1,337 research outputs found
SOM+PSO : A novel method to obtain classification rules
Currently, most processes have a volume of historical information that makes its manual processing difficult. Data mining, one of the most significant stages in the Knowledge Discovery in Databases (KDD) process, has a set of techniques capable of modeling and summarizing these historical data, making it easier to understand them and helping the decision making process in future situations. This article presents a new data mining adaptive technique called SOM+PSO that can build, from the available information, a reduced set of simple classification rules from which the most significant relations between the features recorded can be derived. These rules operate both on numeric and nominal attributes, and they are built by combining a variation of a population metaheuristic and a competitive neural network. The method proposed was compared with the PART method and measured over 19 databases (mostly from the UCI repository), and satisfactory results were obtained.Facultad de Informátic
SOM+PSO : A novel method to obtain classification rules
Currently, most processes have a volume of historical information that makes its manual processing difficult. Data mining, one of the most significant stages in the Knowledge Discovery in Databases (KDD) process, has a set of techniques capable of modeling and summarizing these historical data, making it easier to understand them and helping the decision making process in future situations. This article presents a new data mining adaptive technique called SOM+PSO that can build, from the available information, a reduced set of simple classification rules from which the most significant relations between the features recorded can be derived. These rules operate both on numeric and nominal attributes, and they are built by combining a variation of a population metaheuristic and a competitive neural network. The method proposed was compared with the PART method and measured over 19 databases (mostly from the UCI repository), and satisfactory results were obtained.Facultad de Informátic
Handshape recognition for Argentinian Sign Language using ProbSom
Automatic sign language recognition is an important topic within the areas of
human-computer interaction and machine learning. On the one hand, it poses a
complex challenge that requires the intervention of various knowledge areas,
such as video processing, image processing, intelligent systems and
linguistics. On the other hand, robust recognition of sign language could
assist in the translation process and the integration of hearing-impaired
people.
This paper offers two main contributions: first, the creation of a database
of handshapes for the Argentinian Sign Language (LSA), which is a topic that
has barely been discussed so far. Secondly, a technique for image processing,
descriptor extraction and subsequent handshape classification using a
supervised adaptation of self-organizing maps that is called ProbSom. This
technique is compared to others in the state of the art, such as Support Vector
Machines (SVM), Random Forests, and Neural Networks.
The database that was built contains 800 images with 16 LSA handshapes, and
is a first step towards building a comprehensive database of Argentinian signs.
The ProbSom-based neural classifier, using the proposed descriptor, achieved an
accuracy rate above 90%
Distribution of Action Movements (DAM): A Descriptor for Human Action Recognition
Human action recognition from skeletal data is an important and active area
of research in which the state of the art has not yet achieved near-perfect
accuracy on many well-known datasets. In this paper, we introduce the
Distribution of Action Movements Descriptor, a novel action descriptor based on
the distribution of the directions of the motions of the joints between frames,
over the set of all possible motions in the dataset. The descriptor is computed
as a normalized histogram over a set of representative directions of the
joints, which are in turn obtained via clustering. While the descriptor is
global in the sense that it represents the overall distribution of movement
directions of an action, it is able to partially retain its temporal structure
by applying a windowing scheme.
The descriptor, together with a standard classifier, outperforms several
state-of-the-art techniques on many well-known datasets
Handshape recognition for Argentinian Sign Language using ProbSom
Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearingimpaired people.
This paper offers two main contributions: first, the creation of a database of handshapes for the Argentinian Sign Language (LSA), which is a topic that has barely been discussed so far. Secondly, a technique for image processing, descriptor extraction and subsequent handshape classification using a supervised adaptation of self-organizing maps that is called ProbSom. This technique is compared to others in the state of the art, such as Support Vector Machines (SVM), Random Forests, and Neural Networks.
The database that was built contains 800 images with 16 LSA conjurations, and is a first step towards building a comprehensive database of Argentinian signs. The ProbSom-based neural classifier, using the proposed descriptor, achieved an accuracy rate above 90%.Facultad de Informátic
Handshape recognition for Argentinian Sign Language using ProbSom
Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearingimpaired people.
This paper offers two main contributions: first, the creation of a database of handshapes for the Argentinian Sign Language (LSA), which is a topic that has barely been discussed so far. Secondly, a technique for image processing, descriptor extraction and subsequent handshape classification using a supervised adaptation of self-organizing maps that is called ProbSom. This technique is compared to others in the state of the art, such as Support Vector Machines (SVM), Random Forests, and Neural Networks.
