Deep Learning and Internet of Things (IoT) Based Monitoring System for Miners
Yazarlar (3)
Öğr. Gör. Tuğba SARAY ÇETİNKAYA Istanbul Gelisim University, Türkiye
S. Senan
Istanbul University - Cerrahpasa, Türkiye
Istanbul University - Cerrahpasa, Türkiye
Prof. Dr. Zeynep Orman Istanbul University - Cerrahpasa, Türkiye
| Makale Türü |
Diğer (Teknik, not, yorum, vaka takdimi, editöre mektup, özet, kitap krıtiği,
araştırma notu, bilirkişi raporu ve benzeri)
(SCI, SSCI, AHCI, SCI-Exp dergilerinde yayınlanan teknik not,
editöre mektup, tartışma, vaka takdimi ve özet türünden makale)
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| Dergi Adı | JOURNAL OF MINING SCIENCE (Q4) | ||
| Dergi ISSN | 1062-7391 Dergi Bilgileri (2022) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 04-2022 |
| Cilt / Sayı / Sayfa | 58 / 2 / 325–337 | DOI | 10.1134/S1062739122020156 |
| Makale Linki | http://dx.doi.org/10.1134/s1062739122020156 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| In this study, a miner monitoring system is designed using the Deep Learning (DL) approach and the IoT technology together. It is aimed to determine the area where the miners are located while a possible accident occurs by the proposed system. Experiments were carried out to analyze the effectiveness of the proposed system and the performance evaluations were made. The best result was obtained with an accuracy rate of 97.14%. This rate indicates that the designed miner monitoring system can be used effectively in practice. |
| Anahtar Kelimeler |
| Internet of Things (IoT) | miner monitoring | artificial neural networks | deep learning | LSTM model |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 2 |
| Google Scholar | 6 |