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| 1 | Overexpression of mitogen-activated protein kinase phosphatase-2 enhances adhesion molecule expression and protects against apoptosis in human endothelial cells显示文摘 | Mashael Al-Mutairi Sameer Al-Harthi Laurence Cadalbert | | 0，，04： | 1 |
| 2 | Genetic variation at ?1878 (rs2596542) in MICA gene region is associated with chronic hepatitis B virus infection in Saudi Arabian patients显示文摘 | Ahmed A. Al-Qahtani Mashael Al-Anazi Ayman A. Abdo Faisal M. Sanai Waleed Al-Hamoudi Khalid A. Alswat Hamad I. Al-Ashgar Nisreen Khalaf Nisha Viswan Mohammed N. Al-Ahdal | 2013 | Experimental and Molecular Pathology2013，，3： | 1 |
| 3 | Genetic variation in interleukin 28B and correlation with chronic hepatitis B virus infection in Saudi Arabian patients显示文摘 | Ahmed A. Al‐Qahtani Mashael R. Al‐Anazi Ayman A. Abdo Faisal M. Sanai Waleed K. Al‐Hamoudi Khalid A. Alswat Hamad I. Al‐Ashgar Nisreen Z. Khalaf Nisha A. Viswan Mohammed N. Al Ahdal | 2014 | Liver Int2014，，7： | 1 |
| 4 | Burnout during pandemic COVID-19 in Saudi and non-Saudi nurses in King Abdulaziz hospital,Makkah显示文摘Background:COVID-19 put the global health system in a disastrous situation.Nursing plays a vital role in healthcare services.The ratio of burnout increased during this period.In the context of Saudi Arabia,nurses’whether these are Saudi or non-Saudi the burnout due to emotional exhaustion,depersonalization,and personal accomplishment,the situation of burnout could be there.It is,therefore,important to understand the phenomenon of nurse burnout and the factors that contribute to it.This study aims to understand burnout among nurses and the factors that affect nurses during the COVID-19 pandemic.Methods:The design of this study was quantitative cross-sectional and correlational.This study population included 255 nurses working in the King Abdulaziz hospital,Makkah,in 2021.Self-administered questionnaire(google forms)was distributed through email and WhatsApp.Statistical analysis system version 9.4 for data analysis and reporting.Result:Most of the nurse participants were in the age range 31-40,were females and were Saudi nationals.A Chi-square analysis showed a significant burnout level on the sub-scale of emotional exhaustion and personal accomplishment,while a partial burnout level was observed on the sub-scale of depersonalization.The level of burnout was higher and more significant among Saudi nurses compared to non-Saudi nationals.The impact of demographic variables on burnout showed that nationality,level of education,and duty type were the most influential and significant variables in burnout among Saudi and non-Saudi nurses.Conclusion:The findings indicated that nurses’burnout is higher during COVID-19 and is closely related to their working hours.In addition,when nurses are more nervous and depressed,a higher level of burnout will be witnessed.Since depression and frustration are influenced by working hours,attention should be given to this factor,focusing on interventions to alleviate the causes that lead to nurses’burnout. | Majda Rashed Khairi Mashael Abdulnasser Ahmad Abdulrahman Mohammed Salah Alshmemri May Hassan Bagadood Sanaa Awwad Alsulami | 2023 | Nursing Communications2023，7，1： | 0 |
| 5 | Morphological and Phylogenetic Studies of a Copepod Species, Irodes parupenei Ho and Lin (2007), Infecting Parupeneus rubescens in Saudi Arabia显示文摘Dammam City is one of the gorgeous coastal areas in the Arabian Gulf of Saudi Arabia.The present study aimed to ex-amine one of the copepod species infecting the rosy goatfish that represents a highly consumed fish species by the local population in the Arabian Gulf.The copepod species isolated from the infected fish specimens belong to the family Taeniacanthidae and was iden-tified as Irodes parupenei Ho and Lin(2007),primarily based on its morphological,morphometric,and ultrastructural characteris-tics,especially the structures of the dorsal cephalic area,segmentation of the first antenna,the absence of the maxilliped claw in the fe-male specimens,and the setation and spinulation of the legs 2-4 for the adult females are of great significance in the taxonomic iden-tification.The 18S rRNA gene sequence was analyzed to ensure the precise identity and exact taxonomic status of the copepod species.The result showed that this copepod species belong to Taenicanthidae and closely related to Irodes sauridi(gb|JF781550.1)in the same taxon.More details on the specificity of the goatfish for Irodes species and identifying these parasitic taxa using molecular analysis are given in the present study. | DKHIL Mohamed A ALHAFIDH Wejdan AL-QURAISHY Saleh ALOTAIBI Mashael BANAEEM Manal ALSALEH Thekra ABDEL-GABER Rewaida | 2022 | Journal of Ocean University of China2022，21，2： | 0 |
