Event detection has advanced significantly in the past decades relying on pixeland feature-level representations of video-clips. Although effective those representations have difficulty on incorporating scene semantics. Ontology and description-based approaches can explicitly embed scene semantics, but their deterministic nature is susceptible to noise from underlying components of vision syste...

In recent years, modeling techniques to study land-use change and to develop scenarios for highlighting possible future pathways of land-use change have been increasingly applied and studied. While modeling results always incorporate uncertainties in terms of input data, model parameters, model boundaries, and model structure, up to now, only few land-use change modeling approaches have explici...

In construction planning, decision making has a great impact on final project performance. Hence, it is essential for managers to assess the planning and make informed decisions. However, disproportionately large uncertainties occur during stage; in worst case, reliable probability distributions of are sometimes unavailable due lack information before implementation. This situation constitutes ...

[1] Despite significant recent developments in computational power and distributed hydrologic modeling, the issue of how to adequately address the uncertainty associated with hydrological predictions remains a critical and challenging one. This issue needs to be properly addressed for hydrological modeling to realize its maximum practical potential in environmental decision-making processes. Ar...

This study was conducted in 2016 aimed to investigate the relationship between empowering leadership, uncertainty avoidance, trust, and employee creativity with regard to interaction effects and mediating mechanism. The population consists of 205 employees from the Nomadic affairs of Fars Province. In accordance with the Cochran formula, the sample size of 134 was calculated that were participa...

Stormwater quality modeling results are subject to uncertainty. The variability of input parameters is an important source of overall model error. An in-depth understanding of the variability associated with input parameters can provide knowledge on the uncertainty associated with these parameters and can assist in uncertainty analysis of stormwater quality models and decision making based on m...

Latest scientific and engineering advances have started to recognize the need of defining multiple types of uncertainty. Probabilistic modeling cannot handle situations with incomplete or little information on which to evaluate a probability, or when that information is nonspecific, ambiguous, or conflicting [1]. Many generalized models of uncertainty have been developed to treat such situation...

There is a growing body of evidence to support the proposition of product take back for remanufacturing particularly within the context of Extended Producer Responsibility (EPR). Remanufacturing however presents challenges unlike that of traditional manufacturing environments due to its high levels of uncertainty which may further distract organizations from considering its potential benefits. ...

This research focuses on the methods and application of optimization under uncertainty to radiation therapy planning, where it is natural and useful to model the uncertainty of the problem directly. In particular, we present methods for optimization under uncertainty in radiation therapy of tumors and compare their results. Two themes are developed in this study: (1) the modeling of inherent un...

Information-gap (info-gap) modeling is put forth as a basic approach for enhancing decision making under uncertainty, especially when there is a high level of uncertainty and little information is available. The great need for having realistic techniques for describing severe uncertainty can be illustrated in water resources management by pointing out the wide range of uncertainties present in ...