Probabilistic finite-state machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked: computational linguistics, machine learning, time series analysis, circuit testing, computational biology, speech recognition, and machine translation are some of them. In Part I of this paper, we survey these generative objects and study their definitions and properties. In Part II, we will study the relation of probabilistic finite-state automata with other well-known devices that generate strings as hidden Markov models and n-grams and provide theorems, algorithms, and properties that represent a current state of the art of these objects.
This thesis deals with the development and evaluation of a system which integrates distributed computing frameworks into RapidMiner. A special focus is put on utilizing MapReduce as a programming model. The software frameworks Hadoop, GridGain and Oracle Coherence are reviewed and evaluated with respect to their suitablility to t into the context of RapidMiner. The developed system provides effective means for transparently utilizing these frameworks and enabling RapidMiner processes to parallelize their computations within a distributed environment.
In recent years, diseases of cardiovascular and cerebrovascular have attracted much attention due to main causes in death in human beings. To reduce mortality, there are lots of efforts which are focused on early diagnosis and prevention. It is an important reference index for cardiovascular
diseases through the endovascular membrane in carotid artery by medical ultrasound images. The paper proposes a method which finds the region of interest (ROI) by convolutional neural network, segments and measures intima-media membrane mainly using support vector machine (SVM). Essentially,
the task of detecting the membrane is one target detection problem. This paper adopts the strategy, named Yon Only Look Once (YOLO), a new detection algorithm, and follows the convolution neural network algorithm based on end-to-end training. Firstly, sufficient samples are extracted according
to certain characteristics in the special region. It can be trained by the SVM classification model. Then the ROI is processed and all the pixels are classified into boundary points and non-boundary points through the classification model. Thirdly, the boundary points are selected to obtain
the accurate boundary and calculate the intima-media thickness (IMT). In experiments, two hundred ultrasound images are tested, and the results verify that our algorithm is consistent with the results by ground truth (GT). The detection speed of the algorithm in this paper is in real time,
and it has high generalization characteristics. The algorithm computes the intima-media thickness in ultrasound images accurately and quickly with 95% consistence to ground truth.
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