| 1. |
- Phu, Vu Dinh, et al.
(författare)
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Ventilator-associated respiratory infection in a resource-restricted setting: impact and etiology
- 2017
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Ingår i: Journal of Intensive Care. - : BioMed Central (BMC). - 2052-0492. ; 5
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Tidskriftsartikel (refereegranskat)abstract
- Ventilator-associated respiratory infection (VARI) is a significant problem in resource-restricted intensive care units (ICUs), but differences in casemix and etiology means VARI in resource-restricted ICUs may be different from that found in resource-rich units. Data from these settings are vital to plan preventative interventions and assess their cost-effectiveness, but few are available.
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| 2. |
- Tran, Ngoc Hieu, et al.
(författare)
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Genetic profiling of Vietnamese population from large-scale genomic analysis of non-invasive prenatal testing data
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- The under-representation of several ethnic groups in existing genetic databases and studies have undermined our understanding of the genetic variations and associated traits or diseases in many populations. Cost and technology limitations remain the challenges in performing large-scale genome sequencing projects in many developing countries, including Vietnam. As one of the most rapidly adopted genetic tests, non-invasive prenatal testing (NIPT) data offers an alternative untapped resource for genetic studies. Here we performed a large-scale genomic analysis of 2683 pregnant Vietnamese women using their NIPT data and identified a comprehensive set of 8,054,515 single-nucleotide polymorphisms, among which 8.2% were new to the Vietnamese population. Our study also revealed 24,487 disease-associated genetic variants and their allele frequency distribution, especially 5 pathogenic variants for prevalent genetic disorders in Vietnam. We also observed major discrepancies in the allele frequency distribution of disease-associated genetic variants between the Vietnamese and other populations, thus highlighting a need for genome-wide association studies dedicated to the Vietnamese population. The resulted database of Vietnamese genetic variants, their allele frequency distribution, and their associated diseases presents a valuable resource for future genetic studies.
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| 3. |
- Nguyen, Binh, et al.
(författare)
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Distributed formation trajectory planning for multi-vehicle systems
- 2023
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Ingår i: 2023 American Control Conference (ACC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350328066 - 9798350328073 - 9781665469524 ; , s. 1325-1330
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the problem of distributed formation trajectory planning for multi-vehicle systems with collision avoidance among vehicles. Unlike some previous distributed formation trajectory planning methods, our proposed approach offers great flexibility in handling computational tasks for each vehicle when the global formation of all the vehicles changes. It affords the system the ability to adapt to the computational capabilities of the vehicles. Furthermore, global formation constraints can be handled at any selected vehicles. Thus, any formation change can be effectively updated without recomputing all local formations at all the vehicles. To guarantee the above features, we first formulate a dynamic consensus-based optimization problem to achieve desired formations while guaranteeing collision avoidance among vehicles. Then, the optimization problem is effectively solved by ADMM-based or alternating projection-based algorithms, which are also presented. Theoretical analysis is provided not only to ensure the convergence of our method but also to show that the proposed algorithm can surely be implemented in a fully distributed manner. The effectiveness of the proposed method is illustrated by a numerical example of a 9-vehicle system.
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| 4. |
- Nguyen, Binh, et al.
(författare)
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Real-time distributed trajectory planning for mobile robots
- 2023
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Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 56:2, s. 2152-2157
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Tidskriftsartikel (refereegranskat)abstract
- Efficiently planning trajectories for nonholonomic mobile robots in formation tracking is a fundamental yet challenging problem. Nonholonomic constraints, complexity in collision avoidance, and limited computing resources prevent the robots from being practically deployed in realistic applications. This paper addresses these difficulties by modeling each mobile platform as a nonholonomic motion and formulating trajectory planning as an optimization problem using model predictive control (MPC). That is, the optimization problem is subject to both nonholonomic motions and collision avoidance. To reduce computing costs in real time, the nonholonomic constraints are convexified by finding the closest nominal points to the nonholonomic motion, which are then incorporated into a convex optimization problem. Additionally, the predicted values from the previous MPC step are utilized to form linear avoidance conditions for the next step, preventing collisions among robots. The formulated optimization problem is solved by the alternating direction method of multiplier (ADMM) in a distributed manner, which makes the proposed trajectory planning method scalable. More importantly, the convergence of the proposed planning algorithm is theoretically proved while its effectiveness is validated in a synthetic environment.
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| 5. |
- Thanh Hoan, Nguyen, et al.
(författare)
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Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam
- 2022
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Ingår i: CMES - Computer Modeling in Engineering & Sciences. - : Tech Science Press. - 1526-1492 .- 1526-1506. ; 131:3, s. 1431-1449
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Tidskriftsartikel (refereegranskat)abstract
- Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best tool for accurate prediction. In this study, we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely: Bagging (RF), Bagging (SOM) and Bagging (M5P) to predict historical water levels in the study area. Performances of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees (REPT), which is a benchmark ML model. The data of 19 years period was divided into 70:30 ratio for the modeling. The data of the period 1/2000 to 5/2013 (which is about 70% of total data) was used for the training and for the period 5/2013 to 12/2018 (which is about 30% of total data) was used for testing (validating) the models. Performance of the models was evaluated using standard statistical measures: Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that the performance of all the developed models is good (R2 > 0.9) for the prediction of water levels in the study area. However, the Bagging-based hybrid models are slightly better than another model such as REPT. Thus, these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.
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| 6. |
- Tran, Dien M., et al.
