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As the "Internet of Medical Things (IoMT)" grows, healthcare systems can collect and process data. It is also challenging to study public health prevention requirements. Virus transmission can be prevented by wearing a mask. The World Health Organization (WHO) recommends wearing a facemask to protect against the COVID‐19 pandemic—the levels of a pandemic rise across almost all regions of the world. By following the WHO rules, we support the development of face mask‐detecting technologies and determine whether or not people are using masks in public locations. The proposed paradigm in this paper will work in three stages. Firstly, we use an Image data generator to import the images. In addition to using a Haar cascade (HC) classifier for detecting faces, residual learning (ResNet152V2) trains a model that detects whether someone is wearing a face mask. Detection and classification are carried out in real‐time with high precision. Compared with other recently proposed methods, the model achieved 99.65% accuracy during training and 99.63% during validation. [ABSTRACT FROM AUTHOR]

Brain tumors are still diagnosed and classified based on the results of histopathological examinations of biopsy samples. The existing method requires extra effort from the user, takes too long, and can lead to blunders. These limitations underline the need of employing a fully automated deep learning system for the multi‐classification of brain tumors. In order to facilitate early detection, this study employs a convolutional neural network (CNN) to multi‐classify brain tumors. In this research, we present three distinct CNN models for use in three separate categorization tasks. The first CNN model can correctly categorize brain tumors 99.74% of the time. The second CNN model is 96.27% accurate in differentiating between normal, glioma, meningioma, pituitary, and metastatic brain tumors. The third CNN model successfully distinguishes between Grades II, III, and IV brain tumors 99.18% of the time. The Hybrid Particle Swarm Grey Wolf Optimization (HPSGWO) technique is used to quickly and accurately determine optimal values for all of CNN models most important hyperparameters. An HPSGWO algorithm is used to fine‐tune all the necessary hyperparameters for optimal classification performance. The results are compared with standard existing CNN models across a range of performance measures. The proposed models are trained using publicly available large clinical datasets. To verify their initial multi‐classification of brain tumors, clinicians and radiologists might use the proposed CNN models. [ABSTRACT FROM AUTHOR]

With Deep Learning (DL) outperforming previous Machine Learning (ML) techniques in classifying images, the remote sensing community has recently shown an increased interest in using these algorithms to classify Land Use and Land Cover (LULC) using multispectral and hyperspectral data. Land Use (LU) and Land Cover (LC) are two types of cartographic data that are used to develop smart cities and monitor the environment. LULC classification can benefit greatly from successfully applying remote sensing Image Classification (IC) using high spatial resolution data. The acquisition of spatiotemporal data for LULC classification has been made more accessible because of recent improvements in spatial analysis and Deep Learning (DL) technology. Considering the quality of Deep Neural Networks (DNN) in related Computer Vision (CV) tasks and the enormous volume of remotely sensed data accessible, DL methods appear to be particularly promising for modelling many remote sensing problems. However, there are several issues with ground-truth, resolution, and the nature of data that have a significant impact on categorization performance. We propose a Reversible Residual Network (RAVNet), a hybrid residual attention sensitive segmentation approach, to precisely categorize LULC in this study. The suggested network is based on the VNet model, which extracts relevant information by mixing low-level and high-level Feature Maps (FM). The attention-aware features change adaptively to the integration of residual modules. Our system was tested on the National Agriculture Imagery Program (NAIP) dataset, and the findings demonstrate that our architecture is competitive against other learning models. • RAVNet, a hybrid residual attention-aware LULC extraction NN inspired by ResNet. • A residual VNet architecture allowing for DCNN optimization and performance enhancement. • ResNet tested on NAIP and demonstrates that it outperforms existing learning models. [ABSTRACT FROM AUTHOR]