AIMC Topic: Random Forest

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Application of Machine Learning Methods for an Analysis of E-Nose Multidimensional Signals in Wastewater Treatment.

Sensors (Basel, Switzerland)
The work represents a successful attempt to combine a gas sensors array with instrumentation (hardware), and machine learning methods as the basis for creating numerical codes (software), together constituting an electronic nose, to correct the class...

Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data.

Sensors (Basel, Switzerland)
This paper explores the feasibility of using low-resolution infrared (LRIR) image streams for human activity recognition (HAR) with potential application in e-healthcare. Two datasets based on synchronized multichannel LRIR sensors systems are consid...

Application of machine learning techniques for predicting survival in ovarian cancer.

BMC medical informatics and decision making
BACKGROUND: Ovarian cancer is the fifth leading cause of mortality among women in the United States. Ovarian cancer is also known as forgotten cancer or silent disease. The survival of ovarian cancer patients depends on several factors, including the...

Statistical approaches to identifying significant differences in predictive performance between machine learning and classical statistical models for survival data.

PloS one
Research that seeks to compare two predictive models requires a thorough statistical approach to draw valid inferences about comparisons between the performance of the two models. Researchers present estimates of model performance with little evidenc...

Prediction of coronary heart disease in gout patients using machine learning models.

Mathematical biosciences and engineering : MBE
Growing evidence shows that there is an increased risk of cardiovascular diseases among gout patients, especially coronary heart disease (CHD). Screening for CHD in gout patients based on simple clinical factors is still challenging. Here we aim to b...

A New Approach to Optimize SVM for Insulator State Identification Based on Improved PSO Algorithm.

Sensors (Basel, Switzerland)
The failure of insulators may seriously threaten the safe operation of the power system, where the state detection of high-voltage insulators is a must for the normal and safe operation of the power system. Based on the data of insulators in aerial i...

Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach.

Genes
Genetic disorders are the result of mutation in the deoxyribonucleic acid (DNA) sequence which can be developed or inherited from parents. Such mutations may lead to fatal diseases such as Alzheimer's, cancer, Hemochromatosis, etc. Recently, the use ...

Data-Driven Low-Frequency Oscillation Event Detection Strategy for Railway Electrification Networks.

Sensors (Basel, Switzerland)
Low-frequency oscillations (LFO) occur in railway electrification systems due to the incorporation of new trains with switching converters. As a result, the increased harmonic content can cause catenary stability problems under certain conditions. Mo...

Emotion Detection Using Deep Normalized Attention-Based Neural Network and Modified-Random Forest.

Sensors (Basel, Switzerland)
In the contemporary world, emotion detection of humans is procuring huge scope in extensive dimensions such as bio-metric security, HCI (human-computer interaction), etc. Such emotions could be detected from various means, such as information integra...

Integrating transformer and autoencoder techniques with spectral graph algorithms for the prediction of scarcely labeled molecular data.

Computers in biology and medicine
In molecular and biological sciences, experiments are expensive, time-consuming, and often subject to ethical constraints. Consequently, one often faces the challenging task of predicting desirable properties from small data sets or scarcely-labeled ...