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Intermarket analysis studies interrelationships between various related markets. Standard correlations between markets are not useful if our goal is to either predict future prices or generate profitable signals because current correlation does not tell us anything about future prices. A methodology we originally developed in the mid 1990's called intermarket divergence allows us to gauge the predictive...
Traumatic brain injury (TBI) endangers many patients and lays great burden on the neural intensive-care units in the whole world. To improve the outcome of TBI patients, it is desirable to forecast the intracranial Pressure (ICP) so to enable timely or early interventions to control the ICP level. Past research mainly focused on ICP pulse morphology analysis and ICP waveform forecast, but results...
Rank Aggregation problem is to find a combined ordering for objects, given a set of rankings obtained from different rankers. Rank aggregation is a technique that combines results of various rankings on the sets of entities (e.g. Documents or web pages of search result) to generate an overall ranking of the entities. In the context of the World Wide Web, Rank aggregation is frequently used in metasearching...
In this present work, a technique fordiscrimination between normal and cirrhotic liversegmented regions of interest (SROIs) based on singularvalue decomposition (SVD) of GLCM matrix is reported.Thirty four B-mode ultrasound images taken from 22normal volunteers and 12 patients suffering from livercirrhosis were collected from Department of Radiodiagnosisand Imaging, PGIMER, Chandigarh, India. Firstly,...
Load and price prediction are an important component in the economic and secures operation of the competitive restructured power system energy market. This paper presents the use of an artificial neural network to half hourly ahead load prediction and half hourly ahead price prediction applications. By using historical weather, load consumption, price and calendar data, a multi-layer feed forward...
Application of genetic algorithm to determine structure of Neural Networks based Additive Nonlinear eXogenous (NN-ANARX) model and if possible to simplify the architecture of corresponding neural network constitutes subject of present paper. In this paper, we construct a specific fitness function, which depends on mean square error, certain cross correlation coefficients and an order of the model.
That GIS-based hydrological response units (HRUs) incorporated watershed variables and their potential spatial correlation into ANN modeling was clarified in the paper. The process and final results of neural network modeling were both assessed by the deterministic or statistical methods, spatial regression kriging (RK). The relation of prediction errors and HRUs area scale can provide useful information...
In this paper, a new measure of correlation is introduced in undirected network. In order to get accurately degree distribution for the vertex at the end of a randomly chosen edge, we considered the information of edge, rather than just the degree distribution of node. We analysis the Enron Email Network with the three measures via degree-degree correlation, and get more information than the traditional...
In this paper, the vigilance levels during day time short nap sleep were estimated on the basis of Markov Process Amplitude (MPA) EEG model. The ultimate purpose was to adopt the MPA model to discriminate three levels of vigilance through a simple neural network. A set of parameters were firstly calculated based on MPA EEG model. Secondly, correlation analysis was adopted to extract effective parameters...
This paper presents the performance's comparison of the Modified Double Weight (MDW), Enhanced Double Weight (EDW) and Zero Cross Correlation (ZCC) codes using different detections technique, which are complementary subtraction, AND subtraction and spectral direct detection (SDD). The MDW and EDW codes are the modification of the double weight (DW) code, which has the variable code weight that greater...
Current SCADA (Supervisory Control and Data Acquisition) system architecture increases the interconnectivity to/from other distributed networks and services. In addition, within the SCADA networks there are different types of sub-networks and protocols that are used to monitor and control industrial operations. This complex expansion increases the productivity of SCADA networks; however, it also increases...
Real-Time detecting abnormal formation pressure can not only prevent the happening of drilling hazard, but also effective protect the pollution of reservoir. A detective model can be made from some drilling-logging parameters because these parameters collected by comprehensive logging instrument can indicate the abnormal pressure information existing in the formation. First, a PCA method is used to...
Network traffic exhibits strong correlations which make it suitable for prediction. Real-time forecasting of network traffic load accurately and in a computationally efficient manner is the key element of proactive network management and congestion control. This paper compares predictions produced by different types of neural networks (NN) with forecasts from statistical time series models (ARMA,...
As concerning the problems of intelligent construction existed in the decision support system (DSS) model, one construction plan and one system structure of intelligent construction of DSS model based on integration of neural network (NN) and expert system (ES) are presented. On the basis of separating the literal description and the data description, ES was applied to select the model type. On the...
This paper constructs the annual competitive relationship complex models of Boolean network and weighted network among the logistics enterprises according to the data of the logistics enterprises' locate and turnover from the year 2006 to 2009 in Dongguan, China. Then, the author analyzes network structure properties each year with the different static indexes, and discusses the dynamic mechanism...
Since the artificial neural networks were put forward, they have been used widely in predicting, and achieved good effect. But few pay attention to what an effect input variables with the linear correlation will have on the artificial neural network. Based on one example, I analyzed and studied an influence which the input variables with linear relation have on stability and prediction effect of BP...
An artificial neural network (ANN) was applied to predict monthly shoreline changes at various locations along 25km of the Noor Bay, southern Caspian Sea. The shoreline variations in 8 stations for a period of about 11 years were studied using ANN. The model results were compared with field data. The properties of the wave (height, period, energy by different equations) and wind data were fed to a...
In this research, we proposed that high frequency component of HFECG was applicable biometric feature for new identification system. We developed identification method by using neural network (NN), and aimed at the improvement of the classification rate. Preprocessing prior to NN is performed by justification on time axis and normalization on amplitude. As a result, an average of 99% classification...
The aim of the paper is to identify the key physiological variables and ventilator settings involved in ventilation management, and required for an appropriate Clinical Decision Support System (CDSS). Based on the results of a questionnaire designed for the purpose of the research, 70 hours of physiological and ventilation data were recorded. Recorded data were classified by clinicians into three...
Purpose of the present paper is to examine the predictability of the occurrence of the severe pre-monsoon thunderstorm over Gangetic West Bengal. Instead of considering various meteorological predictors, the daily total ozone concentration is chosen as the predictor because of the influence of tropospheric as well as stratospheric ozone on the genesis of meteorological phenomena. Considering the occurrence/non-occurrence...
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