AIMC Topic: Neural Networks, Computer

Clear Filters Showing 12231 to 12240 of 31376 articles

Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition.

Sensors (Basel, Switzerland)
Human monitoring applications in indoor environments depend on accurate human identification and activity recognition (HIAR). Single modality sensor systems have shown to be accurate for HIAR, but there are some shortcomings to these systems, such as...

Electromagnetic Interference Effects of Continuous Waves on Memristors: A Simulation Study.

Sensors (Basel, Switzerland)
As two-terminal passive fundamental circuit elements with memory characteristics, memristors are promising devices for applications such as neuromorphic systems, in-memory computing, and tunable RF/microwave circuits. The increasingly complex electro...

ContransGAN: Convolutional Neural Network Coupling Global Swin-Transformer Network for High-Resolution Quantitative Phase Imaging with Unpaired Data.

Cells
Optical quantitative phase imaging (QPI) is a frequently used technique to recover biological cells with high contrast in biology and life science for cell detection and analysis. However, the quantitative phase information is difficult to directly o...

Application of artificial intelligence techniques for automated detection of myocardial infarction: a review.

Physiological measurement
Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals worldwide. To diagnose MI, clinicians need to interpret electrocardiog...

Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI.

IEEE transactions on neural networks and learning systems
Accurate prediction of clinical scores (of neuropsychological tests) based on noninvasive structural magnetic resonance imaging (MRI) helps understand the pathological stage of dementia (e.g., Alzheimer's disease (AD)) and forecast its progression. E...

Convolutional Ordinal Regression Forest for Image Ordinal Estimation.

IEEE transactions on neural networks and learning systems
Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression (OR) problem. Recent methods formulate an OR problem as a series of binary classification problems. Such methods cannot ensur...

Beneficial Perturbation Network for Designing General Adaptive Artificial Intelligence Systems.

IEEE transactions on neural networks and learning systems
The human brain is the gold standard of adaptive learning. It not only can learn and benefit from experience, but also can adapt to new situations. In contrast, deep neural networks only learn one sophisticated but fixed mapping from inputs to output...

Massive-Scale Aerial Photo Categorization by Cross-Resolution Visual Perception Enhancement.

IEEE transactions on neural networks and learning systems
Categorizing aerial photographs with varied weather/lighting conditions and sophisticated geomorphic factors is a key module in autonomous navigation, environmental evaluation, and so on. Previous image recognizers cannot fulfill this task due to thr...

Toward Full-Stack Acceleration of Deep Convolutional Neural Networks on FPGAs.

IEEE transactions on neural networks and learning systems
Due to the huge success and rapid development of convolutional neural networks (CNNs), there is a growing demand for hardware accelerators that accommodate a variety of CNNs to improve their inference latency and energy efficiency, in order to enable...

Spiking Neural Network Regularization With Fixed and Adaptive Drop-Keep Probabilities.

IEEE transactions on neural networks and learning systems
Dropout and DropConnect are two techniques to facilitate the regularization of neural network models, having achieved the state-of-the-art results in several benchmarks. In this paper, to improve the generalization capability of spiking neural networ...