5,735 research outputs found
Using Neural Networks for Relation Extraction from Biomedical Literature
Using different sources of information to support automated extracting of
relations between biomedical concepts contributes to the development of our
understanding of biological systems. The primary comprehensive source of these
relations is biomedical literature. Several relation extraction approaches have
been proposed to identify relations between concepts in biomedical literature,
namely, using neural networks algorithms. The use of multichannel architectures
composed of multiple data representations, as in deep neural networks, is
leading to state-of-the-art results. The right combination of data
representations can eventually lead us to even higher evaluation scores in
relation extraction tasks. Thus, biomedical ontologies play a fundamental role
by providing semantic and ancestry information about an entity. The
incorporation of biomedical ontologies has already been proved to enhance
previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1
A Computer Vision System to Localize and Classify Wastes on the Streets
Littering quantification is an important step for improving cleanliness of
cities. When human interpretation is too cumbersome or in some cases
impossible, an objective index of cleanliness could reduce the littering by
awareness actions. In this paper, we present a fully automated computer vision
application for littering quantification based on images taken from the streets
and sidewalks. We have employed a deep learning based framework to localize and
classify different types of wastes. Since there was no waste dataset available,
we built our acquisition system mounted on a vehicle. Collected images
containing different types of wastes. These images are then annotated for
training and benchmarking the developed system. Our results on real case
scenarios show accurate detection of littering on variant backgrounds
Fruit Shop Tool: Fruit Classification and Recognition using Deep Learning
Fruit image classification and recognition is a challenging application of computer vision. The computer vision system is used to recognize a fruit based on artificial neural networks. Deep neural network is widely used for various classification problems. In this paper Convolutional Neural Network (CNN) is used to recognize the fruits. The dataset contains 1877 images of ten categories which are used for the experimental purpose. CNN is constructed with sixteen layers which are used to extract the features from images and Support Vector Machine (SVM) classifier is used for classification. The proposed system has the classification accuracy of 99.2% and the recognition accuracy of 99.02%
Encoding of phonology in a recurrent neural model of grounded speech
We study the representation and encoding of phonemes in a recurrent neural
network model of grounded speech. We use a model which processes images and
their spoken descriptions, and projects the visual and auditory representations
into the same semantic space. We perform a number of analyses on how
information about individual phonemes is encoded in the MFCC features extracted
from the speech signal, and the activations of the layers of the model. Via
experiments with phoneme decoding and phoneme discrimination we show that
phoneme representations are most salient in the lower layers of the model,
where low-level signals are processed at a fine-grained level, although a large
amount of phonological information is retain at the top recurrent layer. We
further find out that the attention mechanism following the top recurrent layer
significantly attenuates encoding of phonology and makes the utterance
embeddings much more invariant to synonymy. Moreover, a hierarchical clustering
of phoneme representations learned by the network shows an organizational
structure of phonemes similar to those proposed in linguistics.Comment: Accepted at CoNLL 201
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