AIMC Topic: Algorithms

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A Diadochokinesis-based expert system considering articulatory features of plosive consonants for early detection of Parkinson's disease.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: A new expert system is proposed to discriminate healthy people from people with Parkinson's Disease (PD) in early stages by using Diadochokinesis tests.

A regularization path algorithm for support vector ordinal regression.

Neural networks : the official journal of the International Neural Network Society
Support vector ordinal regression (SVOR) is a popular method for tackling ordinal regression problems. Solution path provides a compact representation of optimal solutions for all values of regularization parameter, which is extremely useful for mode...

Forecasting carbon dioxide emissions based on a hybrid of mixed data sampling regression model and back propagation neural network in the USA.

Environmental science and pollution research international
The accurate forecast of carbon dioxide emissions is critical for policy makers to take proper measures to establish a low carbon society. This paper discusses a hybrid of the mixed data sampling (MIDAS) regression model and BP (back propagation) neu...

3D multi-view convolutional neural networks for lung nodule classification.

PloS one
The 3D convolutional neural network (CNN) is able to make full use of the spatial 3D context information of lung nodules, and the multi-view strategy has been shown to be useful for improving the performance of 2D CNN in classifying lung nodules. In ...

Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks.

PloS one
A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when lear...

A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data.

Computational intelligence and neuroscience
Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observatio...

Underwater Inherent Optical Properties Estimation Using a Depth Aided Deep Neural Network.

Computational intelligence and neuroscience
Underwater inherent optical properties (IOPs) are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measur...

Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12.

Proteins
We develop two complementary pipelines, "Zhang-Server" and "QUARK", based on I-TASSER and QUARK pipelines for template-based modeling (TBM) and free modeling (FM), and test them in the CASP12 experiment. The combination of I-TASSER and QUARK successf...

Learning normalized inputs for iterative estimation in medical image segmentation.

Medical image analysis
In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual Networks (FC-ResNets). We propose and examine a design that takes particula...

Sequential Probability Ratio Testing with Power Projective Base Method Improves Decision-Making for BCI.

Computational and mathematical methods in medicine
Obtaining a fast and reliable decision is an important issue in brain-computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this study, the EEG signals were firstly analyzed with...