Abstract. In recent years, Vietnamese economy has been growing up rapidly and caused serious environmental quality plunging, especially in industrial and mining areas. It brings an enormous threat to a socially sustainable development and the health of human beings. Environmental quality assessment and protection are complex and dynamic processes, since it involves spatial information from multi-sector, multi-region and multi-field sources and needs complicated data processing. Therefore, an effective environmental protection information system is needed, in which considerable factors hidden in the complex relationships will become clear and visible. In this paper, the authors present the methodology which was used to generate environmental hazard maps which are applied to the integration of Analytic Hierarchy Process (AHP) and Geographical Information system (GIS). We demonstrate the results that were obtained from the study area in Dong Trieu district. This research study has contributed an overall perspective of environmental quality and identified the devastated areas where the administration urgently needs to establish an appropriate policy to improve and protect the environment.
Classification of multilabel documents is essential to information retrieval and text mining. Most of existing approaches to multilabel text classification do not pay attention to relationship between class labels and input documents and also rely on labeled data all the time for classification. In fact, unlabeled data is readily available whereas generation of labeled data is expensive and error prone as it needs human annotation. In this paper, we propose a novel multilabel document classification approach based on semi-supervised mixture model of Watson distributions on document manifold which explicitly considers the manifold structure of document space to exploit efficiently both labeled and unlabeled data for classification. Our proposed approach models all labels within a dataset simultaneously, which lends itself well to the task of considering the relationship between these labels. The experimental results show that proposed method outperforms the state-of-the-art methods applying to multilabeled text classification.
Automatic text summarization plays an important role in information retrieval and text mining. Furthermore, it provides an useful solution to the information overload problem. In this paper, we propose a simplicial NMF-based unsupervised generic document summarization method which can inherit some advantages of simplicial NMF such as easy interpretability, low complexity, convexity and sparsity. By focusing on the major topics contained within every sentence as well as entire document, our method generates better summaries with less repetition. The effectiveness of our method is proved by experimental results. On the summarization performance, our approach obtains mostly higher ROUGE scores than NMF-based method.
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