AIMC Topic: Neural Networks, Computer

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Prediction of coronary heart disease based on combined reinforcement multitask progressive time-series networks.

Methods (San Diego, Calif.)
Coronary heart disease is the first killer of human health. At present, the most widely used approach of coronary heart disease diagnosis is coronary angiography, a surgery that could potentially cause some physical damage to the patients, together w...

MLCV: Bridging Machine-Learning-Based Dimensionality Reduction and Free-Energy Calculation.

Journal of chemical information and modeling
Importance-sampling algorithms leaning on the definition of a model reaction coordinate (RC) are widely employed to probe processes relevant to chemistry and biology alike, spanning time scales not amenable to common, brute-force molecular dynamics (...

Transparent Photovoltaics for Self-Powered Bioelectronics and Neuromorphic Applications.

The journal of physical chemistry letters
Inspired by the brain, future computation depends on creating a neuromorphic device that is energy-efficient for information processing and capable of sensing and learning. The current computation-chip platform is not capable of self-power and neurom...

Deep-Learning-Based Adaptive Advertising with Augmented Reality.

Sensors (Basel, Switzerland)
In this work we describe a system composed of deep neural networks that analyzes characteristics of customers based on their face (age, gender, and personality), as well as the ambient temperature, with the purpose of generating a personalized signal...

Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA.

Frontiers in public health
Over the last decade, the field of bioinformatics has been increasing rapidly. Robust bioinformatics tools are going to play a vital role in future progress. Scientists working in the field of bioinformatics conduct a large number of researches to ex...

MKA: A Scalable Medical Knowledge-Assisted Mechanism for Generative Models on Medical Conversation Tasks.

Computational and mathematical methods in medicine
Using natural language processing (NLP) technologies to develop medical chatbots makes the diagnosis of the patient more convenient and efficient, which is a typical application in healthcare AI. Because of its importance, lots of researches have com...

Multicomponent Spatial-Temporal Graph Attention Convolution Networks for Traffic Prediction with Spatially Sparse Data.

Computational intelligence and neuroscience
Predicting traffic data on traffic networks is essential to transportation management. It is a challenging task due to the complicated spatial-temporal dependency. The latest studies mainly focus on capturing temporal and spatial dependencies with sp...

Exploiting deep neural network and long short-term memory method-ologies in bioacoustic classification of LPC-based features.

PloS one
The research describes the recognition and classification of the acoustic characteristics of amphibians using deep learning of deep neural network (DNN) and long short-term memory (LSTM) for biological applications. First, original data is collected ...

Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning.

Sensors (Basel, Switzerland)
Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlyin...

Clustering by Errors: A Self-Organized Multitask Learning Method for Acoustic Scene Classification.

Sensors (Basel, Switzerland)
Acoustic scene classification (ASC) tries to inference information about the environment using audio segments. The inter-class similarity is a significant issue in ASC as acoustic scenes with different labels may sound quite similar. In this paper, t...