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| 1 | Eff-ect of dexmedetomidine premedication on the intraocular pressure changes after succinylcholine and intubation显示文摘 | MOWAFI H A ALDOSSARY N ISMAIL S A | 2008 | Br J Anaesth2008，100，4： | 1 |
| 2 | Effect of dexmedetomidine premedication on the intraocular pressure changes after succinylcholine and intubation 显示文摘 | Mowafi HA Aldossary N Ismail SA | 2008 | Br J Anaesth2008，100，4： | 1 |
| 3 | Effect of dexmedetomidine premedication on the intraocular pressure changes after succinylcholine and intubation 显示文摘 | Mowafi HA Aldossary N Ismail SA | 2008 | Br J Anaesth2008，100，4： | 1 |
| 4 | Effect of dexmedeto- midine premedication on the intraocular pressure changes after succinylcholine and intubation 显示文摘 | Mowafi HA Aldossary N Ismail SA | 2008 | Br J Anaesth2008，100，4： | 1 |
| 5 | Effect of dexmedetomidine premedication on the intraocular pressure changes after succinylcholine and intubation显示文摘 | Mowafi HA Aldossary N Ismail SA | | 0，，04： | 1 |
| 6 | Effect of Dexmedetomidine premedication on the intraocular pressure changes after succinylcholine and intubation显示文摘 | Mowafi HA Aldossary N Ismail SA | 2008 | Br J Anaesth2008，100，4： | 1 |
| 7 | Domestic Energy ConsumptionPatterns in a Hot and Arid Climate: A multiple-case Study Analysis显示文摘 | Aldossary N A Rezgui Y Kwan A | 2013 | Renewable Energy2013，，62： | 1 |
| 8 | Effect of dexmedeto- midine premedication on the intraocular pressure changes after succinylcholine and intubation显示文摘 | Mowafi HA Aldossary N Ismail SA | 2008 | Br J Anaesth2008，100，4： | 1 |
| 9 | DeepGan-Privacy Preserving of HealthCare System Using DL显示文摘The challenge of encrypting sensitive information of a medical image in a healthcare system is still one that requires a high level of computing complexity,despite the ongoing development of cryptography.After looking through the previous research,it has become clear that the security issues still need to be looked into further because there is room for expansion in the research field.Recently,neural networks have emerged as a cost-effective and effective optimization strategy in terms of providing security for images.This revelation came about as a result of current developments.Nevertheless,such an implementation is a technique that is expensive to compute and does not handle the huge variety of different assaults that may be made on pictures.The primary objective of the system that has been described is to provide evidence of a complex framework in which deep neural networks have been applied to improve the efficiency of basic encryption techniques.Our research has led to the development and proposal of an enhanced version of methods that have previously been used to encrypt pictures.Instead,the generative adversarial network(GAN),commonly known as GAN,will serve as the learning network that generates the private key.The transformation domain,which reflects the one-of-a-kind fashion of the private key that is to be formed,is also meant to lead the learning network in the process of actually accomplishing the private key creation procedure.This scheme may be utilized to train an excellent Deep Neural Networks(DNN)model while instantaneously maintaining the confidentiality of training medical images.It was tested by the proposed approach DeepGAN on open-source medical datasets,and three sets of data:The Ultrasonic Brachial Plexus,the Montgomery County Chest X-ray,and the BraTS18.The findings indicate that it is successful in maintaining both performance and privacy,and the findings of the assessment and the findings of the security investigation suggest that the development of suitable generation technologies is capable of generating private keys with a high level of security. | Sultan Mesfer Aldossary | 2023 | Intelligent Automation & Soft Computing2023，37，8： | 0 |
| 10 | A Hybrid Approach for Performance and Energy-Based Cost Prediction in Clouds显示文摘With the striking rise in penetration of Cloud Computing,energy consumption is considered as one of the key cost factors that need to be managed within cloud providers’infrastructures.Subsequently,recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources,where the energy consumption and the operational costs are minimized.However,to make better cost decisions in these strategies,the performance and energy awareness should be supported at both Physical Machine(PM)and Virtual Machine(VM)levels.Therefore,in this paper,a novel hybrid approach is proposed,which jointly considered the prediction of performance variation,energy consumption and cost of heterogeneous VMs.This approach aims to integrate auto-scaling with live migration as well as maintain the expected level of service performance,in which the power consumption and resource usage are utilized for estimating the VMs’total cost.Specifically,the service performance variation is handled by detecting the underloaded and overloaded PMs;thereby,the decision(s)is made in a cost-effective manner.Detailed testbed evaluation demonstrates that the proposed approach not only predicts the VMs workload and consumption of power but also estimates the overall cost of live migration and auto-scaling during service operation,with a high prediction accuracy on the basis of historical workload patterns. | Mohammad Aldossary | 2021 | Computers, Materials & Continua2021，，9： | 0 |