(författare)
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High prevalence of colonisation with carbapenem-resistant Enterobacteriaceae among patients admitted to Vietnamese hospitals : Risk factors and burden of disease
- 2019
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Ingår i: Journal of Infection. - : Saunders Elsevier. - 0163-4453 .- 1532-2742. ; 79:2, s. 115-122
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundCarbapenem-resistant Enterobacteriaceae (CRE) is an increasing problem worldwide, but particularly problematic in low- and middle-income countries (LMIC) due to limitations of resources for surveillance of CRE and infection prevention and control (IPC).MethodsA point prevalence survey (PPS) with screening for colonisation with CRE was conducted on 2233 patients admitted to neonatal, paediatric and adult care at 12 Vietnamese hospitals located in northern, central and southern Vietnam during 2017 and 2018. CRE colonisation was determined by culturing of faecal specimens on selective agar for CRE. Risk factors for CRE colonisation were evaluated. A CRE admission and discharge screening sub-study was conducted among one of the most vulnerable patient groups; infants treated at an 80-bed Neonatal ICU from March throughout June 2017 to assess CRE acquisition, hospital-acquired infection (HAI) and treatment outcome.ResultsA total of 1165 (52%) patients were colonised with CRE, most commonly Klebsiella pneumoniae (n=805), Escherichia coli (n=682) and Enterobacter spp. (n=61). Duration of hospital stay, HAI and treatment with a carbapenem were independent risk factors for CRE colonisation. The PPS showed that the prevalence of CRE colonisation increased on average 4.2 % per day and mean CRE colonisation rates increased from 13% on the day of admission to 89% at day 15 of hospital stay. At the NICU CRE colonisation increased from 32% at admission to 87% at discharge, mortality was significantly associated (OR 5•5, P < 0•01) with CRE colonisation and HAI on admission.ConclusionThese data indicate that there is an epidemic spread of CRE in Vietnamese hospitals with rapid transmission to hospitalised patients.
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| 7. |
- Dung, Nguyen Van, et al.
(författare)
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Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin
- 2021
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Ingår i: Geomatics, Natural Hazards and Risk. - : Taylor & Francis. - 1947-5705 .- 1947-5713. ; 12:1, s. 1688-1714
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Tidskriftsartikel (refereegranskat)abstract
- In this study, two novel hybrid models namely Bagging-based Rough Set (BRS) and AdaBoost-based Rough Set (ABRS) were used to generate landslide susceptibility maps of Son La hydropower reservoir basin, Vietnam. In total, 186 past landslide events and twelve landslides affecting factors (slope degree, slope aspect, elevation, curvature, focal flow, river density, rainfall, aquifer, weathering crust, lithology, fault density and road density) were considered in the modeling study. The landslide data was split into training (70%) and testing (30%) for the model's development and validation. One R feature selection method was used to select and prioritize the landslide affecting factors based on their importance in model prediction. Performance of the hybrid developed models was evaluated and also compared with single rough set (RS) and support vector machine (SVM) models using various standard statistical measures including area under the curve (AUC)-receiver operating characteristics (ROC) curve. The results show that the developed hybrid model BRS (AUC = 0.845) is the most accurate model in comparison to other models (ABRS, SVM and RS) in predicting landslide susceptibility. Therefore, the BRS model can be used as an effective tool in the development of an accurate landslide susceptibility map of the hilly area.
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| 8. |
- Ha, Duong Hai, et al.
(författare)
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Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping
- 2021
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Ingår i: Water resources management. - : Springer. - 0920-4741 .- 1573-1650. ; 35:13, s. 4415-4433
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Tidskriftsartikel (refereegranskat)abstract
- In this study, the AdaBoost, MultiBoost and RealAdaBoost methods were combined with the Quadratic Discriminant Analysis method to develop three new GIS-based Machine Learning ensemble models, i.e., ABQDA, MBQDA, and RABQDA for groundwater potential mapping in the Dak Nong Province, Vietnam. In total, 227 groundwater wells and 12 conditioning factors (infiltration, rainfall, river density, topographic wetness index, sediment transport index, stream power index, elevation, aspect, curvature, slope, soil, and land use) were used for this study. Performance of the models was evaluated using the Area Under the Receiver Operating Characteristics Curve AUC (AUC) and several other performance metrics. The results showed that the ABQDA model that achieved AUC = 0.741 was superior to the other models in producing an accurate map of groundwater potential for the Dak Nong Province. The models and potential maps produced here can help policymakers and water resources managers to preserve an optimal exploit from these vital resources.
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| 9. |
- Klingberg-Allvin, Marie, et al.
(författare)
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One foot wet and one foot dry : transition into motherhood among married adolescent women in rural Vietnam
- 2008
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Ingår i: Journal of Transcultural Nursing. - : Sage Publications. - 1043-6596 .- 1552-7832. ; 19:4, s. 338-346
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Tidskriftsartikel (refereegranskat)abstract
- This study explores married Vietnamese adolescents' perceptions and experiences related to transition into motherhood and their encounter with health care service. In-depth interviews were conducted with 22 women younger than 20 who were either pregnant or had newly delivered. It emerged from the narratives that young women experienced ambivalence in the transition to motherhood in that they felt too young but also happy to be able to please their husband and the extended family. Patterns were shown indicating that the participants experienced lacking power with regard to decisions in relation to pregnancy, delivery, and contraceptive usage. Feelings of being patronized and ignored in the encounter with health care providers were seen in the narratives. Findings might be used for reproductive health care providers, social workers, and educators in their contact with young mothers to empower them to make their own decisions with regard to marriage, childbearing, and contraception.
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| 10. |
- Pham, Binh Thai, et al.
(författare)
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A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil
- 2020
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:6
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Tidskriftsartikel (refereegranskat)abstract
- Determination of shear strength of soil is very important in civilengineering for foundation design, earth and rock fill dam design, highway and airfield design,stability of slopes and cuts, and in the design of coastal structures. In this study, a novel hybrid softcomputing model (RF-PSO) of random forest (RF) and particle swarm optimization (PSO) wasdeveloped and used to estimate the undrained shear strength of soil based on the clay content (%),moisture content (%), specific gravity (%), void ratio (%), liquid limit (%), and plastic limit (%). Inthis study, the experimental results of 127 soil samples from national highway project Hai Phong-Thai Binh of Vietnam were used to generate datasets for training and validating models. Pearsoncorrelation coefficient (R) method was used to evaluate and compare performance of the proposedmodel with single RF model. The results show that the proposed hybrid model (RF-PSO) achieveda high accuracy performance (R = 0.89) in the prediction of shear strength of soil. Validation of themodels also indicated that RF-PSO model (R = 0.89 and Root Mean Square Error (RMSE) = 0.453) issuperior to the single RF model without optimization (R = 0.87 and RMSE = 0.48). Thus, theproposed hybrid model (RF-PSO) can be used for accurate estimation of shear strength which canbe used for the suitable designing of civil engineering structures.
