Deep learning is a cutting-edge image processing method that is still relatively new but produces reliable results. Leaf disease detection and categorization employ a variety of deep learning approaches. Tomatoes are one of the most popular vegetables and can be found in every kitchen in various forms, no matter the cuisine. After potato and sweet potato, it is the third most widely produced crop. The second-largest tomato grower in the world is India. However, many diseases affect the quality and quantity of tomato crops. This article discusses a deep-learning-based strategy for crop disease detection. A Convolutional-Neural-Network-based technique is used for disease detection and classification. Inside the model, two convolutional and two pooling layers are used. The results of the experiments show that the proposed model outperformed pre-trained InceptionV3, ResNet 152, and VGG19. The CNN model achieved 98% training accuracy and 88.17% testing accuracy.

Nowadays people use social networks such as Facebook, Twitter, Orkut for sharing personal information and also significant events that occurs all over the world. Online news broadcast the most momentous events among users. Then the users discuss those events and post their reviews in micro blogs or in blogosphere. Since the web seems to be too huge and also the information available on the web is constantly updating, the task of identifying such events that are accessible on the web and the comments that are discussed related to those events in social networks is much daunting. Usually an event is temporal in nature, they change in time. Due to the temporal nature of events, the identification of event becomes even more difficult. Existing visual text analysis system extracts temporal themes from the large document stream and with that information; it provides temporal views of changes that occur on the event. Consequently, events that cause the changes can't be identified. In this paper, an Event Identification System is proposed to identify the most important events that occur and to identify the user discussion and also to rate their reviews. The proposed methodology make use of topic clustering, named entity recognition, latent dirichlet allocation, topic modeling to identify the significant events that are available on the web.

E-transactions have become prominent and highly convenient due to the widespread usage of the internet. The number of consumer reviews on various products is increasing day-by-day. These vast number of reviews are beneficial to manufacturers and consumers alike. It is a challenging task for a potential consumer to read all reviews to make a better purchase decision. It is beneficial to mine available consumer reviews for popular products from various consumer product review sites. The first step to do this is to decide the polarity of a review by performing sentiment analysis. We can then classify the review based on the polarity. This paper presents a comparison of a sentiment analyzer with other existing classifiers.