Recent advances in decision trees: An updated survey
Abstract Decision Trees (DTs) are predictive models in supervised learning, known not only
for their unquestionable utility in a wide range of applications but also for their interpretability …
for their unquestionable utility in a wide range of applications but also for their interpretability …
[HTML][HTML] A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery
Lithium-ion batteries are used in a wide range of applications including energy storage
systems, electric transportations, and portable electronic devices. Accurately obtaining the …
systems, electric transportations, and portable electronic devices. Accurately obtaining the …
Selecting critical features for data classification based on machine learning methods
Feature selection becomes prominent, especially in the data sets with many variables and
features. It will eliminate unimportant variables and improve the accuracy as well as the …
features. It will eliminate unimportant variables and improve the accuracy as well as the …
A survey on text classification algorithms: From text to predictions
In recent years, the exponential growth of digital documents has been met by rapid progress
in text classification techniques. Newly proposed machine learning algorithms leverage the …
in text classification techniques. Newly proposed machine learning algorithms leverage the …
Neural decoding of EEG signals with machine learning: a systematic review
Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
Overview and comparative study of dimensionality reduction techniques for high dimensional data
The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …
capabilities are leading towards huge volume of data. The dimensions of data indicate the …
Brain tumor detection and classification using deep learning and sine-cosine fitness grey wolf optimization
Diagnosing a brain tumor takes a long time and relies heavily on the radiologist's abilities
and experience. The amount of data that must be handled has increased dramatically as the …
and experience. The amount of data that must be handled has increased dramatically as the …
Recent progresses in machine learning assisted Raman spectroscopy
With the development of Raman spectroscopy and the expansion of its application domains,
conventional methods for spectral data analysis have manifested many limitations. Exploring …
conventional methods for spectral data analysis have manifested many limitations. Exploring …
A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models
Pneumonia is one of the diseases that people may encounter in any period of their lives.
Approximately 18% of infectious diseases are caused by pneumonia. This disease may …
Approximately 18% of infectious diseases are caused by pneumonia. This disease may …
A comprehensive analysis of dermoscopy images for melanoma detection via deep CNN features
Melanoma is the fastest growing and most lethal cancer among all forms of skin cancer.
Deep learning methods, mainly convolutional neural networks (CNNs) have recently …
Deep learning methods, mainly convolutional neural networks (CNNs) have recently …