Multilayer perceptron (MLP) and support vector machine (SVM), two popular learning
machines, are increasingly being used as alternatives to classical statistical models for …

Temporal attention mechanism has been applied to get state-of-the-art results in neural
machine translation. LSTMs can capture the long-term temporal dependencies in a …

Forecasting air-pollutant levels is an important issue, due to their adverse effects on public
health, and often a legislative necessity. The advantage of Bayesian methods is their ability …

In Data mining and Knowledge Discovery hidden and valuable knowledge from the data
sources is discovered. The traditional algorithms used for knowledge discovery are bottle …

In many practical engineering applications, data tend to be collected in online sequential
way with imbalanced class. Many traditional machine learning methods such as support …

This paper proposes a Genetic Algorithm for jointly performing a feature selection and
granularity learning for Fuzzy Rule-Based Classification Systems in the scenario of highly …

Imbalanced datasets typically make prediction accuracy difficult. Most of the real-world data
are imbalanced in nature. The traditional classifiers assume a well-balanced class …

Data distribution has an important role in classification. The problem of imbalanced data has
occurred when the distribution of one class, which usually attends more interest, is …

Previous research studies have revealed human susceptibility to tropospheric ozone and
consequently huge amount of investments allocating to monitor and research about this …

Insolvency is a crucial problem for several insurance companies that suffer from it. This
problem has direct or indirect effects on both the people working in the financial business …