AIMC Topic: Machine Learning

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Marker-Independent Food Identification Enabled by Combing Machine Learning Algorithms with Comprehensive GC × GC/TOF-MS.

Molecules (Basel, Switzerland)
Reliable methods are always greatly desired for the practice of food inspection. Currently, most food inspection techniques are mainly dependent on the identification of special components, which neglect the combination effects of different component...

Modeling DTA by Combining Multiple-Instance Learning with a Private-Public Mechanism.

International journal of molecular sciences
The prediction of the strengths of drug-target interactions, also called drug-target binding affinities (DTA), plays a fundamental role in facilitating drug discovery, where the goal is to find prospective drug candidates. With the increase in the nu...

Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm.

BMC medical informatics and decision making
BACKGROUND: More than 115,000 maternal deaths and 591,000 prenatal deaths occurred in the world per year with anemia, the reduction of red blood cells or hemoglobin in the blood. The world health organization divides anemia in pregnancy into mild ane...

Impact of Label Noise on the Learning Based Models for a Binary Classification of Physiological Signal.

Sensors (Basel, Switzerland)
Label noise is omnipresent in the annotations process and has an impact on supervised learning algorithms. This work focuses on the impact of label noise on the performance of learning models by examining the effect of random and class-dependent labe...

Recover User's Private Training Image Data by Gradient in Federated Learning.

Sensors (Basel, Switzerland)
Exchanging gradient is a widely used method in modern multinode machine learning system (e.g., distributed training, Federated Learning). Gradients and weights of model has been presumed to be safe to delivery. However, some studies have shown that g...

Graph-based representation for identifying individual travel activities with spatiotemporal trajectories and POI data.

Scientific reports
Individual daily travel activities (e.g., work, eating) are identified with various machine learning models (e.g., Bayesian Network, Random Forest) for understanding people's frequent travel purposes. However, labor-intensive engineering work is ofte...

New venues in electron density analysis.

Physical chemistry chemical physics : PCCP
We provide a comprehensive overview of the chemical information from electron density: not only how to extract information, but also how to obtain and how to assess the quality of the electron density itself. After introducing several indexes derived...

Modelling membrane curvature generation using mechanics and machine learning.

Journal of the Royal Society, Interface
The deformation of cellular membranes regulates trafficking processes, such as exocytosis and endocytosis. Classically, the Helfrich continuum model is used to characterize the forces and mechanical parameters that cells tune to accomplish membrane s...

Automated assessment of balance: A neural network approach based on large-scale balance function data.

Frontiers in public health
Balance impairment (BI) is an important cause of falls in the elderly. However, the existing balance estimation system needs to measure a large number of items to obtain the balance score and balance level, which is less efficient and redundant. In t...