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Nursing Communications 2024年第2期8卷 1-5页

作者： Reham suliman al Kharyef Aisha ali al Harbi Mohammed Abdelhamied Genedy mohamed K.M.Khalil suliman mohamed al eidi Ministry of Health Riyadh 13352Saudi Arabia Consultant Public Health and Research Development.Cairo 11728 Egypt

Background:This study aimed to assess the occupational risks encountered by Healthcare Workers(HCWs)in Saudi Arabia during the COVID-19 ***:A cross-sectional survey was carried out from May to October *** a multi-stage stratified random sampling technique,an online questionnaire was sent to the recruited HCWs,across Saudi ***:Of the 768 HCWs recruited,702 participated in the survey.A significant majority(over 80%)reported working beyond 8 hours ***-19 infection,confirmed via PCR,was identified in 25%of the ***,infection was significantly correlated with direct or close contact(p=0.0007).Psychological distress was reported by 81%,with anxiety being the most prevalent(33%),followed by stress(19%),depression(17%),and insomnia(12%).Around 20%experienced headaches post-PPE use,while 14%reported adverse skin reactions,predominantly allergy and dermatitis.A concerning one-third of respondents reported exposure to ***:High infection rate,physical and psychological risks among HCWs especially those in direct contact with patients,reflect the need for enhancing the entirety of outbreak preparedness and response,specifically *** surveillance system,is crucial to adequately monitor and support HCWs during pandemic scenarios.

关键词： COVID-19 pandemic healthcare worker risk Saudi Arabia

Location-Aware Personalized Traveler Recommender System(LAPTA)Using Collaborative Filtering KNN

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Computers, Materials & Continua 2021年第11期69卷 1553-1570页

作者： Mohanad al-Ghobari Amgad Muneer suliman mohamed Fati School of Information Technology Asia Pacific UniversityKuala Lumpur57000Malaysia Department of Computer and Information Sciences Universiti Teknologi PETRONASSeri Iskandar32160Malaysia Department of Information Systems Prince Sultan UniversityRiyadh11586Saudi Arabia

Many tourists who travel to explore different cultures and cities worldwide aim to find the best tourist sites,accommodation,and food according to their *** objective makes it harder for tourists to decide and plan where to go and what to *** from hiring a local guide,an option which is beyond most travelers’budgets,the majority of sojourners nowadays use mobile devices to search for or recommend interesting sites on the basis of user ***,this work utilizes the prevalent recommender systems and mobile app technologies to overcome this ***,this study proposes location-aware personalized traveler assistance(LAPTA),a system which integrates user preferences and the global positioning system(GPS)to generate personalized and location-aware *** integration will enable the enhanced recommendation of the developed scheme relative to those from the traditional recommender systems used in customer ***,LAPTA separates the data obtained from Google locations into name and category *** the data separation,the system fetches the keywords from the user’s input according to the user’s past research *** proposed system uses the K-Nearest algorithm to match the name and category tags with the user’s input to generate personalized *** system also provides suggestions on the basis of nearby popular attractions using the Google point of interest feature to enhance system *** experimental results showed that LAPTA could provide more reliable and accurate recommendations compared to the reviewed recommendation applications.

关键词： LAPTA recommender system KNN collaborative filtering users’preference mobile application location awareness

Knowledge, attitude and practice of patients with type 2 diabetes mellitus towards complementary and alternative medicine

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Journal of Integrative Medicine 2016年第3期14卷 187-196页

作者： Sulaiman al-eidi Salwa Tayel Fatima al-Slail Naseem Akhtar Qureshi Imen Sohaibani mohamed Khalil Abdullah Mohammad al-Bedah Family & Community Medicine Faculty of Medicine King Saud University Ministry of Health National Center for Complementary and Alternative Medicine Ministry of Health National Diabetes Prevention & Control Program