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| 11. |
- Dam, Nguyen Duc, et al.
(författare)
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Evaluation of Shannon Entropy and Weights of Evidence Models in Landslide Susceptibility Mapping for the Pithoragarh District of Uttarakhand State, India
- 2022
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Ingår i: Advances in Civil Engineering / Hindawi. - : Hindawi Publishing Corporation. - 1687-8086 .- 1687-8094. ; 2022
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Tidskriftsartikel (refereegranskat)abstract
- Landslide susceptibility mapping is considered a useful tool for planning, disaster management, and natural hazard mitigation of a region. Although there are different methods for predicting landslide susceptibility, the bivariate statistical analysis method is considered to be simple and popular. In this study, the main aim is to evaluate the performance of Shannon entropy (SE) and weights of evidence (WOE) statistical models in landslide susceptibility mapping of Pithoragarh district of Uttarakhand state, India. For this purpose, ten landslide affecting factors, namely, slope degree, aspect, curvature, elevation, land cover, slope forming materials, geomorphology (landforms), distance to rivers, distance to roads, and overburden depth were used for the development of landslide susceptibility maps using the SE and WOE methods. Data extracted from the Google Earth images, Aster Digital Elevation Model, and Geological Survey of India report were used for the construction and evaluation of landslide susceptibility models and maps. The landslide data of 91 locations were randomly divided into two parts in the ratio of 70 : 30 using GIS software that is 70% data was used for training the models and 30% data was used for testing and validating the models. Performance of the applied models was evaluated using area under the AUC (area under the curve) ROC (receiver operating characteristics) curve. Results indicated that the WOE model is having better accuracy (AUCWOE = 68.75%) than the SE model (AUCSE = 52.17%) in the development of landslide susceptibility maps. Hence, WOE model can be used for the development of accurate landslide susceptibility maps which can provide useful information to decision maker and policy planner in better development of landslide prone areas.
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| 12. |
- Dao, Trong Tuan, et al.
(författare)
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Resveratrol suppressed lps-induced cox-2 VIA miR-146a-5p inhibition in raw246.7 cells
- 2017
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Ingår i: Farmacia. - 0014-8237. ; 65:2, s. 214-218
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Tidskriftsartikel (refereegranskat)abstract
- Trans-resveratrol (Res) is a well-known natural stilbene frequently found in grapes which have been reported to possess antioxidant, anti-cancer activities and inhibited COX-2 expression. MicroRNAs (miRNAs) are short endogenous non-coding RNAs involved in the regulation of mRNA stability and protein synthesis. In our research, resveratrol isolated from Vitis heyneana Roem. & Schult Vitis heyneana was observed to suppress lipopolysaccharides (LPS)-induced COX-2 expression in Raw264.7 cells in a dose dependent manner. Using qPCR it was revealed that LPS induced the expression of miR-25, miR- 125a, miR-125b, miR-146a-5p, miR-146a-3p and miR-455. However, we only observed miR-146a-5p expression significantly decreased in resveratrol compared to untreated-control group. In addition, resveratrol abrogated the effect of miR-146a-5p mimic induced-COX-2 expression in Raw264.7 cells. Taken together, this study demonstrated for the first time the involvement of miR-146a-5p in resveratrol inhibited LPS-induced COX-2 expression in Raw264.7 cells.
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| 13. |
- Luu, Chinh, et al.
(författare)
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Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam
- 2020
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:7, s. 1-17
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Tidskriftsartikel (refereegranskat)abstract
- Vietnam has been extensively affected by floods, suffering heavy losses in human life andproperty. While the Vietnamese government has focused on structural measures of flood defence such as levees and early warning systems, the country still lacks flood risk assessment methodologies and frameworks at local and national levels. In response to this gap, this study developed a flood risk assessment framework that uses historical flood mark data and a high- resolution digital elevation model to create an inundation map, then combined this map with exposure and vulnerability data to develop a holistic flood risk assessment map. The case study is the October 2010 flood event in Quang Binh province, which caused 74 deaths, 210 injuries, 188,628 flooded properties, 9019 ha of submerged and damaged agricultural land, and widespread damages to canals, levees, and roads. The final flood risk map showed a total inundation area of 64348 ha, in which 8.3% area of low risk, 16.3% area of medium risk, 12.0% area of high risk, 37.1% area of very high risk, and 26.2% area of extremely high risk. The holistic flood risk assessment map of QuangBinh province is a valuable tool and source for flood preparedness activities at the local scale.
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| 14. |
- Ngo, Trinh Quoc, et al.
