AIMC Topic: Machine Learning

Clear Filters Showing 15971 to 15980 of 34417 articles

Zero-Shot Deep Domain Adaptation With Common Representation Learning.

IEEE transactions on pattern analysis and machine intelligence
Domain Adaptation aims at adapting the knowledge learned from a domain (source-domain) to another (target-domain). Existing approaches typically require a portion of task-relevant target-domain data a priori. We propose an approach, zero-shot deep do...

Edge-Computing and Machine-Learning-Based Framework for Software Sensor Development.

Sensors (Basel, Switzerland)
The present research presents a framework that supports the development and operation of machine-learning (ML) algorithms to develop, maintain and manage the whole lifecycle of modeling software sensors related to complex chemical processes. Our moti...

Empirical analyses and simulations showed that different machine and statistical learning methods had differing performance for predicting blood pressure.

Scientific reports
Machine learning is increasingly being used to predict clinical outcomes. Most comparisons of different methods have been based on empirical analyses in specific datasets. We used Monte Carlo simulations to determine when machine learning methods per...

A new active learning approach for adsorbate-substrate structural elucidation in silico.

Journal of molecular modeling
Adsorbate interactions with substrates (e.g. surfaces and nanoparticles) are fundamental for several technologies, such as functional materials, supramolecular chemistry, and solvent interactions. However, modeling these kinds of systems in silico, s...

Decisions are not all equal-Introducing a utility metric based on case-wise raters' perceptions.

Computer methods and programs in biomedicine
Background and Objective Evaluation of AI-based decision support systems (AI-DSS) is of critical importance in practical applications, nonetheless common evaluation metrics fail to properly consider relevant and contextual information. In this articl...

Personalized On-Device E-Health Analytics With Decentralized Block Coordinate Descent.

IEEE journal of biomedical and health informatics
Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating. Nowadays, enhancement on medical diagnosis via machine learning models has been highly effective in many aspects of e-health analy...

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review.

Frontiers in public health
Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed ...

Evaluation of surgical skill using machine learning with optimal wearable sensor locations.

PloS one
Evaluation of surgical skills during minimally invasive surgeries is needed when recruiting new surgeons. Although surgeons' differentiation by skill level is highly complex, performance in specific clinical tasks such as pegboard transfer and knot t...

Genetic Analysis of Coronary Artery Disease Using Tree-Based Automated Machine Learning Informed By Biology-Based Feature Selection.

IEEE/ACM transactions on computational biology and bioinformatics
Machine Learning (ML) approaches are increasingly being used in biomedical applications. Important challenges of ML include choosing the right algorithm and tuning the parameters for optimal performance. Automated ML (AutoML) methods, such as Tree-ba...

EPGAT: Gene Essentiality Prediction With Graph Attention Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Identifying essential genes and proteins is a critical step towards a better understanding of human biology and pathology. Computational approaches helped to mitigate experimental constraints by exploring machine learning (ML) methods and the correla...