Monkeypox is a viral disease that has recently rapidly spread. Experts have trouble diagnosing the disease because it is similar to other smallpox diseases. For this reason, researchers are working on artificial intelligence-based computer vision systems for the diagnosis of monkeypox to make it easier for experts, but a professional dataset has not yet been created. Instead, studies have been carried out on datasets obtained by collecting informal images from the Internet. The accuracy of state-of-the-art deep learning models on these datasets is unknown. Therefore, in this study, monkeypox disease was detected in cowpox, smallpox, and chickenpox diseases using the pre-trained deep learning models VGG-19, VGG-16, MobileNet V2, GoogLeNet, and EfficientNet-B0. In experimental studies on the original and augmented datasets, MobileNet V2 achieved the highest classification accuracy of 99.25% on the augmented dataset. In contrast, the VGG-19 model achieved the highest classification accuracy with 78.82% of the original data. Considering these results, the shallow model yielded better results for the datasets with fewer images. When the amount of data increased, the success of deep networks was better because the weights of the deep models were updated at the desired level.

Brain tumors can have very dangerous and fatal effects if not diagnosed early. These are diagnosed by specialized doctors using biopsy samples taken from the brain. This process is exhausting and wastes doctors' time too much. Researchers have been working to develop a quick and accurate way for identifying and classifying brain tumors in order to overcome these drawbacks. Computer-assisted technologies are utilized to support doctors and specialists in making more efficient and accurate decisions. Deep learning-based methods are one of these technologies and have been used extensively in recent years. However, there is still a need to explore architectures with higher accuracy performance. For this purpose, in this paper proposed a novel convolutional neural network (CNN) which has twenty-four layers to multi-classify brain tumors from brain MRI images for early diagnosis. In order to demonstrate the effectiveness of the proposed model, various comparisons and tests were carried out. Three different state-of-the-art CNN models were used in the comparison: AlexNet, ShuffleNet and SqueezeNet. At the end of the training, proposed model is achieved highest accuracy of 92.82% and lowest loss of 0.2481. In addition, ShuflleNet determines the second highest accuracy at 90.17%. AlexNet has the lowest accuracy at 80.5% with 0.4679 of loss. These results demonstrate that the proposed CNN model provides greater precision and accuracy than the state-of-art CNN models.

ÖzetLiteratürde otonom kara araçları yol takibi problemini çözmek için farklı yöntemler önerilmiştir. Bu yöntemler geometrik tabanlı ve model tabanlı yöntemler olarak iki ana gruba ayrılabilir. Model tabanlı yöntemlerde aracın dinamik modeli kullanılırken, geometrik tabanlı yöntemlerde sadece araç ve yol arasındaki geometrik ilişkilerden yararlanılır. Yapılarının basit olması nedeniyle geometrik tabanlı yöntemler uygulamalarda sıklıkla kullanılmaktadır. Stanley ve Pure Pursuit yöntemleri en yaygın kullanılan geometrik tabanlı yöntemlerdir. Stanley yöntemi düz yolda daha iyi bir yol takip performansı gösterirken, dönüşlerde daha düşük bir performans sergilemektedir. Pure Pursuit yöntemi ise dönüşlerde daha iyi bir performans sergilerken, düz yolda daha düşük bir performans göstermektedir. Bu çalışmada Pure Pursuit ve Stanley yöntemlerinin üstün yanlarını bir arada kullanabilmek için bulanık mantık tabanlı bir hibrit kontrol yöntemi önerilmiştir. Bu yöntemde yolun geometrisine bağlı olarak Stanley ve Pure Pursuit yöntemleri ile elde edilen direksiyon açı değerleri ağırlıklandırılarak tek bir direksiyon açısı değeri hesaplanmaktadır. Ağırlıklandırma parametresi dinamik olup bir bulanık çıkarım mekanizması tarafından ileri bakma açısı değerlendirilerek ayarlanmaktadır. Önerilen yöntemin performansı farklı yol şartlarında test edilmiş ve elde edilen sonuçlar Stanley, Pure Pursuit yöntemleri ve mevcut bir hibrit yöntem ile karşılaştırılmıştır. Benzetim sonuçları önerilen yöntemin diğer klasik iki yönteme ve mevcut hibrit yönteme göre daha üstün bir yol takip performansı sergilediğini göstermiştir.

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In this paper, a waypoint-based path tracking approach is suggested for the swarm robots to follow the desired path in an organized way. In the study, the applicability of the waypoint-based path tracking on the swarm robots that show flexible and scalable behavior has been demonstrated. To evaluate the proposed path planing approach with regard to scalability and flexibility, simulations have been applied in with/without obstacle arenas with different numbers of robots and according to different lookahead distances. With the proposed approach, each swarm robots exhibit swarm behavior in an organized manner depending on the distance of the lookahead to the path to track in the with / without obstacle arenas.