Theoretical Foundations of Deep Resonance Interference Network

2020 ◽  
pp. 340-362
Author(s):  
Christophe Thovex
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Reverse Engineer ◽  
Finite Space ◽  
Systemic Nature ◽  
Hidden Data ◽  
Probabilistic Machine Learning ◽  
Digital Processes ◽  
Theoretical Foundations

Digital processes for banks, insurances, or public services generate big data. Hidden networks and weak signals from frauds activities are sometimes statistically undetectable in the endogenous data respective to processes. The organic intelligence of human experts is able to reverse-engineer new fraud scenarios without statistically significant characteristics, but machine learning usually needs to be taught about them or fails to this task. Deep resonance interference network is a multidisciplinary attempt in probabilistic machine learning inspired from waves temporal reversal in finite space, introduced for big data analysis and hidden data mining. It proposes a theoretical alternative to artificial neural networks for deep learning. It is presented along with experimental outcomes related to fraudulent processes generating data statistically similar to legal endogenous data. Results show particular findings probably due to the systemic nature of the model, which appears closer to reasoning and intuition processes than to the perception processes mainly simulated in deep learning.

Deep Learning Applications in Agriculture

2021 ◽  
pp. 325-345
Author(s):  
Hari Kishan Kondaveeti ◽  
Gonugunta Priyatham Brahma ◽  
Dandhibhotla Vijaya Sahithi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Rapid Growth ◽  
Agricultural Sector ◽  
The Other ◽  
Learning Methods ◽  
Agriculture Sector ◽  
Learning Techniques ◽  
Recent Trends

Deep learning (DL), a part of machine learning (ML), comprises a contemporary technique for processing the images and analyzing the big data with promising outcomes. Deep learning methods are successfully being used in various sectors to gain better results. Agriculture sector is one of the sectors that could be benefitted from the deep learning techniques since the current agriculture techniques cannot keep up with the rapid growth in population. In this chapter, the recent trends in the applications of deep learning techniques in the agricultural sector and the survey of the research efforts that employ deep learning techniques are going to be discussed. Also, the models that are implemented are going to be analyzed and compared with the other existing models.

scholarly journals Inteligência Artificial e os desafios éticos

2021 ◽  
Vol 5 (9) ◽  
Author(s):  
Dora Kaufman
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Luciano Floridi

Prolifera na sociedade o uso de tec- nologias de inteligência artificial (IA). A maior parte das implementações atuais de IA é baseada na técnica de aprendizado de máquina (machine learning), subárea da IA, denomina- da de redes neurais de aprendizado profundo (Deep Learning Neural Networks – DLNNs) cujos algoritmos “aprendem” a partir de exemplos extraídos do big data. Nesse processo de automação de decisões, intensifica-se o debate da IA ética, concentrado em princípios gerais de aplicabilidade restrita, não traduzíveis em boas práticas para nortear o ecossistema de IA. Ademais, alguns desses princípios, como justiça e dignidade, não são universais e desconhece-se como decodificá-los em termos matemáticos. O artigo pondera sobre algumas soluções para mitigar as externalidades negativas sugeridas por Luciano Floridi, Mark Coeckelbergh e Cetric Villani.

scholarly journals State of the Art in Computational Bioacoustics and Machine Learning: How far have we come?

2019 ◽  
Vol 3 ◽  
Author(s):  
Dan Stowell
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
State Of The Art ◽  
Learning Networks ◽  
Recent Developments ◽  
Audio Data ◽  
Ecological Applications ◽  
Near Future ◽  
Statistical Ecology

Terrestrial bioacoustics, like many other domains, has recently witnessed some transformative results from the application of deep learning and big data (Stowell 2017, Mac Aodha et al. 2018, Fairbrass et al. 2018, Mercado III and Sturdy 2017). Generalising over specific projects, which bioacoustic tasks can we consider "solved"? What can we expect in the near future, and what remains hard to do? What does a bioacoustician need to understand about deep learning? This contribution will address these questions, giving the audience a concise summary of recent developments and ways forward. It builds on recent projects and evaluation campaigns led by the author (Stowell et al. 2015, Stowell et al. 2018), as well as broader developments in signal processing, machine learning and bioacoustic applications of these. We will discuss which type of deep learning networks are appropriate for audio data, how to address zoological/ecological applications which often have few available data, and issues in integrating deep learning predictions with existing workflows in statistical ecology.

scholarly journals Testando a Methodology Multicriteria Decision Aid – Constructivist (MCDA-C) na construção de algoritmos de apoio à estabilidade das decisões judiciais

2019 ◽  
Vol 15 (2) ◽  
pp. 281
Author(s):  
Alexandre José Mendes ◽  
Alexandre Morais da Rosa ◽  
Izaias Otacílio da Rosa
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Decision Aid ◽  
Multicriteria Decision Aid ◽  
Multicriteria Decision

O presente trabalho expõe e examina o resultado parcial dos testes com a Methodology Multicriteria Decision Aid – Constructivist ou MCDA-C, estes realizados no âmbito do PPDG da PUC-PR, em parceria com o PPGD da UNIVALI e a empresa Neoway Informática Ltda. Na perspectiva de utilização da MDCA-C enquanto base lógico-algorítmica aplicada às decisões judiciais, a hipótese central é de que esta é capaz de transcender limites e desafios metodológico-jurídico-algorítmicos. Para tanto, busca-se testar a capacidade da metodologia MCDA-C de incorporar as subjetividades do decisor, in casu, o magistrado, ao tempo em que mantém a coerência e integridade ao replicar decisões judiciais, distanciando-se do trivial ao trazer abordagem multidisciplinar. Sob método procedimental indutivo e método interventivo da MDCA-C, o presente artigo utiliza-se de técnicas de big data, machine learning e deep learning para propor uma calibragem de sistema realizado pelo próprio magistrado. Nesse contexto, a utilização da MDCA-C às decisões judicias busca alcançar um produto final de tamanha precisão a ponto de não ser possível a distinção entre o decisium humano e o realizado pela máquina. A relevância da temática evidencia-se ante a repercussão nacional e internacional de tal aplicação, bem como ante possibilidade de revolucionar o método e atuação do Poder Judiciário brasileiro.

scholarly journals Analyzing Large Microbiome Datasets Using Machine Learning and Big Data

2021 ◽  
Vol 1 (3) ◽  
pp. 138-165
Author(s):  
Thomas Krause ◽  
Jyotsna Talreja Wassan ◽  
Paul Mc Kevitt ◽  
Haiying Wang ◽  
Huiru Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Metagenomic Data ◽  
Data Sets ◽  
Raw Data ◽  
Public And Private ◽  
Rumen Microbiome

Metagenomics promises to provide new valuable insights into the role of microbiomes in eukaryotic hosts such as humans. Due to the decreasing costs for sequencing, public and private repositories for human metagenomic datasets are growing fast. Metagenomic datasets can contain terabytes of raw data, which is a challenge for data processing but also an opportunity for advanced machine learning methods like deep learning that require large datasets. However, in contrast to classical machine learning algorithms, the use of deep learning in metagenomics is still an exception. Regardless of the algorithms used, they are usually not applied to raw data but require several preprocessing steps. Performing this preprocessing and the actual analysis in an automated, reproducible, and scalable way is another challenge. This and other challenges can be addressed by adjusting known big data methods and architectures to the needs of microbiome analysis and DNA sequence processing. A conceptual architecture for the use of machine learning and big data on metagenomic data sets was recently presented and initially validated to analyze the rumen microbiome. The same architecture can be used for clinical purposes as is discussed in this paper.

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