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| 1 | Sepsis in European intensive care units: Results of the SOAP study显示文摘 | Vincent JL SakrY SprungCL etal | 2006 | Crit Care Med2006，34，： | 1 |
| 2 | The effects of a visual fideli- ty criterion of the encoding of images显示文摘 | MANNOS J SAKRIS()N D | 1974 | IEEE Transac tions on Information Theory1974，20，4： | 1 |
| 3 | SepsisinEuropeanin-tensivecareunits:resultsoftheSOAPstudy显示文摘 | VincentJL SakrY SprungCL etal | 2006 | CritCareMed2006，34，2： | 1 |
| 4 | Synapses between NG2 glia and neurons 显示文摘 | Sakry D Karram K Trotter J | 2011 | J Anat2011，219，： | 1 |
| 5 | Arebloodtransfusionassociated with greater mortalityrates-ResultsoftheSepsis显示文摘 | VincentJL SakrY SprungC etal | 2008 | Anesthesiology2008，108，26： | 1 |
| 6 | The NG2 Proteoglycan Pro- tects Oligodendrocyte Precursor Cells against Oxidative Stress via Interaction with OMI/HtrA2 显示文摘 | Maus F Sakry D Binam6 F | 2015 | PLoS One2015，10，01： | 1 |
| 7 | Feeding ecology of two sillaginid fishes and trophic interrelations with other co-existing species in the southern part of South China Sea显示文摘 | Sukree Hajisamae Pun Yeesin Sakri Ibrahim | 2006 | Environmental Biology of Fishes (-)2006，，2： | 1 |
| 8 | Lipopolysaccharide binding protein in a surgical intensive care unit:a marker of sepsis?显示文摘 | SakrY BurgettU NaculFE | 2008 | Crit Care Med2008，36，7： | 1 |
| 9 | Estimation of fatigue-life of electronic packages subjected to random vibration load 显示文摘 | SAKRI M I SARAVANAN S MOHANRAM P V | 2009 | Defenee Sei J2009，59，1： | 1 |
| 10 | Sepsis in European intensive care units:results of the SOAP study显示文摘 | VincentJL SakrY SprungCL | 2006 | Crit Care Med2006，34，2： | 1 |
| 11 | Feeding ecology of two sillaginid fishes and trophic interrelations with other co-existing species in the southern part of South China Sea显示文摘 | SUKREE H PUN Y SAKRI I | 2006 | Environ Bio! Fish2006，76，： | 1 |
| 12 | Effectsofhydroxyethyl starch administrantion on renal function in critically ill patients显示文摘 | SakrY PayenD Reinhart K | 2007 | Br J Anaesth2007，980，： | 1 |
| 13 | Low serum high de- nsity lipoprotein cholesterol concentration is an independent p- redictor for enhanced inflammation and endothelial activation 显示文摘 | Wan Ahmad WN Sakri F Mokhsin A | 2015 | PLoS One2015，10，01： | 1 |
| 14 | Synapses between NG2 glia and neurons 显示文摘 | Sakry D Karram K Trotter J | 2011 | J Anat2011，219，1： | 1 |
| 15 | Microcirculatory alterations in patients with severe sepsis:impact of time of assessment and relationship with outcome显示文摘 | De BackerD DonadelloK SakrY | 2013 | Crit Care Med2013，41，3： | 1 |
| 16 | Evaluating and selecting E-commerce software and communication systems for a supply chain显示文摘 | Sakris J Taullri S | 2004 | Euorpean J of Opeartional Research2004，159，2： | 1 |
| 17 | LexDeep:Hybrid Lexicon and Deep Learning Sentiment Analysis Using Twitter for Unemployment-Related Discussions During COVID-19显示文摘The COVID-19 pandemic has spread globally,resulting in financialinstability in many countries and reductions in the per capita grossdomestic product.Sentiment analysis is a cost-effective method for acquiringsentiments based on household income loss,as expressed on social media.However,limited research has been conducted in this domain using theLexDeep approach.This study aimed to explore social trend analytics usingLexDeep,which is a hybrid sentiment analysis technique,on Twitter to capturethe risk of household income loss during the COVID-19 pandemic.First,tweet data were collected using Twint with relevant keywords before(9 March2019 to 17 March 2020)and during(18 March 2020 to 21 August 2021)thepandemic.Subsequently,the tweets were annotated using VADER(lexiconbased)and fed into deep learning classifiers,and experiments were conductedusing several embeddings,namely simple embedding,Global Vectors,andWord2Vec,to classify the sentiments expressed in the tweets.The performanceof each LexDeep model was evaluated and compared with that of a supportvector machine(SVM).Finally,the unemployment rates before and duringCOVID-19 were analysed to gain insights into the differences in unemploymentpercentages through social media input and analysis.The resultsdemonstrated that all LexDeep models with simple embedding outperformedthe SVM.This confirmed the superiority of the proposed LexDeep modelover a classical machine learning classifier in performing sentiment analysistasks for domain-specific sentiments.In terms of the risk of income loss,the unemployment issue is highly politicised on both the regional and globalscales;thus,if a country cannot combat this issue,the global economy will alsobe affected.Future research should develop a utility maximisation algorithmfor household welfare evaluation,given the percentage risk of income lossowing to COVID-19. | Azlinah Mohamed Zuhaira Muhammad Zain Hadil Shaiba Nazik Alturki Ghadah Aldehim Sapiah Sakri Saiful Farik Mat Yatin Jasni Mohamad Zain | 2023 | Computers, Materials & Continua2023，，4： | 0 |
| 18 | Software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure显示文摘Developing successful software with no defects is one of the main goals of software projects.In order to provide a software project with the anticipated software quality,the prediction of software defects plays a vital role.Machine learning,and particularly deep learning,have been advocated for predicting software defects,however both suffer from inadequate accuracy,overfitting,and complicated structure.In this paper,we aim to address such issues in predicting software defects.We propose a novel structure of 1-Dimensional Convolutional Neural Network(1D-CNN),a deep learning architecture to extract useful knowledge,identifying and modelling the knowledge in the data sequence,reduce overfitting,and finally,predict whether the units of code are defects prone.We design large-scale empirical studies to reveal the proposed model’s effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA datasets.The experimental results demonstrate that in terms of f-measure,an optimal and modest 1DCNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79%and 23.88%,respectively,in ways that minimize overfitting and improving prediction performance for software defects.According to the results,1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software engineering.This,in turn,could lead to saving software development resources and producing more reliable software. | Zuhaira Muhammad Zain Sapiah Sakri Nurul Halimatul Asmak Ismail Reza M.Parizi | 2022 | Computers, Materials & Continua2022，，4： | 0 |
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