18,404 research outputs found
Type of Tomato Classification Using Deep Learning
Abstract: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South
American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts,
and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific
studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which helps in
protecting them from developing deadly blood clots. A tomato classification approach is presented with a data set containing
approximately 5,266 images with 7 species belonging to tomatoes. The Neural Network Algorithms (CNN), a deep learning
technique applied widely in image recognition, is used for this task
Multimodal Classification of Urban Micro-Events
In this paper we seek methods to effectively detect urban micro-events. Urban
micro-events are events which occur in cities, have limited geographical
coverage and typically affect only a small group of citizens. Because of their
scale these are difficult to identify in most data sources. However, by using
citizen sensing to gather data, detecting them becomes feasible. The data
gathered by citizen sensing is often multimodal and, as a consequence, the
information required to detect urban micro-events is distributed over multiple
modalities. This makes it essential to have a classifier capable of combining
them. In this paper we explore several methods of creating such a classifier,
including early, late, hybrid fusion and representation learning using
multimodal graphs. We evaluate performance on a real world dataset obtained
from a live citizen reporting system. We show that a multimodal approach yields
higher performance than unimodal alternatives. Furthermore, we demonstrate that
our hybrid combination of early and late fusion with multimodal embeddings
performs best in classification of urban micro-events
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