OBJECTIVE： The use of complementary and alternative medicine（CAM） is relatively common among patients with type 2 diabetes mellitus（T2DM） around the world. Like many other countries, Saudi Arabia has a high incidence of T2DM, and incorporates the use of CAM in its treatment. This study explores the knowledge, attitude and practice（KAP） of CAM therapies among patients with T2DM and explores the relationships between sociodemographics and the KAP of CAM modalities. METHODS： This is a hospital-based, observational, cross-sectional study that selected 302 outpatients with T2DM, from Diabetic Center of King Salman bin Abdul-Aziz Hospital, in Riyadh city, in May 2014. Patients were interviewed using a predesigned questionnaire. RESULTS： The mean age of patients was（51.6 ± 10.6） years and 43.4% of them were males. The prevalence of CAM practices was 30.5%; 30.39% of them used herbs, 20.58% used wet cupping and 17.64% used nutritional supplements and other therapies. The factors found to predict the use of CAM therapies among patients with T2DM were： age above 51 years, unemployment and the participants’ knowledge about the effectiveness of CAM products. CONCLUSION： This preliminary study estimated the prevalence of CAM use, revealed positive attitude towards CAM and found three significant predictors of CAM use among patients with T2DM, which is consistent with regional data. A community-based research with a larger sample that targets T2DM population is needed in Saudi Arabia.

关键词： complementary therapies diabetes mellitus,type 2 prevalence Saudi Arabia

Finger Vein Authentication Based on Wavelet Scattering Networks

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Computers, Materials & Continua 2022年第8期72卷 3369-3383页

作者： Amjad Rehman Majid Harouni Maedeh Omidiravesh suliman mohamed Fati Saeed ali Bahaj Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering Dolatabad BranchIslamic Azad UniversityIsfahanIran Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityIsfahanIran MIS Department College of Business Administration Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia

Biometric-based authentication systems have attracted more attention than traditional authentication techniques such as passwords in the last two *** biometrics such as fingerprint,palm,iris,palm vein and finger vein and other biometrics have been *** of the challenges in biometrics is physical *** of finger vein is of the biometrics least exposed to physical *** methods have been proposed for authentication with the help of this biometric that suffer from weaknesses such as high computational complexity and low identification *** paper presents a novel method of scattering wavelet-based identity *** wavelet extracts image features from Gabor wavelet filters in a structure similar to convolutional neural *** distinguishes this algorithm from other popular feature extraction methods such as deep learning methods,filter-based methods,statistical methods,etc.,is that this algorithm has very high skill and accuracy in differentiating similar images but belongs to different classes,even when the image is subject to serious damage such as noise,angle changes or pixel location,this descriptor still generates feature vectors in away thatminimizes classifier *** improves classification and *** proposed method has been evaluated using two databases Finger Vein USM(FV-USM)and Homologous Multimodal biometrics Traits(SDUMLA-HMT).In addition to having reasonable computational complexity,it has recorded excellent identification rates in noise,rotation,and transmission *** best,it has a 98.2%identification rate for the SDUMLA-HMT database and a 96.1%identification rate for the FV-USM database.

关键词： Biometrics finger veins wavelet scattering vein authentication disaster risk reduction

Factors affecting improvement of fluorescence intensity of quartet and doublet state of NO diatomic molecule excited by glow discharge

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Chinese Physics B 2018年第8期27卷 356-363页

作者： Ahmed Asaad I Khalilt Reem A1-Tuwirqi mohamed Gondal Noura A1-suliman Department of Laser Sciences and Interactions National Institute of Laser Enhanced Sciences(NILES)Cairo UniversityGiza12613 Egypt Department of Physics Faculty of ScienceKing Abdulaziz UniversityJeddah 21551Saudi Arabia Department of Physics King Fahd University of Petroleum&MineralsDhahran 31261Saudi Arabia Department of Physics Faculty of Science for GirlsImam Abdulrahman Ben Faisal UniversityDammam 31441Saudi Arabia

