scholarly journals A Blended Artificial Intelligence Approach for Spectral Classification of Stars in Massive Astronomical Surveys

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 518
Author(s):  
Carlos Dafonte ◽  
Alejandra Rodríguez ◽  
Minia Manteiga ◽  
Ángel Gómez ◽  
Bernardino Arcay
Keyword(s):  
Artificial Intelligence ◽  
Hybrid Approach ◽  
Success Rates ◽  
Stellar Spectra ◽  
Knowledge Based ◽  
Astronomical Surveys ◽  
Classification Project ◽  
Intelligent Models ◽  
The Individual

This paper analyzes and compares the sensitivity and suitability of several artificial intelligence techniques applied to the Morgan–Keenan (MK) system for the classification of stars. The MK system is based on a sequence of spectral prototypes that allows classifying stars according to their effective temperature and luminosity through the study of their optical stellar spectra. Here, we include the method description and the results achieved by the different intelligent models developed thus far in our ongoing stellar classification project: fuzzy knowledge-based systems, backpropagation, radial basis function (RBF) and Kohonen artificial neural networks. Since one of today’s major challenges in this area of astrophysics is the exploitation of large terrestrial and space databases, we propose a final hybrid system that integrates the best intelligent techniques, automatically collects the most important spectral features, and determines the spectral type and luminosity level of the stars according to the MK standard system. This hybrid approach truly emulates the behavior of human experts in this area, resulting in higher success rates than any of the individual implemented techniques. In the final classification system, the most suitable methods are selected for each individual spectrum, which implies a remarkable contribution to the automatic classification process.

Cooperative AI Techniques for Stellar Spectra Classification

2006 ◽  
pp. 332-346
Author(s):  
Alejandra Rodriguez ◽  
Carlos Dafonte ◽  
Bernardino Arcay ◽  
Iciar Carricajo ◽  
Minia Manteiga
Keyword(s):  
Artificial Intelligence ◽  
Spectral Data ◽  
Hybrid System ◽  
Stellar Evolution ◽  
Hybrid Approach ◽  
Stellar Spectra ◽  
Artificial Intelligence Techniques ◽  
Manual Methods ◽  
Short Time

This chapter describes a hybrid approach to the unattended classification of low-resolution optical spectra of stars. The classification of stars in the standard MK system constitutes an important problem in the astrophysics area, since it helps to carry out proper stellar evolution studies. Manual methods, based on the visual study of stellar spectra, have been frequently and successfully used by researchers for many years, but they are no longer viable because of the spectacular advances of the objects collection technologies, which gather a huge amount of spectral data in a relatively short time. Therefore, we propose a cooperative system that is capable of classifying stars automatically and efficiently, by applying to each spectrum the most appropriate method or combined methods, which guarantees a reliable, consistent, and adapted classification. Our final objective is the integration of several artificial intelligence techniques in a unique hybrid system.

Cooperative AI Techniques for Stellar Spectra Classification

2011 ◽  
pp. 1471-1483
Author(s):  
Alejandra Rodriguez ◽  
Carlos Dafonte ◽  
Bernardino Arcay ◽  
Iciar Carricajo ◽  
Minia Manteiga
Keyword(s):  
Artificial Intelligence ◽  
Spectral Data ◽  
Hybrid System ◽  
Stellar Evolution ◽  
Hybrid Approach ◽  
Stellar Spectra ◽  
Artificial Intelligence Techniques ◽  
Manual Methods ◽  
Short Time

This chapter describes a hybrid approach to the unattended classification of low-resolution optical spectra of stars. The classification of stars in the standard MK system constitutes an important problem in the astrophysics area, since it helps to carry out proper stellar evolution studies. Manual methods, based on the visual study of stellar spectra, have been frequently and successfully used by researchers for many years, but they are no longer viable because of the spectacular advances of the objects collection technologies, which gather a huge amount of spectral data in a relatively short time. Therefore, we propose a cooperative system that is capable of classifying stars automatically and efficiently, by applying to each spectrum the most appropriate method or combined methods, which guarantees a reliable, consistent, and adapted classification. Our final objective is the integration of several artificial intelligence techniques in a unique hybrid system.

ARTIFICIAL INTELLIGENCE IN THE CONTEXT OF CULTUROLOGICAL DIMENSION

2020 ◽  
Vol 1 (1) ◽  
pp. 33-40
Author(s):  
D. A. Funtova ◽  
Keyword(s):  
Artificial Intelligence ◽  
Philosophy Of Mind ◽  
Subjective Experience ◽  
Modern Science ◽  
Human Mind ◽  
Scientific Data ◽  
Knowledge Based ◽  
The Individual ◽  
The Brain

High technologies have stimulated a rapidly growing knowledge-based paradigm. Therewith particular sciences seem to have separated from each other. Respectively, it brought to a certain misunderstanding about knowledge being differently directed and unreliable. Take, for instance, artificial intelligence, which is often discussed today by science and mass media. This phenomenon serves as a good example of a knowledge-based paradigm in action: it combines chemistry, computer science, engineering, linguistics, medicine, physics, philosophy and psychology. Culturology, as the broadest of the sciences, allows to comprehend artificial intelligence and opportunities it grants. Theoretically, a complete decoding of the brain cognitive processes will allow to predict the actions of the individual, to imitate and prototype him, as well as to create a model of artificial intelligence based on human intelligence. However, the modern science has not yet produced the method of such a decoding. The article considers the key differences between artificial intelligence and the human mind in accordance with relevant scientific data. The philosophy of mind and sensual subjective experience (qualia) are discussed, with the latter’s impact on culture and on individual’s life (a case study of the author’s experience of smell loss and its transformation) being analyzed. The article specifies how artificial intelligence shapes the axiological dimension of culture.

