AIMC Topic: Machine Learning

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Towards a Resilience to Stress Index Based on Physiological Response: A Machine Learning Approach.

Sensors (Basel, Switzerland)
This study proposes a new index to measure the resilience of an individual to stress, based on the changes of specific physiological variables. These variables include electromyography, which is the muscle response, blood volume pulse, breathing rate...

Hyperparameter Optimization for Transfer Learning of VGG16 for Disease Identification in Corn Leaves Using Bayesian Optimization.

Big data
One of the world's most widely grown crops is corn. Crop loss due to diseases has a major economic effect, putting the food supply in jeopardy. In many parts of the world, lack of infrastructure still slows disease diagnosis. In this context, effecti...

Artificial intelligence in functional imaging of the lung.

The British journal of radiology
Artificial intelligence (AI) is transforming the way we perform advanced imaging. From high-resolution image reconstruction to predicting functional response from clinically acquired data, AI is promising to revolutionize clinical evaluation of lung ...

Machine learning models on chemical inhibitors of mitochondrial electron transport chain.

Journal of hazardous materials
Chemicals can induce adverse effects in humans by inhibiting mitochondrial electron transport chain (ETC) such as disrupting mitochondrial membrane potential, enhancing oxidative stress and causing some diseases. Thus, identifying ETC inhibitors (ETC...

Explanation of machine learning models using shapley additive explanation and application for real data in hospital.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: When using machine learning techniques in decision-making processes, the interpretability of the models is important. In the present paper, we adopted the Shapley additive explanation (SHAP), which is based on fair profit al...

Machine Learning Models Prognosticate Functional Outcomes Better than Clinical Scores in Spontaneous Intracerebral Haemorrhage.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVE: This study aims to develop and compare the use of deep neural networks (DNN) and support vector machines (SVM) to clinical prognostic scores for prognosticating 30-day mortality and 90-day poor functional outcome (PFO) in spontaneous intra...

RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy.

Medical physics
PURPOSE: Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate the accuracy and time-savings resulting from the use of an inte...

Synergy and Complementarity between Focused Machine Learning and Physics-Based Simulation in Affinity Prediction.

Journal of chemical information and modeling
We present results on the extent to which physics-based simulation (exemplified by FEP) and focused machine learning (exemplified by QuanSA) are complementary for ligand affinity prediction. For both methods, predictions of activity for LFA-1 inhibit...

Combining an Integrated Sensor Array with Machine Learning for the Simultaneous Quantification of Multiple Cations in Aqueous Mixtures.

Analytical chemistry
The direct quantification of multiple ions in aqueous mixtures is achieved by combining an automated machine learning pipeline with transient potentiometric data obtained from a single miniaturized array of polymeric sensors electrodeposited on a con...

Phishing Website Detection Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning.

Sensors (Basel, Switzerland)
Phishing has become one of the biggest and most effective cyber threats, causing hundreds of millions of dollars in losses and millions of data breaches every year. Currently, anti-phishing techniques require experts to extract phishing sites feature...