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
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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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
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UT code for WoS chapter
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