Chest diseases, such as COVID-19, viral pneumonia, and lung opacity, in most severe cases, call for quick diagnosis and accurate treatment. Applying deep learning methods, mainly convolutional neural networks (CNNs), has become attractive among machine learning techniques for automated image diagnostics. This paper reports a new ensemble approach that utilizes CNN-1, CNN-3 and VGG-16 structures to classify the disease and U-Net for segmenting the chest diseases in X-ray pictures considered from the Kaggle repository. Data augmentation is applied to original samples to increase the size and performance of a model. The segmentation procedure shows a high capability to define interested regions in the lung, which contributes to higher accuracy, performed comparison between the proposed segmentation method and existing methods. The following results obtained by the model are 96% in segmentation and 87.7% in classification- reflecting the preferred method's high accuracy. The proposed model was evaluated on performance metrics like F1-score, Precision, and Recall. Therefore, this result may lead the way for enhanced diagnostic accuracy and treatment decisions in clinical settings.

Sentiment analysis has its large application in a natural language processing. Natural language processing have a large range of applications like machine translation, aspect-oriented product analysis, product reviews, text classification and sentiment analysis for spam filtering and email categorization.

Inspection methods that are both quick and non-destructive, such as those enabled by deep learning and image processing, could be extremely beneficial for monitoring the quality of potatoes and other agricultural products. This research work proposes a Deep Neural Network (DNN) model to visually classify the diseased potatoes.