AIMC Topic: Neural Networks, Computer

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i4mC-Deep: An Intelligent Predictor of N4-Methylcytosine Sites Using a Deep Learning Approach with Chemical Properties.

Genes
DNA is subject to epigenetic modification by the molecule N4-methylcytosine (4mC). N4-methylcytosine plays a crucial role in DNA repair and replication, protects host DNA from degradation, and regulates DNA expression. However, though current experim...

Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances.

Sensors (Basel, Switzerland)
We use the recent advances in Deep Learning to solve an underwater motion planning problem by making use of optimal control tools-namely, we propose using the Deep Galerkin Method (DGM) to approximate the Hamilton-Jacobi-Bellman PDE that can be used ...

A Time-Series-Based New Behavior Trace Model for Crowd Workers That Ensures Quality Annotation.

Sensors (Basel, Switzerland)
Crowdsourcing is a new mode of value creation in which organizations leverage numerous Internet users to accomplish tasks. However, because these workers have different backgrounds and intentions, crowdsourcing suffers from quality concerns. In the l...

Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network.

Skeletal radiology
PURPOSE: To develop a deep convolutional neural network capable of detecting spinal sclerotic metastases on body CTs.

Reflectance spectroscopy with operator difference for determination of behenic acid in edible vegetable oils by using convolutional neural network and polynomial correction.

Food chemistry
A novel polynomial correction method, order-adaptive polynomial correction (OAPC), was proposed to correct reflectance spectra with operator differences, and convolutional neural network (CNN) was used to develop analysis model to predict behenic aci...

A computational model of familiarity detection for natural pictures, abstract images, and random patterns: Combination of deep learning and anti-Hebbian training.

Neural networks : the official journal of the International Neural Network Society
We present a neural network model for familiarity recognition of different types of images in the perirhinal cortex (the FaRe model). The model is designed as a two-stage system. At the first stage, the parameters of an image are extracted by a pretr...

Continual learning for recurrent neural networks: An empirical evaluation.

Neural networks : the official journal of the International Neural Network Society
Learning continuously during all model lifetime is fundamental to deploy machine learning solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with recurrent neural networks could pave the way to a large number of ...

A CNN model embedded with local feature knowledge and its application to time-varying signal classification.

Neural networks : the official journal of the International Neural Network Society
A novel convolutional neural network is proposed for local prior feature embedding and imbalanced dataset modeling for multi-channel time-varying signal classification. This model consists of a single-channel signal feature parallel extraction unit, ...

Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks.

Computers in biology and medicine
Malaria is a serious disease responsible for thousands of deaths each year. Many efforts have been made to aid in the diagnosis of malaria using machine learning techniques, but to date, the presence of other elements that may interfere with the reco...

Protein-Peptide Binding Site Detection Using 3D Convolutional Neural Networks.

Journal of chemical information and modeling
Peptides and peptide-based molecules represent a promising therapeutic modality targeting intracellular protein-protein interactions, potentially combining the beneficial properties of biologics and small-molecule drugs. Protein-peptide complexes occ...