The database that was built contains 800 images with 16 LSA conjurations, and is a first step towards building a comprehensive database of Argentinian signs. The ProbSom-based neural classifier, using the proposed descriptor, achieved an accuracy rate above 90%.Facultad de Informátic
Eicosapentaenoic acid induces DNA demethylation in carcinoma cells through a TET1-dependent mechanism
In cancer cells, global genomic hypomethylation is found together with localized hypermethylation of CpG islands within the promoters and regulatory regions of silenced tumor suppressor genes. Demethylating agents may reverse hypermethylation, thus promoting gene re-expression. Unfortunately, demethylating strategies are not efficient in solid tumor cells. DNA demethylation is mediated by ten-eleven translocation enzymes (TETs). They sequentially convert 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC), which is associated with active transcription; 5-formylcytosine; and finally, 5-carboxylcytosine. Although α-linolenic acid, eicosapentaenoic acid (EPA), and docosahexaenoic acid, the major n-3 polyunsaturated fatty acids, have anti-cancer effects, their action, as DNA-demethylating agents, has never been investigated in solid tumor cells. Here, we report that EPA demethylates DNA in hepatocarcinoma cells. EPA rapidly increases 5hmC on DNA, inducing p21Waf1/Cip1 gene expression, which slows cancer cell-cycle progression. We show that the underlying molecular mechanism involves TET1. EPA simultaneously binds peroxisome proliferator-activated receptor γ (PPARγ) and retinoid X receptor α (RXRα), thus promoting their heterodimer and inducing a PPARγ-TET1 interaction. They generate a TET1-PPARγ-RXRα protein complex, which binds to a hypermethylated CpG island on the p21 gene, where TET1 converts 5mC to 5hmC. In an apparent shuttling motion, PPARγ and RXRα leave the DNA, whereas TET1 associates stably. Overall, EPA directly regulates DNA methylation levels, permitting TET1 to exert its anti-tumoral function.-Ceccarelli, V., Valentini, V., Ronchetti, S., Cannarile, L., Billi, M., Riccardi, C., Ottini, L., Talesa, V. N., Grignani, F., Vecchini, A., Eicosapentaenoic acid induces DNA demethylation in carcinoma cells through a TET1-dependent mechanism
Automatic vehicle parking using an evolution-obtained neural controller
Within the problems that can be solved with autonomous robots, automatic parking is an area of great interest, since it presents a complex scenario where the agent must go through a series of obstacles to reach its goal. Existing solutions usually require some kind of external mark for monitoring or global vision that indicates where the agent is at a given time. This article presents an evolutionary strategy to generate a robotic controller based on a neural network that successfully solves the problem of vehicle parallel parking using only local information. The performance of the tness function is analyzed, focusing not only on the agent reaching its goal, but also on it doing so in a manner that is appropriate for the physics of a vehicle. Additionally, the Player/Stage simulator is broadly discussed, since it is one of the most widely used simulators nowadays in robotics.Presentado en el XII Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI
Sign Languague Recognition without frame-sequencing constraints: A proof of concept on the Argentinian Sign Language
Automatic sign language recognition (SLR) is an important topic within the
areas of human-computer interaction and machine learning. On the one hand, it
poses a complex challenge that requires the intervention of various knowledge
areas, such as video processing, image processing, intelligent systems and
linguistics. On the other hand, robust recognition of sign language could
assist in the translation process and the integration of hearing-impaired
people, as well as the teaching of sign language for the hearing population.
SLR systems usually employ Hidden Markov Models, Dynamic Time Warping or
similar models to recognize signs. Such techniques exploit the sequential
ordering of frames to reduce the number of hypothesis. This paper presents a
general probabilistic model for sign classification that combines
sub-classifiers based on different types of features such as position, movement
and handshape. The model employs a bag-of-words approach in all classification
steps, to explore the hypothesis that ordering is not essential for
recognition. The proposed model achieved an accuracy rate of 97% on an
Argentinian Sign Language dataset containing 64 classes of signs and 3200
samples, providing some evidence that indeed recognition without ordering is
possible.Comment: IBERAMIA 201
LSA64: An Argentinian Sign Language Dataset
Automatic sign language recognition is a research area that encompasses
human-computer interaction, computer vision and machine learning. Robust
automatic recognition of sign language could assist in the translation process
and the integration of hearing-impaired people, as well as the teaching of sign
language to the hearing population. Sign languages differ significantly in
different countries and even regions, and their syntax and semantics are
different as well from those of written languages. While the techniques for
automatic sign language recognition are mostly the same for different
languages, training a recognition system for a new language requires having an
entire dataset for that language. This paper presents a dataset of 64 signs
from the Argentinian Sign Language (LSA). The dataset, called LSA64, contains
3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first
step towards building a comprehensive research-level dataset of Argentinian
signs, specifically tailored to sign language recognition or other machine
learning tasks. The subjects that performed the signs wore colored gloves to
ease the hand tracking and segmentation steps, allowing experiments on the
dataset to focus specifically on the recognition of signs. We also present a
pre-processed version of the dataset, from which we computed statistics of
movement, position and handshape of the signs.Comment: Published in CACIC XXI
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