AIMC Topic:
Models, Theoretical

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Measurement and ANN prediction of pH-dependent solubility of nitrogen-heterocyclic compounds.

Chemosphere May 15, 2015
Based on the solubility of 25 nitrogen-heterocyclic compounds (NHCs) measured by saturation shake-flask method, artificial neural network (ANN) was employed to the study of the quantitative relationship between the structure and pH-dependent solubili...
Heterocyclic Compounds Models, Theoretical Environmental Pollutants Nitrogen Hydrogen-Ion Concentration Solubility Neural Networks, Computer Quantitative Structure-Activity Relationship Water
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Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas.

PloS one May 11, 2015
BACKGROUND: In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when t...
Environment Male Insect Vectors Thailand Severe Dengue Morbidity Female Models, Theoretical Incidence Humans Climate Support Vector Machine Reproducibility of Results Algorithms Dengue Virus Aedes Animals
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Accuracy of latent-variable estimation in Bayesian semi-supervised learning.

Neural networks : the official journal of the International Neural Network Society May 9, 2015
Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which represent the observable data and the underlying data-generation proce...
Algorithms Regression Analysis Bayes Theorem Supervised Machine Learning Models, Theoretical Cluster Analysis Models, Statistical Normal Distribution Data Accuracy Likelihood Functions
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Asymptotic accuracy of Bayesian estimation for a single latent variable.

Neural networks : the official journal of the International Neural Network Society May 8, 2015
In data science and machine learning, hierarchical parametric models, such as mixture models, are often used. They contain two kinds of variables: observable variables, which represent the parts of the data that can be directly measured, and latent v...
Biometry Likelihood Functions Regression Analysis Bayes Theorem Data Accuracy Machine Learning Models, Theoretical
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A Robust Deep Model for Improved Classification of AD/MCI Patients.

IEEE journal of biomedical and health informatics May 4, 2015
Accurate classification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many rese...
Cognitive Dysfunction Models, Theoretical Support Vector Machine Humans Machine Learning Positron-Emission Tomography Image Interpretation, Computer-Assisted Early Diagnosis Magnetic Resonance Imaging Principal Component Analysis Alzheimer Disease
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On simplicity and complexity in the brave new world of large-scale neuroscience.

Current opinion in neurobiology Apr 29, 2015
Technological advances have dramatically expanded our ability to probe multi-neuronal dynamics and connectivity in the brain. However, our ability to extract a simple conceptual understanding from complex data is increasingly hampered by the lack of ...
Neural Networks, Computer Animals Models, Theoretical Datasets as Topic Nerve Net Neurosciences
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Perception Evolution Network Based on Cognition Deepening Model--Adapting to the Emergence of New Sensory Receptor.

IEEE transactions on neural networks and learning systems Apr 28, 2015
The proposed perception evolution network (PEN) is a biologically inspired neural network model for unsupervised learning and online incremental learning. It is able to automatically learn suitable prototypes from learning data in an incremental way,...
Sensory Receptor Cells Adaptation, Biological Learning Humans Cognition Animals Neural Networks, Computer Models, Theoretical Perception
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High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm.

Journal of chemical theory and computation Apr 28, 2015
Artificial neural networks (NNs) represent a relatively recent approach for the prediction of molecular potential energies, suitable for simulations of large molecules and long time scales. By using NNs to fit electronic structure data, it is possibl...
Thermodynamics Neural Networks, Computer Algorithms Models, Theoretical
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Application of chemometric analysis and self Organizing Map-Artificial Neural Network as source receptor modeling for metal speciation in river sediment.

Environmental pollution (Barking, Essex : 1987) Apr 26, 2015
Present study deals with the river Ganga water quality and its impact on metal speciation in its sediments. Concentration of physico-chemical parameters was highest in summer season followed by winter and lowest in rainy season. Metal speciation stud...
Water Pollutants, Chemical Seasons India Water Quality Rivers Neural Networks, Computer Models, Theoretical Metals, Heavy Environmental Monitoring Geologic Sediments
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Modeling total phosphorus removal in an aquatic environment restoring horizontal subsurface flow constructed wetland based on artificial neural networks.

Environmental science and pollution research international Apr 23, 2015
A horizontal subsurface flow constructed wetland (HSSF-CW) was designed to improve the water quality of an artificial lake in Beijing Wildlife Rescue and Rehabilitation Center, Beijing, China. Artificial neural networks (ANNs), including multilayer p...
Water Quality Wetlands Beijing Water Movements Models, Theoretical Water Pollutants, Chemical Neural Networks, Computer Phosphorus Water
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