AIMC Topic: Machine Learning

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A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method.

Sensors (Basel, Switzerland)
Due to the rapid growth in IT technology, digital data have increased availability, creating novel security threats that need immediate attention. An intrusion detection system (IDS) is the most promising solution for preventing malicious intrusions ...

A Conditional GAN for Generating Time Series Data for Stress Detection in Wearable Physiological Sensor Data.

Sensors (Basel, Switzerland)
Human-centered applications using wearable sensors in combination with machine learning have received a great deal of attention in the last couple of years. At the same time, wearable sensors have also evolved and are now able to accurately measure p...

Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms.

Frontiers in public health
BACKGROUND: In this paper, we examine whether machine learning and deep learning can be used to predict difficult airway intubation in patients undergoing thyroid surgery.

Machine-learning media bias.

PloS one
We present an automated method for measuring media bias. Inferring which newspaper published a given article, based only on the frequencies with which it uses different phrases, leads to a conditional probability distribution whose analysis lets us a...

Machine learning for stone artifact identification: Distinguishing worked stone artifacts from natural clasts using deep neural networks.

PloS one
Stone artifacts are often the most abundant class of objects found in archaeological sites but their consistent identification is limited by the number of experienced analysts available. We report a machine learning based technology for stone artifac...

Exploring the potential of in silico machine learning tools for the prediction of acute Daphnia magna nanotoxicity.

Chemosphere
Engineered nanomaterials (ENMs) are ubiquitous nowadays, finding their application in different fields of technology and various consumer products. Virtually any chemical can be manipulated at the nano-scale to display unique characteristics which ma...

Review and comparison of treatment effect estimators using propensity and prognostic scores.

The international journal of biostatistics
In finding effects of a binary treatment, practitioners use mostly either propensity score matching (PSM) or inverse probability weighting (IPW). However, many new treatment effect estimators are available now using propensity score and "prognostic s...

A Machine Learning-Based Intrauterine Growth Restriction (IUGR) Prediction Model for Newborns.

Indian journal of pediatrics
Intrauterine growth restriction (IUGR) is a condition in which the fetal weight is below the 10th percentile for its gestational age. Prenatal exposure to metals can cause a decrease in fetal growth during gestation thereby reducing birth weight. The...

Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study.

Journal of medical Internet research
BACKGROUND: Heart failure (HF) is a common disease and a major public health problem. HF mortality prediction is critical for developing individualized prevention and treatment plans. However, due to their lack of interpretability, most HF mortality ...

Intelligent nanoscope for rapid nanomaterial identification and classification.

Lab on a chip
Machine learning image recognition and classification of particles and materials is a rapidly expanding field. However, nanomaterial identification and classification are dependent on the image resolution, the image field of view, and the processing ...