Innovative methods have been developed for diagnosis, activity monitoring, and state estimation that achieve high accuracy through the use of stochastic models involving hybrid discrete and continuous behaviors. A key bottleneck is the automated acquisition of these hybrid models, and recent methods have focused predominantly on Jump Markov processes and piecewise autoregressive models. In this...

Hybrid modeling seeks to address problems associated with the representation of complex systems using “single-paradigm” models: where traditional models may represent an entire system as a cellular automaton, for example, the set of submodels within a hybrid model may mix representations as diverse as individual-based models of organisms, Markov chain models, fluid dynamics models of regional o...

nowadays, due to increasing the complexity of ic engines, calibration task becomes more severe and the need to use surrogate models for investigating of the engine behavior arises. accordingly, many black box modeling approaches have been used in this context among which network based models are of the most powerful approaches thanks to their flexible structures. in this paper four network base...

Background: Multiple Sclerosis (MS) is the most frequent non-traumatic neurological disease capable of causing disability in young adults. Detection of MS lesions with magnetic resonance imaging (MRI) is the most common technique. However, manual interpretation of vast amounts of data is often tedious and error-prone. Furthermore, changes in lesions are often subtle and extremely unrepresentati...

Annually, many people are irradiated for diagnostic and therapeutic purposes. Assessment of radiation dose and its related risks to patients are important issues in radiation protection dosimetry. The complex mathematical calculations of the absorbed and effective doses are now done with computers. The calculations are performed with the help of anthropomorphic computational models of human bod...