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| 1 | Total knee arthroplasty:Effect of obesity and other patients' characteristics on operative duration and outcome显示文摘AIM: To examine the effects of patients' characteristics mainly obesity on operative duration and other outcomemeasures of knee arthroplasty. METHODS: This is a retrospective chart review of 204 patients who had knee arthroplasty within the past five years(2007-2011) at King Abdulaziz Medical City in Riyadh, Kingdom of Saudi Arabia. The data collection form was developed utilizing the literature review to gather all the needed variables. Data were gathered from admission notes, nursing notes, operative reports and discharge summaries.RESULTS: A feasible sample of 204 patients were included in the study. Of those patients, 155(76%) were females. The mean age was 70.1 years for males(SD ± 9.4) and 62.7 years(SD ± 8) for females. Regarding the type of total knee replacement(TKR), 163(79.9%) patients had unilateral TKR and 41(20.1%) had bilateral TKR. Nine patients(4.4%) had a normal body mass index(BMI)(18.5 to < 25). Overweight patients(BMI 25 to < 30) represented 18.1%. Obesity class Ⅰ(BMI 30 to < 35) and obesity class Ⅱ(BMI from 35 to < 40) were present in 23% and 29.9% of the patients, respectively. Morbid obesity(BMI greater than 40) was present in 24.5%. The mean duration of surgery was 126.3 min(SD ± 30.8) for unilateral TKR and 216.6 min(SD ± 55.4) for bilateral TKR.The mean length of stay in the hospital was 12 d(SD ± 4.9). The complications that patients had after the operation included 2 patients(1%) who developed deep venous thrombosis, 2 patients(1%) developed surgical wound infections and none had pulmonary embolism. Patients' characteristics(including age, gender, BMI and co-morbidities) did not have an effect on the operative duration of knee replacement nor the length of hospital stay. CONCLUSION: Our study shows that obesity and other patients' characteristics do not have effect on the operative duration nor the length of hospital stay following TKR. | Abdulaziz Saud Al Turki Yazeed Al Dakhil Abdulah Al Turki Mazen Saleh Ferwana | 2015 | World Journal of Orthopedics2015，6，2： | 3 |
| 2 | Mor-phometrical analysis of the human mandibular canal: A CTinvestigation显示文摘 | de Oliveira Junior MR Saud AL Fonseca DR | 2011 | Surg Radiol Anat2011，33，4： | 1 |
| 3 | Morphometrical analysis of the human mandibular canal:a CT investigation显示文摘 | de Oliveira JM Saud AL Fonseca DR | 2011 | Surg Radio Anat2011，33，4： | 1 |
| 4 | Morphometrical analysis of the human mandibular canal: a CT investigation 显示文摘 | de Oliveira JM Saud AL Fonseca DR | 2011 | Surg Radiol Anat2011，33，4： | 1 |
| 5 | Morphometrical analysis of the human mandibular canal: A CT investigation 显示文摘 | de Oliveira Junior MR Saud AL Fonseca DR | 2011 | Surg Radiol Anat2011，33，4： | 1 |
| 6 | Using ASTER images to analyze geologic linear features in Wadi Aurnah basin,western Saudi Arabia显示文摘 | Al Saud M | 2008 | Open Remote Sense J2008，1，： | 1 |
| 7 | Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media显示文摘Cyberbullying(CB)is a challenging issue in social media and it becomes important to effectively identify the occurrence of CB.The recently developed deep learning(DL)models pave the way to design CB classifier models with maximum performance.At the same time,optimal hyperparameter tuning process plays a vital role to enhance overall results.This study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification(TLGODL-CBC)model in Social Media.The proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media context.Initially,the input data is cleaned and pre-processed to make it compatible for further processing.Followed by,independent recurrent autoencoder(IRAE)model is utilized for the recognition and classification of CBs.Finally,the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the work.To assuring the improved outcomes of the TLGODLCBC approach,a wide range of simulations are executed and the outcomes are investigated under several aspects.The simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches. | Mesfer Al Duhayyim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Abdullah Mohamed Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki | 2022 | Computers, Materials & Continua2022，，12： | 1 |
| 8 | Mor- phometrical analysis of the human mandibular canal: a CT investigation显示文摘 | de Oliveira Jfinior MR Saud AL Fonseca DR | 2011 | Surg Radiol Anat2011，33，4： | 1 |
| 9 | Efficient Wideband Channelizer for Software Radio Systems Using Modulated PR Fiherbanks显示文摘 | Wajih A Abu - Al - Saud Gordon L | 2004 | IEEE Transactions on Signal Processing2004，52，10： | 1 |
| 10 | Mor- phometrieal analysis of the human mandibular canal: a CT in- vestigation 显示文摘 | de Oliveira Junior MR Saud AL Fonseea DR | 2011 | Surg Radiol Anat2011，33，4： | 1 |
| 11 | Morphometrical analysis of the human mandibular canal: a CT investigation显示文摘 | de Oliveira JOnior MR Saud AL Fonseea DR | 2011 | Surg Radiol Anat2011，33，4： | 1 |
