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An IoT-Cloud Based Intelligent Computer-Aided Diagnosis of Diabetic Retinopathy Stage Classification Using Deep Learning Approach

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Computers, Materials & Continua 2021年第2期66卷 1665-1680页

作者： K.Shankar Eswaran Perumal Mohamed Elhoseny phong thanh nguyen Department of Computer Applications Alagappa UniversityKaraikudi630001India Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Department of Project Management Ho Chi Minh City Open UniversityHo Chi Minh City7000000Vietnam

Diabetic retinopathy(DR)is a disease with an increasing prevalence and the major reason for blindness among working-age *** possibility of severe vision loss can be extensively reduced by timely diagnosis and *** automated screening for DR has been identified as an effective method for early DR detection,which can decrease the workload associated to manual grading as well as save diagnosis costs and *** studies have been carried out to develop automated detection and classification models for *** paper presents a new IoT and cloud-based deep learning for healthcare diagnosis of Diabetic Retinopathy(DR).The proposed model incorporates different processes namely data collection,preprocessing,segmentation,feature extraction and *** first,the IoT-based data collection process takes place where the patient wears a head mounted camera to capture the retinal fundus image and send to cloud ***,the contrast level of the input DR image gets increased in the preprocessing stage using Contrast Limited Adaptive Histogram Equalization(CLAHE)***,the preprocessed image is segmented using Adaptive Spatial Kernel distance measure-based Fuzzy C-Means clustering(ASKFCM)***,deep Convolution Neural Network(CNN)based Inception v4 model is applied as a feature extractor and the resulting feature vectors undergo classification in line with the Gaussian Naive Bayes(GNB)*** proposed model was tested using a benchmark DR MESSIDOR image dataset and the obtained results showcased superior performance of the proposed model over other such models compared in the study.

关键词： Deep learning classification GaussianNaive Bayes feature extraction diabetic retinopathy

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Energy reduction of a submerged membrane bioreactor using a polytetrafluoroethylene （PTFE） hollow-fiber membrane

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Frontiers of Environmental Science & Engineering 2018年第3期12卷 45-54页

作者： Taro Miyoshi thanh phong nguyen Terumi Tsumuraya Hiromu Tanaka Hiroki Itokawa Toshikazu Hashimoto Maezawa Industries Inc. 5-11 Naka-cho Kawaguchi City Saitama 332-8556 Japan Sumitomo Electric Industries LTD. 1-950 Asashironishi Kumatori-cho Sennan-gun Osaka 590-0458 Japan Japan Sewage Works Agency 2-31-27 Yushima Bunkyo City Tokyo 113-0034 Japan

In this study, we modified a polytetrafluoroethylene （PTFE） hollow-fiber membrane element used for submerged membrane bioreactors （MBRs） to reduce the energy consumption during MBR processes. The high mechanical strength of the PTFE membrane made it possible to increase the effective length of the membrane fiber from 2 to 3 m. In addition, the packing density was increased by 20% by optimizing the membrane element configuration. These modifications improve the efficiency of membrane cleaning associated with aeration. The target of specific energy consumption was less than 0.4 kWh·m^-3 in this study. The continuous operation of a pilot MBR treating real municipal wastewater revealed that the MBR utilizing the modified membrane element can be stably operated under a specific air demand per membrane surface area （SADm） of 0.13 m^3·m^-2. hr I when the daily- averaged membrane fluxes for the constant flow rate and flow rate fluctuating modes of operation were set to 0,6 and 0.5m^3·m^-2·d^-1 respectively. The specific energy consumption under these operating conditions was estimated to be less than 0.37 kWh.m^-3. These results strongly suggest that operating an MBR equipped with the modified membrane element with a specific energy consumption of less than 0.4 kWh·m^-3 is highly possible.

关键词： Energy-saving Membrane bioreactor Polytetrafluoroethylene (PTFE) membrane Hollow fiber Power consumption

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An Optimal Deep Learning Based Computer-Aided Diagnosis System for Diabetic Retinopathy

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Computers, Materials & Continua 2021年第3期66卷 2815-2830页

作者： phong thanh nguyen Vy Dang Bich Huynh Khoa Dang Vo Phuong thanh Phan Eunmok Yang Gyanendra Prasad Joshi Department of Project Management Ho Chi Minh City Open UniversityHo Chi Minh City7000000Vietnam Department of Learning Material Ho Chi Minh City Open UniversityHo Chi Minh City7000000Vietnam Department of Convergence Science Kongju National UniversityGongju32588South Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006South Korea

