AIMC Topic: Machine Learning

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Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar.

PloS one
Using solid waste in building materials is an efficient approach to achieving sustainability goals. Also, the application of modern methods like artificial intelligence is gaining attention. In this regard, the flexural strength (FS) of cementitious ...

Evaluations on supervised learning methods in the calibration of seven-hole pressure probes.

PloS one
Machine learning method has become a popular, convenient and efficient computing tool applied to many industries at present. Multi-hole pressure probe is an important technique widely used in flow vector measurement. It is a new attempt to integrate ...

Predict Ionization Energy of Molecules Using Conventional and Graph-Based Machine Learning Models.

Journal of chemical information and modeling
Ionization energy (IE) is an important property of molecules. It is highly desirable to predict IE efficiently based on, for example, machine learning (ML)-powered quantitative structure-property relationships (QSPR). In this study, we systematically...

Towards a Safe Human-Robot Collaboration Using Information on Human Worker Activity.

Sensors (Basel, Switzerland)
Most industrial workplaces involving robots and other apparatus operate behind the fences to remove defects, hazards, or casualties. Recent advancements in machine learning can enable robots to co-operate with human co-workers while retaining safety,...

DeepTP: A Deep Learning Model for Thermophilic Protein Prediction.

International journal of molecular sciences
Thermophilic proteins have important value in the fields of biopharmaceuticals and enzyme engineering. Most existing thermophilic protein prediction models are based on traditional machine learning algorithms and do not fully utilize protein sequence...

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG.

Sensors (Basel, Switzerland)
Emotion artificial intelligence (AI) is being increasingly adopted in several industries such as healthcare and education. Facial expressions and tone of speech have been previously considered for emotion recognition, yet they have the drawback of be...

Enhancing System Performance through Objective Feature Scoring of Multiple Persons' Breathing Using Non-Contact RF Approach.

Sensors (Basel, Switzerland)
Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring...

Dynamic predictions of postoperative complications from explainable, uncertainty-aware, and multi-task deep neural networks.

Scientific reports
Accurate prediction of postoperative complications can inform shared decisions regarding prognosis, preoperative risk-reduction, and postoperative resource use. We hypothesized that multi-task deep learning models would outperform conventional machin...

Machine learning-based approach reveals essential features for simplified TSPO PET quantification in ischemic stroke patients.

Zeitschrift fur medizinische Physik
INTRODUCTION: Neuroinflammation evaluation after acute ischemic stroke is a promising option for selecting an appropriate post-stroke treatment strategy. To assess neuroinflammation in vivo, translocator protein PET (TSPO PET) can be used. However, t...

Predicting dropout from psychological treatment using different machine learning algorithms, resampling methods, and sample sizes.

Psychotherapy research : journal of the Society for Psychotherapy Research
The occurrence of dropout from psychological interventions is associated with poor treatment outcome and high health, societal and economic costs. Recently, machine learning (ML) algorithms have been tested in psychotherapy outcome research. Dropout...