| 6 | A Hybrid Neural Network-based Approach for Forecasting Water Demand显示文摘Water is a vital resource.It supports a multitude of industries,civilizations,and agriculture.However,climatic conditions impact water availability,particularly in desert areas where the temperature is high,and rain is scarce.Therefore,it is crucial to forecast water demand to provide it to sectors either on regular or emergency days.The study aims to develop an accurate model to forecast daily water demand under the impact of climatic conditions.This forecasting is known as a multivariate time series because it uses both the historical data of water demand and climatic conditions to forecast the future.Focusing on the collected data of Jeddah city,Saudi Arabia in the period between 2004 and 2018,we develop a hybrid approach that uses Artificial Neural Networks(ANN)for forecasting and Particle Swarm Optimization algorithm(PSO)for tuning ANNs’hyperparameters.Based on the Root Mean Square Error(RMSE)metric,results show that the(PSO-ANN)is an accurate model for multivariate time series forecasting.Also,the first day is the most difficult day for prediction(highest error rate),while the second day is the easiest to predict(lowest error rate).Finally,correlation analysis shows that the dew point is the most climatic factor affecting water demand. | Al-Batool Al-Ghamdi Souad Kamel Mashael Khayyat | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 7 | The Impact of International Trade on Economic Growth显示文摘This paper investigates the impact of international trade on the economic growth of Saudi Arabia for the period 1980-2018.The investigation was conducted through Dickey-Fuller(1979)and Phillips-Perron(1988)tests of integration techniques were used in this study to test the log-values of the underlying times series for a unit root and Engle-Granger and Johansen’s(1988)tests for cointegration to test the long-run relationship between real GDP,real exports,and real imports.The variables selected for the study are gross domestic product,real exports,and real imports which were collected mainly from SMSA(Saudi Arabian Monetary Agency)except the consumer prices was collected from World Development Indicators.The results showed that the economic variables under study were included in their constant values at the root of the unit.In order to ensure a balanced relationship between economic growth and exports,imports,and consumer price,in the long run,we used the Granger Engel method,which reveals a common correlation between the variables.Stability of the regression coefficients from the regression of the joint integration result showed that there is a long-term equilibrium relationship between the variables.Tests of joint integration confirmed that GDP growth is affected by long-term exports.And the causal test results indicate that the GDP and import variables are not linked to the short-run economy,which means there is no causal relationship between imports and economic growth in Saudi Arabia.It is recommended that The Saudi economy must expand its scope in international markets by promoting the growth of other export sectors by liberalizing the services and manufacturing sectors and they must shift the dependence on oil revenues to non-oil revenues into intensive efforts to diversify its export-oriented policies and seek alternative commodities(other than oil and related products).Also,an industrial production base should be established to meet the needs of the local economy and then export. | Mashael Eid Alotaibi Mariah Ali Almohaimeed Wjdan Mohammed Alharbi | 2020 | Journal of Modern Accounting and Auditing2020，16，11： | 0 |
| 8 | Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning显示文摘Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era.Malicious Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems.This may result in compromised security of the systems,scams,and other such cyberattacks.These attacks hijack huge quantities of the available data,incurring heavy financial loss.At the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify them.With this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)model.The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs.To attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique.In addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious URLs.Finally,AFSA is applied to the proposed model to enhance the efficiency of GRU