| 12 | Plant communities and reproductive phenology in mountainous regions of northern Libya显示文摘Within the semi-desert landscape of northern Libya, two sub-humid escarpments occur: Al-Akhdar in the east and Nafusa(Jabal Al-Gharbi) in the west. This study compares plant communities in the two regions, which are along an elevation gradient, in terms of species composition and diversity, frequency of different Raunkiaer life forms, and reproductive phenology. The two regions differed in species composition and life-form frequency between regions and between elevation zones within each region. Patterns were associated with the lower rainfall and lower moisture-holding capacity of soils at Nafusa,resulting in more xeric conditions. Only 13% of species were shared between the two regional landscapes. Species diversity, life-form frequency, and duration of the flowering–fruiting phenophase were all affected by elevation above sea level. The duration of flowering and fruiting in spring and fall was associated with environmental conditions, although there were different thresholds in the two regions. There was both a spring and fall episode of flowering at Nafusa, but only spring flowering at Al-Akhdar. It is anticipated that there will be a gradual shift of plant communities to higher elevations and loss of certain sensitive species in response to ongoing climate change. | Ahmad K. Hegazy Hanan F. Kabiel Saud L. Al-Rowaily Lesley Lovett-Doust Abd El-Nasser S. Al Borki | 2017 | Journal of Forestry Research2017，28，4： | 1 |
| 13 | Mor- pbometrical analysis of the human mandibular canal: a CT investigation 显示文摘 | de Oliveira J6nior MR Saud AL Fonseca DR | 2011 | Surg Radiol Anat2011，33，4： | 1 |
| 14 | Efficient wideband channelizer for software radio systems using modulated pr filterbanks显示文摘 | Abu - Al - Saud W A Studer G L | 2004 | IEEE Transactions on Signal Processing2004，52，10： | 1 |
| 15 | Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment显示文摘Cloud Computing(CC)is the most promising and advanced technology to store data and offer online services in an effective manner.When such fast evolving technologies are used in the protection of computerbased systems from cyberattacks,it brings several advantages compared to conventional data protection methods.Some of the computer-based systems that effectively protect the data include Cyber-Physical Systems(CPS),Internet of Things(IoT),mobile devices,desktop and laptop computer,and critical systems.Malicious software(malware)is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity,secrecy,and accessibility of the information.The current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification(OBDDBNCMC)model.The presentedOBDDBN-CMCmodel intends to recognize and classify the malware that exists in IoT-based cloud platform.To attain this,Zscore data normalization is utilized to scale the data into a uniform format.In addition,BDDBN model is also exploited for recognition and categorization of malware.To effectually fine-tune the hyperparameters related to BDDBN model,GrasshopperOptimizationAlgorithm(GOA)is applied.This scenario enhances the classification results and also shows the novelty of current study.The experimental analysis was conducted upon OBDDBN-CMC model for validation and the results confirmed the enhanced performance ofOBDDBNCMC model over recent approaches. | Mohammed Maray Hamed Alqahtani Saud S.Alotaibi Fatma S.Alrayes Nuha Alshuqayran Mrim M.Alnfiai Amal S.Mehanna Mesfer Al Duhayyim | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 16 | An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques显示文摘Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing waste.Therefore,waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management process.This study concentrates on the design of hazardous waste detection and classification using ensemble learning(HWDC-EL)technique to reduce toxicity and improve human health.The goal of the HWDC-EL technique is to detect the multiple classes of wastes,particularly hazardous and non-hazardous wastes.The HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns(DLBP),EfficientNet,and DenseNet121.In addition,the flower pollination algorithm(FPA)based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 models.Moreover,a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine(SVM),extreme learning machine(ELM),and gradient boosting tree(GBT).The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%. | Mesfer Al Duhayyim Saud S.Alotaibi Shaha Al-Otaibi Fahd N.Al-Wesabi Mahmoud Othman Ishfaq Yaseen Mohammed Rizwanullah Abdelwahed Motwakel | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 17 | A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM显示文摘Human-human interaction recognition is crucial in computer vision fields like surveillance,human-computer interaction,and social robotics.It enhances systems’ability to interpret and respond to human behavior precisely.This research focuses on recognizing human interaction behaviors using a static image,which is challenging due to the complexity of diverse actions.The overall purpose of this study is to develop a robust and accurate system for human interaction recognition.This research presents a novel image-based human interaction recognition method using a Hidden Markov Model(HMM).The technique employs hue,saturation,and intensity(HSI)color transformation to enhance colors in video frames,making them more vibrant and visually appealing,especially in low-contrast or washed-out scenes.Gaussian filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical method.Feature extraction uses the features from Accelerated Segment Test(FAST),Oriented FAST,and Rotated BRIEF(ORB)techniques.The application of Quadratic Discriminant Analysis(QDA)for feature fusion and discrimination enables high-dimensional data to be effectively analyzed,thus further enhancing the classification process.It ensures that the final features loaded into the HMM classifier accurately represent the relevant human activities.The impressive accuracy rates of 93%and 94.6%achieved in the BIT-Interaction and UT-Interaction datasets respectively,highlight the success and reliability of the proposed technique.The proposed approach addresses challenges in various domains by focusing on frame improvement,silhouette and feature extraction,feature fusion,and HMM classification.This enhances data quality,accuracy,adaptability,reliability,and reduction of errors. | Tanvir Fatima Naik Bukht Naif Al Mudawi Saud S.Alotaibi Abdulwahab Alazeb Mohammed Alonazi Aisha Ahmed AlArfaj Ahmad Jalal Jaekwang Kim | 2023 | Computers, Materials & Continua2023，77，11： | 0 |
