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Since seizures in general occur infrequently and unpredictably, it's automatic detection during long term electro encephalograph (EEG) recordings is highly recommended. Automatic Seizure Detection Using Higher Order Moments is based on the time domain analysis of EEG signal and extract the features for seizure detection. Each channel of both seizure and normal EEG data were divided into frames of...
How do connected components evolve? What are the regularities that govern the dynamic growth process and the static snapshot of the connected components? In this work, we study patterns in connected components of large, real-world graphs. First, we study one of the largest static Web graphs with billions of nodes and edges and analyze the regularities among the connected components using GFD(Graph...
It is estimated that over 8 million cell phones are lost or stolen each year [7]; often the loss of a cell phone means the loss of personal data, time and enormous aggravation. In this paper we present machine-learning based algorithms by which a cell phone can discern that it may be lost, and take steps to enhance its chances of being successfully recovered. We use data collected from the Reality...
In this paper, a new access method for very high-dimensional data space is proposed. The method uses a graph structure and pivots for indexing objects, such as documents in text mining. It also applies a simple search algorithm that uses distance or similarity based functions in order to obtain the k-nearest neighbors for novel query objects. This method shows a good selectivity over very-high dimensional...
Weka4WS adopts the WSRF technology for implementing remote data mining algorithms and dealing with distributed computation, a WSRF-compliant Web service is used to carry out all the data mining algorithms provided by the Weka library. This paper describes Weka4WS, a framework that extends the widely used open source Weka toolkit to support distributed data mining on WSRF-enabled Grids and have a try...
A quantum neural networks model with learning algorithm is presented. First, based on the information processing modes of biology neuron and quantum computing theory, a quantum neuron model is presented, which is composed of weighting, aggregating, activating, and prompting. Secondly the quantum neural networks model based on quantum neuron is constructed in which the input and the output are real...
Two new similarity indexes are introduced to explain the logic behind the classification methods A and B with attribute hierarchy model. Several kinds of new classification methods proposed are based on variations of the similarity between the expected response pattern and the observed response pattern. The results of simulation indicate that the new methods are better than methods A and B. And with...
Hierarchical phrase-based translation model has been proven to be a simple and powerful machine translation model. However, due to the computational complexity constraints, the extraction and use of hierarchical rules are usually restricted under certain limits, and these limits could have a negative impact on the performance of the translation model, especially for reordering. This paper presents...
State-of-the-art pattern recognition methods have difficulties dealing with problems where the dimension of the output space is large. In this article, we propose a framework based on deep architectures (e. g. deep neural networks) in order to deal with this issue. Deep architectures have proven to be efficient for high dimensional input problems such as image classification, due to their ability...
Multi-agent coordination problems can be cast as distributed optimization tasks. Probability collectives (PCs) are techniques that deal with such problems in discrete and continuous spaces. In this paper we are going to propose a new variation of PCs, sequentially updated probability collectives. Our objective is to show how standard techniques from the statistics literature, sequential Monte Carlo...
We propose a technique to classify characters by two different forms of their symmetry features. The generalized symmetry transform is applied to digits from the USPS data set. These features are then used to train probabilistic neural networks and their performances are compared to the traditional method.
Information Fusion is a valid way which can decrease the uncertainty of making decision, and is also a hotspot. The paper makes some work on a important problem about Fuzzy Integral, that is how to get the Fuzzy Density, and compares two typical means. Based on 11 UCI data set, this paper conducts the compared experiment of several Information Fusion methods. It is compared with references 4 and 5...
Traditional fire detection methods are based on smoke and detectors. They are not suitable for high and large-span space structures because of their limited detection range. The latest fire detection methods are based on video-image processing and data fusion. However, false positive rate and false negative rate still remain unsatisfactory and need improvement. In this paper, some fire video-image...
This paper describes an novel approach towards linguistic processing for robots through integration of a motion language module and a natural language module. The motion language module represents association between symbolized motion patterns and words. The natural language module models sentences. The motion language module and the natural language module are graphically integrated. The integration...
The method of statistical adaptive increment control based on control-cell is on the basis of periodic projection and adaptive increment control for detecting data. Programs have been made for analyzing dynamic parameter about Statistical and adaptive increment control. Analyzing adjustable increment for probability curve of relative increase for effective configuration of extending control-cell,...
This study presents a multilayer perceptron (MLP) cascaded with a logistic regression (LR) model for the probability of default (PD) estimation of real-life small and medium enterprises (SMEs). The MLP does a preprocessing for LR which is used to compute PD value. The obtained raw PD values are calibrated according to the real portfolio default average. In experiments, a Turkish SME database is used...
Research on machine translation has a long history and many methods and techniques have been proposed and developed. However, low quality of translation is still a major problem and many related problems remain unresolved. Super function based machine translation was proposed to perform translation without going through syntactic and semantic analysis as many machine translation systems usually do...
This paper describes a method for environmental audio events analysis. The audio events are modeled using a common universal codebook. The codebook is based on the bag-of-frames (BOF). The features corresponding to the frames and extracted from all audio files are grouped into clusters using the k-means algorithm. The individual audio file is modeled on the normalized distribution of the numbers of...
Enhancing Arabic tagging is of great importance in many NLP applications. This paper presents a simple comparison tool that compares two powerful tagging systems for Arabic, the first one is the ASVM Tagger, by Diab M. et al,. The second one is RDI Arab Tagger that relies on simple powerful long n-grams probability estimation plus A*search algorithm for disambiguation, this comparison is done to superimpose...
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