SEARCH RESULT

LiBerated Social Entrepreneurship in Developing and Emerging Countries consists of a social entrepreneur using business metrics, to sustain social impact. We study differences between developing and developed countries, introducing a new OR approach to development. Commercial entrepreneurs are generally oriented to business metrics like profit, revenues and return. Instead, social entrepreneurs are non-profits or a blend with for-profit goals, generating Return to Society. In DCs, a social entrepreneurship has been uncommon. We introduce a midway as LiBerated Social Entrepreneur, where social businesses should be sustainable. We apply Game and Max-Flow - Min-Cut Theories, Schumpeter’s creative destruction and Adam Smith’s diversification model for our business plan. As a result, B. Kjamili started Migport, formerly QZenobia: a mobile application that runs as a “refugee portal”, supported by “Refugee Big-Data Analytics”: refugees submit data to the application via “questionnaire” and search for opportunities, verified news privatized based on their answers. The idea of both-sided help with benefit generated by D. Czerkawski is an extension of B. Kjamili's conception. Nshareplatform (NSP) will create a friendly public space for people with disabilities, understanding they needs. It tries to facilitate better communication between “two worlds”- Ability and Disability and personalizes an assistant (Special person helping people with disabilities). Multivariate Adaptive Regression Splines (MARS), Conic MARS (CMARS) and its robust version RCMARS have shown their potential for Big-Data and, recently, Small-Data. With that toolbox, we aim to further support our joint and novel project.

International Data Science & Engineering Symposium
IDSES

Gerhard-Wilhelm WEBER Berat KJAMILI Dominik CZERKAWSKI

Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Hence, snow cover mapping with high accuracy is necessary to have a real understanding for present and future climate, water cycle, and ecological changes. This study aims to investigate and compare the design and use of artificial neural networks (ANNs) and support vector machines (SVMs) algorithms for fractional snow cover (FSC) mapping from satellite data. ANN and SVM models with different model building settings are trained by using Moderate Resolution Imaging Spectroradiometer surface reflectance values of bands 1-7, normalized difference snow index and normalized difference vegetation index as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat ETM+ binary snow cover maps. Results on the independent test data set indicate that the developed ANN model with hyperbolic tangent transfer function in the output layer and the SVM model with radial basis function kernel produce high FSC mapping accuracies with the corresponding values of R = 0.93 and R = 0.92, respectively.

International Workshop on GeoInformation Science
GEOADVANCES

B. B. Çiftçi S. Kuter Z. Akyürek Gerhard-Wilhelm WEBER