AIMC Topic: Neural Networks, Computer

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Statistical and artificial neural network-based analysis to understand complexity and heterogeneity in preeclampsia.

Computational biology and chemistry
Preeclampsia is a pregnancy associated disease. It is characterized by high blood pressure and symptoms that are indicative of damage to other organ systems, most often involving the liver and kidneys. If left untreated, the condition could be fatal ...

P Systems-Based Computing Polynomials With Integer Coefficients: Design and Formal Verification.

IEEE transactions on nanobioscience
Automatic design of mechanical procedures solving abstract problems is a relevant scientific challenge. In particular, automatic design of membranes systems performing some prefixed tasks is an important and useful research topic in the area of Natur...

Segmentation of histological images and fibrosis identification with a convolutional neural network.

Computers in biology and medicine
Segmentation of histological images is one of the most crucial tasks for many biomedical analyses involving quantification of certain tissue types, such as fibrosis via Masson's trichrome staining. However, challenges are posed by the high variabilit...

Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques.

Environmental science and pollution research international
Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. Th...

Weed Growth Stage Estimator Using Deep Convolutional Neural Networks.

Sensors (Basel, Switzerland)
This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were...

Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction.

BMC bioinformatics
BACKGROUND: There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) o...

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer.

PloS one
This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in...

Differential Effects of Simulated Cortical Network Lesions on Synchrony and EEG Complexity.

International journal of neural systems
Brain function has been proposed to arise as a result of the coordinated activity between distributed brain areas. An important issue in the study of brain activity is the characterization of the synchrony among these areas and the resulting complexi...

Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models.

International journal of computer assisted radiology and surgery
PURPOSE: Most of the existing convolutional neural network (CNN)-based medical image segmentation methods are based on methods that have originally been developed for segmentation of natural images. Therefore, they largely ignore the differences betw...