Biomedical Improvements of the Technology of Internet of Things in 6G

Öz

With the improvements in nanotechnology last recent years, devices have been reduced, the nanoscale, even nanosized. In addition, Internet of Nano-Things (IoNTs) is considered as the ultimate creation of this trend, with numerous applications in all areas of life with the development of sensor networks and nano-networks. However, communication between such nano-devices is still a problem to be solved. IoNTs are believed to play an important role in the development of Body-centric Nano Networks. Body-centric nano-networks, like wireless sensors, are considered domain-specific and are always applied to support a specific purpose. In these networks, molecular and electromagnetic communication are widely considered to be the two main paradigms, and both follow their own developmental process. By the newl adopted communication technology, it is estimated that the number of devices that will communicate with each other may reach the level of approximately one hundred billion in the next ten years. The transfer of such large data between devices in a faster, high quality and high reliability increases the need for 5G. With the utilization of 6G by THz band, nano-communication systems, molecular networks, hologram communication, which is a higher above the augmented reality, will also possible.

Anahtar Kelimeler

• Nano networks
• wearable technology
• Internet of Nano-Things
• 5G beyond
• 6G

6G'de Nesnelerin İnterneti Teknolojisinin Medikal Alandaki Gelişmeleri

Öz

Günümüzde internet ortamında metne dayalı veri çok hızlı bir şekilde artış göstermektedir ve bu büyük veri içinden istenilen bilgiyi barındıran doğru içeriklere ulaşabilmek önemli bir ihtiyaçtır. İçeriklere ait anahtar sözcüklerin bilinmesi bu ihtiyacı karşılamada olumlu bir etki sağlayabilmektedir. Bu çalışmada, doğal dil işleme ve derin öğrenme modelleri ile Türkçe metinleri temsil eden anahtar sözcüklerin belirlenmesi amaçlanmıştır. Veri kümesi olarak Türkçe Etiketli Metin Derlemi ve Metin Özetleme-Anahtar Kelime Çıkarma Veri Kümesi birlikte kullanılmıştır. Derin öğrenme modeli olarak çalışmada iki farklı model ortaya konmuştur. İlk olarak Uzun Ömürlü Kısa Dönem Belleği ( LSTM) katmanlı bir Diziden Diziye (Seq2Seq) model tasarlanmıştır. Diğer model ise BERT (Transformatörler ile İki Yönlü Kodlayıcı Temsilleri) ile oluşturulmuş Seq2Seq bir modeldir. LSTM katmanlı Seq2seq modelin başarı değerlendirmesinde ROUGE-1 ölçütünde 0,38 F-1 değerine ulaşılmıştır. BERT tabanlı Seq2Seq modelde ROUGE-1 ölçütünde 0,399 F-1 değeri elde edilmiştir. Sonuç olarak dönüştürücü mimarisini temel alan BERT tabanlı Seq2Seq modelin, LSTM tabanlı Seq2seq modele görece daha başarılı olduğu gözlemlenmiştir.

Anahtar Kelimeler

• Anahtar sözcük çıkarımı
• Derin öğrenme
• Seq2Seq mimarisi
• Transformatör mimarisi

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