AIMC Topic: Random Forest

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Application of machine learning algorithms in thermal images for an automatic classification of lumbar sympathetic blocks.

Journal of thermal biology
PURPOSE: There are no previous studies developing machine learning algorithms in the classification of lumbar sympathetic blocks (LSBs) performance using infrared thermography data. The objective was to assess the performance of different machine lea...

Learning Relationships Between Chemical and Physical Stability for Peptide Drug Development.

Pharmaceutical research
PURPOSE OR OBJECTIVE: Chemical and physical stabilities are two key features considered in pharmaceutical development. Chemical stability is typically reported as a combination of potency and degradation product. Moreover, fluorescent reporter Thiofl...

An interpretable machine learning approach to multimodal stress detection in a simulated office environment.

Journal of biomedical informatics
BACKGROUND AND OBJECTIVE: Work-related stress affects a large part of today's workforce and is known to have detrimental effects on physical and mental health. Continuous and unobtrusive stress detection may help prevent and reduce stress by providin...

A hybrid intelligent model for early validation of infectious diseases: An explorative study of machine learning approaches.

Microscopy research and technique
Literature reports several infectious diseases news validation approaches, but none is economically effective for collecting and classifying information on different infectious diseases. This work presents a hybrid machine-learning model that could p...

AI-Driven Validation of Digital Agriculture Models.

Sensors (Basel, Switzerland)
Digital agriculture employs artificial intelligence (AI) to transform data collected in the field into actionable crop management. Effective digital agriculture models can detect problems early, reducing costs significantly. However, ineffective mode...

The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data.

PloS one
Our aim was to predict future high-cost patients with machine learning using healthcare claims data. We applied a random forest (RF), a gradient boosting machine (GBM), an artificial neural network (ANN) and a logistic regression (LR) to predict high...

Classification Approach for Attention Assessment via Singular Spectrum Analysis Based on Single-Channel Electroencephalograms.

Sensors (Basel, Switzerland)
Attention refers to the human psychological ability to focus on doing an activity. The attention assessment plays an important role in diagnosing attention deficit hyperactivity disorder (ADHD). In this paper, the attention assessment is performed vi...

An Insight into the Machine-Learning-Based Fileless Malware Detection.

Sensors (Basel, Switzerland)
In recent years, massive development in the malware industry changed the entire landscape for malware development. Therefore, cybercriminals became more sophisticated by advancing their development techniques from file-based to fileless malware. As f...

A Synthetic Data Generation Technique for Enhancement of Prediction Accuracy of Electric Vehicles Demand.

Sensors (Basel, Switzerland)
In terms of electric vehicles (EVs), electric kickboards are crucial elements of smart transportation networks for short-distance travel that is risk-free, economical, and environmentally friendly. Forecasting the daily demand can improve the local s...

Sleep Classification With Artificial Synthetic Imaging Data Using Convolutional Neural Networks.

IEEE journal of biomedical and health informatics
OBJECTIVE: We propose a new analytic framework, "Artificial Synthetic Imaging Data (ASID) Workflow," for sleep classification from a wearable device comprising: 1) the creation of ASID from data collected by a non-invasive wearable device that permit...