AIMC Topic: Machine Learning

Clear Filters Showing 14981 to 14990 of 34417 articles

Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage.

BMC medical informatics and decision making
BACKGROUND: Outliers and class imbalance in medical data could affect the accuracy of machine learning models. For physicians who want to apply predictive models, how to use the data at hand to build a model and what model to choose are very thorny p...

Early prediction of patient discharge disposition in acute neurological care using machine learning.

BMC health services research
BACKGROUND: Acute neurological complications are some of the leading causes of death and disability in the U.S. The medical professionals that treat patients in this setting are tasked with deciding where (e.g., home or facility), how, and when to di...

Knowledge distillation for multi-depth-model-fusion recommendation algorithm.

PloS one
Recommendation algorithms save a lot of valuable time for people to get the information they are interested in. However, the feature calculation and extraction process of each machine learning or deep learning recommendation algorithm are different, ...

Artificial intelligence and treatment algorithms in spine surgery.

Orthopaedics & traumatology, surgery & research : OTSR
Artificial intelligence (AI) is a set of theories and techniques in which machines are used to simulate human intelligence with complex computer programs. The various machine learning (ML) methods are a subtype of AI. They originate from computer sci...

Raman Spectroscopy in Open-World Learning Settings Using the Objectosphere Approach.

Analytical chemistry
Raman spectroscopy, combined with machine learning techniques, holds great promise for many applications as a rapid, sensitive, and label-free identification method. Such approaches perform well when classifying spectra of chemical species that were ...

Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields.

The journal of physical chemistry letters
Reconstructing force fields (FFs) from atomistic simulation data is a challenge since accurate data can be highly expensive. Here, machine learning (ML) models can help to be data economic as they can be successfully constrained using the underlying ...

Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge.

Sensors (Basel, Switzerland)
The proliferation of sensors to capture parametric measures or event data over a myriad of networking topologies is growing exponentially to improve our daily lives. Large amounts of data must be shared on constrained network infrastructure, increasi...

Improved Feature Parameter Extraction from Speech Signals Using Machine Learning Algorithm.

Sensors (Basel, Switzerland)
Speech recognition refers to the capability of software or hardware to receive a speech signal, identify the speaker's features in the speech signal, and recognize the speaker thereafter. In general, the speech recognition process involves three main...

Modeling and insights into the structural characteristics of drug-induced autoimmune diseases.

Frontiers in immunology
The incidence and complexity of drug-induced autoimmune diseases (DIAD) have been on the rise in recent years, which may lead to serious or fatal consequences. Besides, many environmental and industrial chemicals can also cause DIAD. However, there a...

Maximum Decentral Projection Margin Classifier for High Dimension and Low Sample Size problems.

Neural networks : the official journal of the International Neural Network Society
Compared with relatively easy feature creation or generation in data analysis, manual data labeling needs a lot of time and effort in most cases. Even if automated data labeling​ seems to make it better in some cases, the labeling results still need ...