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| 1 | NAC Transcription Factor ORE1 and Senescence- Induced BIFUNCTIONAL NUCLEASE1 (BFN1) Constitute a Regulatory Cascade in Arabidopsis显示文摘老朽是包含很多抄写因素的行动的一个高度调整的过程。NAC 抄写因素 ORE1 (ANAC092 ) 最近被显示了在断然在 Arabidopsis thaliana 控制老朽起一个关键作用;然而,它通过施加它的分子的功能的直接目标基因都没以前被识别。这里,我们报导那 BIFUNCTIONAL NUCLEASE1 (BFN1 ) ,著名提高老朽的基因,被 ORE1 直接调整。我们已经检测了 BFN1 的提高的表示在在 estradiol 可诱导的 ORE1 overexpression 线的 ORE1 的正式就职以后的 2 h 和在有 35S:ORE1 的 Arabidopsis 叶肉房间原物的 transfection 以后的 6 h 构造。当在衰老的叶子和成熟的花机关的截去地区的 BFN1 表示在 ore1 异种背景是几乎不在的时, ORE1 和 BFN1 表示模式大部分重叠,由 promoterreporter 显示出基因(GUS ) 熔化。在 vitro 绑定地点试金揭示了一个由两部组成的 ORE1 有约束力的地点,类似于 ORS1 的, ORE1 的 paralog。一个由两部组成的 ORE1 有约束力的地点在 BFN1 倡导者被识别;变异在全身的 BFN1 倡导者的上下文以内的 cis 元素急速地减少了调停 ORE1 的 transactivation 能力在短暂地 transfected Arabidopsis 叶肉房间原物。而且,染色质 immunoprecipitation (薄片) 在 ORE1 有约束力的 vivo 示威到 BFN1 倡导者。我们也在 vivo 表明 ORE1 的绑定到二另外的联系老朽的基因的倡导者,也就是 SAG29/SWEET15 和 SINA1,在老朽期间支持 ORE1 的中央角色。 | Lilian R Matallana-Ramirez Mamoona Rauf Sarit Farage-Barhom Hakan Dortay Gang-Ping Xue Wolfgang Droge-Laser Amnon Lers Salma Balazadeh Bernd Mueller-Roeber | 2013 | Molecular Plant2013，6，5： | 8 |
| 2 | 眼血流的变化对青光眼病情进展的影响(英文)显示文摘青光眼是由多种因素引起的神经退行性疾病,眼压过高会损害视神经而导致永久性视力丧失。虽然青光眼的基本病理生理机制尚未确定,但眼组织如视神经,视网膜,脉络膜以及虹膜的血流改变是青光眼发病的重要危险因素。由于不同因素所引发的视神经损害的有限认知,测量方法和治疗方面缺乏,人们对青光眼的理解存在障碍。尽管研究人员在不断地积累证据,力证眼血流的变化在青光眼发病机制中起着重要的作用,但大部分情况下,对于眼血流的变化和青光眼的患病风险之间的关系,他们都持有多样甚至矛盾的结论。本文中,我们回顾了青光眼的不同方面以及眼血流在疾病发展中的影响。 | Sher Zaman Safi Mamoona Noreen Muhammad Imran Yasir Waheed Muhammad Imran Amir Miraj Ul Hussain Shah Nawshad Muhammad | 2017 | 国际眼科杂志2017，17，3： | 3 |
| 3 | Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks显示文摘Internet of Things(IoT)devices work mainly in wireless mediums;requiring different Intrusion Detection System(IDS)kind of solutions to leverage 802.11 header information for intrusion detection.Wireless-specific traffic features with high information gain are primarily found in data link layers rather than application layers in wired networks.This survey investigates some of the complexities and challenges in deploying wireless IDS in terms of data collection methods,IDS techniques,IDS placement strategies,and traffic data analysis techniques.This paper’s main finding highlights the lack of available network traces for training modern machine-learning models against IoT specific intrusions.Specifically,the Knowledge Discovery in Databases(KDD)Cup dataset is reviewed to highlight the design challenges of wireless intrusion detection based on current data attributes and proposed several guidelines to future-proof following traffic capture methods in the wireless network(WN).The paper starts with a review of various intrusion detection techniques,data collection methods and placement methods.The main goal of this paper is to study the design challenges of deploying intrusion detection system in a wireless environment.Intrusion detection system deployment in a wireless environment is not as straightforward as in the wired network environment due to the architectural complexities.So this paper reviews the traditional wired intrusion detection deployment methods and discusses how these techniques could be adopted into the wireless environment and also highlights the design challenges in the wireless environment.The main wireless environments to look into would be Wireless Sensor Networks(WSN),Mobile Ad Hoc Networks(MANET)and IoT as this are the future trends and a lot of attacks have been targeted into these networks.So it is very crucial to design an IDS specifically to target on the wireless networks. | Vasaki Ponnusamy Mamoona Humayun NZJhanjhi Aun Yichiet Maram Fahhad Almufareh | 2022 | Computer Systems Science & Engineering2022，40，3： | 2 |
| 4 | TLR4 polymorphisms and disease susceptibility显示文摘 | Mamoona Noreen Muhammad Ali A. Shah Sheeba Murad Mall Shazia Choudhary Tahir Hussain Iltaf Ahmed Syed Fazal Jalil Muhammad Imran Raza | 2012 | Inflammation Research2012，，3： | 2 |
| 5 | Prediction of COVID-19 Cases Using Machine Learning for Effective Public Health Management显示文摘COVID-19 is a pandemic that has affected nearly every country in the world.At present,sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans.However,widespread diseases,such as COVID-19,create numerous challenges to this goal,and some of those challenges are not yet defined.In this study,a Shallow Single-Layer Perceptron Neural Network(SSLPNN)and Gaussian Process Regression(GPR)model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions:namely,China,South Korea,Japan,Saudi Arabia,and Pakistan.Significant environmental and non-environmental features were taken as the input dataset,and confirmed COVID-19 cases were taken as the output dataset.A correlation analysis was done to identify patterns in the cases related