Introduction Imagine, for a second, a doorman that behaves as automatic doors do. He does not acknowledge you when you pass by, or approach. He gives no hint which door can or will open—until you wander within 10 feet of the door, whereupon he flings the door wide open. If you arrived after hours, you might stand in front of the doors for awhile before you understood that the doors are locked, ...

Driving under the influence of substances (DUI) other than alcohol has been the subject of increasing interest over the past few decades (Schulze et al., 2012). As with alcohol, research has shown that drug intake increases the risk of road traffic accidents (Elvik, 2013; Schulze et al., 2012; Verstraete & Legrand, 2014). Intervention in this area is a priority. A key factor for deterring DUI i...

In this article I examine the constraints that skeptical hypotheses must satisfy in order to be used to raise significant skeptical challenges. I argue that skeptical hypotheses do not have to be logically, metaphysically or epistemically possible. They only need to depict scenarios that are subjectively indistinguishable from the actual world and must provide some indication of how subjects ca...

This study proposes a new fuzzy adaptive filter for the restoration of impulse corrupted digital images. The proposed filter incorporates fuzzy functions to model the uncertainties, while detecting and correcting impulses. The traditional, SMALL fuzzy function is used to identify the non-impulsive nature of the detected corrupted pixels in the initial step. For the better restoration of detecte...

Fuzzy extractors are a powerful tool to extract randomness from noisy data. A fuzzy extractor can extract randomness only if the source data is discrete while in practice source data is continuous. Using quantizers to transform continuous data into discrete data is a commonly used solution. However, as far as we know no study has been made of the effect of the quantization strategy on the perfo...

Hybrid intelligent systems combining fuzzy logic and neural networks are proving their effectiveness in a wide variety of real-world problems. Fuzzy logic and neural nets have particular computational properties that make them suited for particular problems and not for others. For example, while neural networks are good at recognizing patterns, they are not good at explaining how they reach the...

The fuzzy belief Petri net we propose in this paper propagates fuzzy beliefs from observations at nodes that represent measured parameters to fuzzy beliefs of the truths of parameters at hidden and decision nodes. The fuzzy influences spread from the observation nodes throughout our new enhanced bidirectional fuzzy belief Petri net. Compared with Bayesian belief networks, it is simpler and fast...

Uncertainty and vagueness are pervasive phenomena in real-life knowledge. They are supported in extended description logics that adapt classical description logics to deal with numerical probabilities or fuzzy truth values. While the two concepts are distinguished for good reasons, they combine in the notion of probably, which is ultimately a fuzzy qualification of probabilities. Here, we devel...

This paper presents an application of the novel theory of fuzzy networks for optimising models of systems characterised by uncertainty, non-linearity, modular structure and interactions. The application of the theory is demonstrated for retail price models in the context of converting a multiple rule base fuzzy system (MRBFS) into an equivalent single rule base fuzzy system (SRBFS) by linguisti...

In the paper, a three-level thresholding method for image segmentation is presented, based on probability partition, fuzzy partition and entropy theory. A new fuzzy entropy has been defined through probability analysis. The image is divided into three parts, namely, dark, gray and white part, whose member functions of the fuzzy region are Z-function and P-function and S-function, respectively, ...