AIMC Topic: Artificial Intelligence

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A computational model of the short-cut rule for 2D shape decomposition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
We propose a new 2D shape decomposition method based on the short-cut rule. The short-cut rule originates from cognition research, and states that the human visual system prefers to partition an object into parts using the shortest possible cuts. We ...

Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations.

Evolutionary computation
Dispatching rules are frequently used for real-time, online scheduling in complex manufacturing systems. Design of such rules is usually done by experts in a time consuming trial-and-error process. Recently, evolutionary algorithms have been proposed...

Machine learning algorithms and forced oscillation measurements to categorise the airway obstruction severity in chronic obstructive pulmonary disease.

Computer methods and programs in biomedicine
The purpose of this study was to develop automatic classifiers to simplify the clinical use and increase the accuracy of the forced oscillation technique (FOT) in the categorisation of airway obstruction level in patients with chronic obstructive pul...

Identification of human drug targets using machine-learning algorithms.

Computers in biology and medicine
Identification of potential drug targets is a crucial task in the drug-discovery pipeline. Successful identification of candidate drug targets in entire genomes is very useful, and computational prediction methods can speed up this process. In the cu...

HyDRA: gene prioritization via hybrid distance-score rank aggregation.

Bioinformatics (Oxford, England)
UNLABELLED: Gene prioritization refers to a family of computational techniques for inferring disease genes through a set of training genes and carefully chosen similarity criteria. Test genes are scored based on their average similarity to the traini...

Permutation entropy analysis of vital signs data for outcome prediction of patients with severe traumatic brain injury.

Computers in biology and medicine
Permutation entropy is computationally efficient, robust to outliers, and effective to measure complexity of time series. We used this technique to quantify the complexity of continuous vital signs recorded from patients with traumatic brain injury (...

Exploiting uncertainty measures in compounds activity prediction using support vector machines.

Bioorganic & medicinal chemistry letters
The great majority of molecular modeling tasks require the construction of a model that is then used to evaluate new compounds. Although various types of these models exist, at some stage, they all use knowledge about the activity of a given group of...

Towards an intelligent framework for multimodal affective data analysis.

Neural networks : the official journal of the International Neural Network Society
An increasingly large amount of multimodal content is posted on social media websites such as YouTube and Facebook everyday. In order to cope with the growth of such so much multimodal data, there is an urgent need to develop an intelligent multi-mod...

Intra-axiom redundancies in SNOMED CT.

Artificial intelligence in medicine
OBJECTIVE: Intra-axiom redundancies are elements of concept definitions that are redundant as they are entailed by other elements of the concept definition. While such redundancies are harmless from a logical point of view, they make concept definiti...