AIMC Topic: Algorithms

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Event-triggered impulsive control on quasi-synchronization of memristive neural networks with time-varying delays.

Neural networks : the official journal of the International Neural Network Society
This paper discusses the quasi-synchronization of memristive neural networks (MNNs) with time-varying delays via event-triggered impulsive and state feedback control approaches. The choice of different initial conditions may lead to the unexpected pa...

Artificial Intelligence Using Open Source BI-RADS Data Exemplifying Potential Future Use.

Journal of the American College of Radiology : JACR
OBJECTIVES: With much hype about artificial intelligence (AI) rendering radiologists redundant, a simple radiologist-augmented AI workflow is evaluated; the premise is that inclusion of a radiologist's opinion into an AI algorithm would make the algo...

Using neural attention networks to detect adverse medical events from electronic health records.

Journal of biomedical informatics
The detection of Adverse Medical Events (AMEs) plays an important role in disease management in ensuring efficient treatment delivery and quality improvement of health services. Recently, with the rapid development of hospital information systems, a ...

Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning.

Scientific reports
RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow fo...

Recurrent Neural Network for Predicting Transcription Factor Binding Sites.

Scientific reports
It is well known that DNA sequence contains a certain amount of transcription factors (TF) binding sites, and only part of them are identified through biological experiments. However, these experiments are expensive and time-consuming. To overcome th...

Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification.

Sensors (Basel, Switzerland)
Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this p...

Dual vigilance fuzzy adaptive resonance theory.

Neural networks : the official journal of the International Neural Network Society
Clusters retrieved by generic Adaptive Resonance Theory (ART) networks are limited to their internal categorical representation. This study extends the capabilities of ART by incorporating multiple vigilance thresholds in a single network: stricter (...

Deep Geodesic Learning for Segmentation and Anatomical Landmarking.

IEEE transactions on medical imaging
In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identificatio...

An improved support vector machine-based diabetic readmission prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In healthcare systems, the cost of unplanned readmission accounts for a large proportion of total hospital payment. Hospital-specific readmission rate becomes a critical issue around the world. Quantification and early ident...

Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization.

Proceedings of the National Academy of Sciences of the United States of America
Humans and most animals can learn new tasks without forgetting old ones. However, training artificial neural networks (ANNs) on new tasks typically causes them to forget previously learned tasks. This phenomenon is the result of "catastrophic forgett...