model.The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository.The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures. | Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohamed K.Nour Mashael M.Asiri Ali M.Al-Sharafi Mahmoud Othman Abdelwahed Motwakel | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 9 | Cache Memory Design for Single Bit Architecture with Different Sense Amplifiers显示文摘Most modern microprocessors have one or two levels of on-chip caches to make things run faster,but this is not always the case.Most of the time,these caches are made of static random access memory cells.They take up a lot of space on the chip and use a lot of electricity.A lot of the time,low power is more important than several aspects.This is true for phones and tablets.Cache memory design for single bit architecture consists of six transistors static random access memory cell,a circuit of write driver,and sense amplifiers(such as voltage differential sense amplifier,current differential sense amplifier,charge transfer differential sense amplifier,voltage latch sense amplifier,and current latch sense amplifier,all of which are compared on different resistance values in terms of a number of transistors,delay in sensing and consumption of power.The conclusion arises that single bit six transistor static random access memory cell voltage differential sense amplifier architecture consumes 11.34μW of power which shows that power is reduced up to 83%,77.75%reduction in the case of the current differential sense amplifier,39.62%in case of charge transfer differential sense amplifier and 50%in case of voltage latch sense amplifier when compared to existing latch sense amplifier architecture.Furthermore,power reduction techniques are applied over different blocks of cache memory architecture to optimize energy.The single-bit six transistors static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique consumes 8.078μW of power,i.e.,reduce 28%more power that makes single bit six transistor static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique more energy efficient. | Reeya Agrawal Anjan Kumar Salman A.AlQahtani Mashael Maashi Osamah Ibrahim Khalaf Theyazn H.H.Aldhyani | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 10 | Big Data Analytics with Artificial Intelligence Enabled Environmental Air Pollution Monitoring Framework显示文摘Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby highly populated countries like India. Due to increased population, thenumber of vehicles also continues to increase. Each vehicle has its individualemission rate;however, the issue arises when the emission rate crosses thestandard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop predictionapproaches to monitor and control pollution using real time data. With thedevelopment of the Internet of Things (IoT) and Big Data Analytics (BDA),there is a huge paradigm shift in how environmental data are employed forsustainable cities and societies, especially by applying intelligent algorithms.In this view, this study develops an optimal AI based air quality prediction andclassification (OAI-AQPC) model in big data environment. For handling bigdata from environmental monitoring, Hadoop MapReduce tool is employed.In addition, a predictive model is built using the hybridization of ARIMAand neural network (NN) called ARIMA-NN to predict the pollution level.For improving the performance of the ARIMA-NN algorithm, the parametertuning process takes place using oppositional swallow swarm optimization(OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS)classifier is used to classify the air quality into pollutant and non-pollutant.A detailed experimental analysis is performed for highlighting the betterprediction performance of the proposed ARIMA-NN method. The obtainedoutcomes pointed out the enhanced outcomes of the proposed OAI-AQPCtechnique over the recent state of art techniques. | Manar Ahmed Hamza Hadil Shaiba Radwa Marzouk Ahmad Alhindi Mashael M.Asiri Ishfaq Yaseen Abdelwahed Motwakel Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 11 | A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis显示文摘Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment.Classical methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related symptoms.As an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD conditions.However,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 HD.In 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)model.The 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 identication.The CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD features.We 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 criteria.The 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 average.Therefore,the AHDD system proposed herein has the highest capability to identify patients with HD.This system can be used by medical practitioners to diagnose HD efciently。 | Mohamed Elhoseny Mazin Abed Mohammed Salama A.Mostafa Karrar Hameed Abdulkareem Mashael S.Maashi Begonya Garcia-Zapirain Ammar Awad Mutlag Marwah Suliman Maashi | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 12 | Pterostilbene induces cell apoptosis and inhibits lipogenesis in SKOV3 ovarian cancer cells by activation of AMPK-induced inhibition of Akt/mTOR signaling cascade显示文摘This study investigates if the anti-tumor effect of Pterostilbene in the SKOV3 ovarian cancer(OC)cell line involves inhibition of cell metabolism and tested in this effect involves modulating AMPK and Akt-induced regulation of mTORC1.Initially,SKOV3 cells were cultured in the humidified conditions in DMEM media for 24 h with or without increasing concentration of Pterostilbene.Then,the cells were incubated with Pterostilbene(IC_(50)=50μM)under similar conditions with or without pre-incubation with Dorsomorphin,an AMPK inhibitor.In a dose-dependent manner,Pterostilbene inhibited SKOV3 cell survival and increased their lysate levels of lactate dehydrogenase(LDH)and single-stranded DNA(ssDNA).When SKOV3 cells were treated with 50μM Pterostilbene,Pterostilbene significantly suppressed cell migration and invasion,reduced lysate levels of lactic acid and the optical density of Oil Red O staining,and increased lysate glucose levels.It also increased levels of malondialdehyde(MDA),reactive oxygen species(ROS),and induced intrinsic cell apoptosis by upregulating protein levels of Bax and cleaved caspase-3 and reducing protein levels of Bcl-2.Besides,Pterostilbene reduced mRNA levels of sterol regulatory element-binding protein 1(SREBP-1),fatty acid synthase(FAS),acetyl CoA carboxylase-1(ACC-1),and AMP-activated protein kinase(AMPK).Furthermore,Pterostilbene increased the protein levels of p-AMPK,p-p53,p-raptor,p-TSC-2,but significantly decreased protein levels of p-Akt,p-TSC-2,p-mTOR,p-S6K1,and p-4E-BP.Treatment with Dorsomorphin(CC)abolished all the anti-tumorigenesis effects afforded by Pterostilbene and prevented Pterostilbene-induced phosphorylation of Akt,p53,and mTOR.In conclusion,the tumorsuppressive effect of Pterostilbene in SKOV3 cells involves the induction of ROS and inhibition of dysregulation cell metabolism mainly due to AMPK-induced Akt-dependent or independent suppression of mTOR. | ATTALLA EL-KOTT EMAN ELBEALY FAHMY ELSAID HAITHAM EL-MEKKAWY ABD-EL-KARIM ABD-LATEIF ABDULALI TAWEEL HEBA KHALIFA AHMAD KANDEEL KAREEM MORSY ESSAM IBRAHIM MASHAEL MOHAMMED BIN-MEFERIJ | 2021 | BIOCELL2021，45，1： | 0 |
| 13 | Time Series Facebook Prophet Model and Python for COVID-19 Outbreak Prediction显示文摘COVID-19 comes from a large family of viruses identied in 1965;to date,seven groups have been recorded which have been found to affect humans.In the healthcare industry,there is much evidence that Al or machine learning algorithms can provide effective models that solve problems in order to predict conrmed cases,recovered cases,and deaths.Many researchers and scientists in the eld of machine learning are also involved in solving this dilemma,seeking to understand the patterns and characteristics of virus attacks,so scientists may make the right decisions and take specic actions.Furthermore,many models have been considered to predict the Coronavirus outbreak,such as the retro prediction model,pandemic Kaplan’s model,and the neural forecasting model.Other research has used the time series-dependent face book prophet model for COVID-19 prediction in India’s various countries.Thus,we proposed a prediction and analysis model to predict COVID-19 in Saudi Arabia.The time series dependent face book prophet model is used to t the data and provide future predictions.This study aimed to determine the pandemic prediction of COVID-19 in Saudi Arabia,using the Time Series Analysis to observe and predict the coronavirus pandemic’s spread daily or weekly.We found that the proposed model has a low ability to forecast the recovered cases of the COVID-19 dataset.In contrast,the proposed model of death cases has a high ability to forecast the COVID-19 dataset.Finally,obtaining more data could empower the model for further validation. | Mashael Khayyat Kaouther Laabidi Nada Almalki Maysoon Al-zahrani | 2021 | Computers, Materials & Continua2021，，6： | 0 |
