Role of machine learning in medical research: A survey
Abstract Machine learning is one of the essential and effective tools in analyzing highly
complex medical data. With vast amounts of medical data being generated, there is an …
complex medical data. With vast amounts of medical data being generated, there is an …
[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …
belonging to one class is lower than the other. Ensemble learning combines multiple models …
A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities
In modern society, air pollution is an important topic as this pollution exerts a critically bad
influence on human health and the environment. Among air pollutants, Particulate Matter …
influence on human health and the environment. Among air pollutants, Particulate Matter …
A survey on the new generation of deep learning in image processing
L Jiao, J Zhao - Ieee Access, 2019 - ieeexplore.ieee.org
During the past decade, deep learning is one of the essential breakthroughs made in
artificial intelligence. In particular, it has achieved great success in image processing …
artificial intelligence. In particular, it has achieved great success in image processing …
An electricity price forecasting model by hybrid structured deep neural networks
Electricity price is a key influencer in the electricity market. Electricity market trades by each
participant are based on electricity price. The electricity price adjusted with the change in …
participant are based on electricity price. The electricity price adjusted with the change in …
Reviewing ensemble classification methods in breast cancer
Context Ensemble methods consist of combining more than one single technique to solve
the same task. This approach was designed to overcome the weaknesses of single …
the same task. This approach was designed to overcome the weaknesses of single …
[HTML][HTML] Performance analysis of cost-sensitive learning methods with application to imbalanced medical data
Many real-world machine learning applications require building models using highly
imbalanced datasets. Usually, in medical datasets, the healthy patients or samples are …
imbalanced datasets. Usually, in medical datasets, the healthy patients or samples are …
Antlion re-sampling based deep neural network model for classification of imbalanced multimodal stroke dataset
Stroke is enlisted as one of the leading causes of death and serious disability affecting
millions of human lives across the world with high possibilities of becoming an epidemic in …
millions of human lives across the world with high possibilities of becoming an epidemic in …
Predicting and analyzing injury severity: A machine learning-based approach using class-imbalanced proactive and reactive data
Although the utility of the machine learning (ML) techniques is established in occupational
accident domain using reactive data, its exploration in predicting injury severity using both …
accident domain using reactive data, its exploration in predicting injury severity using both …
[HTML][HTML] Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data
Abstract Machine learning (ML) is increasingly used in cognitive, computational and clinical
neuroscience. The reliable and efficient application of ML requires a sound understanding of …
neuroscience. The reliable and efficient application of ML requires a sound understanding of …