Diabetic Retinopathy(DR)is a significant blinding disease that poses serious threat to human vision *** and severity grading of DR are difficult processes to ***,it depends on ophthalmoscopically-visible symptoms of growing severity,which is then ranked in a stepwise scale from no retinopathy to various levels of DR *** paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization(OPSO)algorithm-based Convolutional Neural Network(CNN)Model EOPSO-CNN in order to perform DR detection and *** proposed EOPSO-CNN model involves three main processes such as preprocessing,feature extraction,and *** proposed model initially involves preprocessing stage which removes the presence of noise in the input ***,the watershed algorithm is applied to segment the preprocessed *** by,feature extraction takes place by leveraging EOPSO-CNN ***,the extracted feature vectors are provided to a Decision Tree(DT)classifier to classify the DR *** study experiments were carried out using Messidor DR Dataset and the results showed an extraordinary performance by the proposed method over compared methods in a considerable *** simulation outcome offered the maximum classification with accuracy,sensitivity,and specificity values being 98.47%,96.43%,and 99.02%respectively.

关键词： Diabetic retinopathy convolutional neural network classification image processing computer-aided diagnosis

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Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data

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Computers, Materials & Continua 2021年第3期66卷 2555-2571页

作者： phong thanh nguyen Vy Dang Bich Huynh Khoa Dang Vo Phuong thanh Phan Mohamed Elhoseny Dac-Nhuong Le Department of Project Management Ho Chi Minh City Open UniversityHo Chi Minh City700000Vietnam Department of Learning Material Ho Chi Minh City Open UniversityHo Chi Minh City700000Vietnam Faculty of Computers and Information Mansoura UniversityDakahlia Governorate35516Egypt Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Faculty of Information Technology Duy Tan UniversityDanang550000Vietnam

Data fusion is a multidisciplinary research area that involves different *** is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare *** generation of huge quantity of data from medical devices resulted in the formation of big data during which data fusion techniques become *** medical data is a crucial issue of exponentially-pacing computing world and can be achieved by Intrusion Detection Systems(IDS).In this regard,since singularmodality is not adequate to attain high detection rate,there is a need exists to merge diverse techniques using decision-based multimodal fusion *** this view,this research article presents a new multimodal fusion-based IDS to secure the healthcare data using *** proposed model involves decision-based fusion model which has different processes such as initialization,pre-processing,Feature Selection(FS)and multimodal classification for effective detection of *** FS process,a chaotic Butterfly Optimization(BO)algorithmcalled CBOA is *** the classic BO algorithm offers effective exploration,it fails in achieving faster *** order to overcome this,i.e.,to improve the convergence rate,this research work modifies the required parameters of BO algorithm using chaos ***,to detect intrusions,multimodal classifier is applied by incorporating three Deep Learning(DL)-based classification ***,the concepts like Hadoop MapReduce and Spark were also utilized in this study to achieve faster computation of big data in parallel computation *** validate the outcome of the presented model,a series of experimentations was performed using the benchmark NSLKDDCup99 Dataset *** proposed model demonstrated its effective results on the applied dataset by offering the maximum accuracy of 99.21%,precision of 98.93%and detection rate of 99.59%.The results assured the betterm

关键词： Big data data fusion deep learning intrusion detection bio-inspired algorithm spark

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Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

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Computers, Materials & Continua 2024年第2期78卷 2181-2205页

作者： thanh-Lam nguyen HaoKao thanh-Tuan nguyen Mong-Fong Horng Chin-Shiuh Shieh Department of Electronic Engineering National Kaohsiung University of Science and TechnologyKaohsiung807618Taiwan Department of Electronic and Automation Engineering Nha Trang UniversityNha Trang650000Vietnam

Since its inception,the Internet has been rapidly *** the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the *** applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the ***,malicious threats on the internet are on the rise as *** Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet *** scale and complexity of DDoS attacks are constantly *** Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those *** addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant ***,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown *** attacks,which are not encountered during the system’s training,can lead to misclassification with significant *** this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional *** proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)***,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)*** success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering *** has yielded a novel method in the field of unknown attack *** further expands the trend of applying DL and M

关键词： Cybersecurity DDoS unknown attack detection machine learning deep learning incremental learning convolutional neural networks(CNN) open-set recognition(OSR) spatial location constraint prototype loss fuzzy c-means CICIDS2017 CICDDoS2019

