AIMC Topic: Algorithms

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Continuous patrolling in uncertain environment with the UAV swarm.

PloS one
The research about unmanned aerial vehicle (UAV) swarm has developed rapidly in recent years, especially the UAV swarm with sensors which is becoming common means of achieving situational awareness. Due to inadequate researches of the UAV swarm with ...

Personalized Saliency and Its Prediction.

IEEE transactions on pattern analysis and machine intelligence
Nearly all existing visual saliency models by far have focused on predicting a universal saliency map across all observers. Yet psychology studies suggest that visual attention of different observers can vary significantly under specific circumstance...

Predictors of atrial fibrillation early recurrence following cryoballoon ablation of pulmonary veins using statistical assessment and machine learning algorithms.

Heart and vessels
Inflammation, oxidative stress, myocardial injury biomarkers and clinical parameters (longer AF duration, left atrial enlargement, the metabolic syndrome) are factors commonly related to AF recurrence. This study aims to assess the predictive value o...

Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination.

Sensors (Basel, Switzerland)
, a smut fungus, is studied as an agent for the biological control of (milk thistle) weed. Confirmation of the systemic infection is essential in order to assess the effectiveness of the biological control application and assist decision-making. Non...

Predictive pollen-based biome modeling using machine learning.

PloS one
This paper investigates suitability of supervised machine learning classification methods for classification of biomes using pollen datasets. We assign modern pollen samples from Africa and Arabia to five biome classes using a previously published Af...

Traditional machine learning for limited angle tomography.

International journal of computer assisted radiology and surgery
PURPOSE: The application of traditional machine learning techniques, in the form of regression models based on conventional, "hand-crafted" features, to artifact reduction in limited angle tomography is investigated.

Quiet sleep detection in preterm infants using deep convolutional neural networks.

Journal of neural engineering
OBJECTIVE: Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analy...

Automatic polyp frame screening using patch based combined feature and dictionary learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Polyps in the colon can potentially become malignant cancer tissues where early detection and removal lead to high survival rate. Certain types of polyps can be difficult to detect even for highly trained physicians. Inspired by aforementioned proble...

Combinatorial Scoring of Phylogenetic Trees and Networks Based on Homoplasy-Free Characters.

Journal of computational biology : a journal of computational molecular cell biology
Construction of phylogenetic trees and networks for extant species from their characters represents one of the key problems in phylogenomics. While solution to this problem is not always uniquely defined and there exist multiple methods for tree/netw...

Sequential Integration of Fuzzy Clustering and Expectation Maximization for Transcription Factor Binding Site Identification.

Journal of computational biology : a journal of computational molecular cell biology
The identification of transcription factor binding sites (TFBSs) is a problem for which computational methods offer great hope. Thus far, the expectation maximization (EM) technique has been successfully utilized in finding TFBSs in DNA sequences, bu...