AIMC Topic: Neural Networks, Computer

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Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network.

Dento maxillo facial radiology
OBJECTIVES: The purpose of this study was to automatically diagnose odontogenic cysts and tumors of both jaws on panoramic radiographs using deep learning. We proposed a novel framework of deep convolution neural network (CNN) with data augmentation ...

Self-organizing subspace clustering for high-dimensional and multi-view data.

Neural networks : the official journal of the International Neural Network Society
A surge in the availability of data from multiple sources and modalities is correlated with advances in how to obtain, compress, store, transfer, and process large amounts of complex high-dimensional data. The clustering challenge increases with the ...

Hybrid neural network with cost-sensitive support vector machine for class-imbalanced multimodal data.

Neural networks : the official journal of the International Neural Network Society
Although deep learning exhibits advantages in various applications involving multimodal data, it cannot effectively solve the class-imbalance problem. Herein, we propose a hybrid neural network with a cost-sensitive support vector machine (hybrid NN-...

A learning approach with incomplete pixel-level labels for deep neural networks.

Neural networks : the official journal of the International Neural Network Society
Learning with incomplete labels in Neural Networks has been actively investigated these last years. Among different kinds of incomplete labels, we investigate incomplete pixel-level labels which are tackled in many concrete problems. One of the chall...

Controller design for finite-time and fixed-time stabilization of fractional-order memristive complex-valued BAM neural networks with uncertain parameters and time-varying delays.

Neural networks : the official journal of the International Neural Network Society
In this paper we investigate controller design problem for finite-time and fixed-time stabilization of fractional-order memristive complex-valued BAM neural networks (FMCVBAMNNs) with uncertain parameters and time-varying delays. By using the Lyapuno...

Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm.

Environmental science and pollution research international
Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input sce...

An ensemble learning based hybrid model and framework for air pollution forecasting.

Environmental science and pollution research international
As advance of economy and industry, the impact of air pollution has gradually gained attention. In order to predict air quality, there were many studies that exploited various machine learning techniques to build predictive model for pollutant concen...

SUSSOL-Using Artificial Intelligence for Greener Solvent Selection and Substitution.

Molecules (Basel, Switzerland)
Solvents come in many shapes and types. Looking for solvents for a specific application can be hard, and looking for green alternatives for currently used nonbenign solvents can be even harder. We describe a new methodology for solvent selection and ...

Differentiation Between Anteroposterior and Posteroanterior Chest X-Ray View Position With Convolutional Neural Networks.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
PURPOSE:  Detection and validation of the chest X-ray view position with use of convolutional neural networks to improve meta-information for data cleaning within a hospital data infrastructure.

Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data.

Magnetic resonance in medicine
PURPOSE: To develop a strategy for training a physics-guided MRI reconstruction neural network without a database of fully sampled data sets.