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GLOBAL BOUND ON THE GRADIENT OF SOLUTIONS TO p-LAPLACE TYPE EQUATIONS WITH MIXED DATA

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Acta Mathematica Scientia 2024年第4期44卷 1394-1414页

作者： Minh-Phuong TRAN The-Quang TRAN thanh-Nhan nguyen Applied Analysis Research Group Faculty of Mathematics and StatisticsTon Duc Thang UniversityHo Chi Minh CityVietnam nguyen Huu Huan High School Thu Duc CityVietnam Group of Analysis and Applied Mathematics Department of MathematicsHo Chi Minh City University of EducationVietnam

In this paper,the study of gradient regularity for solutions of a class of elliptic problems of p-Laplace type is *** particular,we prove a global result concerning Lorentz-Morrey regularity of the non-homogeneous boundary data problem:-div((s^(2)+|▽u|^(2)p-2/2)▽u)=-div(|f|^(p-2)f)+g inΩ,u=h in■Ω,with the(sub-elliptic)degeneracy condition s∈[0,1]and with mixed data f∈L^(p)(Q;R^(n)),g∈Lp/(p-1)(Ω;R^(n))for p∈(1,n).This problem naturally arises in various applications such as dynamics of non-Newtonian fluid theory,electro-rheology,radiation of heat,plastic moulding and many *** on the idea of level-set inequality on fractional maximal distribution functions,it enables us to carry out a global regularity result of the solution via fractional maximal *** to the significance of M_(α)and its relation with Riesz potential,estimates via fractional maximal functions allow us to bound oscillations not only for solution but also its fractional derivatives of orderα.Our approach therefore has its own interest.

关键词： gradient estimates p-Laplace quasilinear elliptic equation fractional maximal operators Lorentz-Morrey spaces

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Prevalence and outcomes of neonates with severe COVID-19:An observational study at Children’s Hospital 1 in Ho Chi Minh City,Vietnam

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Asian Pacific Journal of Tropical Medicine 2024年第9期17卷 392-399,I0001页

作者： Phung nguyen The nguyen Tran thanh Thuc nguyen thanh Hung Ngo Ngoc Quang Minh Dang Quoc Duy Tran Minh Nhut nguyen Hoang Dung Tran nguyen Ai Nuong Dinh nguyen Cam Tu Department of Pediatrics University of Medicine and Pharmacy at Ho Chi Minh cityHo Chi Minh CityVietnam Children’s Hospital 1 Ho Chi Minh CityVietnam Pediatric Department School of MedicineVietnam National University Ho Chi Minh CityVietnam

Objective:To evaluate the impact of the severe COVID-19 pandemic on neonates and develop strategies to improve their ***:We conducted an observational cross-sectional study at Children's Hospital 1(CH1)from July 25,2021,to May 31,*** neonates who had fever or respiratory symptoms or were born from mothers with COVID-19 and had a positive RT-PCR SARS-CoV-2 result would be *** classified neonates with COVID-19 into 2 groups:mild/moderate and severe for *** between groups were analyzed using Fisher's exact test/Chi-square test for categorical variables and Student's t-test/Wilcoxon Rank Sum test for continuous ***:This study included 88 newborns who had positive RT-PCR SARS-CoV-2 *** severity COVID-19 rate among neonatal cases was found to be 13.6%(12/88),with a corresponding mortality rate of 1.1%(1/88).All severe cases showed lung abnormalities as evident on chest X-ray *** addition to respiratory symptoms,a higher incidence of gastrointestinal manifestations,such as vomiting and diarrhea,was observed in the severe group,indicating a compelling *** administration of anticoagulant and anti-inflammatory drugs in the study group resulted in a satisfactory outcome with no significant ***:The COVID-19 pandemic has had a substantial impact on the well-being of *** management of COVID-19 in this population presents significant challenges.

关键词： COVID-19 Neonate Anti-inflammatory Anticoagulant

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Machine learning based models for predicting compressive strength of geopolymer concrete

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Frontiers of Structural and Civil Engineering 2024年第7期18卷 1028-1049页

作者： Quang-Huy LE Duy-Hung nguyen thanh SANG-TO Samir KHATIR Hoang LE-MINH Amir H.GANDOMI thanh CUONG-LE Center for Engineering Application and Technology Solutions Ho Chi Minh City Open UniversityHo Chi Minh 700000Vietnam Institute of Civil Engineering Ho Chi Minh City University of TransportHo Chi Minh 700000Vietnam Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia

Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly *** effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important *** facilitate engineering work on the material,an efficient predicting model is *** this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting *** standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is *** effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting *** three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and *** numerical results suggest that the DNN model generally outperforms the other two models.

