AIMC Topic: Algorithms

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Smart sampling and incremental function learning for very large high dimensional data.

Neural networks : the official journal of the International Neural Network Society
Very large high dimensional data are common nowadays and they impose new challenges to data-driven and data-intensive algorithms. Computational Intelligence techniques have the potential to provide powerful tools for addressing these challenges, but ...

Computational probing protein-protein interactions targeting small molecules.

Bioinformatics (Oxford, England)
MOTIVATION: With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug target...

EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks.

Computational and mathematical methods in medicine
In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. How...

Correct machine learning on protein sequences: a peer-reviewing perspective.

Briefings in bioinformatics
Machine learning methods are becoming increasingly popular to predict protein features from sequences. Machine learning in bioinformatics can be powerful but carries also the risk of introducing unexpected biases, which may lead to an overestimation ...

Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery.

International journal of computer assisted radiology and surgery
PURPOSE: Single-incision laparoscopic surgery decreases postoperative infections, but introduces limitations in the surgeon's maneuverability and in the surgical field of view. This work aims at enhancing intra-operative surgical visualization by exp...

Simulative and experimental investigation on stator winding turn and unbalanced supply voltage fault diagnosis in induction motors using Artificial Neural Networks.

ISA transactions
The three-phase shift between line current and phase voltage of induction motors can be used as an efficient fault indicator to detect and locate inter-turn stator short-circuit (ITSC) fault. However, unbalanced supply voltage is one of the contribut...

Extending XCS with Cyclic Graphs for Scalability on Complex Boolean Problems.

Evolutionary computation
A main research direction in the field of evolutionary machine learning is to develop a scalable classifier system to solve high-dimensional problems. Recently work has begun on autonomously reusing learned building blocks of knowledge to scale from ...

Cytotoxicity of chitosan/streptokinase nanoparticles as a function of size: An artificial neural networks study.

Nanomedicine : nanotechnology, biology, and medicine
Predicting the size and toxicity of chitosan/streptokinase nanoparticles at various values of processing parameters was the aim of this study. For the first time, a comprehensive model could be developed to determine the cytotoxicity of the nanoparti...

iTIS-PseKNC: Identification of Translation Initiation Site in human genes using pseudo k-tuple nucleotides composition.

Computers in biology and medicine
Translation is an essential genetic process for understanding the mechanism of gene expression. Due to the large number of protein sequences generated in the post-genomic era, conventional methods are unable to identify Translation Initiation Site (T...