Summary In today's world, identifying the owner and proprietor of a vehicle that violates driving rules or does any unintentional work on the street is a challenging task. Inspection of each driver's license number takes a long time for a highway police officer. To overcome this, many researchers have introduced an automated number plate recognition approach which is usually a computer vision‐based technique to identify the vehicle's registration plate. However, the existing recognition approaches are lagged to extract the influential features which degrade the detection accuracy and increase the misclassification errors. In this article, a novel automated number plate recognition methodology has been proposed to identify the number plates accurately with minimal error rates. Primary, a new pretrained location‐dependent ultra convolutional neural network (LUCNN) is employed to learn the influential features from the input images. These obtained features are then fed into hybrid single‐shot fully convolutional detectors with a support vector machine (SSVM) classifier to separate the vehicle's city, model, and number from the registration location. At varied automobile distances, the proposed LUCNN + SSVM model is able to retrieve the number plate regions in the picture acquired from its back end. The performance results manifest that the proposed LUCNN + SSVM model attains a better accuracy of 98.75% and a lesser error range of 1.25% than the existing recognition models.

Summary In today's world, cancers are becoming a crucial warning in current medical applications where they show a significant part in the prognosis and appraisal of ovarian malignancies in histopathological imaging. Automated approaches to systematize the formation and categorization of cancers in periodic acid‐Schiff (PAS)‐stained images have recently been quickly growing in the area of digital pathology. However, the existing literature lacks computerized approaches to systematize the localization and categorization of cancers in PAS‐stained images. In this work, a new deep fully connected convolutional neural network (DFCNN) along with hybridized channel selection (HCS) strategy has been proposed to diagnose ovarian cancers accurately in PAS‐stained images. Primarily, the HCS strategy selects the optimal channel for managing the input dataset. Afterward, the DFCNN is employed to extract the influential features from the PAS‐stained images. The autoencoder modeling can be carried out to build the proposed DFCNN layers. The appropriate layer selection in the proposed model facilitates to obtaining accurate cancer classification with minimal misclassification errors. The performance results manifest that the proposed model achieves a superior accuracy of 99.22% when compared with existing CNN+softmax, CNN+decision tree, and CNN+radial basis function models.