Open AccessA NONLINEAR MODEL FOR A SMART SEMANTIC BROWSER BOT FOR A TEXT ATTRIBUTE RECOGNITION
Author(s) -
Ricardo Carreño Aguilera,
Marco Antonio Acevedo Mosqueda,
Sandra L. Gomez-Coronel,
Ignacio AlgredoBadillo,
Daniel Pacheco Bautista,
Miguel Patiño Ortíz,
Julián Patiño Ortíz,
Miguel Ángel Martínez Cruz
Publication year - 2020
Publication title -
fractals
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 44
eISSN - 1793-6543
pISSN - 0218-348X
DOI - 10.1142/s0218348x20500450
Subject(s) - computer science , scalability , deep learning , artificial intelligence , process (computing) , cryptocurrency , artificial neural network , machine learning , data science , natural language processing , world wide web , database , operating system
In spite of the advances in the state of the art in semantic artificial intelligence applications, there is still a long way to go to bring it to a level of mass adoption. Thus, in order to contribute to the advancement of this topic, this study develops a feasible model with a potential scalability for semantic applications’ mass adoption, specifically for news or statement cluster attribute identification, either positive, negative or neutral. This paper proposes a disruptive system based on Blockchain using a Semantic Browser Expert System Bot with artificial intelligence called Blockchain Semantic Browser Expert System (BSBES) to look for and analyze relevant information that significantly represents the cryptocurrencies adoption patterns. The artificial intelligence in this study consists of a deep learning neural network to process the input information to identify the news pattern in a semantic way using deep learning based on two aspects of the news: technical aspect and adoption aspect of the cryptocurrencies. BSBES performance is achieved based on deep learning tools, and scalability is supported by a blockchain system including a stability study.