50,388 research outputs found
Microservices and Machine Learning Algorithms for Adaptive Green Buildings
In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings
Meta-Learning for Phonemic Annotation of Corpora
We apply rule induction, classifier combination and meta-learning (stacked
classifiers) to the problem of bootstrapping high accuracy automatic annotation
of corpora with pronunciation information. The task we address in this paper
consists of generating phonemic representations reflecting the Flemish and
Dutch pronunciations of a word on the basis of its orthographic representation
(which in turn is based on the actual speech recordings). We compare several
possible approaches to achieve the text-to-pronunciation mapping task:
memory-based learning, transformation-based learning, rule induction, maximum
entropy modeling, combination of classifiers in stacked learning, and stacking
of meta-learners. We are interested both in optimal accuracy and in obtaining
insight into the linguistic regularities involved. As far as accuracy is
concerned, an already high accuracy level (93% for Celex and 86% for Fonilex at
word level) for single classifiers is boosted significantly with additional
error reductions of 31% and 38% respectively using combination of classifiers,
and a further 5% using combination of meta-learners, bringing overall word
level accuracy to 96% for the Dutch variant and 92% for the Flemish variant. We
also show that the application of machine learning methods indeed leads to
increased insight into the linguistic regularities determining the variation
between the two pronunciation variants studied.Comment: 8 page
Machine Learning in Automated Text Categorization
The automated categorization (or classification) of texts into predefined
categories has witnessed a booming interest in the last ten years, due to the
increased availability of documents in digital form and the ensuing need to
organize them. In the research community the dominant approach to this problem
is based on machine learning techniques: a general inductive process
automatically builds a classifier by learning, from a set of preclassified
documents, the characteristics of the categories. The advantages of this
approach over the knowledge engineering approach (consisting in the manual
definition of a classifier by domain experts) are a very good effectiveness,
considerable savings in terms of expert manpower, and straightforward
portability to different domains. This survey discusses the main approaches to
text categorization that fall within the machine learning paradigm. We will
discuss in detail issues pertaining to three different problems, namely
document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey
Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach
Recent years have witnessed the rapid development of human activity
recognition (HAR) based on wearable sensor data. One can find many practical
applications in this area, especially in the field of health care. Many machine
learning algorithms such as Decision Trees, Support Vector Machine, Naive
Bayes, K-Nearest Neighbor, and Multilayer Perceptron are successfully used in
HAR. Although these methods are fast and easy for implementation, they still
have some limitations due to poor performance in a number of situations. In
this paper, we propose a novel method based on the ensemble learning to boost
the performance of these machine learning methods for HAR
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Integrative machine learning approach for multi-class SCOP protein fold classification
Classification and prediction of protein structure has been a central research theme in structural bioinformatics. Due to the imbalanced distribution of proteins over multi SCOP classification, most discriminative machine learning suffers the well-known âFalse Positives â problem when learning over these types of problems. We have devised eKISS, an ensemble machine learning specifically designed to increase the coverage of positive examples when learning under multiclass imbalanced data sets. We have applied eKISS to classify 25 SCOP folds and show that our learning system improved over classical learning methods
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