This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas.

Breastfeeding provides benefits to the infant and mother; however, the rates of breastfeeding, particularly exclusive breastfeeding, remain below optimal levels in many Asian countries. The aim of this study is to review the benefits of breastfeeding to mothers and infants and current rates of breastfeeding in Vietnam, and to evaluate the effectiveness of a mobile application on exclusive breastfeeding among mothers in Vietnam. A two-arm, parallel triple-blinded randomised controlled trial will be conducted among 1000 mothers in Hanoi City, Vietnam, during 2020–2021. Eligible participants are pregnant women who will seek antenatal care from health facilities at 24–36 weeks of gestation and plan to deliver at two participating hospitals, own a smartphone, and carry a singleton foetus. Permuted-block randomisation method stratified by maternal age, education and parity will be used to ensure an equal number of participants in each group. A smartphone app will be developed to deliver breastfeeding and non-breastfeeding information to the intervention and control group, respectively. Data will be collected at baseline, before hospital discharge, and at 1, 4, and 6 months postpartum. This study envisages demonstrating whether a smartphone-based intervention can be effective at improving breastfeeding in Vietnam. Trials registration: ACTRN12619000531112.

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