AIMC Topic: Machine Learning

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Implicit data crimes: Machine learning bias arising from misuse of public data.

Proceedings of the National Academy of Sciences of the United States of America
SignificancePublic databases are an important resource for machine learning research, but their growing availability sometimes leads to "off-label" usage, where data published for one task are used for another. This work reveals that such off-label u...

Understanding, discovery, and synthesis of 2D materials enabled by machine learning.

Chemical Society reviews
Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as input computed or experimental materials data, ML algorithms predict the structural, electronic, mechanical, and chemical properties of 2D materials that have ye...

Tuberculosis Disease Diagnosis Based on an Optimized Machine Learning Model.

Journal of healthcare engineering
Computer science plays an important role in modern dynamic health systems. Given the collaborative nature of the diagnostic process, computer technology provides important services to healthcare professionals and organizations, as well as to patients...

Developing random forest hybridization models for estimating the axial bearing capacity of pile.

PloS one
Accurate determination of the axial load capacity of the pile is of utmost importance when designing the pile foundation. However, the methods of determining the axial load capacity of the pile in the field are often costly and time-consuming. Theref...

Lower-Limb Joint Torque Prediction Using LSTM Neural Networks and Transfer Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Estimation of joint torque during movement provides important information in several settings, such as effect of athletes' training or of a medical intervention, or analysis of the remaining muscle strength in a wearer of an assistive device. The abi...

Prediction of the performance of pre-packed purification columns through machine learning.

Journal of separation science
Pre-packed columns have been increasingly used in process development and biomanufacturing thanks to their ease of use and consistency. Traditionally, packing quality is predicted through rate models, which require extensive calibration efforts throu...

Investigation of the Temperature Compensation of Piezoelectric Weigh-In-Motion Sensors Using a Machine Learning Approach.

Sensors (Basel, Switzerland)
Piezoelectric ceramics have good electromechanical coupling characteristics and a high sensitivity to load. One typical engineering application of piezoelectric ceramic is its use as a signal source for Weigh-In-Motion (WIM) systems in road traffic m...

Hierarchical representation for PPI sites prediction.

BMC bioinformatics
BACKGROUND: Protein-protein interactions have pivotal roles in life processes, and aberrant interactions are associated with various disorders. Interaction site identification is key for understanding disease mechanisms and design new drugs. Effectiv...

Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning.

Scientific reports
Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual drug screening. Most DTI prediction methods cast the problem as a binary classification task to predict if interactions exist or as a regression task to p...