AIMC Topic: Neural Networks, Computer

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Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images.

Nature biomedical engineering
In machine learning for image-based medical diagnostics, supervised convolutional neural networks are typically trained with large and expertly annotated datasets obtained using high-resolution imaging systems. Moreover, the network's performance can...

Deep Learning Assisted Neonatal Cry Classification Support Vector Machine Models.

Frontiers in public health
Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio sign...

HiAM: A Hierarchical Attention based Model for knowledge graph multi-hop reasoning.

Neural networks : the official journal of the International Neural Network Society
Learning to reason in large-scale knowledge graphs has attracted much attention from research communities recently. This paper targets a practical task of multi-hop reasoning in knowledge graphs, which can be applied in various downstream tasks such ...

Dynamical robustness and its structural dependence in biological networks.

Journal of theoretical biology
We discuss the dynamical robustness of biological networks represented by directed graphs, such as neural networks and gene regulatory networks. The theoretical results indicate that networks with low indegree variance and high outdegree variance are...

Semi-Supervised Anomaly Detection in Video-Surveillance Scenes in the Wild.

Sensors (Basel, Switzerland)
Surveillance cameras are being installed in many primary daily living places to maintain public safety. In this video-surveillance context, anomalies occur only for a very short time, and very occasionally. Hence, manual monitoring of such anomalies ...

Forecasting Air Temperature on Edge Devices with Embedded AI.

Sensors (Basel, Switzerland)
With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network...

Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing.

Sensors (Basel, Switzerland)
Forage dry matter is the main source of nutrients in the diet of ruminant animals. Thus, this trait is evaluated in most forage breeding programs with the objective of increasing the yield. Novel solutions combining unmanned aerial vehicles (UAVs) an...

Benefits of deep learning classification of continuous noninvasive brain-computer interface control.

Journal of neural engineering
. Noninvasive brain-computer interfaces (BCIs) assist paralyzed patients by providing access to the world without requiring surgical intervention. Prior work has suggested that EEG motor imagery based BCI can benefit from increased decoding accuracy ...

Scaling Human-Object Interaction Recognition in the Video through Zero-Shot Learning.

Computational intelligence and neuroscience
Recognition of human activities is an essential field in computer vision. The most human activity consists of the interaction between humans and objects. Many successful works have been done on human-object interaction (HOI) recognition and achieved ...

Choosing a Metamodel of a Simulation Model for Uncertainty Quantification.

Medical decision making : an international journal of the Society for Medical Decision Making
BACKGROUND: Metamodeling may substantially reduce the computational expense of individual-level state transition simulation models (IL-STM) for calibration, uncertainty quantification, and health policy evaluation. However, because of the lack of gui...