We report on the observation of new fluorescence emission spectral transitions obtained from NO diatomic molecule in the region from ultraviolet （UV） to near infrared （NIR） in a low power glow discharge system. This glow discharge electronic excitation populates different quartet and doublet states of NO in its proximity such as the A2Z （/9 = 2）, b4∑- （υ= 3）, B2М （1） = 4）, and X2Л （u= 33-32） states. Due to inter-system crossing, emission lines originating from these levels to lower lying states are recorded and spectral line assignments are performed. The observed systems include b4∑- a4∏, B21∏-a4∏, a4∏-X2I∏, A2∑X2∏, X2∏-X2∏ （33-15）, X2∏-X2∏ （33-17）, X2∏-X2∏ （33-20）, and X2∏-X2∏ （33- 18）. This new information will conduce to the better understanding of the interesting features of NO molecule. Such parameters that affect the recording of low density of NO molecules are also discussed In addition to the factors such as the time evolution, argon gas concentration relative to NO mixture, the percentage of NO molecular gas concentration, discharge electric current signals and discharge applied voltage are studied. Those factors would enhance the fluorescence signal intensity of NO molecules. The recent results might be significant as reference data for optimizing the glow discharge spectrometer and diagnostics of NO gas.

关键词： fluorescence emission NO molecule quartet states intersystem crossing glow discharge

Optimal Deep Learning-based Cyberattack Detection and Classification Technique on Social Networks

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Computers, Materials & Continua 2022年第7期72卷 907-923页

作者： Amani Abdulrahman albraikan Siwar Ben Haj Hassine suliman mohamed Fati Fahd Nal-Wesabi Anwer Mustafa Hilal Abdelwahed Motwakel Manar Ahmed Hamza Mesfer al Duhayyim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self-Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Cyberbullying(CB)is a distressing online behavior that disturbs mental health *** studies have employed statistical and Machine Learning(ML)techniques for CB *** this motivation,the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification(ODL-CDC)technique for CB detection in social *** proposed ODL-CDC technique involves different processes such as pre-processing,prediction,and hyperparameter *** addition,GloVe approach is employed in the generation of word ***,the pre-processed data is fed into BidirectionalGated Recurrent Neural Network(BiGRNN)model for ***,hyperparameter tuning of BiGRNN model is carried out with the help of Search and Rescue Optimization(SRO)*** order to validate the improved classification performance of ODL-CDC technique,a comprehensive experimental analysis was carried out upon benchmark dataset and the results were inspected under varying aspects.A detailed comparative study portrayed the superiority of the proposed ODL-CDC technique over recent techniques,in terms of performance,with the maximum accuracy of 92.45%.

关键词： Cybersecurity cyberbullying social networks parameter tuning deep learning metaheuristics

A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

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Computers, Materials & Continua 2022年第3期70卷 5363-5381页

作者： Amgad Muneer Shakirah Mohd Taib suliman mohamed Fati Abdullateef O.Balogun Izzatdin Abdul Aziz Department of Computer and Information Sciences Universiti Teknologi PETRONASSeri Iskandar32160Malaysia Centre for Research in Data Science(CERDAS) Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Information Systems Department Prince Sultan UniversityRiyadh11586Saudi Arabia

Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world *** issues in this field still unsolved,so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big *** a phenomenon is referred to as the“curse of dimensionality”that affects traditional techniques in terms of both accuracy and ***,this research proposed a hybrid model based on Deep Autoencoder Neural Network(DANN)with five layers to reduce the difference between the input and *** proposed model was applied to a real-world gas turbine(GT)dataset that contains 87620 columns and 56 *** the experiment,two issues have been investigated and solved to enhance the *** first is the dataset class imbalance,which solved using SMOTE *** second issue is the poor performance,which can be solved using one of the optimization *** optimization algorithms have been investigated and tested,including stochastic gradient descent(SGD),RMSprop,Adam and ***,Adamax optimization algorithm showed the best results when employed to train *** experimental results show that our proposed model can detect the anomalies by efficiently reducing the high dimensionality of dataset with accuracy of 99.40%,F1-score of 0.9649,Area Under the Curve(AUC)rate of 0.9649,and a minimal loss function during the hybrid model training.