(författare)
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Landslide Susceptibility Mapping Using Single Machine Learning Models : A Case Study from Pithoragarh District, India
- 2021
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Ingår i: Advances in Civil Engineering / Hindawi. - : Hindawi Publishing Corporation. - 1687-8086 .- 1687-8094. ; 2021
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Tidskriftsartikel (refereegranskat)abstract
- Landslides are one of the most devastating natural hazards causing huge loss of life and damage to properties and infrastructures and adversely affecting the socioeconomy of the country. Landslides occur in hilly and mountainous areas all over the world. Single, ensemble, and hybrid machine learning (ML) models have been used in landslide studies for better landslide susceptibility mapping and risk management. In the present study, we have used three single ML models, namely, linear discriminant analysis (LDA), logistic regression (LR), and radial basis function network (RBFN), for landslide susceptibility mapping at Pithoragarh district, as these models are easy to apply and so far they have not been used for landslide study in this area. The main objective of this study is to evaluate the performance of these single models for correctly identifying landslide susceptible zones for their further application in other areas. For this, ten important landslide affecting factors, namely, slope, aspect, curvature, elevation, land cover, lithology, geomorphology, distance to rivers, distance to roads, and overburden depth based on the local geoenvironmental conditions, were considered for the modeling. Landslide inventory of past 398 landslide events was used in the development of models. The data of past landslide events (locations) was randomly divided into a 70/30 ratio for training (70%) and validation (30%) of the models. Standard statistical measures, namely, accuracy (ACC), specificity (SPF), sensitivity (SST), positive predictive value (PPV), negative predictive value (NPV), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC), were used to evaluate the performance of the models. Results indicated that the performance of all the models is very good (AUC > 0.90) and that of the LR model is the best (AUC = 0.926). Therefore, these single ML models can be used for the development of accurate landslide susceptibility maps. Our study demonstrated that the single models which are easy to use and can compete with the complex ensemble/hybrid models can be applied for landslide susceptibility mapping in landslide-prone areas.
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| 15. |
- Nguyen, Manh Duc, et al.
(författare)
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Hybridization of Differential Evolution and Adaptive-Network-Based Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil
- 2022
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Ingår i: CMES - Computer Modeling in Engineering & Sciences. - : Tech Science Press. - 1526-1492 .- 1526-1506. ; 130:1, s. 149-166
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Tidskriftsartikel (refereegranskat)abstract
- One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil. In this study, the main purpose is to develop a novel hybrid Machine Learning (ML) model (ANFIS-DE), which used Differential Evolution (DE) algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System (ANFIS), for estimating soil Compression coefficient (Cc) from other geotechnical parameters namely Water Content, Void Ratio, Specific Gravity, Liquid Limit, Plastic Limit, Clay content and Depth of Soil Samples. Validation of the predictive capability of the novel model was carried out using statistical indices: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R). In addition, two popular ML models namely Reduced Error Pruning Trees (REPTree) and Decision Stump (Dstump) were used for comparison. Results showed that the performance of the novel model ANFIS-DE is the best (R = 0.825, MAE = 0.064 and RMSE = 0.094) in comparison to other models such as REPTree (R = 0.7802, MAE = 0.068 and RMSE = 0.0988) and Dstump (R = 0.7325, MAE = 0.0785 and RMSE = 0.1036). Therefore, the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc, which can be employed in the design and construction of civil engineering structures.
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| 16. |
- Nguyen, Phong Tung, et al.
(författare)
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Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique : The DakNong Province Case-study, Vietnam
- 2020
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Ingår i: International Journal of Environmental Research and Public Health. - Switzerland : MDPI. - 1661-7827 .- 1660-4601. ; 17:7
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Tidskriftsartikel (refereegranskat)abstract
- The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.
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| 17. |
- Nguyen, Phong Tung, et al.
(författare)
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Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling
- 2020
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:7
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Tidskriftsartikel (refereegranskat)abstract
- Groundwater is one of the most important sources of fresh water all over the world, especially in those countries where rainfall is erratic, such as Vietnam. Nowadays, machine learning (ML) models are being used for the assessment of groundwater potential of the region. Credal decision trees (CDT) is one of the ML models which has been used in such studies. In the present study, the performance of the CDT has been improved using various ensemble frameworks such as Bagging, Dagging, Decorate, Multiboost, and Random SubSpace. Based on these methods, five hybrid models, namely BCDT, Dagging-CDT, Decorate-CDT, MBCDT, and RSSCDT, were developed and applied for groundwater potential mapping of DakLak province of Vietnam. Data of 227 groundwater wells of the study area were utilized for the construction and validation of the models. Twelve groundwater potential conditioning factors, namely rainfall, slope, elevation, river density, Sediment Transport Index (STI), curvature, flow direction, aspect, soil, land use, Topographic Wetness Index (TWI), and geology, were considered for the model studies. Various statistical measures, including area under receiver operating characteristic (AUC) curve, were applied to validate and compare the performance of the models. The results show that performance of the hybrid CDT ensemble models MBCDT (AUC = 0.770), BCDT (AUC = 0.731), Dagging-CDT (AUC = 0.763), Decorate-CDT (AUC = 0.750), and RSSCDT (AUC = 0.766) improved significantly in comparison to the single CDT (AUC = 0.722) model. Therefore, these developed hybrid models can be applied for better ground water potential mapping and groundwater resources management of the study area as well as other regions of the world.
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| 18. |
- Nguyen, Phong Tung, et al.
(författare)
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Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping
- 2020
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Ingår i: Applied Sciences. - Switzerland : MDPI. - 2076-3417. ; 10:7
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Tidskriftsartikel (refereegranskat)abstract
- Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) ensemble techniques for groundwater potential mapping in Dak Lak Province, Vietnam. A suite of well yield data and twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic Wetness Index, flow direction, rainfall, river density, soil, land use, and geology) were used for generating the training and validation datasets required for the building and validation of the models. Based on the area under the receiver operating characteristic curve (AUC) and several other validation methods (negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, and Kappa), it was revealed that all four ensemble learning techniques were successful in enhancing the validation performance of the base LR model. The ensemble DLR model (AUC = 0.77) was the most successful model in identifying the groundwater potential zones in the study area, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and single LR model (AUC = 0.71), respectively. The models developed in this study and the resulting potential maps can assist decision-makers in the development of effective adaptive groundwater management plans.
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| 19. |
- Nguyen, Viet-Tien, et al.
(författare)
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GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility : A Case Study at Da Lat City, Vietnam
- 2019
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 11:24
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Tidskriftsartikel (refereegranskat)abstract
- Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.
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| 20. |
- Pham, Binh Thai, et al.
