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Machine Learning Tools for Ensuring Optimized Agribusiness Recovery Strategies

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24310%2F23%3A00009896" target="_blank" >RIV/46747885:24310/23:00009896 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.igi-global.com/gateway/chapter/317180" target="_blank" >https://www.igi-global.com/gateway/chapter/317180</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.4018/978-1-6684-4649-2" target="_blank" >10.4018/978-1-6684-4649-2</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Machine Learning Tools for Ensuring Optimized Agribusiness Recovery Strategies

  • Original language description

    The chapter presents machine learning approaches for business continuity and recovery optimization in agribusiness. Firstly, a mathematical method, entitled business continuity points (BCPTs) is tested with domain data for its potential to predict process recovery results, namely recovery time and criticality ranking of key operations. A 72.22% accuracy has been estimated. Then, decision tree prediction with 10-fold cross validation and random forest has been 92.31% accurate in classifying business functions as critical or not. Additionally, a new multi-approach and multi-class decision tree classifier with some of the BCPTs input variables is presented, with 55.36% accuracy, and 70.37% and 88.89% accuracy rates when boosted with the 10 folds and the random forest. Finally, regression analysis techniques are used to improve the initial recovery time BCPTs formula. Exponential regression has been more precise compared to the quadratic model (R2exp=0.954, R2quad=0.85). Despite current data limitations, the inferred prediction patterns are robust and highly accurate in the given field.

  • Czech name

  • Czech description

Classification

  • Type

    C - Chapter in a specialist book

  • CEP classification

  • OECD FORD branch

    10200 - Computer and information sciences

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Book/collection name

    A. Karmaoui

  • ISBN

    978-1-6684-4649-2

  • Number of pages of the result

    38

  • Pages from-to

    33-70

  • Number of pages of the book

    269

  • Publisher name

    IGI Global

  • Place of publication

  • UT code for WoS chapter