16,661 research outputs found
Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome
We evaluate a version of the recently-proposed classification system named
Optimized Dissimilarity Space Embedding (ODSE) that operates in the input space
of sequences of generic objects. The ODSE system has been originally presented
as a classification system for patterns represented as labeled graphs. However,
since ODSE is founded on the dissimilarity space representation of the input
data, the classifier can be easily adapted to any input domain where it is
possible to define a meaningful dissimilarity measure. Here we demonstrate the
effectiveness of the ODSE classifier for sequences by considering an
application dealing with the recognition of the solubility degree of the
Escherichia coli proteome. Solubility, or analogously aggregation propensity,
is an important property of protein molecules, which is intimately related to
the mechanisms underlying the chemico-physical process of folding. Each protein
of our dataset is initially associated with a solubility degree and it is
represented as a sequence of symbols, denoting the 20 amino acid residues. The
herein obtained computational results, which we stress that have been achieved
with no context-dependent tuning of the ODSE system, confirm the validity and
generality of the ODSE-based approach for structured data classification.Comment: 10 pages, 49 reference
Data granulation by the principles of uncertainty
Researches in granular modeling produced a variety of mathematical models,
such as intervals, (higher-order) fuzzy sets, rough sets, and shadowed sets,
which are all suitable to characterize the so-called information granules.
Modeling of the input data uncertainty is recognized as a crucial aspect in
information granulation. Moreover, the uncertainty is a well-studied concept in
many mathematical settings, such as those of probability theory, fuzzy set
theory, and possibility theory. This fact suggests that an appropriate
quantification of the uncertainty expressed by the information granule model
could be used to define an invariant property, to be exploited in practical
situations of information granulation. In this perspective, a procedure of
information granulation is effective if the uncertainty conveyed by the
synthesized information granule is in a monotonically increasing relation with
the uncertainty of the input data. In this paper, we present a data granulation
framework that elaborates over the principles of uncertainty introduced by
Klir. Being the uncertainty a mesoscopic descriptor of systems and data, it is
possible to apply such principles regardless of the input data type and the
specific mathematical setting adopted for the information granules. The
proposed framework is conceived (i) to offer a guideline for the synthesis of
information granules and (ii) to build a groundwork to compare and
quantitatively judge over different data granulation procedures. To provide a
suitable case study, we introduce a new data granulation technique based on the
minimum sum of distances, which is designed to generate type-2 fuzzy sets. We
analyze the procedure by performing different experiments on two distinct data
types: feature vectors and labeled graphs. Results show that the uncertainty of
the input data is suitably conveyed by the generated type-2 fuzzy set models.Comment: 16 pages, 9 figures, 52 reference
A Convolutional Neural Network model based on Neutrosophy for Noisy Speech Recognition
Convolutional neural networks are sensitive to unknown noisy condition in the
test phase and so their performance degrades for the noisy data classification
task including noisy speech recognition. In this research, a new convolutional
neural network (CNN) model with data uncertainty handling; referred as NCNN
(Neutrosophic Convolutional Neural Network); is proposed for classification
task. Here, speech signals are used as input data and their noise is modeled as
uncertainty. In this task, using speech spectrogram, a definition of
uncertainty is proposed in neutrosophic (NS) domain. Uncertainty is computed
for each Time-frequency point of speech spectrogram as like a pixel. Therefore,
uncertainty matrix with the same size of spectrogram is created in NS domain.
In the next step, a two parallel paths CNN classification model is proposed.
Speech spectrogram is used as input of the first path and uncertainty matrix
for the second path. The outputs of two paths are combined to compute the final
output of the classifier. To show the effectiveness of the proposed method, it
has been compared with conventional CNN on the isolated words of Aurora2
dataset. The proposed method achieves the average accuracy of 85.96 in noisy
train data. It is more robust against Car, Airport and Subway noises with
accuracies 90, 88 and 81 in test sets A, B and C, respectively. Results show
that the proposed method outperforms conventional CNN with the improvement of
6, 5 and 2 percentage in test set A, test set B and test sets C, respectively.
It means that the proposed method is more robust against noisy data and handle
these data effectively.Comment: International conference on Pattern Recognition and Image Analysis
(IPRIA 2019
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
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