed on CICIDs-2017 dataset to exhibit the promising performance of the MM-WMVEDL *** comparison study pointed out the supremacy of the MM-WMVEDL method over other recent methods with accuracy of 99.67%.

关键词： Internet of Things intrusion detection system machine learning ensemble deep learning metaheuristics

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