TY  - JOUR
T1  - Akıllı Ev Bileşenlerinde Kullanılan Farklı Kablosuz İletişim Standartları için Makine Öğrenmesi Tabanlı Öneri Sistemi
TT  - Machine Learning-Based Recommendation System for Different Wireless Communications Standards Used in Smart Home Components
AU  - Yazar, Ahmet
AU  - Şentürk, Ahmet Ata
AU  - Çörez, Şulenur
AU  - Satır, Olçan
AU  - Kosova, Burak
PY  - 2024
DA  - April
DO  - 10.7212/karaelmasfen.1362548
JF  - Karaelmas Fen ve Mühendislik Dergisi
PB  - Zonguldak Bülent Ecevit Üniversitesi
WT  - DergiPark
SN  - 2146-7277
SP  - 11
EP  - 22
VL  - 14
IS  - 1
LA  - tr
AB  - Akıllı ev sistemlerinin giderek yaygınlaştığı son yıllarda bu sistemlere ait kablosuz iletişim altyapısı da daha çok önem kazanmıştır. Birbirlerine karşı farklı üstünlükleri olan kablosuz iletişim standartlarının bir akıllı ev sistemi için ne tür ihtiyaçlar ve hangi şartlar altında daha kullanılabilir olduğunun kestirilmesi yeni bir problem haline gelmiştir. Bu çalışmada, yeni nesil akıllı ev bileşenlerinde kullanılan farklı kablosuz iletişim standartları için makine öğrenmesi tabanlı özgün öneri yöntemleri geliştirilmiştir. Ayrıca bu amaca yönelik olarak ortamsal ve çevresel farkındalığı yükseltecek düzeyde bilgi girdilerinden yararlanılmıştır. Makine öğrenmesi uygulamasına yönelik yeni bir yapay veri kümesi oluşturulmuştur. Alınan sonuçlara bakıldığında, geliştirilen özgün çözümlerin akıllı ev tasarımlarına fayda getireceği görülmüştür.
KW  - 5G
KW  - Akıllı Evler
KW  - Kablosuz İletişim
KW  - Makine Öğrenmesi
KW  - Öneri Sistemi
N2  - In recent years, as smart home systems have become increasingly widespread, the wireless communication infrastructure associated with these systems has gained more importance. Assessing the needs and determining the conditions under which different wireless communication standards, each with its own advantages, can be more suitable for a smart home system has become a new challenge. In this study, a machine learning-based recommendation methods have been developed for various wireless communication standards used in next-generation smart home components. Additionally, a level of ambient and environmental awareness has been utilized to enhance the information inputs for this purpose. A new artificial dataset has been created for the machine learning application. When examining the results, it can be observed that the proposed novel solution would be beneficial in smart home designs.
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UR  - https://doi.org/10.7212/karaelmasfen.1362548
L1  - https://dergipark.org.tr/tr/download/article-file/3417268
ER  -