AIMC Topic: Neural Networks, Computer

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Dynamic network modeling and dimensionality reduction for human ECoG activity.

Journal of neural engineering
OBJECTIVE: Developing dynamic network models for multisite electrocorticogram (ECoG) activity can help study neural representations and design neurotechnologies in humans given the clinical promise of ECoG. However, dynamic network models have so far...

Age grading An. gambiae and An. arabiensis using near infrared spectra and artificial neural networks.

PloS one
BACKGROUND: Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into < or ≥ 7 days old with an average accuracy of 80%, achieved by training a re...

Siamese Neural Networks for User Identity Linkage Through Web Browsing.

IEEE transactions on neural networks and learning systems
Linking online identities of users among countless heterogeneous network services on the Internet can provide an explicit digital representation of users, which can benefit both research and industry. In recent years, user identity linkage (UIL) thro...

Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation.

IEEE transactions on medical imaging
Accurate segmentation of the prostate from magnetic resonance (MR) images provides useful information for prostate cancer diagnosis and treatment. However, automated prostate segmentation from 3D MR images faces several challenges. The lack of clear ...

Transfer learning for neural network model in chlorophyll-a dynamics prediction.

Environmental science and pollution research international
Neural network models have been used to predict chlorophyll-a concentration dynamics. However, as model generalization ability decreases, (i) the performance of the models gradually decreases over time; (ii) the accuracy and performance of the models...

Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.

EBioMedicine
BACKGROUND: Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information.

Analyzing Learned Molecular Representations for Property Prediction.

Journal of chemical information and modeling
Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or ex...

Classification and Segmentation of Hyperspectral Data of Hepatocellular Carcinoma Samples Using 1-D Convolutional Neural Network.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Pathological diagnosis plays an important role in the diagnosis and treatment of hepatocellular carcinoma (HCC). The traditional method of pathological diagnosis of most cancers requires freezing, slicing, hematoxylin and eosin staining, and manual a...

Automated brain extraction of multisequence MRI using artificial neural networks.

Human brain mapping
Brain extraction is a critical preprocessing step in the analysis of neuroimaging studies conducted with magnetic resonance imaging (MRI) and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, op...