| 14 | Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model显示文摘One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of lesions.Thus,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images.The 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 cases.In addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion boundaries.This study uses two databases in training and testing the proposed segmentation approach.The breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq.Results 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 images.The 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 set.For the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity. | Ihsan Jasim Hussein M.A.Burhanuddin Mazin Abed Mohammed Mohamed Elhoseny Begonya Garcia-Zapirain Marwah Suliman Maashi Mashael S.Maashi | 2021 | Computers, Materials & Continua2021，，3： | 0 |
| 15 | TG-SMR:AText Summarization Algorithm Based on Topic and Graph Models显示文摘Recently,automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization.However,most of the computing methods that are used in real systems are based on graph models,which are characterized by their simplicity and stability.Thus,this paper proposes an improved extractive text summarization algorithm based on both topic and graph models.The methodology of this work consists of two stages.First,the well-known TextRank algorithm is analyzed and its shortcomings are investigated.Then,an improved method is proposed with a new computational model of sentence weights.The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods.Finally,through experiments on the DUC2004 and DUC2006 datasets,our proposed improved graph model algorithm TG-SMR(Topic Graph-Summarizer)is compared to other text summarization systems.The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores.It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators. | Mohamed Ali Rakrouki Nawaf Alharbe Mashael Khayyat Abeer Aljohani | 2023 | Computer Systems Science & Engineering2023，45，4： | 0 |
| 16 | Exendin-4 inhibits the survival and invasiveness of two colorectal cancer cell lines via suppressing GS3Kβ/β-catenin/NF-κB axis through activating SIRT1显示文摘This study examined if the anti-tumorigenesis effect of Exendin-4 in HT29 and HCT116 colorectal cancer(CRC)involves modulation of SIRT1 and Akt/GSR3K/β-catenin/NF-κB axis.HT29 and HCT116 cells were treated either with increasing levels of Exendin-4(0.0-200μM)or with Exendin-4(at its IC50)in the presence or absence of EX-527(10μM/a selective SIRT1 inhibitor)or Exendin-4(9-39)amide(E(9-39)A)(1μM/an Exendin-4 antagonist).In a dose-dependent manner,Exendin-4 inhibited cell survival,but enhanced levels of lactate dehydrogenase(LDH)and single-stranded DNA(ssDNA)in both HT29 and HCT116.In both cell lines and at it has an IC50(45μM for HT29 and 35μM for HCT1165),Exendin-4 also significantly reduced cell survival,migration,and invasion of both cell types,with no effect on the expression GLP-1 receptors(GLPRs)nor of the activity of Akt.At these doses,Exendin-4 also increased the expression of SIRT1 but reduced the acetylation of NF-κB and the expression of Bax and cleaved caspase-3 and in both cell lines.Concomitantly,protein levels of p-GS3Kβ(Ser9),total and acetylatedβ-catenin,and Anix2 were significantly decreased,but levels of p-GS3Kβ(Ser9)and p-β-catenin(Ser33/37/Thr41)were significantly increased in both HT29 and HCT116-exendin-4 treated cells.All the effects exerted by Exendin-4 were completely prevented by Ex527 or E(9-39)A.In conclusion,Exendin-4 suppresses the tumorigenesis of HT29 and HCT116 CRC cell activation of GS3Kβ-induced inhibition ofβ-catenin and NF-κβin a SIRT1-dependent mechanism. | ATTALLA F.EL-KOTT AYMAN E.EL-KENAWY EMAN R.ELBEALY ALI S.ALSHEHRI HEBA S.KHALIFA MASHAEL MOHAMMED BIN-MEFERIJ EHAB E.MASSOUD AMIRA M.ALRAMLAWY | 2021 | BIOCELL2021，45，5： | 0 |
| 17 | A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images显示文摘The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019. | Mazin Abed Mohammed Karrar Hameed Abdulkareem Begonya Garcia-Zapirain Salama A.Mostafa Mashael S.Maashi Alaa S.Al-Waisy Mohammed Ahmed Subhi Ammar Awad Mutlag Dac-Nhuong Le | 2021 | Computers, Materials & Continua2021，，3： | 0 |
