AIMC Topic: Machine Learning

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Learning Receptive Fields and Quality Lookups for Blind Quality Assessment of Stereoscopic Images.

IEEE transactions on cybernetics
Blind quality assessment of 3D images encounters more new challenges than its 2D counterparts. In this paper, we propose a blind quality assessment for stereoscopic images by learning the characteristics of receptive fields (RFs) from perspective of ...

Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism.

NeuroImage. Clinical
Despite consensus on the neurological nature of autism spectrum disorders (ASD), brain biomarkers remain unknown and diagnosis continues to be based on behavioral criteria. Growing evidence suggests that brain abnormalities in ASD occur at the level ...

Woods: A fast and accurate functional annotator and classifier of genomic and metagenomic sequences.

Genomics
Functional annotation of the gigantic metagenomic data is one of the major time-consuming and computationally demanding tasks, which is currently a bottleneck for the efficient analysis. The commonly used homology-based methods to functionally annota...

Multi-Step Protocol for Automatic Evaluation of Docking Results Based on Machine Learning Methods--A Case Study of Serotonin Receptors 5-HT(6) and 5-HT(7).

Journal of chemical information and modeling
Molecular docking, despite its undeniable usefulness in computer-aided drug design protocols and the increasing sophistication of tools used in the prediction of ligand-protein interaction energies, is still connected with a problem of effective resu...

Intensity Estimation of Spontaneous Facial Action Units Based on Their Sparsity Properties.

IEEE transactions on cybernetics
Automatic measurement of spontaneous facial action units (AUs) defined by the facial action coding system (FACS) is a challenging problem. The recent FACS user manual defines 33 AUs to describe different facial activities and expressions. In spontane...

Machine learning assisted design of highly active peptides for drug discovery.

PLoS computational biology
The discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning ap...

Incremental learning for ν-Support Vector Regression.

Neural networks : the official journal of the International Neural Network Society
The ν-Support Vector Regression (ν-SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ν on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to...

Relative Attribute SVM+ Learning for Age Estimation.

IEEE transactions on cybernetics
When estimating age, human experts can provide privileged information that encodes the facial attributes of aging, such as smoothness, face shape, face acne, wrinkles, and bags under-eyes. In automatic age estimation, privileged information is unavai...

Supervised dictionary learning for inferring concurrent brain networks.

IEEE transactions on medical imaging
Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, G...