Classification of Industrial and Commercial Facilities Using Machine Learning Techniques
Yazarlar (7)
Serkan Gerz
Talha Alperen Çengel
Turan Ozturk
Bünyamin Gençtürk
Ender Boz
Dr. Öğr. Üyesi Elham Tahsın Yasın YASIN Sinop Üniversitesi, Türkiye
Doç. Dr. Murat Köklü Selçuk Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SCOPUS dergilerinde yayınlanan tam makale)
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| Dergi Adı | Intelligent Methods in Engineering Sciences | ||
| Dergi ISSN | 2979-9236 | ||
| Dergi Tarandığı Indeksler | Scopus | ||
| Makale Dili | Türkçe | Basım Tarihi | 06-2024 |
| Kabul Tarihi | 30-06-2024 | Yayınlanma Tarihi | 30-06-2024 |
| Cilt / Sayı / Sayfa | 3 / 2 / 46–53 | DOI | 10.58190/imiens.2024.98 |
| Makale Linki | https://doi.org/10.58190/imiens.2024.98 | ||
| UAK Araştırma Alanları |
Bilgi Güvenliği ve Kriptoloji
Görüntü İşleme
Yapay Zeka
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| Özet |
| The performance of machine learning algorithms for the automatic classification of industrial and commercial facilities were examined within the scope of this project. A dataset containing a total of 47,683 data points, including 27,691 warehouses, 6,441 retail stores, and 13,551 factories, was used in this study. To classify these facilities as "factory," "warehouse," or "retail," ANN, RF, and kNN machine learning models were applied and compared. The ANN achieved the highest classification accuracy with 76.9%. This was followed by the RF algorithm with 73.9% and the kNN algorithm with 63.9%. The high performance demonstrated by the ANN indicates that it could be a powerful tool for automatic facility classification in industrial and commercial sectors. This classification can provide significant contributions such as increasing operational efficiency of businesses, more effectively guiding marketing strategies, and better management of resources. Future studies can expand research in this field by further increasing model accuracy and testing in various application scenarios. |
| Anahtar Kelimeler |
| Artificial intelligence | Image Classification | Industrial | Logistics | Machine Learning |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 4 |