to fluctuations in the associated variables.The results of this study established that the population and air quality index of a region had a statistically significant influence on the cases.However,age and the human development index had a negative influence on the cases.The proposed SSLPNN-based classification model performed well when predicting the classes of confirmed cases.During training,the binary classification model was highly accurate,with a Root Mean Square Error(RMSE)of 0.91.Likewise,the results of the regression analysis using the GPR technique with Matern 5/2 were highly accurate(RMSE=0.95239)when predicting the number of confirmed COVID-19 cases in an area.However,dynamic management has occupied a core place in studies on the sustainable development of public health but dynamic management depends on proactive strategies based on statistically verified approaches,like Artificial Intelligence(AI).In this study,an SSLPNN model has been trained to fit public health associated data into an appropriate class,allowing GPR to predict the number of confirmed COVID-19 cases in an area based on the given values of selected parameters. Therefore, this tool can help authorities in different ecological settingseffectively manage COVID-19. | Fahad Ahmad Saleh N.Almuayqil Mamoona Humayun Shahid Naseem Wasim Ahmad Khan Kashaf Junaid | 2021 | Computers, Materials & Continua2021，，3： | 1 |
| 6 | Facial expression and paininthe critically ill non-communicative patient:state of science review显示文摘 | Mamoona AR Mary JG | | 0，，06： | 1 |
| 7 | TLR4 polymorphisms and disease susceptibility显示文摘 | Mamoona Noreen Muhammad Ali A. Shah Sheeba Murad Mall Shazia Choudhary Tahir Hussain Iltaf Ahmed Syed Fazal Jalil Muhammad Imran Raza | 2012 | Inflammation Research2012，，3： | 1 |
| 8 | Polymorphisms in ghrelin and heparan sulfate proteoglycan genes and their association with diabetic nephropathy in Pakistani population显示文摘Diabetic nephropathy(DN),a long term complication of diabetes,is the most common cause of end-stage renal disease,increasing the risk of death.Genetic predispositions play an important role in determining the susceptibility of the development of DN.Heparan sulphate proteoglycan(HSPG) and ghrelin(GH) gene polymorphisms are associated with the risk of DN.T allele frequency of the HSPG gene determined by BamHI polymorphism located in intron 6 may be a risk factor for the development of renal dysfunction in DN(Fisher two tailed test,CI = 95%,d.f.= 29,P = 0.016).The ghrelin gene polymorphism is caused by a cytosine-to-adenine transition in exon 2 of the preproghrelin gene forming Leu72Met variant.In Pakistani population,the preproghrelin Leu72Met polymorphism was observed to be not associated with diabetic nephropathy in patients as indicated by statistical analysis(CI = 95%,d.f.= 29,P = 0.691).The allelic frequencies of HSPG genetic polymorphism has the potential to be used as diagnostic markers for diabetic nephropathy disease. | Khuram Shehzad Maria Rasool Mahjabeen Saleem Mamoona Naz | 2012 | Journal of Chinese Pharmaceutical Sciences2012，21，3： | 1 |
| 9 | TOPLESS promotes plant immunity by repressing auxin signaling and is targeted by the fungal effector Naked1显示文摘In plants,the antagonism between growth and defense is hardwired by hormonal signaling.The perception of pathogen-associatedmolecularpatterns(PAMPs)frominvadingmicroorganismsinhibits auxin signalingand plant growth.Conversely,pathogens manipulate auxin signaling to promote disease,but how this hormone inhibits immunity is not fully understood.Ustilago maydis is a maize pathogen that induces auxin signaling in its host.We characterized a U.maydis effector protein,Naked1(Nkd1),that is translocated into the host nucleus.Through its native ethylene-responsive element binding factor-associated amphiphilic repression(EAR)motif,Nkd1 binds to the transcriptional co-repressors TOPLESS/TOPLESS-related(TPL/TPRs)and prevents the recruitment of a transcriptional repressor involved in hormonal signaling,leading to the derepression of auxin and jasmonate signaling and thereby promoting susceptibility to(hemi)biotrophic pathogens.A moderate upregulation of auxin signaling inhibits the PAMP-triggered reactive oxygen species(ROS)burst,an early defense response.Thus,our findings establish a clear mechanism for auxin-induced pathogen susceptibility.Engineered Nkd1 variants with increased expression or increased EAR-mediated TPL/TPR binding trigger typical salicylic-acid-mediated defense reactions,leading to pathogen resistance.This implies that moderate binding of Nkd1 to TPL is a result of a balancing evolutionary selection process to enable TPL manipulation while avoiding host recognition. | Fernando Navarrete Michelle Gallei Aleksandra EKornienko Indira Saado Mamoona Khan Khong-Sam Chia Martin A.Darino Janos Bindics Armin Djamei | 2022 | Plant Communications2022，3，2： | 1 |
