Development of artificial neural network based mathematical models for predicting small scale quarry powder factor for efficient fragmentation coupled with uniformity index model-Reference-Cited by-同舟云学术

Development of artificial neural network based mathematical models for predicting small scale quarry powder factor for efficient fragmentation coupled with uniformity index model

Published:2023-06-02 Issue:12 Volume:56 Page:14535-14556
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Taiwo Blessing Olamide^ORCID,Yewuhalashet Fissha,Adamolekun Lateef Bankole,Bidemi Ogunyemi Olaoluwa,Famobuwa Oluwaseun Victor,Victoria Adediran Oluwatomisin

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10462-023-10524-1.pdf

Reference62 articles.

1. Adesida PA (2022) Powder factor prediction in blasting operation using rock geo-mechanical properties and geometric parameters. Int J Min Geo-Eng 56(1):25–32

2. Afradi A, Ebrahimabadi A (2020) Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate. SN Appl Sci 2:1–16

3. Agatonovic-Kustrin S, Beresford R (2000) Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 22(5):717–727

4. Al-Bakri AY, Sazid M (2021) Application of artificial neural network (ANN) for prediction and optimization of blast-induced impacts. Mining 1(3):315–334

5. Amoako R, Jha A, Zhong S (2022) Rock fragmentation prediction using an artificial neural network and support vector regression hybrid approach. Mining 2(2):233–247

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