| 11 | Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things显示文摘In this paper,the Internet ofMedical Things(IoMT)is identified as a promising solution,which integrates with the cloud computing environment to provide remote health monitoring solutions and improve the quality of service(QoS)in the healthcare sector.However,problems with the present architectural models such as those related to energy consumption,service latency,execution cost,and resource usage,remain a major concern for adopting IoMT applications.To address these problems,this work presents a four-tier IoMT-edge-fog-cloud architecture along with an optimization model formulated using Mixed Integer Linear Programming(MILP),with the objective of efficiently processing and placing IoMT applications in the edge-fog-cloud computing environment,while maintaining certain quality standards(e.g.,energy consumption,service latency,network utilization).A modeling environment is used to assess and validate the proposed model by considering different traffic loads and processing requirements.In comparison to the other existing models,the performance analysis of the proposed approach shows a maximum saving of 38%in energy consumption and a 73%reduction in service latency.The results also highlight that offloading the IoMT application to the edge and fog nodes compared to the cloud is highly dependent on the tradeoff between the network journey time saved vs.the extra power consumed by edge or fog resources. | Hatem A.Alharbi Barzan A.Yosuf Mohammad Aldossary Jaber Almutairi | 2023 | Computer Systems Science & Engineering2023，47，10： | 0 |
| 12 | Investigating and Modelling of Task Offloading Latency in Edge-Cloud Environment显示文摘Recently,the number of Internet of Things(IoT)devices connected to the Internet has increased dramatically as well as the data produced by these devices.This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing.However,different service architecture and offloading strategies have a different impact on the service time performance of IoT applications.Therefore,this paper presents an Edge-Cloud system architecture that supports scheduling offloading tasks of IoT applications in order to minimize the enormous amount of transmitting data in the network.Also,it introduces the offloading latency models to investigate the delay of different offloading scenarios/schemes and explores the effect of computational and communication demand on each one.A series of experiments conducted on an EdgeCloudSim show that different offloading decisions within the Edge-Cloud system can lead to various service times due to the computational resources and communications types.Finally,this paper presents a comprehensive review of the current state-of-the-art research on task offloading issues in the Edge-Cloud environment. | Jaber Almutairi Mohammad Aldossary | 2021 | Computers, Materials & Continua2021，，9： | 0 |
| 13 | DL-Powered Anomaly Identification System for Enhanced IoT Data Security显示文摘In many commercial and public sectors,the Internet of Things(IoT)is deeply embedded.Cyber security threats aimed at compromising the security,reliability,or accessibility of data are a serious concern for the IoT.Due to the collection of data from several IoT devices,the IoT presents unique challenges for detecting anomalous behavior.It is the responsibility of an Intrusion Detection System(IDS)to ensure the security of a network by reporting any suspicious activity.By identifying failed and successful attacks,IDS provides a more comprehensive security capability.A reliable and efficient anomaly detection system is essential for IoT-driven decision-making.Using deep learning-based anomaly detection,this study proposes an IoT anomaly detection system capable of identifying relevant characteristics in a controlled environment.These factors are used by the classifier to improve its ability to identify fraudulent IoT data.For efficient outlier detection,the author proposed a Convolutional Neural Network(CNN)with Long Short Term Memory(LSTM)based Attention Mechanism(ACNN-LSTM).As part of the ACNN-LSTM model,CNN units are deployed with an attention mechanism to avoid memory loss and gradient dispersion.Using the N-BaIoT and IoT-23 datasets,the model is verified.According to the N-BaIoT dataset,the overall accuracy is 99%,and precision,recall,and F1-score are also 0.99.In addition,the IoT-23 dataset shows a commendable accuracy of 99%.In terms of accuracy and recall,it scored 0.99,while the F1-score was 0.98.The LSTM model with attention achieved an accuracy of 95%,while the CNN model achieved an accuracy of 88%.According to the loss graph,attention-based models had lower loss values,indicating that they were more effective at detecting anomalies.In both the N-BaIoT and IoT-23 datasets,the receiver operating characteristic and area under the curve(ROC-AUC)graphs demonstrated exceptional accuracy of 99%to 100%for the Attention-based CNN and LSTM models.This indicates that these models are capable of making precise predictions. | Manjur Kolhar Sultan Mesfer Aldossary | 2023 | Computers, Materials & Continua2023，77，12： | 0 |
