AIMC Topic: Algorithms

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Modular Grammatical Evolution for the Generation of Artificial Neural Networks.

Evolutionary computation
This article presents a novel method, called Modular Grammatical Evolution (MGE), toward validating the hypothesis that restricting the solution space of NeuroEvolution to modular and simple neural networks enables the efficient generation of smaller...

Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multiobjective Evolutionary Algorithm.

Evolutionary computation
An important challenge in reinforcement learning is to solve multimodal problems, where agents have to act in qualitatively different ways depending on the circumstances. Because multimodal problems are often too difficult to solve directly, it is of...

Modified generalized neo-fuzzy system with combined online fast learning in medical diagnostic task for situations of information deficit.

Mathematical biosciences and engineering : MBE
In the paper, we propose the modified generalized neo-fuzzy system. It is designed to solve the pattern-image recognition task by working with data that are fed to the system in the image form. The neo-fuzzy system can work with small training datase...

[Challenges and Prospects of Medical Device Containing Adaptive Algorithms to Supervision].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
With the market development and demand change, the use of adaptive algorithms in medical devices has become a possible trend. However, some uncertainties in the adaptive algorithm itself will bring challenges to the existing current supervisory work ...

Atom typing using graph representation learning: How do models learn chemistry?

The Journal of chemical physics
Atom typing is the first step for simulating molecules using a force field. Automatic atom typing for an arbitrary molecule is often realized by rule-based algorithms, which have to manually encode rules for all types defined in this force field. The...

Comparing machine learning techniques for predicting glassy dynamics.

The Journal of chemical physics
In the quest to understand how structure and dynamics are connected in glasses, a number of machine learning based methods have been developed that predict dynamics in supercooled liquids. These methods include both increasingly complex machine learn...

Enriching UMLS-Based Phenotyping of Rare Diseases Using Deep-Learning: Evaluation on Jeune Syndrome.

Studies in health technology and informatics
The wide adoption of Electronic Health Records (EHR) in hospitals provides unique opportunities for high throughput phenotyping of patients. The phenotype extraction from narrative reports can be performed by using either dictionary-based or data-dri...

The Prediction of Fall Circumstances Among Patients in Clinical Care - A Retrospective Observational Study.

Studies in health technology and informatics
Standardized fall risk scores have not proven to reliably predict falls in clinical settings. Machine Learning offers the potential to increase the accuracy of such predictions, possibly vastly improving care for patients at high fall risks. We devel...

Constructive Fuzzy Cognitive Map for Depression Severity Estimation.

Studies in health technology and informatics
Depression is a common and serious medical disorder that negatively affects the mood and the emotions of people, especially adolescents. In this paper, a novel framework for automatically creating Fuzzy Cognitive Maps (FCMs) is proposed. It is applie...

Beyond the Brain: MIDS Extends BIDS to Multiple Modalities and Anatomical Regions.

Studies in health technology and informatics
Brain Imaging Data Structure (BIDS) provides a valuable tool to organise brain imaging data into a clear and easy standard directory structure. Moreover, BIDS is widely supported by the scientific community and has been established as a powerful stan...