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This paper proposes a new decision fusion method accounting for conditional dependence (correlation) between land-cover classifications from multi-sensor data. The dependence structure between different classification results is calculated and used as weighting parameters for the subsequent fusion scheme. An algorithm for fusion of correlated probabilities (FCP) is adopted to fuse the prior probability, conditional probability, and obtained weighting parameters to generate a posterior probability for each class. A maximum posterior probability rule is then used to combine the posterior probabilities generated for each class to produce the final fusion result. The proposed FCP-based decision fusion method is assessed in land-cover classification over two study areas. The experimental results demonstrate that the proposed decision fusion method outperformed the existing decision fusion methods that do not take into account the correlation or dependence. The proposed decision fusion method can also be applied to other applications with different sensor data. [ABSTRACT FROM PUBLISHER]

The study objective is to predict the epidemiological impact of dengue fever arbovirosis in urban tropical areas of Pakistan. To do so, we used the GPS-based data of the Aedes larvae collected during 2014–2015 in Lahore. We developed a Geographically Weighted Logistic Regression (GWLR) model for Geospatially predicting larvae presence or absence in Lahore. Data on rainfall, temperature are included along with time series of the normalized difference vegetation index (NDVI) derived from Landsat imagery. We observed a high spatial variability of the GWLR parameter estimates of these variables in the study area. The GWLR model significantly ( R a 2 = 0.78) explained the presence or absence of Aedes larvae with temperature, rainfall and NDVI variables in South and Southeast of the study area. In the North and North-West, however, GWLR relationships were observed weak in highly populated areas. Interpolating GWLR coefficients generate more accurate maps of Aedes larvae presence or absence [ABSTRACT FROM AUTHOR]

The aim of the present study is to determine the collaborative activities of Asian library and information science (LIS) researchers through bibliometric, social network analysis at the author, institution, and country levels of the LIS publications produced by Asian researchers. For the study, research articles were derived from the 1994–2013 Web of Science (WoS) archives. Coauthor analysis software was used to analyze author contributions on the New Modified Author Activity Index (NMAAI1, 2, 3)—a measurement tool to evaluate author research activities. Results reveal (a) that LIS “institutions collaboration pattern” outcomes did not yield strong collaboration with Asian countries or regions, (b) intracontinent and intercontinent collaboration was less harmonious on institutional and author levels, and (c) interpretation through NMAAI1,2,3revealed that Asian countries did not produce sufficient collaborative LIS publications. Taiwan, South Korea, Singapore, India, Iran, Israel, and Turkey have been producing ample LIS papers, but the United States and United Kingdom emerged as leaders. This work provides a holistic view of Asian LIS development and emphasizes individual levels of collaboration, finding that most influential authors serve on coauthor networks, which differentiates this study from previous studies in the LIS field. There is immense need to develop liaisons, sign memoranda of understanding (MOUs), and participate in combined research plans and short-term research exchange programs. [ABSTRACT FROM PUBLISHER]