关键词： Anomaly detection outlier detection unsupervised learning autoencoder deep learning hybrid model

Statistical Histogram Decision Based Contrast Categorization of Skin Lesion Datasets Dermoscopic Images

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Computers, Materials & Continua 2021年第5期67卷 2337-2352页

作者： Rabia Javed Mohd Shafry Mohd Rahim Tanzila Saba suliman mohamed Fati Amjad Rehman Usman Tariq School of Computing Faculty of EngineeringUniversiti Teknologi Malaysia Johor Bahru81310Malaysia Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11589Saudi Arabia College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Most of the melanoma cases of skin cancer are the life-threatening form of *** is prevalent among the Caucasian group of people due to their light skin *** is the second most common cancer that hits the age group of 15–29 *** high number of cases has increased the importance of automated systems for *** diagnosis should be fast and accurate for the early treatment of *** should remove the need for biopsies and provide stable diagnostic *** requires large quantities of *** lesion datasets contain various kinds of dermoscopic images for the detection of *** publicly available benchmark skin lesion datasets,ISIC 2017,ISBI 2016,and PH2,are used for the ***,the ISIC archive and PH2 are the most challenging and demanding dermoscopic *** datasets’pre-analysis is necessary to overcome contrast variations,under or over segmented images boundary extraction,and accurate skin lesion *** this paper,we proposed the statistical histogram-based method for the pre-categorization of skin lesion *** image histogram properties are utilized to check the image contrast variations and categorized these images into high and low contrast *** two performance measures,processing time and efficiency,are computed for evaluation of the proposed *** results showed that the proposed methodology improves the pre-processing efficiency of 77%of ISIC 2017,67%of ISBI 2016,and 92.5%of PH2 datasets.

关键词： Cancer healthcare contrast enhancement dermoscopic images skin lesion low contrast images WHO

Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

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Computers, Materials & Continua 2021年第3期66卷 3161-3182页

作者： Ihsan Jasim Hussein M.A.Burhanuddin Mazin Abed Mohammed mohamed Elhoseny Begonya Garcia-Zapirain Marwah suliman Maashi Mashael S.Maashi BIOCORE Research Group Universiti Teknikal Malaysia MelakaMelaka76100Malaysia Director of UTeM International Centre BIOCORE Research GroupUniversiti Teknikal Malaysia MelakaMelaka76100Malaysia College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates eVIDA Laboratory University of DeustoBilbao48007Spain Medical Laboratory Science Department King Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia

One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of ***,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound *** main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer *** addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion *** study uses two databases in training and testing the proposed segmentation *** breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in *** of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound *** segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data *** the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.

关键词： Viola-Jones model breast cancer segmentation ovarian tumor ovarian tumor segmentation breast cancer ultrasound images active contour cascade model

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

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Computers, Materials & Continua 2021年第4期67卷 51-71页

作者： mohamed Elhoseny Mazin Abed Mohammed Salama A.Mostafa Karrar Hameed Abdulkareem Mashael S.Maashi Begonya Garcia-Zapirain Ammar Awad Mutlag Marwah suliman Maashi Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia College of Agriculture Al-Muthanna UniversitySamawah66001Iraq Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia eVIDA Lab University of DeustoBilbao48007Spain Department of Pure Science Ministry of EducationGeneral Directorate of CurriculaBaghdadIraq Department of Medical Laboratory Science Faculty of Applied Medical ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Heart disease(HD)is a serious widespread life-threatening *** heart of patients with HD fails to pump sufcient amounts of blood to the entire *** the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective *** methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related *** an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD ***,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with *** this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)*** MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD *** CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD *** evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation *** AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on ***,the AHDD system proposed herein has the highest capability to identify patients with *** system can be used by medical

关键词： Heart disease machine learning multi-agent feature wrapper model heart disease diagnosis HD cleveland datasets convolutional neural network