AIMC Topic: Machine Learning

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Mathematical foundations based statistical modeling of software source code for software system evolution.

Mathematical biosciences and engineering : MBE
Source code is the heart of the software systems; it holds a wealth of knowledge that can be tapped for intelligent software systems and leverage the possibilities of reuse of the software. In this work, exploration revolves around making use of the ...

Machine learning models and over-fitting considerations.

World journal of gastroenterology
Machine learning models may outperform traditional statistical regression algorithms for predicting clinical outcomes. Proper validation of building such models and tuning their underlying algorithms is necessary to avoid over-fitting and poor genera...

Artificial Intelligence for Biology.

Integrative and comparative biology
Despite efforts to integrate research across different subdisciplines of biology, the scale of integration remains limited. We hypothesize that future generations of Artificial Intelligence (AI) technologies specifically adapted for biological scienc...

The Axes of Life: A Roadmap for Understanding Dynamic Multiscale Systems.

Integrative and comparative biology
The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care requ...

Deep Learning for Reintegrating Biology.

Integrative and comparative biology
The goal of this vision paper is to investigate the possible role that advanced machine learning techniques, especially deep learning (DL), could play in the reintegration of various biological disciplines. To achieve this goal, a series of operation...

Development and Internal Validation of Machine Learning Algorithms for Predicting Hyponatremia After TJA.

The Journal of bone and joint surgery. American volume
BACKGROUND: The development of hyponatremia after total joint arthroplasty (TJA) may lead to several adverse events and is associated with prolonged inpatient length of stay as well as increased hospital costs. The purpose of this study was to develo...

Simple Linear Cancer Risk Prediction Models With Novel Features Outperform Complex Approaches.

JCO clinical cancer informatics
PURPOSE: The ability to accurately predict an individual's risk for cancer is critical to the implementation of precision prevention measures. Current cancer risk predictions are frequently made with simple models that use a few proven risk factors, ...

A machine learning tutorial for spatial auditory display using head-related transfer functions.

The Journal of the Acoustical Society of America
This review presents a high-level overview of the uses of machine learning (ML) to address several challenges in spatial auditory display research, primarily using head-related transfer functions. This survey also reviews and compares several categor...