Sentence examples for decision making applications from inspiring English sources

So it is efficient and feasible for real-world decision making applications.

With the help of fuzzy sets based TOPSIS, an overwhelming trend of fuzzy decision making applications has been witnessed.

For decision making applications, the Analytic Hierarchy Process (AHP) is considered one of the most popular MCDM methods, because it takes in consideration the quantitative and qualitative performances.

This paper presents a semantic-based approach to the unified representation of healthcare domain knowledge and patient data for practical clinical decision making applications.

The computation of the intersection family of two large families of unsorted sets is an interesting problem from the mathematical point of view which also appears as a subproblem in decision making applications related to market research or temporal evolution analysis problems.

This paper presents an overview on Big Data including four issues, namely: (i) concepts, characteristics and processing paradigms of Big Data; (ii) the state-of-the-art techniques for decision making in Big Data; (iii) felicitous decision making applications of Big Data in social science; and (iv) the current challenges of Big Data as well as possible future directions.

Opponent modeling is another difficult problem in decision-making applications, and it is essential to achieving high performance in poker.

The present paper presents a broad overview of reliability-based assessment methods and will then focus on decision-making applications using updated time-dependent estimates of bridge reliabilities considering a risk-ranking decision analysis.

Therefore, in order to effectively analyze natural signals in artificial intelligence and decision-making applications, it is necessary to develop dimensionality reduction techniques that can remove the redundant data and capture the essential features of the TF representation.

The assumption of skyline operation is settled human preference, which may be subject to huge challenges in practical decision-making applications because it simplifies preference scenarios that are usually dynamic.

Spatially explicit crop-type information can be used to estimate crop areas for a variety of monitoring and decision-making applications such as crop insurance, land rental, supply-chain logistics, and financial market forecasting.