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Landslides are considered one of the most frequent natural disasters that cause human loss and damage property, affecting the sustainability of communities worldwide. Mapping landslide susceptibility has gained a lot of interest from researchers. Using machine learning methods to yield landslide susceptibility maps (LSMs) has been extensively studied in the last decade. However, the problems of sampling non-landslides and weighting classes of conditioning factors are not thoroughly investigated in landslide susceptibility models. Moreover, the process of zoning LSMs for different susceptible areas is often overlooked. To address this gap, we present a study on sampling non-landslides and computing appropriate weights, which are used to train machine learning models to obtain accurate outputs. The outputs are clustered into different susceptible levels by K-means clustering method. Our study was conducted in Nam Pam area, Vietnam. A landslide inventory of 71 landslide points and 44 delineated landslide polygons, and 13 conditioning factors were selected in this study area. Furthermore, three regression methods, i.e., Bayesian, K-Nearest Neighbors, and Support vector machine (SVM), were taken to generate LSMs. We assessed three models based on various metrics, including 5 statistical metrics and AUC. The reliability of LSMs was evaluated by MFR value. Empirical results have shown that SVM method and the 2:1 ratio of non-landslides to landslides are recommended to build landslide susceptibility models. [ABSTRACT FROM AUTHOR]

One of the network performance challenges of Vehicular Ad Hoc Networks (VANET) is the Black Hole attack. This destructive attack severely damages the network performance if succeeded. By replying to a route request with the minimum routing cost and the maximum sequence number (SN) value, the malicious vehicle can fool the source vehicle that it has the "freshest" and best cost-effective route to the destination vehicle. As a result, all data packets intended to a legitimate destination are caught and destroyed by the malicious vehicle. This may also cause a serious risk for autonomous vehicle control system or traffic warning applications. Previous published malicious detection algorithms suffer high error rates when the malicious node uses a "close-to-legitimate" SN value to attack or actively change the information in the route reply packet. This paper proposes a Black Hole Attack Detection Algorithm (BADA) based on a machine learning approach. In VANETs, it is a challenge to differentiate the behavior of malicious vehicles as they try to avoid detection by imitating that of the normal vehicles. The BADA classification algorithm outperforms existing solutions because it relies on the history of each vehicle's route request and response behavior, and it deploys the k-Nearest Neighbors machine learning algorithm to identify malicious vehicles. The paper also proposes a Black hole Attack Detection Routing Protocol which utilizes the proposed BADA solution to realize a more secured and improved AODV-based protocol. Using the NS2 simulation system, the paper evaluates the performance of the proposed protocol and compares it with related solutions on the traffic network model in Ho Chi Minh city, Vietnam under black hole attacks using minimum SN values. Simulation results have shown that the proposed algorithm can correctly detect the malicious node higher than 99.0%, outperforming previously published algorithms. [ABSTRACT FROM AUTHOR]

Labor exports are currently considered among the most important foreign economic sectors, implying that they contribute to a country's economic development and serve as a strategic solution for employment creation. Therefore, with the support of data collected between 1992 and 2020, this paper proposes that labor exports contribute significantly to Vietnam's socio-economic development. This study also aims to employ the Backpropagation Neural Network (BPNN), k-Nearest Neighbor (kNN), and Random Forest Regression (RFR) models to analyze labor migration forecasting in Taiwan, Korea, and Japan. The study results indicate that the BPNN model was able to achieve the highest accuracy regarding the actual labor exports. In terms of these accuracy metrics, this study will aid the Vietnamese government in establishing new legislation for Vietnamese migrant workers in order to improve the nation's economic development. [ABSTRACT FROM AUTHOR]

Cost–benefit analysis is widely used to elucidate the association between foraging group size and resource size. Despite advances in the development of theoretical frameworks, however, the empirical systems used for testing are hindered by the vagaries of field surveys and incomplete data. This study developed the three approaches to data imputation based on machine learning (ML) algorithms with the aim of rescuing valuable field data. Using 163 host spider webs (132 complete data and 31 incomplete data), our results indicated that the data imputation based on random forest algorithm outperformed classification and regression trees, the k-nearest neighbor, and other conventional approaches (Wilcoxon signed-rank test and correlation difference have p-value from < 0.001–0.030). We then used rescued data based on a natural system involving kleptoparasitic spiders from Taiwan and Vietnam (Argyrodes miniaceus, Theridiidae) to test the occurrence and group size of kleptoparasites in natural populations. Our partial least-squares path modelling (PLS-PM) results demonstrated that the size of the host web (T = 6.890, p = 0.000) is a significant feature affecting group size. The resource size (T = 2.590, p = 0.010) and the microclimate (T = 3.230, p = 0.001) are significant features affecting the presence of kleptoparasites. The test of conformation of group size distribution to the ideal free distribution (IFD) model revealed that predictions pertaining to per-capita resource size were underestimated (bootstrap resampling mean slopes