Convolutional neural networks (CNNs) have become very popular, as they can successfully solve problems in many areas by obtaining representations of input data at different layers with tuned hyperparameters. A CNN’s hyperparameters include design parameters (DPs), which describe the depth of the CNN and order of layers; layer parameters (LPs), which are used for each CNN layer; and training parameters, which are used for training the CNN. The performance of CNNs depends on these hyperparameters, but setting them properly remains a very difficult and important problem. Although there are studies in the literature that optimize each of these three parameter groups separately, there is a lack of methodologies for simultaneous optimization of DPs and LPs in a nested framework. This study proposes a novel method called SwarmCNN, which combines particle swarm optimization and artificial bee colony algorithms to optimize both DPs and LPs. The effectiveness of SwarmCNN was evaluated across various datasets, including Mnist, Mnist RD, Mnist BN, Mnist BI, Mnist RD + BI, Convex, Rectangles, Mnist Fashion, and CIFAR-10. The results demonstrate promising accuracy rates: 99.58%, 96.20%, 97.56%, 96.39%, 83.39%, 96.92%, 100%, 93.47%, and 84.77%, respectively. Comparative analysis against numerous competitors revealed SwarmCNN’s superiority on five datasets and its second-place ranking on four datasets. The results demonstrated that SwarmCNN emerges as a powerful and competitive solution for optimizing hyperparameters and conducting neural architecture searches with high accuracy on various datasets.

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.

Background/Objectives: Skin cancer is a major public health concern, where early diagnosis and effective treatment are essential for prevention. To enhance diagnostic accuracy, researchers have increasingly utilized computer vision systems, with deep learning-based approaches becoming the primary focus in recent studies. Nevertheless, there is a notable research gap in the effective optimization of hyperparameters to design optimal deep learning architectures, given the need for high accuracy and lower computational complexity. Methods: This paper puts forth a robust metaheuristic optimization-based approach to develop novel deep learning architectures for multi-class skin cancer classification. This method, designated as the SADASNet (Selective and Adaptive Deep Architecture Search Network by Hyperparameter Optimization) algorithm, is developed based on the Particle Swarm Optimization (PSO) technique. The SADASNet method is adapted to the HAM10000 dataset. Innovative data augmentation techniques are applied to overcome class imbalance issues and enhance the performance of the model. The SADASNet method has been developed to accommodate a range of image sizes, and six different original deep learning models have been produced as a result. Results: The models achieved the following highest performance metrics: 99.31% accuracy, 97.58% F1 score, 97.57% recall, 97.64% precision, and 99.59% specificity. Compared to the most advanced competitors reported in the literature, the proposed method demonstrates superior performance in terms of accuracy and computational complexity. Furthermore, it maintains a broad solution space during parameter optimization. Conclusions: With these outcomes, this method aims to enhance the classification of skin cancer and contribute to the advancement of deep learning.

Automatic classification of aerial images has become one of the topics studied in recent years. Especially for the use of drones in different fields such as agricultural applications, smart city applications, surveillance and security applications, it is necessary to automatically classify the images obtained with the camera during autonomous mission execution. For this purpose, researchers have created new data sets and some computer vision methods have been developed to achieve high accuracy. However, in addition to increasing the accuracy of the developed methods, the computational complexity should also be reduced. Because the methods to be used in devices such as drones where energy consumption is important should have low computational complexity. In this study, firstly, five different state-of-art deep learning models were used to obtain high accuracy values in the classification of aerial images. Among these models, the VGG19 model achieved the highest accuracy with 94.21%. In the second part of the study, the parameters of this model were analyzed and the model was reconstructed. The number of 143.6 million parameters of the VGG19 model was reduced to 34 million. The accuracy of the model obtained by reducing the number of parameters is 93.56% on the same test data. Thus, despite the 66.5% decrease in the parameter ratio, there was only a 0.7% decrease in the accuracy value. When compared to previous studies, the results show improved performance.

Soil temperature not only affects many soil properties, but also has a significant effect on plant development. Knowing and correct estimation of soil temperature is important for both soil management and crop production. The accuracy of temperature forecasts is very important, especially for the countries that stand out with their agriculture-based economies. Therefore, in recent years, different artificial intelligence methods have been used in soil temperature predictions. Deep learning methods lead the way in achieving high prediction accuracy. In this study, a Long Short-Term Memory (LSTM) network, which is a deep learning (DL) sub-architecture, is proposed to create an effective model for soil temperature prediction. The data used in the study are the daily soil temperatures at a depth of 50 cm for the years 2013-2021 of Bingöl province. For the training of the proposed LSTM model, 89% of the data set within the scope of the study was used, and. The remaining 11% was estimated by the model for assessing model success. The RMSE value as a result of the estimation made by the trained LSTM model was obtained as 1,25. The high estimation accuracy of the proposed model showed that this model could be successfully applied in temperature data estimation studies.