AIMC Topic: Neural Networks, Computer

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MaTPIP: A deep-learning architecture with eXplainable AI for sequence-driven, feature mixed protein-protein interaction prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Protein-protein interaction (PPI) is a vital process in all living cells, controlling essential cell functions such as cell cycle regulation, signal transduction, and metabolic processes with broad applications that include ...

Sampled-data controller scheme for multi-agent systems and its Application to circuit network.

Neural networks : the official journal of the International Neural Network Society
The objective of this study is to investigate the synchronization criteria under the sampled-data control method for multi-agent systems (MASs) with state quantization and time-varying delay. Currently, a looped Lyapunov-Krasovskii Functional (LKF) h...

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.

Nature genetics
Deep learning methods have recently become the state of the art in a variety of regulatory genomic tasks, including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the ful...

SWSSL: Sliding Window-Based Self-Supervised Learning for Anomaly Detection in High-Resolution Images.

IEEE transactions on medical imaging
Anomaly detection (AD) aims to determine if an instance has properties different from those seen in normal cases. The success of this technique depends on how well a neural network learns from normal instances. We observe that the learning difficulty...

Stable Deep MRI Reconstruction Using Generative Priors.

IEEE transactions on medical imaging
Data-driven approaches recently achieved remarkable success in magnetic resonance imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to a lack of generalizability and interpretability. In this paper, we addres...

Frame-Level Teacher-Student Learning With Data Privacy for EEG Emotion Recognition.

IEEE transactions on neural networks and learning systems
Recently, electroencephalogram (EEG) emotion recognition has gradually attracted a lot of attention. This brief designs a novel frame-level teacher-student framework with data privacy (FLTSDP) for EEG emotion recognition. The framework first proposes...

Skeleton-Based Human Motion Prediction With Privileged Supervision.

IEEE transactions on neural networks and learning systems
Existing supervised methods have achieved impressive performance in forecasting skeleton-based human motion. However, they often rely on action class labels in both training and inference phases. In practice, it could be a burden to request action cl...

A Spatial-Temporal Graph Model for Pronunciation Feature Prediction of Chinese Poetry.

IEEE transactions on neural networks and learning systems
With the development of artificial intelligence, speech recognition and prediction have become one of the important research domains with wild applications, such as intelligent control, education, individual identification, and emotion analysis. Chin...

Glove-Based Hand Gesture Recognition for Diver Communication.

IEEE transactions on neural networks and learning systems
We have developed a smart dive glove that recognizes 13 static hand gestures used in diving communication. The smart glove employs five dielectric elastomer sensors to capture finger motion and implements a machine learning classifier in the onboard ...

Supervised Learning in Multilayer Spiking Neural Networks With Spike Temporal Error Backpropagation.

IEEE transactions on neural networks and learning systems
The brain-inspired spiking neural networks (SNNs) hold the advantages of lower power consumption and powerful computing capability. However, the lack of effective learning algorithms has obstructed the theoretical advance and applications of SNNs. Th...