Malaria remains a significant global health issue. In Indonesia, >400,000 cases of malaria were reported, with the highest prevalence in Papua Province. However, limited access to healthcare services in remote area...
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The recent development of large-scale multimedia concept ontologies has provided a new momentum for research in the semantic analysis of multimedia repositories. Different methods for generic concept detection have be...
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The recent development of large-scale multimedia concept ontologies has provided a new momentum for research in the semantic analysis of multimedia repositories. Different methods for generic concept detection have been extensively studied, but the question of how to exploit the structure of a multimedia ontology and existing inter-concept relations has not received similar attention. In this paper, we present a clustering-based method for modeling semantic concepts on low-level feature spaces and study the evaluation of the quality of such models with entropy-based methods. We cover a variety of methods for assessing the similarity of different concepts in a multimedia ontology. We study three ontologies and apply the proposed techniques in experiments involving the visual and semantic similarities, manual annotation of video, and concept detection. The results show that modeling inter-concept relations can provide a promising resource for many different application areas in semantic multimedia processing.
State failure has been traditionally defined as the collapse of national authority, which may be reflected in disasters such as wars and disruptive regime transitions. The availability of comprehensive datasets and th...
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ISBN:
(纸本)9781424401956
State failure has been traditionally defined as the collapse of national authority, which may be reflected in disasters such as wars and disruptive regime transitions. The availability of comprehensive datasets and the limitations exhibited by previous forecasting analyses led us to integrate different predictive resources and models through statistical analysis and machine learning. Here we demonstrate the predictive ability of unsupervised and supervised learning approaches to detecting meaningful relationships between country cases, encoded by several socio-economic indicators, and the emergence of violent conflicts. Two clustering-based analyses (Kohonen maps and a network-based approach) provided the basis for exploratory analyses that confirmed hypotheses about the relevance of the data and the differences between state failure types. We also illustrate the potential of a novel network-basedclustering approach for sub-class discovery in the area of political instability analysis. Furthermore, we show significant relationships between the emergence of violent conflicts and a dataset of quantitative indicators of good governance, which allows the design of effective supervised and unsupervised classifiers. This study contributes to the development of intelligent data analysis techniques for supporting hypothesis generation and testing in international conflict analyses.
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