| 18 | Historical Arabic Images Classification and Retrieval Using Siamese Deep Learning Model显示文摘Classifying the visual features in images to retrieve a specific image is a significant problem within the computer vision field especially when dealing with historical faded colored images.Thus,there were lots of efforts trying to automate the classification operation and retrieve similar images accurately.To reach this goal,we developed a VGG19 deep convolutional neural network to extract the visual features from the images automatically.Then,the distances among the extracted features vectors are measured and a similarity score is generated using a Siamese deep neural network.The Siamese model built and trained at first from scratch but,it didn’t generated high evaluation metrices.Thus,we re-built it from VGG19 pre-trained deep learning model to generate higher evaluation metrices.Afterward,three different distance metrics combined with the Sigmoid activation function are experimented looking for the most accurate method formeasuring the similarities among the retrieved images.Reaching that the highest evaluation parameters generated using the Cosine distance metric.Moreover,the Graphics Processing Unit(GPU)utilized to run the code instead of running it on the Central Processing Unit(CPU).This step optimized the execution further since it expedited both the training and the retrieval time efficiently.After extensive experimentation,we reached satisfactory solution recording 0.98 and 0.99 F-score for the classification and for the retrieval,respectively. | Manal M.Khayyat Lamiaa A.Elrefaei Mashael M.Khayyat | 2022 | Computers, Materials & Continua2022，，7： | 0 |
| 19 | Clustered Single-Board Devices with Docker Container Big Stream Processing Architecture显示文摘The expanding amounts of information created by Internet of Things(IoT)devices places a strain on cloud computing,which is often used for data analysis and storage.This paper investigates a different approach based on edge cloud applications,which involves data filtering and processing before being delivered to a backup cloud environment.This Paper suggest designing and implementing a low cost,low power cluster of Single Board Computers(SBC)for this purpose,reducing the amount of data that must be transmitted elsewhere,using Big Data ideas and technology.An Apache Hadoop and Spark Cluster that was used to run a test application was containerized and deployed using a Raspberry Pi cluster and Docker.To obtain system data and analyze the setup’s performance a Prometheusbased stack monitoring and alerting solution in the cloud based market is employed.This Paper assesses the system’s complexity and demonstrates how containerization can improve fault tolerance and maintenance ease,allowing the suggested solution to be used in industry.An evaluation of the overall performance is presented to highlight the capabilities and limitations of the suggested architecture,taking into consideration the suggested solution’s resource use in respect to device restrictions. | N.Penchalaiah Abeer S.Al-Humaimeedy Mashael Maashi J.Chinna Babu Osamah Ibrahim Khalaf Theyazn H.H.Aldhyani | 2022 | Computers, Materials & Continua2022，，12： | 0 |
| 20 | Metaheuristics Enabled Clustering with Routing Scheme for Wireless Sensor Networks显示文摘Wireless Sensor Network(WSN)is a vital element in Internet of Things(IoT)as the former enables the collection of huge quantities of data in energy-constrained environment.WSN offers independent access to the target region and performs data collection in an effective manner.But energy constraints remain a challenging issue in WSN since it operates on in-built battery.The studies conducted earlier recommended that the energy spent on communication processmust be considerably reduced to improve the efficiency of WSN.Cluster organization and optimal selection of the routes are considered as NP hard optimization problems which can be resolved with the help of metaheuristic algorithms.Clustering and routing are considered as effective approaches in enhancing the energy effectiveness and lifespan of WSN.In this background,the current study develops an Improved Duck and Traveller Optimization(IDTO)-enabled cluster-based Multi-Hop Routing(IDTOMHR)technique for WSN.Primarily,IDTO algorithm is exploited for the selection of Cluster Head(CH)and construction of clusters.Besides,Artificial Gorilla Troops Optimization(ATGO)technique is also used to derive an optimal set of routes to the destination.Both clustering and routing approaches derive a fitness function with the inclusion of multiple input parameters.The proposed IDTOMHR model was experimentally validated for its performance under different aspects.The extensive experimental results confirmed the better performance of IDTOMHR model over other recent approaches. | Mashael M.Asiri Saud S.Alotaibi Dalia H.Elkamchouchi Amira Sayed A.Aziz Manar Ahmed Hamza Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen | 2022 | Computers, Materials & Continua2022，，12： | 0 |
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