Artificial Neural Networks Approach in Determining Factors of Death Caused by Coronavirus in the World with Unbalanced Panel Data Models

2021 ◽  
pp. 139-158
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Determining Factors ◽  
The World ◽  
Artificial Neural ◽  
Unbalanced Panel Data

Knowledge Discovery with Artificial Neural Networks

2011 ◽  
pp. 669-673
Author(s):  
Juan R. Rabuñal Dopico ◽  
Daniel Rivero Cebrian ◽  
Julián Dorado de la Calle ◽  
Nieves Pedreira Souto
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Knowledge Discovery ◽  
Powerful Technique ◽  
Complex Problems ◽  
New Information ◽  
Wide Range ◽  
The World ◽  
Artificial Neural

The world of Data Mining (Cios, Pedrycz & Swiniarrski, 1998) is in constant expansion. New information is obtained from databases thanks to a wide range of techniques, which are all applicable to a determined set of domains and count with a series of advantages and inconveniences. The Artificial Neural Networks (ANNs) technique (Haykin, 1999; McCulloch & Pitts, 1943; Orchad, 1993) allows us to resolve complex problems in many disciplines (classification, clustering, regression, etc.), and presents a series of advantages that convert it into a very powerful technique that is easily adapted to any environment. The main inconvenience of ANNs, however, is that they can not explain what they learn and what reasoning was followed to obtain the outputs. This implies that they can not be used in many environments in which this reasoning is essential.

scholarly journals A study and identification of COVID-19 viruses using N-grams with Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine

2020 ◽  
Author(s):  
Mohamed El Boujnouni
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Naive Bayes ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The World ◽  
Artificial Neural

Abstract Coronavirus disease 2019 or COVID-19 is a global health crisis caused by a virus officially named as severe acute respiratory syndrome coronavirus 2 and well known with the acronym (SARS-CoV-2). This very contagious illness has severely impacted people and business all over the world and scientists are trying so far to discover all useful information about it, including its potential origin(s) and inter-host(s). This study is a part of this scientific inquiry and it aims to identify precisely the origin(s) of a large set of genomes of SARS-COV-2 collected from different geographic locations in all over the world. This research is performed through the combination of five powerful techniques of machine learning (Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine) and a widely known tool of language modeling (N-grams). The experimental results have shown that the majority of techniques gave the same global results concerning the origin(s) and inter-host(s) of SARS-COV-2. These results demonstrated that this virus has one zoonotic source which is Pangolin.

scholarly journals A Developmental Model for the Evolution of Artificial Neural Networks

2000 ◽  
Vol 6 (3) ◽  
pp. 189-218 ◽  
Author(s):  
J. C. Astor ◽  
C. Adami
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Information ◽  
Developmental Model ◽  
Complex Structures ◽  
Artificial Neuron ◽  
Artificial Chemistry ◽  
The World ◽  
Artificial Neural ◽  
Distributed Genetic Algorithm

We present a model of decentralized growth and development for artificial neural networks (ANNs), inspired by developmental biology and the physiology of nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentrations of substrates. The chemicals and substrates, in turn, are modeled by a simple artificial chemistry. While the system is designed to allow for the evolution of complex networks, we demonstrate the power of the artificial chemistry by analyzing engineered (handwritten) genomes that lead to the growth of simple networks with behaviors known from physiology. To evolve more complex structures, a Java-based, platform-independent, asynchronous, distributed genetic algorithm (GA) has been implemented that allows users to participate in evolutionary experiments via the World Wide Web.

scholarly journals LITHOLOGICAL INTERPRETATION ACCORDING TO WELL-LOGGING DATA FOR THE BAZHENOV FORMATION WITH USAGE OF ARTIFICIAL NEURAL NETWORKS

2021 ◽  
Vol 2 (3) ◽  
pp. 3-9
Author(s):  
Elena U. Temnikova ◽  
Serafim I. Grubas ◽  
Arsenii A. Fedoseev
Keyword(s):  
Neural Networks ◽  
Spatial Distribution ◽  
Artificial Neural Networks ◽  
Well Logging ◽  
Data Models ◽  
Relative Content ◽  
Material Composition ◽  
Bazhenov Formation ◽  
Modern Classification ◽  
Artificial Neural

Using artificial neural networks for lithological interpretation according to well logging data, models of the relative content of rock-forming components of the Bazhenov Formation were constructed and its main types of rocks were identified in accordance with a modern classification. Results of lithological interpretation were used for building correlation schemes, which made it possible to trace the spatial distribution of the material composition and main types of rocks of the Bazhenov Formation for the Salym field.

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