(författare)
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Extreme learning machine based prediction of soil shear strength : A sensitivity analysis using Monte Carlo simulations and feature backward elimination
- 2020
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 12:6, s. 1-29
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Tidskriftsartikel (refereegranskat)abstract
- Machine Learning (ML) has been applied widely in solving a lot of real-world problems. However, this approach is very sensitive to the selection of input variables for modeling and simulation. In this study, the main objective is to analyze the sensitivity of an advanced ML method, namely the Extreme Learning Machine (ELM) algorithm under different feature selection scenarios for prediction of shear strength of soil. Feature backward elimination supported by Monte Carlo simulations was applied to evaluate the importance of factors used for the modeling. A database constructed from 538 samples collected from Long Phu 1 power plant project was used for analysis. Well-known statistical indicators, such as the correlation coefficient (R), root mean squared error (RMSE), and mean absolute error (MAE), were utilized to evaluate the performance of the ELM algorithm. In each elimination step, the majority vote based on six elimination indicators was selected to decide the variable to be excluded. A number of 30,000 simulations were conducted to find out the most relevant variables in predicting the shear strength of soil using ELM. The results show that the performance of ELM is good but very different under different combinations of input factors. The moisture content, liquid limit, and plastic limit were found as the most critical variables for the prediction of shear strength of soil using the ML model.
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| 21. |
- Ta, Duong Nguyen Binh, et al.
(författare)
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Interactivity-Constrained Server Provisioning in Large-Scale Distributed Virtual Environments
- 2012
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Ingår i: IEEE Transactions on Parallel and Distributed Systems. - 1045-9219 .- 1558-2183. ; 23:2, s. 304-312
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Tidskriftsartikel (refereegranskat)abstract
- Maintaining interactivity is one of the key challenges in distributed virtual environments (DVEs). In this paper, we consider a new problem, termed the interactivity-constrained server provisioning problem, whose goal is to minimize the number of distributed servers needed to achieve a prespecified level of interactivity. We identify and formulate two variants of this new problem and show that they are both NP-hard via reductions to the set covering problem. We then propose several computationally efficient approximation algorithms for solving the problem. The main algorithms exploit dependencies among distributed servers to make provisioning decisions. We conduct extensive experiments to evaluate the performance of the proposed algorithms. Specifically, we use both static Internet latency data available from prior measurements and topology generators, as well as the most recent, dynamic latency data collected via our own large-scale deployment of a DVE performance monitoring system over PlanetLab. The results show that the newly proposed algorithms that take into account interserver dependencies significantly outperform the well-established set covering algorithm for both problem variants.
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| 22. |
- Tran, Quoc Cuong, et al.
(författare)
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Novel Ensemble Landslide Predictive Models Based on the Hyperpipes Algorithm : A Case Study in the Nam Dam Commune, Vietnam
- 2020
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Ingår i: Applied Sciences. - Switzerland : MDPI. - 2076-3417. ; 10:11
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Tidskriftsartikel (refereegranskat)abstract
- Development of landslide predictive models with strong prediction power has become a major focus of many researchers. This study describes the first application of the Hyperpipes (HP) algorithm for the development of the five novel ensemble models that combine the HP algorithm and the AdaBoost (AB), Bagging (B), Dagging, Decorate, and Real AdaBoost (RAB) ensemble techniques for mapping the spatial variability of landslide susceptibility in the Nam Dan commune, Ha Giang province, Vietnam. Information on 76 historical landslides and ten geo-environmental factors (slope degree, slope aspect, elevation, topographic wetness index, curvature, weathering crust, geology, river density, fault density, and distance from roads) were used for the construction of the training and validation datasets that are the prerequisites for building and testing the proposed models. Using different performance metrics (i.e., the area under the receiver operating characteristic curve (AUC), negative predictive value, positive predictive value, accuracy, sensitivity, specificity, root mean square error, and Kappa), we verified the proficiency of all five ensemble learning techniques in increasing the fitness and predictive powers of the base HP model. Based on the AUC values derived from the models, the ensemble ABHP model that yielded an AUC value of 0.922 was identified as the most efficient model for mapping the landslide susceptibility in the Nam Dan commune, followed by RABHP (AUC = 0.919), BHP (AUC = 0.909), Dagging-HP (AUC = 0.897), Decorate-HP (AUC = 0.865), and the single HP model (AUC = 0.856), respectively. The novel ensemble models proposed for the Nam Dan commune and the resultant susceptibility maps can aid land-use planners in the development of efficient mitigation strategies in response to destructive landslides.
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| 23. |
- Tran, Trung-Hieu, et al.
(författare)
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GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India
- 2021
-
Ingår i: Mathematical problems in engineering (Print). - : Hindawi Publishing Corporation. - 1024-123X .- 1563-5147. ; 2021
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Tidskriftsartikel (refereegranskat)abstract
- The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely, Positive Predicted Values (PPV), Negative Predicted Values (NPV), Sensitivity, Specificity, Mean Absolute Error (MAE), Root Mean Squire Error (RMSE), and Area under the ROC Curve (AUC) were used for the evaluation of the models. All the three soft computing models used in this study have shown good performance in the accurate development of landslide susceptibility maps, but performance of the ADT and MLP is better than NB. Therefore, these models can be used for the construction of accurate landslide susceptibility maps in other landslide-prone areas also.
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| 24. |
- Duong, Quang Trung, et al.
(författare)
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On The Symbol Error Probability of Distributed-Alamouti Scheme
- 2009
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Ingår i: Journal of Communications. - : Academy Publisher. - 1796-2021 .- 2374-4367. ; 4:7, s. 437-444
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Tidskriftsartikel (refereegranskat)abstract
- Taking into account the relay’s location, we analyze the maximum likelihood (ML) decoding performance ofdualhop relay network, in which two amplify-and-forward (AF) relays employ the Alamouti code in a distributed fashion. In particular, using the well-known moment generating function (MGF) approach we derive the closed-form expressions of the average symbol error probability (SEP) for M-ary phase-shift keying (M-PSK) when the relays are located nearby either the source or destination. The analytical result is obtained as a single integral with finite limits and the integrand composed solely of trigonometric functions. Assessing the asymptotic characteristic of SEP formulas in the high signal-to-noise ratio regime, we show that the distributed-Alamouti protocol achieves a full diversity order. We also perform the Monte-Carlo simulations to validate our analysis. In addition, based on the upper bound of SEP we propose an optimal power allocation between the first-hop (the source-to-relay link) and second-hop (the relay-to-destination link) transmission. We further show that as the two relays are located nearby the destination most of the total power should be allocated to the broadcasting phase (the first-hop transmission). When the two relays are placed close to the source, we propose an optimal transmission scheme which is a non-realtime processing, hence, can be applied for practical applications. It is shown that the optimal power allocation scheme outperforms the equal power scheme with a SEP performance improvement by 2-3 dB.