| 10 | Role of Fuzzy Approach towards Fault Detection for Distributed Components显示文摘Component-based software development is rapidly introducing numerous new paradigms and possibilities to deliver highly customized software in a distributed environment.Among other communication,teamwork,and coordination problems in global software development,the detection of faults is seen as the key challenge.Thus,there is a need to ensure the reliability of component-based applications requirements.Distributed device detection faults applied to tracked components from various sources and failed to keep track of all the large number of components from different locations.In this study,we propose an approach for fault detection from componentbased systems requirements using the fuzzy logic approach and historical information during acceptance testing.This approach identified error-prone components selection for test case extraction and for prioritization of test cases to validate components in acceptance testing.For the evaluation,we used empirical study,and results depicted that the proposed approach significantly outperforms in component selection and acceptance testing.The comparison to the conventional procedures,i.e.,requirement criteria,and communication coverage criteria without irrelevancy and redundancy successfully outperform other procedures.Consequently,the F-measures of the proposed approach define the accurate selection of components,and faults identification increases in components using the proposed approach were higher(i.e.,more than 80 percent)than requirement criteria,and code coverage criteria procedures(i.e.,less than 80 percent),respectively.Similarly,the rate of fault detection in the proposed approach increases,i.e.,92.80 compared to existing methods i.e.,less than 80 percent.The proposed approach will provide a comprehensive guideline and roadmap for practitioners and researchers. | Yaser Hafeez Sadia Ali Nz Jhanjhi Mamoona Humayun Anand Nayyar Mehedi Masud | 2021 | Computers, Materials & Continua2021，，5： | 0 |
| 11 | Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis显示文摘Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes.People express their unique ideas and views onmultiple topics thus providing vast knowledge.Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making.Since the proliferation of COVID-19,it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked.The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering the various textual characteristics that must be analyzed.Hence,this research presents a deep learning-based model that utilizes the positives of random minority oversampling combined with class label analysis to achieve the best results for sentiment analysis.This research specifically focuses on utilizing class label analysis to deal with the multiclass problem by combining the class labels with a similar overall sentiment.This can be particularly helpful when dealing with smaller datasets.Furthermore,our proposed model integrates various preprocessing steps with random minority oversampling and various deep learning algorithms including standard deep learning and bi-directional deep learning algorithms.This research explores several algorithms and their impact on sentiment analysis tasks and concludes that bidirectional neural networks do not provide any advantage over standard neural networks as standard Neural Networks provide slightly better results than their bidirectional counterparts.The experimental results validate that our model offers excellent results with a validation accuracy of 92.5%and an F1 measure of 0.92. | Mamoona Humayun Danish Javed Nz Jhanjhi Maram Fahaad Almufareh Saleh Naif Almuayqil | 2023 | Computer Systems Science & Engineering2023，47，10： | 0 |
| 12 | Cyber Security and Privacy Issues in Industrial Internet of Things显示文摘The emergence of industry 4.0 stems from research that has received a great deal of attention in the last few decades.Consequently,there has been a huge paradigm shift in the manufacturing and production sectors.However,this poses a challenge for cybersecurity and highlights the need to address the possible threats targeting(various pillars of)industry 4.0.However,before providing a concrete solution certain aspect need to be researched,for instance,cybersecurity threats and privacy issues in the industry.To fill this gap,this paper discusses potential solutions to cybersecurity targeting this industry and highlights the consequences of possible attacks and countermeasures(in detail).In particular,the focus of the paper is on investigating the possible cyber-attacks targeting 4 layers of IIoT that is one of the key pillars of Industry 4.0.Based on a detailed review of existing literature,in this study,we have identified possible cyber threats,their consequences,and countermeasures.Further,we have provided a comprehensive framework based on an analysis of cybersecurity and privacy challenges.The suggested framework provides for a deeper understanding of the current state of cybersecurity and sets out directions for future research and applications. | NZ Jhanjhi Mamoona Humayun Saleh NAlmuayqil | 2021 | Computer Systems Science & Engineering2021，37，6： | 0 |