| 14 | Secured Framework for Assessment of Chronic Kidney Disease in Diabetic Patients显示文摘With the emergence of cloud technologies,the services of healthcare systems have grown.Simultaneously,machine learning systems have become important tools for developing matured and decision-making computer applications.Both cloud computing and machine learning technologies have contributed significantly to the success of healthcare services.However,in some areas,these technologies are needed to provide and decide the next course of action for patients suffering from diabetic kidney disease(DKD)while ensuring privacy preservation of the medical data.To address the cloud data privacy problem,we proposed a DKD prediction module in a framework using cloud computing services and a data control scheme.This framework can provide improved and early treatment before end-stage renal failure.For prediction purposes,we implemented the following machine learning algorithms:support vector machine(SVM),random forest(RF),decision tree(DT),naïve Bayes(NB),deep learning(DL),and k nearest neighbor(KNN).These classification techniques combined with the cloud computing services significantly improved the decision making in the progress of DKD patients.We applied these classifiers to the UCI Machine Learning Repository for chronic kidney disease using various clinical features,which are categorized as single,combination of selected features,and all features.During single clinical feature experiments,machine learning classifiers SVM,RF,and KNN outperformed the remaining classification techniques,whereas in combined clinical feature experiments,the maximum accuracy was achieved for the combination of DL and RF.All the feature experiments presented increased accuracy and increased F-measure metrics from SVM,DL,and RF. | Sultan Mesfer Aldossary | 2023 | Intelligent Automation & Soft Computing2023，，6： | 0 |
| 15 | An Eco-Friendly Approach for Reducing Carbon Emissions in Cloud Data Centers显示文摘Based on the Saudi Green initiative,which aims to improve the Kingdom’s environmental status and reduce the carbon emission of more than 278 million tons by 2030 along with a promising plan to achieve netzero carbon by 2060,NEOM city has been proposed to be the“Saudi hub”for green energy,since NEOM is estimated to generate up to 120 Gigawatts(GW)of renewable energy by 2030.Nevertheless,the Information and Communication Technology(ICT)sector is considered a key contributor to global energy consumption and carbon emissions.The data centers are estimated to consume about 13%of the overall global electricity demand by 2030.Thus,reducing the total carbon emissions of the ICT sector plays a vital factor in achieving the Saudi plan to minimize global carbon emissions.Therefore,this paper aims to propose an eco-friendly approach using a Mixed-Integer Linear Programming(MILP)model to reduce the carbon emissions associated with ICT infrastructure in Saudi Arabia.This approach considers the Saudi National Fiber Network(SNFN)as the backbone of Saudi Internet infrastructure.First,we compare two different scenarios of data center locations.The first scenario considers a traditional cloud data center located in Jeddah and Riyadh,whereas the second scenario considers NEOM as a potential cloud data center new location to take advantage of its green energy infrastructure.Then,we calculate the energy consumption and carbon emissions of cloud data centers and their associated energy costs.After that,we optimize the energy efficiency of different cloud data centers’locations(in the SNFN)to reduce the associated carbon emissions and energy costs.Simulation results show that the proposed approach can save up to 94%of the carbon emissions and 62%of the energy cost compared to the current cloud physical topology.These savings are achieved due to the shifting of cloud data centers from cities that have conventional energy sources to a city that has rich in renewable energy sources.Finally,we design a heuristic algorithm to verify the proposed approach,and it gives equivalent results to the MILP model. | Mohammad Aldossary Hatem A.Alharbi | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 16 | Relative Time Quantum-based Enhancements in Round Robin Scheduling显示文摘Modern human life is heavily dependent on computing systems and one of the core components affecting the performance of these systems is underlying operating system.Operating systems need to be upgraded to match the needs of modern-day systems relying on Internet of Things,Fog computing and Mobile based applications.The scheduling algorithm of the operating system dictates that how the resources will be allocated to the processes and the Round Robin algorithm(RR)has been widely used for it.The intent of this study is to ameliorate RR scheduling algorithm to optimize task scheduling.We have carried out an experimental study where we have developed four variations of RR,each algorithm considers three-time quanta and the performance of these variations was compared with the RR algorithm,and results highlighted that these variations performed better than conventional RR algorithm.In the future,we intend to develop an automated scheduler that can determine optimal algorithm based on the current set of processes and will allocate time quantum to the processes intelligently at the run time.This way the task performance of modern-day systems can be improved to make them more efficient. | Sardar Zafar Iqbal Hina Gull Saqib Saeed Madeeha Saqib Mohammed Alqahtani Yasser A.Bamarouf Gomathi Krishna May Issa Aldossary | 2022 | Computer Systems Science & Engineering2022，41，5： | 0 |
