AIMC Topic: Random Forest

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Process identification and discrimination in the environmental dose rate time series of a radiopharmaceutical facility using machine learning techniques.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Multi-facility nuclear sites with research reactors have several environmental area gamma monitors in a network as a part of their surveillance capability. However, the routine release of low levels of Ar gas from the reactor is prone to interfere wi...

Prospective Validation of Machine Learning Algorithms for Absorption, Distribution, Metabolism, and Excretion Prediction: An Industrial Perspective.

Journal of chemical information and modeling
Absorption, distribution, metabolism, and excretion (ADME), which collectively define the concentration profile of a drug at the site of action, are of critical importance to the success of a drug candidate. Recent advances in machine learning algori...

Using Natural Language Processing to Predict Fatal Drug Overdose From Autopsy Narrative Text: Algorithm Development and Validation Study.

JMIR public health and surveillance
BACKGROUND: Fatal drug overdose surveillance informs prevention but is often delayed because of autopsy report processing and death certificate coding. Autopsy reports contain narrative text describing scene evidence and medical history (similar to p...

Using a stacked ensemble learning framework to predict modulators of protein-protein interactions.

Computers in biology and medicine
Identifying small molecule protein-protein interaction modulators (PPIMs) is a highly promising and meaningful research direction for drug discovery, cancer treatment, and other fields. In this study, we developed a stacking ensemble computational fr...

A machine learning model for orthodontic extraction/non-extraction decision in a racially and ethnically diverse patient population.

International orthodontics
INTRODUCTION: The purpose of the present study was to create a machine learning (ML) algorithm with the ability to predict the extraction/non-extraction decision in a racially and ethnically diverse sample.

Henry gas solubility optimization double machine learning classifier for neurosurgical patients.

PloS one
This study aims to predict head trauma outcome for Neurosurgical patients in children, adults, and elderly people. As Machine Learning (ML) algorithms are helpful in healthcare field, a comparative study of various ML techniques is developed. Several...

Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery.

Environmental science and pollution research international
Despite the substantial impact of rivers on the global marine litter problem, riverine litter has been accorded inadequate consideration. Therefore, our objective was to detect riverine litter by utilizing middle-scale multispectral satellite images ...

Improved accuracy and less fault prediction errors via modified sequential minimal optimization algorithm.

PloS one
The benefits and opportunities offered by cloud computing are among the fastest-growing technologies in the computer industry. Additionally, it addresses the difficulties and issues that make more users more likely to accept and use the technology. T...

Automatic extraction of ranked SNP-phenotype associations from text using a BERT-LSTM-based method.

BMC bioinformatics
Extraction of associations of singular nucleotide polymorphism (SNP) and phenotypes from biomedical literature is a vital task in BioNLP. Recently, some methods have been developed to extract mutation-diseases affiliations. However, no accessible met...

Comparing machine learning methods for predicting land development intensity.

PloS one
Land development intensity is a comprehensive indicator to measure the degree of saving and intensive land construction and economic production activities. It is also the result of the joint action of natural, social, economic, and ecological element...