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Grasshoppers damage crops and causes millions of tons of food to be destroyed every year. The development of effective and accurate locust identification systems is critical in controlling locust species and preventing food loss. In this study, 11 different plant pest grasshopper species seen in various parts of our country and the world were classified using various convolutional neural network models. The dataset used in the study was obtained by observing the Eastern and Southeastern Anatolia regions of our country. The novelity of this study is that a dataset named GHCD11 has been created for 11 different plant pest grasshopper species in our country. In addition, VGG16, VGG19, ResNet50, DenseNet121, EfficientNet, MobileNet, which are in the Keras library and are widely used in image classification, were used for the automatic classification of 11 different grasshopper species in the study. As a result of experimental studies on the GHCD11 dataset with learning transfer, remarkable classification accuracies in the range of 95% to 99% were obtained. The study is important because it not only presents a novel dataset, but also demonstrates that automatic identification and detection of plant pest grasshopper species can be done with high accuracy using convolutional neural network architectures. [ABSTRACT FROM AUTHOR]

The duration of the smart intersection system lights is determined automatically according to the nearest busy. The vehicle at the intersection with the camera is calculated by the image processing process. optimizing the signaling time in traffic signaling. It will be passed to be passed by a system that can be reached later. Also the system can be entered with this remote central management. Manually switch to roads. In this study, it is a smart intersection system used with special permission from Malatya Metropolitan Municipality transportation units. These studies and the benefits they have provided are highlighted. In addition, DARKNET's real-time object detection YOLOV3 deep learning model is used within the scope of in-vehicle realtime traffic system from data images on websites for traffic. The vehicles are placed in the targeted and future-determined database. Positive signaling with information from the designed Process-Based Intersection Management System. Agricultural bounty takes advantage of little stealing gases to be grown to take advantage of time and small items. A clean environment will be created. [ABSTRACT FROM AUTHOR]

The skin, in which our body is completely covered, both provides the heat balance of our body and protects our body against external factors. Skin cancer, which occur as a result of the uncontrolled proliferation of cells on the skin surface, are one of the most common types of cancer in the world. Early detection of skin cancer means early treatment of the disease. With early diagnosis, patients can be cured earlier and mortality rates can be reduced. The hardest part of skin cancer diagnosis is that skin lesions are very similar to each other. Therefore, it is of great importance that skin cancer can be diagnosed and classified as benign or malignant tumor. In this study, Convolutional Neural Network were used to determine whether skin cancer is benign or malignant. Separate results are obtained with Alexnet, Resnet50, Densenet201 and Googlenet. Then the performance rates of the models used have been compared. The highest accuracy rate was achieved with the Resnet50 model with 83.49%. Then, the results were obtained with the proposed hybrid model. In the proposed hybrid model, the accuracy rate was 84.11%. This rate is an important value for early diagnosis and treatment of the disease. [ABSTRACT FROM AUTHOR]

Deep learning, one of the most popular topics of recent years, is frequently used in the biomedical field. Disease and fracture detection, data estimation, tissue and organ segmentation, and many other applications are successfully carried out with deep learning algorithms. However, while these applications are valid for human medicine, veterinary medicine is left behind. So, the deficiency in the literature about animal's X-Ray images processing has been the biggest motivation for this study. In this work, a comprehensive dataset containing dog X-Rays taken from Ankara Metropolitan Municipality Stray Animals Temporary Nursing Home, was processed with deep learning algorithms. It is aimed to determine the type of long bone. Like many other studies, Convolutional Neural Networks'(CNN) architectures are utilized in this study, too. Transfer learning was carried out with AlexNet, GoogLeNet and VGG-19 deep learning models, and the classification performance was tested with Support Vector Machines (SVM). [ABSTRACT FROM AUTHOR]

It is important for the sensitivity of ecological balance that image processing methods and techniques give better results day by day. This article deals with the classification of flower images which are the basic elements of ecological balance. Recently the use of deep learning methods on flower images has increased. In this study, the dataset available on the internet was used for the classification of flower images. The dataset consists of 4326 images. In the obtained dataset, convolutional neural network (CNN), one of the deep learning models, was used for feature extraction. The CNN architectures AlexNet, VGG-16 and VGG-19 are used in this study. The common feature of the three models is that they have a fully connected layer that give 1000 features. The features obtained from flower images were classified with support vector machines (SVM) and the results were compared. As a result of the comparison, the best classification performance was achieved with VGG-16 architecture. Obtained classification accuracy rate is 86.56%. In the next phase, 1000 features obtained from the last fully connected layer of CNN architectures were combined and 3000 feature set were created. Then, the most efficient 300 features were selected from the 3000 set of features using the Maximum Information Coefficient (MIC), Ridge regression, and Recursive Feature Elimination (RFE) feature selection methods. The most efficient features extracted by feature selection methods were reclassified by SVM method. The classification success rate increased by approximately 4.54% to 91.10%. In this study, it has been observed that using CNN architectures and feature selection methods are effective in the classification of flower images. [ABSTRACT FROM AUTHOR]

Recently, rapid developments in image processing have gained different perspective in deep learning models. Deep learning models continue to contribute to the areas of human health. Pneumonia is one of the diseases that people may encounter in any period of their lives. Pneumonia accounts for about 18% of infectious diseases. In some cases, this disease can cause death. In this study, lung x-ray images were used for the diagnosis of pneumonia. The convolutional neural network (CNN) from deep learning models was used for feature extraction in the resulting image set. The results of CNN with different classifiers were compared. As a result of the comparison, a success rate of approximately 95.8% was obtained with support vector machines (SVM). In the early diagnosis of deadly diseases such as pneumonia, deep learning models were found to be faster and more accurate. This study has shown that feature extraction with CNN provides better results in terms of performance than current methods in biomedical field. [ABSTRACT FROM AUTHOR]

Recent developments in image processing have contributed to the advancement of rapidly developing technological systems. In particular, work on image processing in the health field has further increased its popularity. Whether it is medical images or images on the other side, although the success of existing methods is ensured, the deeper learning model is a model that contributes more in terms of time and performance compared to existing methods. Highperformance results can be obtained on multi-layered images with deep learning model while the existing methods are operated on single layer images. The most important feature of deep learning is that it can process the operations on the image in a single pass and discover the parameters that need to be entered manually. Moreover, as technology companies try to deepen their learning and increase their competitive power among themselves, the methods they have built on deep learning in the scientific sense have begun to be preferred over existing methods. It is envisaged that the data sets in the biomedical field, which is one of the limited access areas of the dataset, to be obtained quickly in the recent times will contribute more to the image processing studies in this area together with the deep learning model. [ABSTRACT FROM AUTHOR]