Machine Learning-Based Recommendation System for Different Wireless Communications Standards Used in Smart Home Components

Ahmet Yazar; Ahmet Ata Şentürk; Şulenur Çörez; Olçan Satır; Burak Kosova

Makale detayları

Machine Learning-Based Recommendation System for Different Wireless Communications Standards Used in Smart Home Components

Yazarlar:

Ahmet Yazar | Ahmet Ata Şentürk | Şulenur Çörez | Olçan Satır | Burak Kosova

Yayıncı:

Zonguldak Bulent Ecevit University

Ana Başlıklar:

Karar Desteği ve Grup Destek Sistemleri

Dergi:

Karaelmas Fen ve Mühendislik Dergisi Vol.14 Issue 1, pp.11-22

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001316427

pISSN:

2146-7277

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

Machine Learning-Based Recommendation System for Different Wireless Communications Standards Used in Smart Home Components

Yazarlar:

Ahmet Yazar | Ahmet Ata Şentürk | Şulenur Çörez | Olçan Satır | Burak Kosova

Yayıncı:

Zonguldak Bulent Ecevit University

Ana Başlıklar:

Karar Desteği ve Grup Destek Sistemleri

Dergi:

Karaelmas Fen ve Mühendislik Dergisi Vol.14 Issue 1, pp.11-22

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001316427

pISSN:

2146-7277

5G

Smart Homes

Wireless Communications

Machine Learning

Recommendation System5G

Akıllı Evler

Kablosuz İletişim

Makine Öğrenmesi

Öneri Sistemi

Sayı

Makale detayları

Yazarlar: Ahmet Yazar | Ahmet Ata Şentürk | Şulenur Çörez | Olçan Satır | Burak Kosova

Yayıncı: Zonguldak Bulent Ecevit University

Ana Başlıklar: Karar Desteği ve Grup Destek Sistemleri

Dergi: Karaelmas Fen ve Mühendislik Dergisi Vol.14 Issue 1, pp.11-22

Belge Tipi: Makale

Yayın Tarihi: 4/2024

Belge Dili: Türkçe

Document ID (Org No.): 2001316427

pISSN: 2146-7277

Sayı

Yazarlar:

Ahmet Yazar | Ahmet Ata Şentürk | Şulenur Çörez | Olçan Satır | Burak Kosova

Yayıncı:

Zonguldak Bulent Ecevit University

Ana Başlıklar:

Karar Desteği ve Grup Destek Sistemleri

Dergi:

Karaelmas Fen ve Mühendislik Dergisi Vol.14 Issue 1, pp.11-22

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001316427

pISSN:

2146-7277