Machine Learning Applications for Demand Driven in Supply Chain: Literature Review

Garinian, Eric Octavio Mayoral; Salais-Fierro, Tomás Eloy; Marmolejo-Saucedo, José Antonio; Rodríguez-Aguilar, Román

doi:10.1007/978-3-031-09753-9_59

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article1

Other1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These benefits are resulting in cost savings and increased competitiveness for manufacturing companies. [16][17][18][19][20] In healthcare, machine learning is used to improve patient outcomes and reduce costs. For example, machine learning algorithms can be used to envisage patient outcomes, identify patients at high risk of complications, and optimize treatment plans.…”

Section: Introductionmentioning

confidence: 99%

Potable Water Identification with Machine Learning: An Exploration of Water Quality Parameters

Mohan¹,

Dileep

Bhuria

et al. 2023

IJRITCC

View full text Add to dashboard Cite

In this research, we aim to determine the water potability using three machine learning classification algorithms: decision tree, gradient boosting and bagging classifier. These algorithms were trained and tested on a dataset of water quality measurements. The outcomes of the experiment showed that the gradient boosting algorithm achieved the highest F1-score of 0.78 among all the algorithms. This indicates that the gradient boosting algorithm was most effective in correctly identifying both the safe and contaminated water samples. The results of this study demonstrate that gradient boosting is a promising approach for determining water potability and can be used as a reliable method for water quality assessment.

show abstract