International Journal of Machine Intelligence and Sensory Signal Processing
These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.
Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.
Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.
International Journal of Machine Intelligence and Sensory Signal Processing (3 papers in press)
Self-Configuring Artificial Neural Network Applied to Unmanned Aerial Position Estimation with use of Thermal Infrared Images by Wanessa Da Silva, Nandamudi Lankalapalli Vijaykumar, Haroldo Fraga De Campos Velho, Elcio Hideiti Shiguemori Abstract: Applications of Unmanned Aerial Vehicles (UAVs) have had an exponential
growth. Employing this technology is useful where the human intervention could
be impossible, exhaustive, risky, or expensive. Many low cost applications for
UAVhave motivated research for autonomous navigation. However, flying during
night periods is still a challenge for UAV. For autonomous navigation, some
approaches can be applied: the use of information from a Global Navigation
Satellite System (GNSS), and image processing. In the case of GNSS, signal
may be lost or blocked. Therefore, alternative ways other than GNSS signal
deserve to be investigated for critical missions. This research proposes a method
to estimate the geographic position of a UAV during the night based on thermal
infrared images (TIR). An image processing procedure is applied to extract edges
from satellite images under the visible band and UAV thermal infrared image.
The latter process is performed by means of Artificial Neural Networks (ANNs)
and correlation index. The automatic configuration of Artificial Neural Network
(ANN) has been designed by optimization approach, solved by the Multiple-
Particle Collision Algorithm. Keywords: Unmanned Aerial Vehicles; Thermal Infrared Images; Artificial Neural Network; Multiple-Particle Collision Algorithm.
Mixture parameter estimation for Higgs classification by Inga Strümke, Steffen Maeland Abstract: We study a particle physics scenario involving new Higgs bosons of degenerate mass. This paper is intended for readers with a background in statistics, mathematics or data science, and a brief introduction to the physical processes, data gathering and simulation as well as the underlying physical theory is given. We then explore how deep learning can be used to estimate the relative occurence of the mass degenerate particles in experiments at the Large Hadron Collider (LHC). The measurable quantities, such as energies and momenta of the Higgs decay products, are used as input features for a neural network. We first demonstrate that a simple probabilistic interpretation of the network output can give rise to highly prior dependent results, and then show how the issue can be circumvented by expressing the task as a parameter estimation problem in a two-class mixture model. The contents of this paper form a basis for arXiv:1804.07737, which has been accepted for publication in The European Physics Journal C. Keywords: BSM; Beyond Standard Model physics; Machine Learning; Two-Higgs Doublet Models,rnMixture models; Parameter estimation; Prior probability shift.
Group decision making reuse with introspection learning for case-based reasoning by Aijun Yan Abstract: A reuse method of group decision making based on introspection learning is proposed for the accuracy of case-based reasoning classifiers with the K nearest neighbors (KNN) reuse method is not high. First, the KNN strategy is adopted to retrieve K most similar cases from the case base and regarding its conclusions as experts in decision-making. Then, the group decision results of the target case can be calculated by introducing into the group cardinal utility function based on expert authority; if the decision is incorrect, the authority of experts is adjusted via failure-triggered strategy in the introspection learning automatically. In this manner, an improved methodology for case reuse is obtained for CBR problem solving. The experiment results show that the proposed method can effectively improve the classification accuracy, which substantially outperform the traditional means in potential information mining of most similar cases, having application advantages in the pattern classification fields. Keywords: case reuse; group decision making; introspection learning; expert authority; classification.