| 1. |
- 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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| 2. |
- 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
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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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| 3. |
- Trung, Hieu Tran, et al.
(författare)
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Anti-inflammatory and Antiphytopathogenic Fungal Activity of 2,3-seco-Tirucallane Triterpenoids Meliadubins A and B from Melia dubia Cav. Barks with ChemGPS-NP and In Silico Prediction
- 2023
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Ingår i: ACS Omega. - : American Chemical Society (ACS). - 2470-1343. ; 8:40, s. 37116-37127
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Tidskriftsartikel (refereegranskat)abstract
- Two new rearranged 2,3-seco-tirucallane triterpenoids, meliadubins A (1) and B (2), along with four known compounds, 3-6, were isolated from the barks of Melia dubia Cav. Compound 2 exhibited a significant inflammatory inhibition effect toward superoxide anion generation in human neutrophils (EC50 at 5.54 +/- 0.36 mu M). It bound to active sites of a human inducible nitric oxide synthase (3E7G) through interactions with the residues of GLU377 and PRO350, which may benefit in reducing the neutrophilic inflammation effect. The ChemGPS-NP interpretation combined with bioactivity assay and in silico prediction results suggested 2 to be an agent for targeting iNOS with different mechanisms as compared to a selected set of current approved drugs. Moreover, compounds 1 and 2 showed remarkable inhibition against the rice pathogenic fungus Magnaporthe oryzae in a dose-dependent manner with IC50 values of 137.20 +/- 9.55 and 182.50 +/- 18.27 mu M, respectively. Both 1 and 2 displayed interactions with the residue of TYR223, a key active site of trihydroxynaphthalene reductase (1YBV). The interpretation of 1 and 2 in the ChemGPS-NP physical-chemical property space indicated that both compounds are quite different compared to all members of a selected set of reference compounds. In light of demonstrated biological activity and in silico prediction experiments, both compounds possibly exhibited activity against phytopathogenic fungi via a novel mode of action.
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| 4. |
- Jha, Debesh, et al.
(författare)
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A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging
- 2021
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Ingår i: Medical Image Analysis. - 1361-8415 .- 1361-8423. ; 70
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Tidskriftsartikel (refereegranskat)abstract
- Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years.
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