关键词： geopolymer concrete compressive strength prediction machine-learning based model deep neural network K-nearest neighbor support vector machines

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Prevalence and risk factors associated with long COVID symptoms in children and adolescents in a southern province of Vietnam

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Asian Pacific Journal of Tropical Medicine 2024年第3期17卷 119-128,I0001,I0002页

作者： Huynh Ngoc Linh nguyen The Tan Le Thi Minh Thu nguyen Tu Loan nguyen Thi To Uyen Le thanh Thao Trang Truong thanh Nam Doan Hoang Phu Faculty of Medicine Ca Mau Medical CollegeCa Mau 98000Vietnam Ca Mau Obstetrics and Pediatrics Hospital Ca Mau 98000Vietnam Faculty of Basic Science and Public Health Dong Thap Medical CollegeDong Thap 81000Vietnam Graduate University of Sciences and Technology Vietnam Academy of Science and TechnologyHa Noi 10000Vietnam Community Public Health Research Program School of Public HealthWalailak UniversityNakhon Si Thammarat 80160Thailand Faculty Public Health Can Tho University of Medicine and PharmacyCan Tho City 94000Vietnam Doctoral Program in Health Sciences College of Graduate StudiesWalailak UniversityNakhon Si Thammarat 80160Thailand

Objective:To investigate the prevalence and risk factors associated with long COVID symptoms among children and adolescents who have recovered from ***:This study applied a cross-sectional approach within community settings in a southern province of Vietnam.A structured questionnaire featuring socio-demographic information and common long COVID symptoms was *** correlation coefficients assessed associations among pairs of long COVID ***,multivariable logistic regression models were performed to investigate the risk factors of long COVID in recovered COVID-19 children and ***:Among 422 participants,39.3%reported long COVID symptoms,with a prevalence of 45.2%(SD=0.5)in children and 22.2%(SD=0.4)in *** symptoms reported were cough 34.6%(SD=0.5),fatigue 20.6%(SD=0.4),shortness of breath 10.9%(SD=0.3),and lack of appetite 6.6%(SD=0.3).Concerning risk factors of long COVID,a higher risk was observed among demographic groups,including girls(OR 1.25,95%CI 1.15-1.37;P<0.001,reference:boys),children compared to adolescents(OR 1.24,95%CI 1.12-1.37;P<0.001),overweight individuals(OR 1.14,95%CI 1.02-1.27;P=0.018,reference:healthy weight),and participants without any COVID-19 vaccination(OR 1.36,95%CI 1.20-1.54;P<0.001),or have received only one single dose(OR 1.35,95%CI 1.10-1.64;P=0.004)compared to those who have received two ***,patients with a COVID-19 treatment duration exceeding two weeks also had a higher risk of long COVID(OR 1.32,95%CI 1.09-1.60;P=0.003)than those who recovered less than seven ***:The insights from this study provide crucial guidance for predicting the factors associated with the occurrence of long COVID in pediatric patients,contributing to strategic interventions aimed at mitigating the long COVID risks among children and adolescents in Vietnam.

关键词： Long COVID Prevalence Risk factors Children Adolescent Vietnam

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Anti‐sloshing control:Flatness‐based trajectory planning and tracking control with an integrated extended state observer

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IET Cyber-Systems and Robotics 2024年第3期6卷 31-48页

作者： Khanh nguyen Viet Minh Do Duc thanh Cao Duc Tung Lam nguyen School of Electrical and Electronic Engineering Hanoi University of Science and TechnologyHanoiVietnam Vietnam Maritime University HaiphongVietnam

The phenomenon of sloshing causes a significantly negative impact on a wide range of industries.A time‐optimal flatness‐based trajectory planning and Lyapunov‐based model predictive control(LMPC)is proposed for trajectory tracking of a transmitting cylindrical container filled with ***,this research presents an equivalent discrete model based on a mass‐spring‐damper ***,after the flatness of the adopted non‐linear model for 2D is established,time‐optimal trajectories are introduced.A control method called LMPC is shown to solve the problem of orbital tracking,which allows setting limits for state *** addition,to ensure system performance,a linear extended state observer(LESO)is integrated to cope with system ***,the efficiency of the proposed approach for liquid sloshing suppression and tracking is illustrated by simulations.

关键词： control dynamic model motion planning

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