| 13 | Improved Video Steganography with Dual Cover Medium,DNA and Complex Frames显示文摘The most valuable resource on the planet is no longer oil,but data.The transmission of this data securely over the internet is another challenge that comes with its ever-increasing value.In order to transmit sensitive information securely,researchers are combining robust cryptography and steganographic approaches.The objective of this research is to introduce a more secure method of video steganography by using Deoxyribonucleic acid(DNA)for embedding encrypted data and an intelligent frame selection algorithm to improve video imperceptibility.In the previous approach,DNA was used only for frame selection.If this DNA is compromised,then our frames with the hidden and unencrypted data will be exposed.Moreover the frame selected in this way were random frames,and no consideration was made to the contents of frames.Hiding data in this way introduces visible artifacts in video.In the proposed approach rather than using DNA for frame selection we have created a fakeDNA out of our data and then embedded it in a video file on intelligently selected frames called the complex frames.Using chaotic maps and linear congruential generators,a unique pixel set is selected each time only from the identified complex frames,and encrypted data is embedded in these random locations.Experimental results demonstrate that the proposed technique shows minimum degradation of the stenographic video hence reducing the very first chances of visual surveillance.Further,the selection of complex frames for embedding and creation of a fake DNA as proposed in this research have higher peak signal-to-noise ratio(PSNR)and reduced mean squared error(MSE)values that indicate improved results.The proposed methodology has been implemented in Matlab. | Asma Sajjad Humaira Ashraf NZ Jhanjhi Mamoona Humayun Mehedi Masud Mohammed A.AlZain | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 14 | IoT Wireless Intrusion Detection and Network Traffic Analysis显示文摘Enhancement in wireless networks had given users the ability to use the Internet without a physical connection to the router.Almost every Internet of Things(IoT)devices such as smartphones,drones,and cameras use wireless technology(Infrared,Bluetooth,IrDA,IEEE 802.11,etc.)to establish multiple interdevice connections simultaneously.With the flexibility of the wireless network,one can set up numerous ad-hoc networks on-demand,connecting hundreds to thousands of users,increasing productivity and profitability significantly.However,the number of network attacks in wireless networks that exploit such flexibilities in setting and tearing down networks has become very alarming.Perpetrators can launch attacks since there is no first line of defense in an ad hoc network setup besides the standard IEEE802.11 WPA2 authentication.One feasible countermeasure is to deploy intrusion detection systems at the edge of these ad hoc networks(Network-based IDS)or at the node level(Host-based IDS).The challenge here is that there is no readily available benchmark data available for IoT network traffic.Creating this benchmark data is very tedious as IoT can work on multiple platforms and networks,and crafting and labelling such dataset is very labor-intensive.This research aims to study the characteristics of existing datasets available such as KDD-Cup and NSL-KDD,and their suitability for wireless IDS implementation.We hypothesize that network features are parametrically different depending on the types of network and assigning weight dynamically to these features can potentially improve the subsequent threat classifications.This paper analyses packet and flow features for the data packet captured on a wireless network rather than a wired network.Combining domain heuristcs and early classification results,the paper had identified 19 header fields exclusive to wireless network that contain high information gain to be used as ML features in Wireless IDS. | Vasaki Ponnusamy Aun Yichiet NZ Jhanjhi Mamoona humayun MaramFahhad Almufareh | 2022 | Computer Systems Science & Engineering2022，40，3： | 0 |
