Robustness of Fuzzy Modeling
Martha G Smons (Marthasimons)
on
March 9, 2021
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Robustness of Fuzzy Modeling and Its Applications in
Clustering and Classification Problems
This paper investigates the use of deep learning for a
classification task in a natural scene context. Deep
Learning is a powerful data-driven approach to learning
for scene analysis given the natural visual world as a
whole. Although the deep learning algorithms used in
this work are not fully-trained, it could be considered
a natural data-driven approach to learning for this
task given the natural visual world and the natural
objects themselves in the context of the scene. In this
paper, we present a novel framework for applying the
deep learning method to natural scenes for natural
object detection. The proposed method is designed to
solve for the problem of natural object detection.
Extensive experimental study on real images from the
field show that the proposed method is a promising
approach for object detection in real real-world
environments.
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Video In HV range prediction from the scientific
literature
We present a novel method for learning supervised
learning problems based on an adversarial learning
algorithm. The adversarial technique is motivated by
the fact that it is the least-squares model used in
practice. The approach exploits the adversarial
learning principle to minimize the influence of its
adversarial input and to reduce the adversarial output
to a set of small, weighted minimizers. The objective
is to minimize the total variance of the squared loss
function over adversarial input and minimizes the
adversarial output. We apply our learned adversarial
algorithm to various supervised learning tasks,
including classification, clustering, and
classification with a single pass of the training
images. Our results show that the proposed approach
provides a simple yet effective learning technique to
improve both prediction accuracy and performance. Using
this approach, we found that the proposed approach
significantly outperforms competing methods on three
datasets.
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Predicting the Treatment of Medulloblastoma Patients
Based on Functional Connectomes Using Deep
Convolutional Neural Network
The research on the potential use of deep learning for
medical machine translation (MT) has focused on
identifying the source of textural patterns in human
speech. In this work we study the effect of MT on the
transcription of the patient-related speech in response
to a question posed by the human in the context of a
medical evaluation. To this end, we used a recurrent
neural network to learn the structure and dynamics of a
patient's speech with a high-quality corpus. We
investigated the effect of MT on the translation
process of the translated speech and the ability of the
human-AI community to generate appropriate speech
patterns for translation. On the basis of the results
presented we conducted experiments to investigate the
effect of MT and its effects on translation
performance. The results indicate that MT's effects
also extend to the training stage.
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