| 17 | Exploring and Modelling IoT Offloading Policies in Edge Cloud Environments显示文摘The Internet of Things(IoT)has recently become a popular technology that can play increasingly important roles in every aspect of our daily life.For collaboration between IoT devices and edge cloud servers,edge server nodes provide the computation and storage capabilities for IoT devices through the task offloading process for accelerating tasks with large resource requests.However,the quantitative impact of different offloading architectures and policies on IoT applications’performance remains far from clear,especially with a dynamic and unpredictable range of connected physical and virtual devices.To this end,this work models the performance impact by exploiting a potential latency that exhibits within the environment of edge cloud.Also,it investigates and compares the effects of loosely-coupled(LC)and orchestrator-enabled(OE)architecture.The LC scheme can smoothly address task redistribution with less time consumption for the offloading sceneries with small scale and small task requests.Moreover,the OE scheme not only outperforms the LC scheme in the large-scale tasks requests and offloading occurs but also reduces the overall time by 28.19%.Finally,to achieve optimized solutions for optimal offloading placement with different constraints,orchestration is important. | Jaber Almutairi Mohammad Aldossary | 2022 | Computer Systems Science & Engineering2022，41，5： | 0 |
| 18 | Experimental Study of Heat Transfer Enhancement in Solar Tower Receiver Using Internal Fins显示文摘The receiver is an important element in solar energy plants.The principal receiver’s tubes in power plants are devised to work under extremely severe conditions,including excessive heat fluxes.Half of the tube’s circumference is heated whilst the other half is insulated.This study aims to improve the heat transfer process and reinforce the tubes’structure by designing a new receiver;by including longitudinal fins of triangular,circular and square shapes.The research is conducted experimentally using Reynolds numbers ranging from 28,000 to 78,000.Triangular fins have demonstrated the best improvement for heat transfer.For Reynolds number value near 43,000 Nusselt number(Nu)is higher by 3.5%and 7.5%,sequentially,compared to circular and square tube fins,but varies up to 6.5%near Re=61000.The lowest friction factor is seen in a triangular fin receiver;where it deviates from circular fins by 4.6%,and square fin tubes by 3.2%.Adding fins makes the temperature decrease gradually,and in the case of no fins,the temperature gradient between the hot tube and water drops sharply in the planed tube by 7%. | Hashem Shatnawi Chin Wai Lim Firas Basim Ismail Abdulrahman Aldossary | 2021 | Computers, Materials & Continua2021，，8： | 0 |
| 19 | Synchronous carotid endarterectomy and coronary artery bypass graft: Four case reports显示文摘BACKGROUND One of the major perioperative complications for coronary artery bypass graft(CABG)is stroke.The risk of perioperative stroke after CABG is approximately 2%.Carotid stenosis(CS)is considered an independent predictor of perioperative stroke risk in CABG patients.The optimal management of such patients has been a source of controversy.One of the possible surgical options is synchronous carotid endarterectomy(CEA)and CABG.Here,we have presented 4 cases of successful synchronous CEA and CABG.Our center’s experience with 4 cases of significant carotid artery stenosis,which were successfully managed with combined CEA and CABG,are detailed.The first case was a female who presented for CABG after a ST-elevation myocardial infarction.She had right internal carotid artery(ICA)occlusion and 90%left ICA stenosis.The second case was a male who was electively admitted for CABG.It was discovered that he had left ICA occlusion and 90%right ICA stenosis.The third case was a male with a history of stroke,two months prior to admission.He presented with non-ST-elevation myocardial infarction.Preoperatively,it was discovered that he had>90%right ICA stenosis.The final case was a male who was electively admitted for CABG.It was discovered that he had bilateral>90%ICA stenosis.We have also reviewed the current evidence and guidelines for managing CS in patients undergoing CABG.CONCLUSION Our case series demonstrated that synchronous CEA and CABG was safe.A multicenter study with additional patients is needed.It is necessary for clinicians to screen for CS in high-risk patients with features. | Faisal Khader AlGhamdi Abdulmajeed Altoijry Abdulrahman AlQahtani Mohammed Yousef Aldossary Sultan Omar AlSheikh Kaisor Iqbal Walid Abdulaziz Alayadhi | 2023 | World Journal of Clinical Cases2023，11，36： | 0 |
| 20 | A Review of Energy-Related Cost Issues and Prediction Models in Cloud Computing Environments显示文摘With the expansion of cloud computing,optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important,since it directly affects providers’revenue and customers’payment.Thus,providing prediction information of the cloud services can be very beneficial for the service providers,as they need to carefully predict their business growths and efficiently manage their resources.To optimize the use of cloud services,predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs.However,such mechanisms need to be provided with energy awareness not only at the level of the Physical Machine(PM)but also at the level of the Virtual Machine(VM)in order to make improved cost decisions.Therefore,this paper presents a comprehensive literature review on the subject of energy-related cost issues and prediction models in cloud computing environments,along with an overall discussion of the closely related works.The outcomes of this research can be used and incorporated by predictive resource management techniques to make improved cost decisions assisted with energy awareness and leverage cloud resources efficiently. | Mohammad Aldossary | 2021 | Computer Systems Science & Engineering2021，36，2： | 0 |
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