Electronic components are basic elements that are widely used in many industrial and technological fields. With the development of technology, their dimensions are being produced in smaller and smaller sizes. As a result, making fast distinctions becomes difficult. Being able to classify electronic components quickly and accurately will save labor and time in all areas where these elements are used. Recently, deep learning algorithms have become preferential in product classification studies due to their high accuracy and speed. In this paper, a classification study of electronic components was carried out with the deep learning method. A new convolutional neural network (CNN) model is proposed in the study. The model has six convolution layers, four pooling layers, two fully connected layers, softmax, and a classification layer. The training parameters of the network were determined as an ensemble size of 16, maximum period of 100, initial learning rate of 1 × 10⁻³, and the optimizing method sgdm. While determining the CNN model layers and training parameters, the values with the highest predictive values were selected as a result of the trials. Classification research was conducted using the pre-trained networks AlexNet, ShuffleNet, SqueezeNet, and GoogleNet for the same data, and their performance success parameters were compared to those of the proposed CNN model. The proposed CNN model showed higher performance than the other methods, and an accuracy value of 98.99% was obtained.

This study proposes a new convolutional neural network (CNN) method with an input-signal decomposition algorithm. With the proposed CNN architecture, hourly electricity consumption data for the Covid-19 period in Turkey were used as input data, and the short-term electricity consumption was forecasted. The input data were decomposed into its subcomponents using a signal decomposition process called Empirical Mode Decomposition (EMD). To extract the deep features, all input data were transformed into 2D feature maps and fed into the CNN. The obtained results were compared with the pre-trained models GoogleNet, AlexNet, SqueezeNet, and ResNet18. Model-wise comparisons showed that the proposed method had the highest correlation coefficient (R) and lowest root mean square error (RMSE) and mean absolute error (MAE) values for 1-h, 2-h, and 3-h. The mean R-values of the proposed method were 95.6%, 95.2%, and 94.0% for 1h, 2h and 3h ahead, respectively. The mean RMSE values were 8.2%, 8.7%, and 10.2% for 1h, 2h and 3h ahead, respectively. The experimental results confirm that the proposed method outperforms other pretrained methods despite its simpler structure.

The use of remote sensing has great potential for detecting many natural differences, such as disasters, climate changes, and urban changes. Due to technological advances in imaging, remote sensing has become an increasingly popular topic. One of the significant benefits of technological advancement has been the ease with which remote sensing data is now accessible. Physical and spatial information is detected by remote sensing, which can be described as the process of identifying distinctive characteristics of an environment. Resolution is one of the most important factors influencing the success of the detection processes. As a result of the resolution being below the necessary level, features of the objects to be differentiated become incomprehensible and therefore constitute a significant barrier to differentiation. The use of deep learning methods for classifying remote sensing data has become prevalent and successful in recent years. This study classified Satellite images using deep learning and machine learning methods. Based on the transfer learning strategy, a parallel convolutional neural network (CNN) was designed in the study. To improve the feature mapping of an image, convolutional branches use pre-trained knowledge of the transmitted network. Using the offline augmentation method, the raw data set was balanced to overcome its unbalanced class distribution and increased network performance. A total of 35 classes of landforms have been studied in the experiments. The accuracy value of the developed model in the classification study of landforms was 97.84%. According to experimental results, the proposed method provides high classification accuracy in detecting landforms and outperforms existing studies.

The effect of Coddington factors on aberration functions has been analysed using thin lens approximation with optical glass parameters. The dependence of spherical aberration on Coddington shape factor for the various optical glasses in real lens design was discussed using exact ray tracing for the optics education and training purposes. Thin lens approximation and thick lens design are generally taught with only lecturing method. But, thick lens design is closely related to the real life. Hence, it is more appropriate to teach thin lens approximation and thick lens design with real-life context based approach. Context based teaching can be effective in solving problems in which the subject is very difficult and irrelevant. It also provides extensive evidence for optics education that students are generally unable to correctly apply the concepts of lens design to optical instruments currently used. Therefore, the outline of real-life context based thick lens design lessons were proposed and explained in detail considering thin lens approximation.

Background: Accurate sex estimation is crucial in forensic identification when DNA analysis is impractical or remains are fragmented. Traditional anthropometric approaches often rely on single bone measurements and yield moderate levels of accuracy. Objective: This study aimed to evaluate deep convolutional neural networks (CNNs) for automated sex estimation using entire foot radiographs, an approach rarely explored. Methods: Digital foot radiographs from 471 adults (238 men, 233 women, aged 18–65 years) without deformities or prior surgery were retrospectively collected at a single tertiary center. Six CNN architectures (AlexNet, ResNet-18, ResNet-50, ShuffleNet, GoogleNet, and InceptionV3) were trained using transfer learning (70/15/15 train–validation–test split, data augmentation). The model performance was assessed using accuracy, sensitivity, specificity, precision, and F1-score. Results: InceptionV3 achieved the highest accuracy (97.1%), surpassing previously reported methods (typically 72–89%), with balanced sensitivity (97.5%) and specificity (96.8%). ResNet-50 followed closely (95.7%), whereas simpler networks, such as AlexNet, underperformed (90%). Conclusions: Deep learning applied to whole-foot radiographs delivers state-of-the-art accuracy for sex estimation, enabling rapid, reproducible, and cost-effective forensic identification when DNA analysis is delayed or unavailable, such as in mass disasters or clinical emergency settings.