| 15 | Prediction Model for Coronavirus Pandemic Using Deep Learning显示文摘The recent global outbreak of COVID-19 damaged the world health systems,human health,economy,and daily life badly.None of the countries was ready to face this emerging health challenge.Health professionals were not able to predict its rise and next move,as well as the future curve and impact on lives in case of a similar pandemic situation happened.This created huge chaos globally,for longer and the world is still struggling to come up with any suitable solution.Here the better use of advanced technologies,such as artificial intelligence and deep learning,may aid healthcare practitioners in making reliable COVID-19 diagnoses.The proposed research would provide a prediction model that would use Artificial Intelligence and Deep Learning to improve the diagnostic process by reducing unreliable diagnostic interpretation of chest CT scans and allowing clinicians to accurately discriminate between patients who are sick with COVID-19 or pneumonia,and also empowering health professionals to distinguish chest CT scans of healthy people.The efforts done by the Saudi government for the management and control of COVID-19 are remarkable,however;there is a need to improve the diagnostics process for better perception.We used a data set from Saudi regions to build a prediction model that can help distinguish between COVID-19 cases and regular cases from CT scans.The proposed methodology was compared to current models and found to be more accurate(93 percent)than the existing methods. | Mamoona Humayun Ahmed Alsayat | 2022 | Computer Systems Science & Engineering2022，40，3： | 0 |
| 16 | Smart-City-based Data Fusion Algorithm for Internet of Things显示文摘Increasingly,Wireless Sensor Networks(WSNs)are contributing enormous amounts of data.Since the recent deployments of wireless sensor networks in Smart City infrastructures,significant volumes of data have been produced every day in several domains ranging from the environment to the healthcare system to transportation.Using wireless sensor nodes,a Smart City environment may now be shown for the benefit of residents.The Smart City delivers intelligent infrastructure and a stimulating environment to citizens of the Smart Society,including the elderly and others.Weak,Quality of Service(QoS)and poor data performance are common problems in WSNs,caused by the data fusion method,where a small amount of bad data can significantly impact the total fusion outcome.In our proposed research,a WSN multisensor data fusion technique employing fuzzy logic for event detection.Using the new proposed Algorithm,sensor nodes will collect less repeated data,and redundant data will be used to increase the data’s overall reliability.The network’s fusion delay problem is investigated,and a minimum fusion delay approach is provided based on the nodes’fusion waiting time.The proposed algorithm performs well in fusion,according to the results of the experiment.As a result of these discoveries,It is concluded that the algorithm describe here is effective and dependable instrument with a wide range of applications. | Jawad Khan Muhammad Amir Khan N.Z.Jhanjhi Mamoona Humayun Abdullah Alourani | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 17 | An Insight into Different Strategies for Control and Prophylaxis of Fasciolosis:A Review显示文摘Fasciolosis is one of the important diseases of livestock and has zoonotic importance.Fasciolosis can cause huge economic losses due to decrease in milk and meat production,decreased feed conversion ratio,and cost of treatment.Treatment and prophylaxis strategies for Fasciola infection are formed based on epidemiological data.The control of Fasciola infection can be attained by treating the animals with active anthelmintics.The use of different combinations of anthelmintics with a possible rotation is more effective against immature as well as adult flukes.Control of the intermediate host(snail)is vital for the reduction of fasciolosis.Due to the rapid growth of snails,the eradication is quite difficult in waterlogged and marshy areas.The use of different grazing methods and treatment of grazing areas can also help to control fasciolosis.A variety of antigens generated by Fasciola spp.have been shown to protect against liver fluke infection.The crude antigens,excretory/secretory,and refined antigens and their combination can be used as prophylactic treatment for the control of fasciolosis.The use of any of the single or combination of these methods can be very effective for the control of fasciolosis. | Hafiz Muhammad Rizwan Muhammad Sohail Sajid Haider Abbas Sadia Ghazanfer Mamoona Arshad | 2022 | Journal of Advances in International Veterinary Research2022，4，1： | 0 |
