Mitigating the multicollinearity problem and its machine learning approach: a review
Technologies have driven big data collection across many fields, such as genomics and
business intelligence. This results in a significant increase in variables and data points …
business intelligence. This results in a significant increase in variables and data points …
[HTML][HTML] Stability of feature selection algorithm: A review
UM Khaire, R Dhanalakshmi - Journal of King Saud University-Computer …, 2022 - Elsevier
Feature selection technique is a knowledge discovery tool which provides an understanding
of the problem through the analysis of the most relevant features. Feature selection aims at …
of the problem through the analysis of the most relevant features. Feature selection aims at …
Binary grasshopper optimisation algorithm approaches for feature selection problems
Feature Selection (FS) is a challenging machine learning-related task that aims at reducing
the number of features by removing irrelevant, redundant and noisy data while maintaining …
the number of features by removing irrelevant, redundant and noisy data while maintaining …
How interpretable machine learning can benefit process understanding in the geosciences
Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering
new opportunities to improve our understanding of the complex Earth system. IML goes …
new opportunities to improve our understanding of the complex Earth system. IML goes …
[HTML][HTML] Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel
Land use maps describe the spatial distribution of natural resources, cultural landscapes,
and human settlements, serving as an important planning tool for decision makers. In the …
and human settlements, serving as an important planning tool for decision makers. In the …
Generating energy data for machine learning with recurrent generative adversarial networks
The smart grid employs computing and communication technologies to embed intelligence
into the power grid and, consequently, make the grid more efficient. Machine learning (ML) …
into the power grid and, consequently, make the grid more efficient. Machine learning (ML) …
Impact of land use and urbanization on river water quality and ecology in a dam dominated basin
Improving water quality and ecological status is an important foundation for sustainability
development. Increasing human activity and the corresponding land use change lead to the …
development. Increasing human activity and the corresponding land use change lead to the …
Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: a neuro-oncological investigation
Abstract Background The O6-methylguanine-DNA methyltransferase (MGMT) is a
deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential …
deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential …
Anomaly detection with machine learning algorithms and big data in electricity consumption
When analyzing smart metering data, both reading errors and frauds can be identified. The
purpose of this analysis is to alert the utility companies to suspicious consumption behavior …
purpose of this analysis is to alert the utility companies to suspicious consumption behavior …
Malware detection: a framework for reverse engineered android applications through machine learning algorithms
Today, Android is one of the most used operating systems in smartphone technology. This is
the main reason, Android has become the favorite target for hackers and attackers …
the main reason, Android has become the favorite target for hackers and attackers …