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Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review
Recent crises, recessions and bubbles have stressed the non-stationary nature and the presence of drastic structural changes in the financial domain. The most recent literature suggests the use of conventional machine ...
Árvore de predição semi-supervisionada para predição de localização subcelular de proteínas
(Universidade Federal de São CarlosUFSCarCâmpus São CarlosEngenharia de Computação - EC, 2021-11-19)
Protein subcellular localization is a really important classification task, because the location of proteins inside a cell is directly related to these protein’s functions. As there are a lot of proteins that reside at the ...
Using metrics from complex networks to evaluate machine translation
(ELSEVIER SCIENCE BV, 2011)
Establishing metrics to assess machine translation (MT) systems automatically is now crucial owing to the widespread use of MT over the web. In this study we show that such evaluation can be done by modeling text as complex ...
Energy-Based Dropout in Restricted Boltzmann Machines: Why Not Go Random
(2020-01-01)
Deep learning architectures have been widely fostered throughout the last years, being used in a wide range of applications, such as object recognition, image reconstruction, and signal processing. Nevertheless, such models ...
Model selection for discriminative restricted boltzmann machines through meta-heuristic techniques
(2015)
Discriminative learning of Restricted Boltzmann Machines has been recently introduced as an alternative to provide a self-contained approach for both unsupervised feature learning and classification purposes. However, one ...
A Fully Unsupervised Machine Learning Framework for Algal Bloom Forecasting in Inland Waters Using MODIS Time Series and Climatic Products
(Mdpi, 2022-09-01)
Progressively monitoring water quality is crucial, as aquatic contaminants can pose risks to human health and other organisms. Machine learning can support the development of new effective tools for water monitoring, ...
A machine learning approach to dengue forecasting: comparing LSTM, Random Forest and Lasso
(2018-04-12)
We used the Infodengue database of incidence and weather time-series, to train predictive models for the weekly number of cases of dengue in 790 cities of Brazil. To overcome a limitation in the length of time-series ...
Internet of Things: A survey on machine learning-based intrusion detection approaches
(Elsevier B.V., 2019-03-14)
In the world scenario, concerns with security and privacy regarding computer networks are always increasing. Computer security has become a necessity due to the proliferation of information technologies in everyday life. ...
Workforce Optimization for Bank Operation Centers: A Machine Learning Approach
Online Banking Systems evolved and improved in recent years with the use of mobile and online technologies, performing money transfer transactions on these channels can be done without delay and human interaction, however ...