| 18 | The expressional level of tankyrase-1 gene and its regulation in colorectal cancer in a Saudi population显示文摘Tankyrase1(TNKS1)plays an essential role in cancer progression by regulating telomere length.The study aimed to determine expression of TNKS1 and its regulation in colorectal cancer(CRC)in 20 samples from Saudi patients.mRNA expression of TNKS1 in CRC and paired normal tissues was measured by qRT-PCR.Epigenetic modification of TNKS1 promoter was determined by methylation-specific PCR while somatic mutation was analyzed by Sanger sequencing in exon 10 of the gene.All cancerous and normal tissues expressed TNKS1,but level of expression in CRC tissues was significantly associated with tumor stage though no other parameters;age,gender,and tumor location,showed any correlation.Expression of TNKS1 was markedly higher in earlier(I,II)than in later(Ⅲ,Ⅳ)stages of CRC development.Both cancerous and healthy tissues had unmethylated promoters.Sanger sequencing of exon 10 masked any somatic mutation in the samples.Our findings suggest that up-regulation of TNKS1 was inversely correlated with cancer progression in CRC,indicating that TNKS1 participates in the initiation of CRC by stabilizing telomere length in the first phase of cancer progression.Mechanisms other than TNKS1 might play a role in malignant tumor progression and telomere maintenance in the late stages of CRC. | HALA ABDULAZIZ M ALWARTHAN MOHAMMAD SAUD AL ANAZI NARASIMHA RPARINE RAMESA SHAFI BHAT GHADAH ALAMRO FTOON ALJARBOU SOOAD K AL-DAIHAN | 2019 | BIOCELL2019，43，2： | 0 |
| 19 | Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment显示文摘In recent times,cities are getting smart and can be managed effectively through diverse architectures and services.Smart cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic diseases.With latest developments in digital data collection,AI techniques can be employed for clinical decision making process.On the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the globe.Generally,wearables can be employed in healthcare systems that instigate the development of CVD detection and classification.With this motivation,the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC technique.The proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data collection.Then,the collected data is saved in cloud server for examination.Followed by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health condition.To accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)technique.In addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of CVD.The experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models. | Hanan Abdullah Mengash Lubna A.Alharbi Saud S.Alotaibi Sarab AlMuhaideb Nadhem Nemri Mrim M.Alnfiai Radwa Marzouk Ahmed S.Salama Mesfer Al Duhayyim | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 20 | Evaluation of blended internet and face-to-face continuous medical education for cupping providers in Saudi Arabia显示文摘Background:In regulating cupping therapy in Saudi Arabia,there was a need to develop a system for continuous medical and professional development for cupping practitioners.The current study aimed to evaluate the effect of blended-internet continuing-medical-education(CME)on knowledge,knowledge retention,and intellectual skills in cupping providers compared with conventional,face-to-face CME.Methods:Participants were recruited from a list of registered cupping providers(physicians,nurses,and physiotherapists trained and licensed to practice cupping therapy)for a randomized,open,controlled trial.After providing informed consent,participants were allocated into three groups(classes).Classes were randomized to(1)blended-internet CME(n=31);(2)conventional,face-to-face CME(n=31);and(3)control group receiving no intervention(n=32).Results:Posttest knowledge scores were increased to a greater extent by blended-internet CME training compared to conventional CME(P=0.002)or no training(P<0.001).Similarly,knowledge retention was significantly higher in the blended-internet CME group compared to the conventional CME group(P=0.011).Posttest skills scores immediately after the course were significantly higher in the blended-internet CME group compared to the conventional CME group(mean difference:1.408,95%confidence interval:0.728–2.088,P<0.001).Conclusion:Although the current study aimed to determine if blended-internet CME is as effective as conventional,face-to-face CME,the results showed that cupping providers who participated in blended-internet CME had significantly higher scores for posttest knowledge,knowledge retention,and posttest intellectual skills. | Abdullah A.Al-Mudaiheem Nasser A.Al-Hamdan Abdullah AlBedah Saud Al Sanad Tamer S.Aboushanab Ahmed T.Elolemy Mohamed K.M.Khalil | 2021 | TMR Non-Drug Therapy2021，4，1： | 0 |
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