AIMC Topic: Algorithms

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Reward-predictive representations generalize across tasks in reinforcement learning.

PLoS computational biology
In computer science, reinforcement learning is a powerful framework with which artificial agents can learn to maximize their performance for any given Markov decision process (MDP). Advances over the last decade, in combination with deep neural netwo...

Online Doctor Recommendation with Convolutional Neural Network and Sparse Inputs.

Computational intelligence and neuroscience
The recommendation system in the online medical consultation website is a system to assist patients to find appropriate doctors. Based on the analysis of the current situation of the development of an online medical community (Haodf.com) in China, th...

Accelerating Surgical Site Infection Abstraction With a Semi-automated Machine-learning Approach.

Annals of surgery
OBJECTIVE: To demonstrate that a semi-automated approach to health data abstraction provides significant efficiencies and high accuracy.

Enhancing the performance of data-driven models for monthly reservoir evaporation prediction.

Environmental science and pollution research international
The accuracy level for reservoir evaporation prediction is an important issue for decision making in the water resources field. The traditional methods for evaporation prediction could encounter numerous obstacles owing to the effect of several param...

Implementing logical inference based on DNA assembly.

Bio Systems
Algorithms and information processing, fundamental to biological system, are an essential aspect of many elementary physical phenomena, such as molecular self-assembly. Self-assembly system has been proved to be capable of performing many logic opera...

[Epicardial fat Tissue Volumetry: Comparison of Semi-Automatic Measurement and the Machine Learning Algorithm].

Kardiologiia
Aim        To compare assessments of epicardial adipose tissue (EAT) volumes obtained with a semi-automatic, physician-performed analysis and an automatic analysis using a machine-learning algorithm by data of low-dose (LDCT) and standard computed to...

Statistical and Machine-Learning Analyses in Nutritional Genomics Studies.

Nutrients
Nutritional compounds may have an influence on different OMICs levels, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metagenomics. The integration of OMICs data is challenging but may provide new knowledge to explain...

Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network.

International journal of environmental research and public health
A better understanding of circumstances contributing to the severity outcome of traffic crashes is an important goal of road safety studies. An in-depth crash injury severity analysis is vital for the proactive implementation of appropriate mitigatio...

Data augmentation for enhancing EEG-based emotion recognition with deep generative models.

Journal of neural engineering
OBJECTIVE: The data scarcity problem in emotion recognition from electroencephalography (EEG) leads to difficulty in building an affective model with high accuracy using machine learning algorithms or deep neural networks. Inspired by emerging deep g...

Transparency and reproducibility in artificial intelligence.

Nature
Breakthroughs in artificial intelligence (AI) hold enormous potential as it can automate complex tasks and go even beyond human performance. In their study, McKinney et al. showed the high potential of AI for breast cancer screening. However, the lac...