Deep learning and ensemble deep learning for circRNA-RBP interaction prediction in the last decade: A review
D Lasantha, S Vidanagamachchi… - … Applications of Artificial …, 2023 - Elsevier
Circular ribonucleic acids (circRNAs) are widely expressed in cells and tissues and play vital
roles in cellular physiological processes. Their expressions are associated with …
roles in cellular physiological processes. Their expressions are associated with …
[HTML][HTML] Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis
D Watts, RF Pulice, J Reilly, AR Brunoni… - Translational …, 2022 - nature.com
Selecting a course of treatment in psychiatry remains a trial-and-error process, and this long-
standing clinical challenge has prompted an increased focus on predictive models of …
standing clinical challenge has prompted an increased focus on predictive models of …
ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis
Sentiment analysis has been a hot research topic in natural language processing and data
mining fields in the last decade. Recently, deep neural network (DNN) models are being …
mining fields in the last decade. Recently, deep neural network (DNN) models are being …
A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets
Abstract Undoubtedly, coronavirus (COVID-19) has caused one of the biggest challenges of
all times. The ongoing COVID-19 pandemic has caused more than 150 million infected …
all times. The ongoing COVID-19 pandemic has caused more than 150 million infected …
[HTML][HTML] A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping
Landslides are highly hazardous geological disasters that can potentially threaten the safety
of human life and property. As a result, landslide susceptibility mapping (LSM) plays an …
of human life and property. As a result, landslide susceptibility mapping (LSM) plays an …
A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping
This study introduces four heterogeneous ensemble-learning techniques, that is, stacking,
blending, simple averaging, and weighted averaging, to predict landslide susceptibility in …
blending, simple averaging, and weighted averaging, to predict landslide susceptibility in …
[HTML][HTML] A deep learning ensemble for network anomaly and cyber-attack detection
V Dutta, M Choraś, M Pawlicki, R Kozik - Sensors, 2020 - mdpi.com
Currently, expert systems and applied machine learning algorithms are widely used to
automate network intrusion detection. In critical infrastructure applications of communication …
automate network intrusion detection. In critical infrastructure applications of communication …
[HTML][HTML] An evaluation of eight machine learning regression algorithms for forest aboveground biomass estimation from multiple satellite data products
Y Zhang, J Ma, S Liang, X Li, M Li - Remote sensing, 2020 - mdpi.com
This study provided a comprehensive evaluation of eight machine learning regression
algorithms for forest aboveground biomass (AGB) estimation from satellite data based on …
algorithms for forest aboveground biomass (AGB) estimation from satellite data based on …
The power of ensemble learning in sentiment analysis
J Kazmaier, JH Van Vuuren - Expert Systems with Applications, 2022 - Elsevier
An ensemble of models is a set of learning models whose individual predictions are
combined in such a way that component models compensate for each other's weaknesses …
combined in such a way that component models compensate for each other's weaknesses …
[HTML][HTML] Hyperspectral leaf reflectance as proxy for photosynthetic capacities: An ensemble approach based on multiple machine learning algorithms
Global agriculture production is challenged by increasing demands from rising population
and a changing climate, which may be alleviated through development of genetically …
and a changing climate, which may be alleviated through development of genetically …