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Application of AI Techniques in Medical Image Segmentation
Application of AI Techniques in Medical Image Segmentation

... Bioelectromagnetism, Vol. 9, No. 2, 2007. [5] M.Rastgarpour and J.Shanbehzadeh, ―The Status Quo of Artificial Intelligence Methods in Automatic Medical Image Segmentation ((Periodical style—Accepted for publication)‖ IEEE proc. Of ICICA, Dubai, 2011- to be published. ...
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... Because our approaches are evaluated using the data provided in this SemEval-2012 task, we describe briefly below how the data was collected, as well as the metrics used to evaluate system performance. The dataset consists of 79 relation classes that are chosen according to (Bejar et al., 1991) and ...
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A Computational Intelligence Approach to Modelling Interstate Conflict

... study of interstate conflict has been the adoption of the generic term of “conflict” rather than “war” or “dispute”. This has led to collection of MID data which allows us, not only to concentrate on intense state interactions, but also on sub war interactions, where militarised behaviour occurs wit ...
CH12
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... The coefficients b0 and b1 will usually be found using computer software, such as Excel or Minitab ...
Extending Powell's Semiparametric Censored Estimator to Include Non-Linear Functional Forms and Extending Buchinsky's Estimation Technique
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... as the degree of censoring is reduced and as the sample size is increased. A recent estimator that is similar to Powell is by Buchinsky and Hahn (1998) who estimate censored quantile regressions (censored LAD is the 50th quantile) by first estimating nonparametric quantiles and conditional distribut ...
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... Semantic Web agent could infer that since "Goose" is a type of "DarkMeatFowl," and "DarkMeatFowl" is a subset of the class "Fowl," which is a subset of the class "EdibleThing," then "Goose" is an "EdibleThing." ...
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... In our opinion, much more attention needs to be devoted to appreciating and understanding these trade-offs. Many econometric textbooks, for example, focus on teaching statistical techniques and not on how to build structural econometric models. This emphasis perhaps has only reinforced many economis ...
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Time series



A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, intelligent transport and trajectory forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called ""time series analysis"", which focuses on comparing values of a single time series or multiple dependent time series at different points in time.Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility.)Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language.).
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