The objective of this review was to present, evaluate and discuss methods for reducing false alarms in sensor-based detection models developed for livestock production as described in the scientific literature. Papers included in this review are all peer-reviewed and present sensor-based detection models developed for modern livestock production with the purpose of optimizing animal health or m...

In a real-world application, Fuzzy Semi Hidden Markov Model (FSHMM) is an inspirational domain for uncertainty. FSHMM has been handled the vagueness in real-life situation. The other models cannot consider uncertainty information, but effectively finds optimal path between states where exists. proposed work described new approach to solving model's two significant problems: decoding and re-esti...

We study a family of Markov processes on P , the space of partitions of the natural numbers with at most k blocks. The process can be constructed from a Poisson point process on R+ ×∏ki=1 P (k) with intensity dt ⊗ (k) ν , where ν is the distribution of the paintbox based on the probability measure ν on Pm, the set of ranked-mass partitions of 1, and (k) ν is the product measure on ∏k i=1 P . We...

In this contribution we deal with the problem of doing computations with a Markov chain when the information about transition probabilities is expressed linguistically. This could be the case, for instance, if the process we are modeling is described by a human expert, for whom the use of linguistic labels is easier than being forced to give inexact numerical probabilities which, in turn, may y...

software architecture is considered one of the most important indices of software engineering today. software architecture is a technical description of a system indicating its component structures and their relationships, and is the principles and rules governing designing. the success of the software depends on whether the system can satisfy the quality attributes. one of the most critical as...

Estimation of the extent and spread of wildland fires is an important application of high spatial resolution multispectral images. This work addresses a fuzzy segmentation algorithm to map fire extent, active fire front, hot burn scar, and smoke regions based on a statistical model. The fuzzy results are useful data sources for integrated fire behavior and propagation models built using Dynamic...

tion problem is to decide, from the observed image, in This paper proposes a new unsupervised fuzzy Bayesian which class each pixel lies. In the first case we speak of image segmentation method using a recent model using hidden fuzzy segmentation, and in the second case of hard segmenfuzzy Markov fields. The originality of this model is to use tation. As we can see, fuzzy and hard segmentations...

Investors use different approaches to select optimal portfolio. so, Optimal investment choices according to return can be interpreted in different models. The traditional approach to allocate portfolio selection called a mean - variance explains. Another approach is Markov chain. Markov chain is a random process without memory. This means that the conditional probability distribution of the nex...

In this paper, an unsupervised multiresolution image segmentation algorithm is put forward, which combines interscale and intrascale Markov random field and fuzzy c-means clustering with spatial constraints. In the initial label determination of wavelet coefficient phase, the statistical distribution property of wavelet coefficients is characterized by Gaussian mixture model, the properties of ...

-Many image segmentation techniques are available in the literature. Some of these techniques use only the gray level histogram, some use spatial details while others use fuzzy set theoretic approaches. Most of these techniques are not suitable for noisy environments. Some works have been done using the Markov Random Field (MRF) model which is robust to noise, but is computationally involved. N...