AIMC Topic: Machine Learning

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Statistical learning of transition patterns in the songbird auditory forebrain.

Scientific reports
Statistical learning of transition patterns between sounds-a striking capability of the auditory system-plays an essential role in animals' survival (e.g., detect deviant sounds that signal danger). However, the neural mechanisms underlying this capa...

An integrated machine learning model for aircraft components rare failure prognostics with log-based dataset.

ISA transactions
Predictive maintenance is increasingly advancing into the aerospace industry, and it comes with diverse prognostic health management solutions. This type of maintenance can unlock several benefits for aerospace organizations. Such as preventing unexp...

Machine learning in GI endoscopy: practical guidance in how to interpret a novel field.

Gut
There has been a vast increase in GI literature focused on the use of machine learning in endoscopy. The relative novelty of this field poses a challenge for reviewers and readers of GI journals. To appreciate scientific quality and novelty of machin...

Improving Docking-Based Virtual Screening Ability by Integrating Multiple Energy Auxiliary Terms from Molecular Docking Scoring.

Journal of chemical information and modeling
Virtual Screening (VS) based on molecular docking is an efficient method used for retrieving novel hit compounds in drug discovery. However, the accuracy of the current docking scoring function (SF) is usually insufficient. In this study, in order to...

A machine learning approach to estimation of phase diagrams for three-component lipid mixtures.

Biochimica et biophysica acta. Biomembranes
The plasma membrane of eukaryotic cells is commonly believed to contain ordered lipid domains. The interest in understanding the origin of such domains has led to extensive studies on the phase behavior of mixed lipid systems. Three-component phase d...

Sparsity through evolutionary pruning prevents neuronal networks from overfitting.

Neural networks : the official journal of the International Neural Network Society
Modern Machine learning techniques take advantage of the exponentially rising calculation power in new generation processor units. Thus, the number of parameters which are trained to solve complex tasks was highly increased over the last decades. How...

Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography.

Osteoarthritis and cartilage
OBJECTIVE: To develop and validate a machine learning (ML) approach for automatic three-dimensional (3D) histopathological grading of osteochondral samples imaged with contrast-enhanced micro-computed tomography (CEμCT).

Alveolar Bone Segmentation in Intraoral Ultrasonographs with Machine Learning.

Journal of dental research
The use of intraoral ultrasound imaging has received great attention recently due to the benefits of being a portable and low-cost imaging solution for initial and continuing care that is noninvasive and free of ionizing radiation. Alveolar bone is a...

Detecting lane change maneuvers using SHRP2 naturalistic driving data: A comparative study machine learning techniques.

Accident; analysis and prevention
Lane change has been recognized as a challenging driving maneuver and a significant component of traffic safety research. Developing a real-time continuous lane change detection system can assist drivers to perform and deal with complex driving tasks...