Automated knowledge-based analysis and classification of stellar spectra using fuzzy reasoning

2004 ◽  
Vol 27 (2) ◽  
pp. 237-244 ◽  
Author(s):  
Alejandra Rodrı́guez ◽  
Bernardino Arcay ◽  
Carlos Dafonte ◽  
Minia Manteiga ◽  
Iciar Carricajo
Keyword(s):  
Fuzzy Reasoning ◽  
Stellar Spectra ◽  
Knowledge Based ◽  
Automated Knowledge

scholarly journals Hybrid artificial intelligence model for prediction of heating energy use

2021 ◽  
pp. 152-152
Author(s):  
Aleksandra Sretenovic ◽  
Radisa Jovanovic ◽  
Vojislav Novakovic ◽  
Natasa Nord ◽  
Branislav Zivkovic
Keyword(s):  
Artificial Intelligence ◽  
Hybrid Model ◽  
Energy Use ◽  
Linear Part ◽  
Hybrid Approach ◽  
Non Linear ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
The Individual ◽  
Hybrid Artificial Intelligence

Currently, in the building sector there is an increase in energy use due to the increased demand for indoor thermal comfort. Proper energy planning based on a real measurement data is a necessity. In this study, we developed and evaluated hybrid artificial intelligence models for the prediction of the daily heating energy use. Building energy use is defined by significant number of influencing factors, while many of them are hard to define and quantify. For heating energy use modelling, complex relationship between the input and output variables is not strictly linear nor non-linear. The main idea of this paper was to divide the heat demand prediction problem into the linear and the non-linear part (residuals) by using different statistical methods for the prediction. The expectations were that the joint hybrid model, could outperform the individual predictors. Multiple Linear Regression (MLR) was selected for the linear modelling, while the non-linear part was predicted using Feedforward (FFNN) and Radial Basis (RBFN) neural network. The hybrid model prediction consisted of the sum of the outputs of the linear and the non-linear model. The results showed that the hybrid FFNN model and the hybrid RBFN model achieved better results than each of the individual FFNN and RBFN neural networks and MLR on the same dataset. It was shown that this hybrid approach improved the accuracy of artificial intelligence models.

Classification of Artificial Intelligence Systems

2020 ◽  
Vol 144 ◽  
pp. 58-67
Author(s):  
Vyacheslav I. Kukshev ◽  
Keyword(s):  
Artificial Intelligence ◽  
International Standards ◽  
Social Sphere ◽  
Knowledge Based ◽  
Industrial Internet ◽  
Multi Agent ◽  
Definition Of ◽  
The Military

The article considers the classification of Artificial Intelligence (AI) systems. The role of AI has increased significantly recently in all areas of life. The use of AI in public administration, in production, in medicine, in the military, in the social sphere, etc., raised a number of questions related to the definition of AI and classification of AI systems. Classification of AI is necessary to understand the role of AI in the digital economy. Classification becomes important in the context of intensive development of international standards for AI systems and knowledge-based systems (expert, neural, multi-agent, cyber-physical systems and systems based on the industrial Internet)

Phonetic iconicity in the evaluative morphology of a sample of Indo-European, Niger-Congo and Austronesian languages

2010 ◽  
Vol 3 (2) ◽  
pp. 156-180 ◽  
Author(s):  
Renáta Gregová ◽  
Lívia Körtvélyessy ◽  
Július Zimmermann
Keyword(s):  
Three Dimensional ◽  
Sound Symbolism ◽  
Three Dimensional Model ◽  
Austronesian Languages ◽  
Place Of Articulation ◽  
European Languages ◽  
Phonetic Symbolism ◽  
The Individual ◽  
Core Vocabulary

Universals Archive (Universal #1926) indicates a universal tendency for sound symbolism in reference to the expression of diminutives and augmentatives. The research ( Štekauer et al. 2009 ) carried out on European languages has not proved the tendency at all. Therefore, our research was extended to cover three language families – Indo-European, Niger-Congo and Austronesian. A three-step analysis examining different aspects of phonetic symbolism was carried out on a core vocabulary of 35 lexical items. A research sample was selected out of 60 languages. The evaluative markers were analyzed according to both phonetic classification of vowels and consonants and Ultan's and Niewenhuis' conclusions on the dominance of palatal and post-alveolar consonants in diminutive markers. Finally, the data obtained in our sample languages was evaluated by means of a three-dimensional model illustrating the place of articulation of the individual segments.

A Human/Computer Learning Network to Improve Biodiversity Conservation and Research

AI Magazine ◽  
2012 ◽  
Vol 34 (1) ◽  
pp. 10 ◽  
Author(s):  
Steve Kelling ◽  
Jeff Gerbracht ◽  
Daniel Fink ◽  
Carl Lagoze ◽  
Weng-Keen Wong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Feedback Loop ◽  
Human Computation ◽  
Computational Power ◽  
Learning Networks ◽  
Learning Network ◽  
Computer Learning ◽  
The Individual ◽  
Citizen Science Project

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both, and thereby continually improves the effectiveness of the network as a whole. In this paper we explore how Human-Computer Learning Networks can leverage the contributions of a broad recruitment of human observers and processes their contributed data with Artificial Intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.

Sign in / Sign up

Export Citation Format

Share Document