| 18 | A Compact Rhombus Shaped Antenna with Extended Stubs for Ultra-Wideband Applications显示文摘Ultra-wideband(UWB)is highly preferred for short distance communication.As a result of this significance,this project targets the design of a compact UWB antennas.This paper describes a printed UWB rhombusshaped antenna with a partial ground plane.To achieve wideband response,two stubs and a notch are incorporated at both sides of the rhombus design and ground plane respectively.To excite the antenna,a simple microstrip feed line is employed.The suggested antenna is built on a 1.6 mm thick FR4 substrate.The proposed design is very compact with overall electrical size of 0.18λ×0.25λ(14×18 mm2).The rhombus shaped antenna covers frequency ranging from 3.5 to 11 GHz with 7.5 GHz impedance bandwidth.The proposed design simulated and measured bandwidths are 83.33%and 80%,respectively.Radiation pattern in terms of E-field and H-field are discussed at 4,5.5 and 10 GHz respectively.The proposed design has 65%radiation efficiency and 1.5 dBi peak gain.The proposed design is simulated in CST(Computer Simulation Technology)simulator and the simulated design is fabricated for the measured results.The simulated and measured findings are in close resemblance.The obtained results confirm the application of the proposed design for the ultra-wide band applications. | Syed Misbah un Noor Muhammad Amir Khan Shahid Khan NZ Jhanjhi Mamoona Humayun Hesham A.Alhumyan | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 19 | A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare显示文摘COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in 2019.It affects the whole world through personto-person communication.This virus spreads by the droplets of coughs and sneezing,which are quickly falling over the surface.Therefore,anyone can get easily affected by breathing in the vicinity of the COVID-19 patient.Currently,vaccine for the disease is under clinical investigation in different pharmaceutical companies.Until now,multiple medical companies have delivered health monitoring kits.However,a wireless body area network(WBAN)is a healthcare system that consists of nano sensors used to detect the real-time health condition of the patient.The proposed approach delineates is to fill a gap between recent technology trends and healthcare structure.If COVID-19 affected patient is monitored through WBAN sensors and network,a physician or a doctor can guide the patient at the right timewith the correct possible decision.This scenario helps the community to maintain social distancing and avoids an unpleasant environment for hospitalized patients Herein,a Monte Carlo algorithm guided protocol is developed to probe a secured cipher output.Security cipher helps to avoid wireless network issues like packet loss,network attacks,network interference,and routing problems.Monte Carlo based covid-19 detection technique gives 90%better results in terms of time complexity,performance,and efficiency.Results indicate that Monte Carlo based covid-19 detection technique with edge computing idea is robust in terms of time complexity,performance,and efficiency and thus,is advocated as a significant application for lessening hospital expenses. | Tallat Jabeen Ishrat Jabeen Humaira Ashraf Nz Jhanjhi Mamoona Humayun Mehedi Masud Sultan Aljahdali | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 20 | Cloud Security Service for Identifying Unauthorized User Behaviour显示文摘Recently,an innovative trend like cloud computing has progressed quickly in InformationTechnology.For a background of distributed networks,the extensive sprawl of internet resources on the Web and the increasing number of service providers helped cloud computing technologies grow into a substantial scaled Information Technology service model.The cloud computing environment extracts the execution details of services and systems from end-users and developers.Additionally,through the system’s virtualization accomplished using resource pooling,cloud computing resources become more accessible.The attempt to design and develop a solution that assures reliable and protected authentication and authorization service in such cloud environments is described in this paper.With the help of multi-agents,we attempt to represent Open-Identity(ID)design to find a solution that would offer trustworthy and secured authentication and authorization services to software services based on the cloud.This research aims to determine how authentication and authorization services were provided in an agreeable and preventive manner.Based on attack-oriented threat model security,the evaluation works.By considering security for both authentication and authorization systems,possible security threats are analyzed by the proposed security systems. | D.Stalin David Mamoona Anam Chandraprabha Kaliappan S.Arun Mozhi Selvi Dilip Kumar Sharma Pankaj Dadheech Sudhakar Sengan | 2022 | Computers, Materials & Continua2022，，2： | 0 |
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