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The assignment of cause. For example, if one attributes a statement to a CEO, one says the CEO made the statement. Likewise, if one attributes a market bounce to GDP growth, one says the GDP growth caused the bounce.
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The plithogenic aggregation operators (intersection, union, complement, inclusion, equality) are based on contradiction degrees between attributes' values, and the first two are linear combinations of the fuzzy operators' [t.sub.norm] and [t.sub.conorm].
Let A be a non-empty set of uni-dimensional attributes A = {[a.sub.1], [a.sub.2], ..., [a.sub.m]}, m [greater than or equal to] 1; and [alpha] [member of] A be a given attribute whose spectrum of all possible values (or states) is the non-empty set S, where S can be a finite discrete set, S = {[s.sub.1], [s.sub.2], .., [s.sub.1]}, 1 [less than or equal to] l < [infinite]), or infinitely countable set S = {[s.sub.1], [s.sub.2], ..., [s.sub.[infinity]]}, or infinitely uncountable (continuum) set S = ]a,b[ a < b, where ] ...
Other attributes may be required dependent on the use of the database table.
Entire books have been written on the subjects of database structure and their management, so we will not try to replicate that depth of information here, but suffice to say that for the purpose of system analysis, you need to be aware of which attributes are required by the current system, that need to be present in the new system.
Keywords: measurable attributes, units of measurement, quantity, word problem solving.
On the other hand, creating these models requires superior understanding of the measurable attributes of the quantities involved in word problems, as well as proficiency with units of measurement (Thompson, 2011).
However, in fuzzy or uncertain decision making environment, for example, the assessment of international cooperation projects across different countries and regions, the experts are invited from different countries and regions and usually have difficulty in giving precise preference information on the attributes, for example, multiplicative preference relations and fuzzy preference relations.
It is desirable to deal with preference orderings, linguistic terms, interval numbers, and inequality constraints among the attribute weights in the MADM process since they are the common ways for experts to use when expressing their subjective preference on attributes easily and accurately.
It consists of three parts: the first part divides the complete entities into different blocks according to the attribute value types; the second part introduces the method of attribute clustering, which is used to remove redundant attributes; the third part performs comparisons solely between the subentities within the scalable window in each block and gathers the outputs from individual blocks to finish the ER problem.
The algorithm introduces a strategy of exchanging attributes between vertices with the same degree randomly to induce attackers to search for false targets and maintain the whole structure of the network.
Keywords: Artificial Neural Network; Petrophysical analysis; Porosity prediction; seismic attributes, Prospective zones.
Sahai and Waters in 2005 proposed the notation of attribute-based encryption (ABE) [1] in which the ciphertext and key are, respectively, associated with a series of attributes, and an access structure is specified to define the attribute set that can be used to decrypt the ciphertext successfully.

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