AIMC Topic: Deep Learning

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Intrusion Detection System for IoT Based on Deep Learning and Modified Reptile Search Algorithm.

Computational intelligence and neuroscience
This study proposes a novel framework to improve intrusion detection system (IDS) performance based on the data collected from the Internet of things (IoT) environments. The developed framework relies on deep learning and metaheuristic (MH) optimizat...

A Deep Learning-Based Piano Music Notation Recognition Method.

Computational intelligence and neuroscience
In the era of rapid development of computer technology, piano music notation and electronic synthesis system can be established using computer technology, and the basic laws of music score can be analyzed from the perspective of image processing, whi...

A Comparative Study of Text Genres in English-Chinese Translation Effects Based on Deep Learning LSTM.

Computational and mathematical methods in medicine
In recent years, neural network-based English-Chinese translation models have gradually supplanted traditional translation methods. The neural translation model primarily models the entire translation process using the "encoder-attention-decoder" str...

The Masking Impact of Intra-Artifacts in EEG on Deep Learning-Based Sleep Staging Systems: A Comparative Study.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Elimination of intra-artifacts in EEG has been overlooked in most of the existing sleep staging systems, especially in deep learning-based approaches. Whether intra-artifacts, originated from the eye movement, chin muscle firing, or heart beating, et...

Deep-SAGA: a deep-learning-based system for automatic gaze annotation from eye-tracking data.

Behavior research methods
With continued advancements in portable eye-tracker technology liberating experimenters from the restraints of artificial laboratory designs, research can now collect gaze data from real-world, natural navigation. However, the field lacks a robust me...

Accelerating multi-echo chemical shift encoded water-fat MRI using model-guided deep learning.

Magnetic resonance in medicine
PURPOSE: To accelerate chemical shift encoded (CSE) water-fat imaging by applying a model-guided deep learning water-fat separation (MGDL-WF) framework to the undersampled k-space data.

Utility of unsupervised deep learning using a 3D variational autoencoder in detecting inner ear abnormalities on CT images.

Computers in biology and medicine
BACKGROUND AND PURPOSE: To examine the diagnostic performance of unsupervised deep learning using a 3D variational autoencoder (VAE) for detecting and localizing inner ear abnormalities on CT images.