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MyJournals.org - Science - 'Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey' (BMC Medical Research Methodology)

Social network technologies have gained widespread attention in many fields. However, the research on Tibetan Social Network (TSN) is limited to the sentiment analysis of micro-blogs, and few researchers focus on text classification and data mining in TSN. It cannot meet the social needs of the majority of Tibetans and the text information they really care about. In this paper, we investigate and compare different models that we adopted for the classification of Tibetan text. Machine learning models including Naive Bayesian (NB), Random Forest (RF), Support Vector Machine (SVM), fastText and t...

In natural free surface flows, sediment particles in the surface layer of a sediment bed are moved and entrained by the fluctuating hydrodynamic forces, such as lift and drag, exerted by the overlying flow. Accurate prediction of near-bed hydrodynamic forces in rapidly varied flows is vital for coastal sediment transport and morphodynamics. Directly measured hydrodynamic forces within the rapidly varied flows over rough bed layer have been limited by previous spatial averaging shear force studies. Therefore, the direct measurements were designed and adapted to estimate tidal bore forces, inclu...

The efficient maintenance and classification of huge amounts of data is a big challenge for the websites which provide services for online businesses. Many of the websites provide multiple services for the customer. In the present work, we have compared various machine learning-based classification methods for the efficient distribution of data. To effectively categorize the data, the Enterprise Interface (El) layer is suggested between the application layer and the physical layer. Methods based on global and local clustering are proposed for the effective distribution of the data in the El la...

As technology continues to advance, algorithms have become an integral part of our everyday lives. From the moment we wake up and check our phones to the moment we go to bed, algorithms are working…