scholarly journals Efficient Data Projection for Visual Analysis of Large Data Sets Using Neural Networks

Informatica ◽  
2011 ◽  
Vol 22 (4) ◽  
pp. 507-520 ◽  
Author(s):  
Viktor Medvedev ◽  
Gintautas Dzemyda ◽  
Olga Kurasova ◽  
Virginijus Marcinkevičius
Keyword(s):  
Neural Networks ◽  
Visual Analysis ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Efficient Data

scholarly journals Generation of geometric interpolations of building types with deep variational autoencoders

2020 ◽  
Vol 6 ◽  
Author(s):  
Jaime de Miguel Rodríguez ◽  
Maria Eugenia Villafañe ◽  
Luka Piškorec ◽  
Fernando Sancho Caparrini
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Large Data ◽  
Learning Model ◽  
Large Data Sets ◽  
Data Sets ◽  
Connectivity Map ◽  
Data Set ◽  
3D Objects ◽  
Machine Learning Model

Abstract This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.

scholarly journals Artificial Intelligence Explained for Nonexperts

2020 ◽  
Vol 24 (01) ◽  
pp. 003-011 ◽  
Author(s):  
Narges Razavian ◽  
Florian Knoll ◽  
Krzysztof J. Geras
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Clinical Practice ◽  
Medical Imaging ◽  
Deep Neural Networks ◽  
Large Data ◽  
Large Data Sets ◽  
Natural Images ◽  
Data Sets ◽  
Current State

AbstractArtificial intelligence (AI) has made stunning progress in the last decade, made possible largely due to the advances in training deep neural networks with large data sets. Many of these solutions, initially developed for natural images, speech, or text, are now becoming successful in medical imaging. In this article we briefly summarize in an accessible way the current state of the field of AI. Furthermore, we highlight the most promising approaches and describe the current challenges that will need to be solved to enable broad deployment of AI in clinical practice.

Modular Grammatical Evolution for the Generation of Artificial Neural Networks

2021 ◽  
pp. 1-36
Author(s):  
Khabat Soltanian ◽  
Ali Ebnenasir ◽  
Mohsen Afsharchi
Keyword(s):  
Neural Networks ◽  
State Of The Art ◽  
Solution Space ◽  
Single Layer ◽  
Large Data ◽  
Grammatical Evolution ◽  
Large Data Sets ◽  
Data Sets ◽  
Novel Method ◽  
Weak Locality

Abstract This paper presents a novel method, called Modular Grammatical Evolution (MGE), towards validating the hypothesis that restricting the solution space of NeuroEvolution to modular and simple neural networks enables the efficient generation of smaller and more structured neural networks while providing acceptable (and in some cases superior) accuracy on large data sets. MGE also enhances the state-of-the-art Grammatical Evolution (GE) methods in two directions. First, MGE's representation is modular in that each individual has a set of genes, and each gene is mapped to a neuron by grammatical rules. Second, the proposed representation mitigates two important drawbacks of GE, namely the low scalability and weak locality of representation, towards generating modular and multi-layer networks with a high number of neurons. We define and evaluate five different forms of structures with and without modularity using MGE and find single-layer modules with no coupling more productive. Our experiments demonstrate that modularity helps in finding better neural networks faster. We have validated the proposed method using ten well-known classification benchmarks with different sizes, feature counts, and output class counts. Our experimental results indicate that MGE provides superior accuracy with respect to existing NeuroEvolution methods and returns classifiers that are significantly simpler than other machine learning generated classifiers. Finally, we empirically demonstrate that MGE outperforms other GE methods in terms of locality and scalability properties.

scholarly journals Biplot Analysis of Host-by-Pathogen Data

Plant Disease ◽  
2002 ◽  
Vol 86 (12) ◽  
pp. 1396-1401 ◽  
Author(s):  
Weikai Yan ◽  
Duane E. Falk
Keyword(s):  
Visual Analysis ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Net Blotch ◽  
Host Genotype ◽  
Visual Evaluation ◽  
Biplot Analysis ◽  
Value Decomposition ◽  
Single Scatter

Effective breeding for disease resistance relies on a thorough understanding of host-by-pathogen relations. Achieving such understanding can be difficult and challenging, particularly for large data sets with complex host genotype-by-pathogen strain interactions. This paper presents a biplot approach that facilitates visual analysis of host-by-pathogen data. A biplot displays both host genotypes and pathogen isolates in a single scatter plot; each genotype or isolate is displayed as a point defined by its scores on the first two principal components derived from subjecting genotype- or strain-centered data to singular value decomposition. From a biplot, clusters of host genotypes and clusters of pathogen strains can be simultaneously visualized. Moreover, the basis for genotype and strain classifications, i.e., interactions between individual genotypes and strains, can be visualized at the same time. A biplot based on genotype-centered data and that based on strain-centered data are appropriate for visual evaluation of susceptibility/resistance of genotypes and virulence/avirulence of strains, respectively. Biplot analysis of genotype-by-strain is illustrated with published response scores of 13 barley line groups to 8 net blotch isolate groups.

Methodological support of the expert system in the problem of interaction of business ecosystems

2021 ◽  
Vol 4 ◽  
pp. 47-53
Author(s):  
K. V. Simonov ◽  
◽  
V. V. Kuimov ◽  
M. V. Kobalinsky ◽  
S. V. Kirillova ◽  
...  
Keyword(s):  
Neural Networks ◽  
Expert System ◽  
Business Models ◽  
Quantitative Description ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Effective Solution ◽  
Business Ecosystems ◽  
The Government

The paper discusses modern approaches and digital transformations in business models and interactions. In this regard for a quantitative description of interactions in ecosystems a variant of methodological support based on neural networks is proposed for fast nonlinear multiparametric regression of large data sets within the projected expert system. The possibility of effective solution of the problem of filling gaps in the observational data arrays and processing of not precisely specified information is shown. This approach is proposed for solving predictive problems in the problem of interaction of objects of interest in business ecosystems. The article was prepared within the framework of the Grant of the RFBR and the Government of the Krasnoyarsk Territory No. 20-410-242916 / 20 r_mk Krasnoyarsk.

Feed Forward Backpropagation Neural Networks and their Use in Predicting the Acute Toxicity of Chemicals to the Fathead Minnow

1997 ◽  
Vol 32 (3) ◽  
pp. 637-658 ◽  
Author(s):  
Klaus L.E. Kaiser ◽  
Stefan P. Niculescu ◽  
Gerrit Schüürmann
Keyword(s):  
Neural Networks ◽  
Fathead Minnow ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Explicit Equation ◽  
Data Set ◽  
Feed Forward ◽  
Backpropagation Neural Networks ◽  
Water Partition Coefficient

Abstract Various aspects connected to the use of feed forward backpropagation neural networks to build multivariate QSARs based on large data sets containing considerable amounts of important information are investigated. Based on such a model and a 419 compound data set, the explicit equation of one of the resulting multivariate QSARs for the computation of toxicity to the fathead minnow is presented as function of measured Microtox, logarithms of molecular weight and octanol/water partition coefficient, and 48 other functional group and discrete descriptors.

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