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| 25. |
- Duong, Quang Trung, et al.
(författare)
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Performance analysis of cooperative spatial multiplexing networks with AF/DF relaying and linear receiver over Rayleigh fading channels
- 2015
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Ingår i: Wireless Communications & Mobile Computing. - : Elsevier. - 1530-8669 .- 1530-8677. ; 15:3, s. 500-509
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Tidskriftsartikel (refereegranskat)abstract
- Cooperative spatial multiplexing (CSM) system has played an important role in wireless networks by offering a substantial improvement in multiplexing gain compared with its cooperative diversity counterpart. However, there is a limited number of research works that consider the performance of CSM systems. As such, in this paper, we have derived exact performance of CSM with amplify-and-forward and decode-and-forward relays in terms of outage capacity and ergodic capacity. We have shown that CSM systems yield a unity diversity order regardless of the number of antennas at the destination and the number of relays in the networks, which is the direct result of diversity and multiplexing gain trade-off. Our analytical expressions are corroborated by Monte-Carlo simulations.
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| 26. |
- Duong, Quang Trung, et al.
(författare)
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Symbol Error Probability of Distributed-Alamouti Scheme in Wireless Relay Networks
- 2008
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Konferensbidrag (refereegranskat)abstract
- In this paper, we analyze the maximum likelihood decoding performance of non-regenerative cooperation employing Alamouti scheme. Specifically, we derive two closed-form expressions for average symbol error probability (SEP) when the relays are located near by the source or destination. The analytical results are obtained as a single integral with finite limits and an integrand composed solely of trigonometric functions. Assessing the asymptotic (high signal-to-noise ratio) behavior of SEP formulas, we show that the distributed-Alamouti codes achieves a full diversity order. We also perform Monte-Carlo simulations to validate the analysis.
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| 27. |
- Hien Tran, Thi, et al.
(författare)
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Triolein from Coix lacryma-jobi induces cell cycle arrest through p53/p21 signaling pathway
- 2016
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Ingår i: Biomedical and Pharmacology Journal. - : Oriental Scientific Publishing Company. - 0974-6242. ; 9:2, s. 519-524
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Tidskriftsartikel (refereegranskat)abstract
- p53, a tumor suppressor protein, has important roles in DNA repair, cell cycle and apoptosis, is a one of the key events in cancer development. Coix lacryma-jobi seed has been used as a food and traditional medicine plant with anti-oxidant, anti-cancer and anti-diabetic effects. In currently research, we identified the most potent p53-increasing compound among 4 compounds (1-4) found in Coix lacryma-jobi and demonstrated its molecular mechanism in MCF-7 cells. Among the four isolated compounds (1-4), triolein most increased p53. Triolein treatment induced p53, p21, p27 and Bax in MCF-7 cells. Moreover, triolein caused S phase arrest through suppression of CDK1, phopho-Rb and E2F1 in dose-dependent manner. We also observed the decreasing of DNA synthesis by triolein. These data suggest that triolein may induced cell cycle restart involve DNA synthesis and apoptosis pathway in MCF-7 cells.
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| 28. |
- Klingberg-Allvin, Marie, et al.
(författare)
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Married men's first time experiences of early childbearing and their role in sexual and reproductive decision making : a qualitative study from rural Vietnam
- 2012
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Ingår i: Culture, Health and Sexuality. - : Informa UK Limited. - 1369-1058 .- 1464-5351. ; 14:4, s. 449-461
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Tidskriftsartikel (refereegranskat)abstract
- Male partners' involvement in women's sexual and reproductive health has been increasingly emphasised in international health. A qualitative approach with open-ended qualitative interviews was used to explore young, married men's first time experiences of early childbearing, their sexual and reproductive decision making and the meanings they make of their role as husbands and fathers. The results offer a nuanced picture of the men's vulnerability in becoming young fathers and having to assume their role as family decision-makers, while still being inexperienced in matters related to the health of their wives and newborn child. Constraints to gender equality and traditional norms and values continue to pose barriers to both young men and women making independent decisions in relation to marriage and childbearing. Men's involvement is necessary in healthcare programmes designed to improve women's sexual and reproductive health and the health of the newborn. Young, first-time fathers, in particular, need support and empowerment.
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| 29. |
- Moreau, Thomas, et al.
(författare)
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Benchopt : Reproducible, efficient and collaborative optimization benchmarks
- 2022
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Ingår i: Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. - 1049-5258. - 9781713871088 ; 35, s. 25404-25421
-
Konferensbidrag (refereegranskat)abstract
- Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice. Yet, the rapid development of the field poses several challenges: researchers are confronted with a profusion of methods to compare, limited transparency and consensus on best practices, as well as tedious re-implementation work. As a result, validation is often very partial, which can lead to wrong conclusions that slow down the progress of research. We propose Benchopt, a collaborative framework to automate, reproduce and publish optimization benchmarks in machine learning across programming languages and hardware architectures. Benchopt simplifies benchmarking for the community by providing an off-the-shelf tool for running, sharing and extending experiments. To demonstrate its broad usability, we showcase benchmarks on three standard learning tasks: ℓ2-regularized logistic regression, Lasso, and ResNet18 training for image classification. These benchmarks highlight key practical findings that give a more nuanced view of the state-of-the-art for these problems, showing that for practical evaluation, the devil is in the details. We hope that Benchopt will foster collaborative work in the community hence improving the reproducibility of research findings.
