Optimization is the process of achieving the best solution for a problem. LabVIEW based on an SVM model is proposed in this paper to get the best SVM parameters using the hybrid CS and PSO method. PCA is used as a preprocessor of SVM for reducing the dimension of data and extracting features of training samples. Also, SVM parameters are optimized for Parkinson’s disease data by combining CS and PSO. The designed system is used to determine the best SVM parameters, and it is compared to PSO and CS optimization methods and found that the used CS-PSO hybrid optimization method is better. The hybrid model shows that the accuracy of the performance achieved is 97.4359%. Also, the data classification results obtained by using SVM parameters determined by optimization are measured by precision, recall, F1 score, false positive rate (FPR), false discovery rate (FDR), false negative rate (FNR), negative predictive value (NPV), and Matthews’ correlation coefficient (MCC) parameters.

Makine öğrenmesi, var olan verilerin çıkarımlarını matematiksel ve istatistiksel yöntemlerle yapan ve bilinmeyen bir yöntem paradigmasıdır. Bu çalışmada, denetimli öğrenme algoritması, EEG (elektroensefalografi) verilerine uygulanmış, sınıflandırma algoritması performans analiz sonuçları MATLAB ile incelenmiştir. Bu çalışmada, algoritma olarak en yakın komşu algoritması (k-NN) kullanılmıştır. Bu sınıflandırma, Temel Bileşen Analizinin (TBA) kullanıldığı ve kullanılmadığı durumlar için iki aşamada değerlendirilmiştir. Boyut azaltma, verilerin boyut boyutunu küçültme işlemidir. TBA ile veri kümesinin boyutunun azaltılarak, önemli veri özelliklerini korunması beklenir. KNN, sınıflandırma doğruluğu açısından önemli sayılabilecek sonuçlar vermiştir. Mevcut çalışma, farklı biyoelektriksel sinyaller için uygun özelliklerin uygun bir sınıflandırıcı ile kombine edildiğinde anlamlı bir sınıflandırma yapılabileceğini göstermiştir.

Foreign Object Debris (FOD) at airports poses a risk to aircraft and passenger safety. FOD can seriously harm aircraft engines and injure personnel. Accurate and careful FOD detection is of great importance for a safe flight. According to the FAA's report, FOD types are aircraft fasteners such as nut, safety; aircraft parts such as fuel blast, landing gear parts, rubber parts; construction materials such as wooden pieces, stones; plastic materials, natural plant and animal parts. For this purpose, in this study, the effect of different networks and optimizer on object detection and accuracy analysis were examined by using a data set of possible materials at the airport. AlexNet, Resnet18 and Squeezenet networks were used. Application is applied two stages. The first one, 3000 data were divided into two parts, 70% to 30%, training and test data, and the results were obtained. The second one, 3000 data were used for training, except for the training data, 440 data were used for validation. Also, for each application, both SGDM and ADAM optimizer are used. The best result is obtained from ADAM optimizer with Resnet18, accuracy rate is %99,56.