OpenStreetMap (OSM) has been demonstrated to be a valuable source of spatial data in the context of many applications. However concerns still exist regarding the quality of such data and this has limited the proliferation of its use. Consequently much research has been invested in the development of methods for assessing and/or improving the quality of OSM data. However most of these methods require ground-truth data, which, in many cases, may not be available. In this paper we present a novel solution for OSM data quality assessment that does not require ground-truth data. We consider the semantic accuracy of OSM street network data, and in particular, the associated semantic class (road class) information. A machine learning model is proposed that learns the geometrical and topological characteristics of different semantic classes of streets. This model is subsequently used to accurately determine if a street has been assigned a correct/incorrect semantic class.

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Since blood centers in most countries typically rely on volunteer donors to meet the hospitals' needs, donor retention is critical for blood banks. Identifying regular donors is critical for the advance planning of blood banks to guarantee a stable blood supply. In this research, donors' data was collected from a Saudi blood bank from 2017 to 2018. Machine learning algorithms such as logistic regression (LG), random forest (RF) and support vector classifier (SVC) were applied to develop and evaluate models for classifying blood donors as return and nonreturn donors. The natural imbalance of the donors' distribution required extra attention and considerations to produce classifiers with good performance. Thus, over-SMOTE sampling was tested. Experiments of different classifiers showed very similar performance results. In addition to the donors return classification, a time series analysis on the donors dataset was also considered to find any seasonal variations that could be captured and delivered to blood banks for better planning and decision making. After aggregating the donation count by month, results showed that the number of donations each year was stable except for two discovered drops in June and September, which for the two observed years coincided with two religious periods: Fasting and Performing Hajj.

Abstract-Pandemics or high impact epidemics are one of the biggest threats facing humanity today. While a complete elimination of the occurrence of such threats is improbable, it is possible to contain their impact by efficient management which in turn depends on effective decision-making. In the event of a pandemic the data flows are enormous and pose severe cognitive overload to the public health decision-makers. In this context, this paper presents PandemCap, an innovative decision support tool that can be used by the public health officials for making better and well informed decisions in the event of pandemics or high impact epidemics. PandemCap provides an interactive, flexible platform to public health decision-makers by making extensive use of techniques from the domains of visual analytics and epidemic modeling. In addition, the tool also allows for the study of the impact of various interventions or control measures such as the use of vaccines, anti-virals, hospital beds, and ventilators.