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| 30. |
- Ngo, Son Tung, et al.
(författare)
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Distal Hydrophobic Loop Modulates the Copper Active Site and Reaction of AA13 Polysaccharide Monooxygenases
- 2022
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Ingår i: Journal of Physical Chemistry B. - : American Chemical Society (ACS). - 1520-6106 .- 1520-5207. ; 126:39, s. 7567-7578
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Tidskriftsartikel (refereegranskat)abstract
- Polysaccharide monooxygenases (PMOs) use a type-2 copper center to activate O2 for the selective hydroxylation of one of the two C-H bonds of glycosidic linkages. Our electron paramagnetic resonance (EPR) analysis and molecular dynamics (MD) simulations suggest the unprecedented dynamic roles of the loop containing the residue G89 (G89 loop) on the active site structure and reaction cycle of starch-active PMOs (AA13 PMOs). In the Cu(II) state, the G89 loop could switch between an open and closed conformation, which is associated with the binding and dissociation of an aqueous ligand in the distal site, respectively. The conformation of the G89 loop influences the positioning of the copper center on the preferred substrate of AA13 PMOs. The dissociation of the distal ligand results in the bending of the T-shaped core of the Cu(II) active site, which could help facilitate its reduction to the active Cu(I) state. In the Cu(I) state, the G89 loop is in the closed conformation with a confined copper center, which could allow for efficient O2 binding. In addition, the G89 loop remains in the closed conformation in the Cu(II)-superoxo intermediate, which could prevent off-pathway superoxide release via exchange with the distal aqueous ligand. Finally, at the end of the reaction cycle, aqueous ligand binding to the distal site could switch the G89 loop to the open conformation and facilitate product release.
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| 31. |
- Nguyen, Quang Hung, et al.
(författare)
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Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
- 2021
-
Ingår i: Mathematical problems in engineering (Print). - UK : Hindawi Publishing Corporation. - 1024-123X .- 1563-5147. ; 2021, s. 1-15
-
Tidskriftsartikel (refereegranskat)abstract
- The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were employed to evaluate the predictive capability of the models under different training and testing ratios. Besides, Monte Carlo simulation was simultaneously carried out to evaluate the performance of the proposed models, taking into account the random sampling effect. The results showed that although all three ML models performed well, the ANN was the most accurate and statistically stable model after 1000 Monte Carlo simulations (Mean R = 0.9348) compared with other models such as Boosted (Mean R = 0.9192) and ELM (Mean R = 0.8703). Investigation on the performance of the models showed that the predictive capability of the ML models was greatly affected by the training/testing ratios, where the 70/30 one presented the best performance of the models. Concisely, the results presented herein showed an effective manner in selecting the appropriate ratios of datasets and the best ML model to predict the soil shear strength accurately, which would be helpful in the design and engineering phases of construction projects.
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| 32. |
- Nhu, Viet-Ha, et al.
(författare)
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Shallow Landslide Susceptibility Mapping : A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms
- 2020
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Ingår i: International Journal of Environmental Research and Public Health. - Switzerland : MDPI. - 1661-7827 .- 1660-4601. ; 17:8, s. 1-30
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Tidskriftsartikel (refereegranskat)abstract
- Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.
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| 33. |
- Pham, Binh Thai, et al.
(författare)
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A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping
- 2020
-
Ingår i: Water. - Switzerland : MDPI. - 2073-4441. ; 12:1, s. 1-21
-
Tidskriftsartikel (refereegranskat)abstract
- Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.
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| 34. |
- Pham, Binh Thai, et al.
(författare)
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A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil
- 2021
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Ingår i: Mathematical problems in engineering (Print). - : Hindawi Limited. - 1024-123X .- 1563-5147.
-
Tidskriftsartikel (refereegranskat)abstract
- Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient (K) of soil (10-9 cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio (e), specific density (g/cm3), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and correlation coefficient (R) were used to evaluate and compare performance of the models. The results show that all the three models performed well (R > 0.8) for the prediction of permeability coefficient of soil, but the RF model (RMSE = 0.0084, MAE = 0.0049, and R = 0.851) is more efficient compared with the other two models, namely, ANN (RMSE = 0.001, MAE = 0.005, and R = 0.845) and SVM (RMSE = 0.0098, MAE = 0.0064, and R = 0.844). Thus, it can be concluded that the RF model can be used for accurate estimation of the permeability coefficient (K) of the soil.
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| 35. |
- Pham, Binh Thai, et al.
(författare)
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Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction
- 2020
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Ingår i: Symmetry. - Switzerland : MDPI. - 2073-8994. ; 12:6
-
Tidskriftsartikel (refereegranskat)abstract
- Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.
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| 36. |
- Rose, Angela M.C., et al.
(författare)
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Vaccine effectiveness against COVID-19 hospitalisation in adults (≥ 20 years) during Omicron-dominant circulation : I-MOVECOVID-19 and VEBIS SARI VE networks, Europe, 2021 to 2022
- 2023
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Ingår i: Eurosurveillance. - 1025-496X. ; 28:47
-
Tidskriftsartikel (refereegranskat)abstract
- Introduction: The I-MOVE-COVID-19 and VEBIS hospital networks have been measuring COVID-19 vaccine effectiveness (VE) in participating European countries since early 2021. Aim: We aimed to measure VE against PCR-confirmed SARS-CoV-2 in patients ≥20 years hospitalised with severe acute respiratory infection (SARI) from December 2021 to July 2022 (Omicron-dominant period). Methods: In both networks, 46 hospitals (13 countries) follow a similar test-negative case–control protocol. We defined complete primary series vaccination (PSV) and first booster dose vaccination as last dose of either vaccine received≥14 days before symptom onset (stratifying first booster into received<150 and≥150 days after last PSV dose). We measured VE overall, by vaccine category/product, age group and time since first mRNA booster dose, adjusting by site as a fixed effect, and by swab date, age, sex, and presence/absence of at least one commonly collected chronic condition. Results: We included 2,779 cases and 2,362 controls. The VE of all vaccine products combined against hospitalisation for laboratory-confirmed SARS-CoV-2 was 43% (95% CI: 29–54) for complete PSV (with last dose received≥150 days before onset), while it was 59% (95% CI: 51–66) after addition of one booster dose. The VE was 85% (95% CI: 78–89), 70% (95% CI: 61–77) and 36% (95% CI: 17–51) for those with onset 14–59 days, 60–119 days and 120–179 days after booster vaccination, respectively. Conclusions: Our results suggest that, during the Omicron period, observed VE against SARI hospitalisation improved with first mRNA booster dose, particularly for those having symptom onset<120 days after first booster dose.
