AIMC Topic: Neural Networks, Computer

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Disease-free survival assessment by artificial neural networks for hepatocellular carcinoma patients after radiofrequency ablation.

Journal of the Formosan Medical Association = Taiwan yi zhi
BACKGROUND/PURPOSE: Radiofrequency ablation (RFA) provides an effective treatment for patients who exhibit early hepatocellular carcinoma (HCC) stages or are waiting for liver transplantation. It is important to assess patients after RFA. The goal of...

Modeling of glucose release from native and modified wheat starch gels during in vitro gastrointestinal digestion using artificial intelligence methods.

International journal of biological macromolecules
Estimation of the amounts of glucose release (AGR) during gastrointestinal digestion can be useful to identify food of potential use in the diet of individuals with diabetes. In this work, adaptive neuro-fuzzy inference system (ANFIS), genetic algori...

Spatio-Temporal Tolerance of Visuo-Tactile Illusions in Artificial Skin by Recurrent Neural Network with Spike-Timing-Dependent Plasticity.

Scientific reports
Perceptual illusions across multiple modalities, such as the rubber-hand illusion, show how dynamic the brain is at adapting its body image and at determining what is part of it (the self) and what is not (others). Several research studies showed tha...

Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm.

Computer methods and programs in biomedicine
Cardiovascular disease is one of the most rampant causes of death around the world and was deemed as a major illness in Middle and Old ages. Coronary artery disease, in particular, is a widespread cardiovascular malady entailing high mortality rates....

A novel multi-target regression framework for time-series prediction of drug efficacy.

Scientific reports
Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods ...

PCM-SABRE: a platform for benchmarking and comparing outcome prediction methods in precision cancer medicine.

BMC bioinformatics
BACKGROUND: Numerous publications attempt to predict cancer survival outcome from gene expression data using machine-learning methods. A direct comparison of these works is challenging for the following reasons: (1) inconsistent measures used to eval...

A fuzzy integral method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification across multiple subjects.

Journal of integrative neuroscience
The huge number of voxels in fMRI over time poses a major challenge to for effective analysis. Fast, accurate, and reliable classifiers are required for estimating the decoding accuracy of brain activities. Although machine-learning classifiers seem ...

Elimination of spiral waves in a locally connected chaotic neural network by a dynamic phase space constraint.

Neural networks : the official journal of the International Neural Network Society
In this study, a method is proposed that eliminates spiral waves in a locally connected chaotic neural network (CNN) under some simplified conditions, using a dynamic phase space constraint (DPSC) as a control method. In this method, a control signal...

Three-Class Mammogram Classification Based on Descriptive CNN Features.

BioMed research international
In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have prese...

Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection.

Computational intelligence and neuroscience
In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection ...