Intelligent Interactive Systems in Knowledge-Based by Maria Virvou

By Maria Virvou

The basic objective of this up to date examine e-book is to document a pattern of the latest advances within the box of clever interactive platforms in knowledge-based environments. It includes contemporary study and case reports of clever interactive structures. This booklet will end up precious to researchers, professors, study scholars and practitioners because it studies novel examine paintings on leading edge themes within the zone of clever interactive systems.

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K6 and m1 . . m7 . A possible situation where a user would use both the keyboard and the microphone could be the following: The specific user knows the correct answer and types in a speed higher than the normal speed of writing. The system confirms that the answer is correct and the user says a word like ‘bravo’ that is included in the specific list of the system for the emotion of happiness. The user also speaks in a higher voice volume. In that case the variables k1, m3 and m6 take the value 1 and all the others are zeroed.

Additionally it is known that cost estimation models are sensitive to the data used for their construction. Estimation models present increased accuracy when the projects used to predict future projects are developed in similar environment and under the same constraints with the projects under estimation. One method has been proposed for estimating the cost of IS development [26] based on COCOMO II model [7] that demands a wealth of data regarding the projects variables. This model is very analytical as it requires the values of 23 cost variables and then calculates a parametric cost equation.

In this chapter, we apply the PART algorithm that is based on extracting partial decision trees. The method also handles missing values by assigning an instance with missing value to each of the tree branches with a weight proportional to the number of training instances descending that branch, normalized by the total number of training instances with known values. In this study for the induction of classification trees and rules we utilize the Weka machine learning library [37]. A simple rule coming from the domain of software cost estimation will have as Rule Body certain software project attribute values and as a Rule Head a productivity (or cost, or effort) value.

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