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| 37. |
- Ta, Duong Nguyen Binh, et al.
(författare)
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Multi-objective zone mapping in large-scale distributed virtual environments
- 2011
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Ingår i: Journal of Network and Computer Applications. - : Elsevier BV. - 1084-8045 .- 1095-8592. ; 34:2, s. 551-561
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Tidskriftsartikel (refereegranskat)abstract
- In large-scale distributed virtual environments (DVEs), the NP-hard zone mapping problem concerns how to assign distinct zones of the virtual world to a number of distributed servers to improve overall interactivity. Previously, this problem has been formulated as a single-objective optimization problem, in which the objective is to minimize the total number of clients that are without QoS. This approach may cause considerable network traffic and processing overhead, as a large number of zones may need to be migrated across servers. In this paper, we introduce a multi-objective approach to the zone mapping problem, in which both the total number of clients without QoS and the migration overhead are considered. To this end, we have proposed several new algorithms based on meta-heuristics such as local search and multi-objective evolutionary optimization techniques. Extensive simulation studies have been conducted with realistic network latency data modeled after actual Internet measurements, and different workload distribution settings. Simulation results demonstrate the effectiveness of the newly proposed algorithms.
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| 38. |
- Thi Thanh Ngo, Huong, et al.
(författare)
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Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network: A Case Study of Vietnam
- 2023
-
Ingår i: CMES - Computer Modeling in Engineering & Sciences. - : Tech Science Press. - 1526-1492 .- 1526-1506. ; 135:3, s. 2219-2241
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Tidskriftsartikel (refereegranskat)abstract
- Flash floods are one of the most dangerous natural disasters, especially in hilly terrain, causing loss of life, property, and infrastructures and sudden disruption of traffic. These types of floods are mostly associated with landslides and erosion of roads within a short time. Most of Vietnam is hilly and mountainous; thus, the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management. In this study, three Machine Learning (ML) methods namely Deep Learning Neural Network (DL), Correlation-based Feature Weighted Naive Bayes (CFWNB), and Adaboost (AB-CFWNB) were used for the development of flash flood susceptibility maps for hilly road section (115 km length) of National Highway (NH)-6 in Hoa Binh province, Vietnam. In the proposed models, 88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors. The performance of the models was evaluated using standard statistical measures including Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC) and Root Mean Square Error (RMSE). The results revealed that all the models performed well (AUC > 0.80) in predicting flash flood susceptibility zones, but the performance of the DL model is the best (AUC: 0.972, RMSE: 0.352). Therefore, the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.
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| 39. |
- Tram, Duc-Dung, et al.
(författare)
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Performance Analysis of DF/AF Cooperative MISO Wireless Sensor Networks With NOMA and SWIPT Over Nakagami-m Fading
- 2018
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Ingår i: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 6, s. 56142-56161
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we investigate downlink cooperative multiple-input single-output wireless sensor networks with the nonorthogonal multiple access technique and simultaneous wireless information and power transfer over Nakagami-m fading. Specifically, the considered network includes a multiantenna sink node, an energy-limited relay cluster, a high-priority sensor node (SN) cluster, and a low-priority SN cluster. Prior to transmission, a transmit antenna, a relay, a high-priority SN, and a low-priority SN are selected. In this paper, we propose three antenna-relay-destination selection schemes, i.e., sink node-high-priority, sink node-relay, and sink node-low-priority. In each proposed scheme, we consider two relaying strategies, i.e., decode-and-forward and amplify-and-forward, and then, we derive the corresponding closed-form expressions of outage probability at the selected SNs. In addition, we introduce two algorithms: 1) the power-splitting ratio optimization algorithm and 2) the best antenna-relay-destination selection determination algorithm. Finally, we utilize the Monte Carlo simulations to verify our analytical results.
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| 40. |
- Tuyen, Tran Thi, et al.
(författare)
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Prediction of white spot disease susceptibility in shrimps using decision trees based machine learning models
- 2024
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Ingår i: Applied water science. - : Springer Science and Business Media Deutschland GmbH. - 2190-5487 .- 2190-5495. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activity worldwide affecting the economy of the countries, especially South-East Asian countries like Vietnam. This deadly disease in shrimps is caused by the White Spot Syndrome Virus (WSSV). Researchers are trying to understand the spread and control of this disease by doing field and laboratory studies considering effect of environmental conditions on shrimps affected by WSSV. Generally, they have not considered spatial factors in their study. Therefore, in the present study, we have used spatial (distances to roads and factories) as well as physio-chemical factors of water: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Salinity, NO3, P3O4 and pH, for developing WSSV susceptibility maps of the area using Decision Tree (DT)-based Machine Learning (ML) models namely Random Tree (RT), Extra Tree (ET), and J48. Model’s performance was evaluated using standard statistical measures including Area Under the Curve (AUC). The results indicated that ET model has the highest accuracy (AUC: 0.713) in predicting disease susceptibility in comparison to other two models (RT: 0.701 and J48: 0.641). The WSSV susceptibility maps developed by the ML technique, using DT (ET) method, will help decision makers in better planning and control of spatial spread of WSSV disease in shrimps.
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| 41. |
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