Abstract Pneumonia is a bacterial, fungal, or viral infection that affects one or both lungs. It is a serious disease in which the air sacs in both lungs become clogged with pus and other substances. So, several frameworks and models have been built to properly assess such diseases, but there is still space for improvement. In this study, we used CXR images to train a CNN model to detect and classify Pneumonia disease in the lungs and also present how training accuracy and validation accuracy as well as training loss and validation loss vary when changing the size of the input image. The Kaggle CXR dataset is used, which was already created and pre-processed. The Convolutional Neural Network method is used in the research in close collaboration with some other data augmentation frameworks to enhance classification accuracy, which also will help to enhance training and validation accuracies, as well as characterize the precision of the Convolutional Neural Network model and achieve various results. The training and validation accuracy of our model are 0.9757 and 0.9568, respectively, and the training and validation loss are 0.0857 and 0.1399.

Millions of patients suffer from epilepsy each year, a chronic nervous illness with a growing global prevalence. In a lot of situations, it might cause critical injuries or patient deaths. So, the automatic prediction of the epileptic seizure saves the patients from injury as well as from death. In this age of technological advancements, the internet of things (IoT) has started to offer a variety of solutions in the healthcare sector through the use of machine learning, deep learning, and cloud-based services. Internet of thing, Edge computing, and Cloud computing helps in the detection of such nervous disorders using deep learning and machine learning. Most of the models were developed preciously but there are many issues related to the prediction and security of the data of patients. This research study proposed an autonomous cloud-edge integrated seizure prediction model based on a convolutional neural network. The proposed model detects and classifying the epileptic seizure using an EEG dataset. The proposed model shows average training accuracy and average validation accuracy are 99.27% and 96.37%, respectively and the average losses for training and validation are 2.10 % and 14.23%, respectively. The result shows that overall accuracies and losses are better than the wellknown existing works. All implementation for seizure prediction is done in Python language.