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anfis based exhaustive search matlab functionality  (MathWorks Inc)


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    MathWorks Inc anfis based exhaustive search matlab functionality
    Standard <t>ANFIS</t> architecture. Number of Story Points and Project Velocity are given as inputs. These inputs have been derived from 21 projects dataset (dataset sample given in Table ). ANFIS based exhaustive search <t>MATLAB</t> functionality has been used to decide the inputs. Different inputs (as feature pairs) tested against Actual Time. The feature pair with minimum error has been chosen in input layer.
    Anfis Based Exhaustive Search Matlab Functionality, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/anfis based exhaustive search matlab functionality/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    anfis based exhaustive search matlab functionality - by Bioz Stars, 2026-03
    90/100 stars

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    1) Product Images from "An efficient ANFIS-EEBAT approach to estimate effort of Scrum projects"

    Article Title: An efficient ANFIS-EEBAT approach to estimate effort of Scrum projects

    Journal: Scientific Reports

    doi: 10.1038/s41598-022-11565-2

    Standard ANFIS architecture. Number of Story Points and Project Velocity are given as inputs. These inputs have been derived from 21 projects dataset (dataset sample given in Table ). ANFIS based exhaustive search MATLAB functionality has been used to decide the inputs. Different inputs (as feature pairs) tested against Actual Time. The feature pair with minimum error has been chosen in input layer.
    Figure Legend Snippet: Standard ANFIS architecture. Number of Story Points and Project Velocity are given as inputs. These inputs have been derived from 21 projects dataset (dataset sample given in Table ). ANFIS based exhaustive search MATLAB functionality has been used to decide the inputs. Different inputs (as feature pairs) tested against Actual Time. The feature pair with minimum error has been chosen in input layer.

    Techniques Used: Derivative Assay



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    Standard ANFIS architecture. Number of Story Points and Project Velocity are given as inputs. These inputs have been derived from 21 projects dataset (dataset sample given in Table ). ANFIS based exhaustive search MATLAB functionality has been used to decide the inputs. Different inputs (as feature pairs) tested against Actual Time. The feature pair with minimum error has been chosen in input layer.

    Journal: Scientific Reports

    Article Title: An efficient ANFIS-EEBAT approach to estimate effort of Scrum projects

    doi: 10.1038/s41598-022-11565-2

    Figure Lengend Snippet: Standard ANFIS architecture. Number of Story Points and Project Velocity are given as inputs. These inputs have been derived from 21 projects dataset (dataset sample given in Table ). ANFIS based exhaustive search MATLAB functionality has been used to decide the inputs. Different inputs (as feature pairs) tested against Actual Time. The feature pair with minimum error has been chosen in input layer.

    Article Snippet: ANFIS based exhaustive search MATLAB functionality has been used to decide the inputs.

    Techniques: Derivative Assay