AIMC Topic: Principal Component Analysis

Clear Filters Showing 521 to 530 of 712 articles

Adult fly age estimations using cuticular hydrocarbons and Artificial Neural Networks in forensically important Calliphoridae species.

Forensic science international
Blowflies (Diptera: Calliphoridae) are forensically important as they are known to be one of the first to colonise human remains. The larval stage is typically used to assist a forensic entomologists with adult flies rarely used as they are difficult...

Low-Dose Lung CT Image Restoration Using Adaptive Prior Features From Full-Dose Training Database.

IEEE transactions on medical imaging
The valuable structure features in full-dose computed tomography (FdCT) scans can be exploited as prior knowledge for low-dose CT (LdCT) imaging. However, lacking the capability to represent local characteristics of interested structures of the LdCT ...

Factors analysis of protein O-glycosylation site prediction.

Computational biology and chemistry
To improve the prediction accuracy of O-glycosylation sites, and analyze the structure of the O-glycosylation sites, factor analysis based prediction is proposed in this study. Our studies show that factor analysis strongly boosts machine learning al...

A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women.

Osteoarthritis and cartilage
OBJECTIVE: Knee osteoarthritis (OA) is among the higher contributors to global disability. Despite its high prevalence, currently, there is no cure for this disease. Furthermore, the available diagnostic approaches have large precision errors and low...

Optimisation of a machine learning algorithm in human locomotion using principal component and discriminant function analyses.

PloS one
Assessment methods in human locomotion often involve the description of normalised graphical profiles and/or the extraction of discrete variables. Whilst useful, these approaches may not represent the full complexity of gait data. Multivariate statis...

Identifying novel factor XIIa inhibitors with PCA-GA-SVM developed vHTS models.

European journal of medicinal chemistry
There currently is renewed interest in blood clotting Factor XII as a potential target for thrombosis inhibition. Historically untargeted, there is little drug information with which to start drug candidate searches. Typical high-throughput screening...

Rapid Life-Cycle Impact Screening Using Artificial Neural Networks.

Environmental science & technology
The number of chemicals in the market is rapidly increasing, while our understanding of the life-cycle impacts of these chemicals lags considerably. To address this, we developed deep artificial neural network (ANN) models to estimate life-cycle impa...

Effects of spatial fMRI resolution on the classification of naturalistic movies.

NeuroImage
Studies involving multivariate pattern analysis (MVPA) of BOLD fMRI data generally attribute the success of the information-theoretic approach to BOLD signal contrast on the fine spatial scale of millimeters facilitating the classification or decodin...

"What is relevant in a text document?": An interpretable machine learning approach.

PloS one
Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatically map documents to these abstract concepts, allowing to annotate ve...

Assessment of the water quality monitoring network of the Piabanha River experimental watersheds in Rio de Janeiro, Brazil, using autoassociative neural networks.

Environmental monitoring and assessment
Water quality monitoring is a complex issue that requires support tools in order to provide information for water resource management. Budget constraints as well as an inadequate water quality network design call for the development of evaluation too...