Beton Ağırlıklı Barajların Simbiyotik Arama Algoritması ile Optimizasyonu

Artan nüfus ve sanayileşme suya olan ihtiyacı hıza arttırmaktadır. Bu artış projelerin boyutlarını da arttırmaktadır. Klasik yöntemlerle yapılan projelendirmelerde maliyetler oldukça yüksek çıkabilmektedir. Bu çalışmada su kaynakları projelerinin en önemlilerinden olan beton ağırlıklı barajların Simbiyotik Arama Algoritması (SOS) kullanılarak optimum boyutlarının bulunması amaçlanmıştır. Çalışmada baraj yükseklikleri ve deprem ivmeleri değişimi ile maliyet artışları da ve bu artışların oranları hesaplanmıştır. Elde edilen sonuçlar grafikler ve tablolar ile düzenlenmiştir. Ayrıca çalışmada her girdi parametresi için modelleme yapabilecek bir programda geliştirilmiştir.

Classifying Protein Sequences Using Convolutional Neural Network

One of the major challenges in bioinformatics is the classification and identification of protein structure and function. Large amounts of RNA data cannot be managed using traditional laboratory methods. For this, proteins should be separated according to their structure and families. Therefore, proteins need to be classified to define their biological families and functions. In traditional machine learning approaches, various feature extraction algorithms are used to classify proteins. In manual feature extraction, the selected features directly affect performance. Therefore, in the proposed method of this study, protein sequences were digitized by amino acid composition technique. The digitized protein sequences were converted to spectrograms, and automatic feature extraction was performed using 2D CNN models (VGG19, ResNet). The extracted features were classified with SVM and kNN. As a result, the accuracy with 95.03% was achieved in the classification of protein sequences using ResNet.

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