Machine Learning Tools for Ensuring Optimized Agribusiness Recovery Strategies

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning Tools for Ensuring Optimized Agribusiness Recovery Strategies

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Machine Learning Tools for Ensuring Optimized Agribusiness Recovery Strategies

Popis výsledku anglicky

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.

Druh

C - Kapitola v odborné knize

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

A. Karmaoui

ISBN

978-1-6684-4649-2

Počet stran výsledku

38

Strana od-do

33-70

Počet stran knihy

269

Název nakladatele

IGI Global

Místo vydání

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Kód UT WoS kapitoly

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