AIMC Topic: Time Factors

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Process identification and discrimination in the environmental dose rate time series of a radiopharmaceutical facility using machine learning techniques.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Multi-facility nuclear sites with research reactors have several environmental area gamma monitors in a network as a part of their surveillance capability. However, the routine release of low levels of Ar gas from the reactor is prone to interfere wi...

Unsupervised Anomaly Detection for Cars CAN Sensors Time Series Using Small Recurrent and Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Predictive maintenance in the car industry is an active field of research for machine learning and anomaly detection. The capability of cars to produce time series data from sensors is growing as the car industry is heading towards more connected and...

Recurrent neural network modeling of multivariate time series and its application in temperature forecasting.

PloS one
Temperature forecasting plays an important role in human production and operational activities. Traditional temperature forecasting mainly relies on numerical forecasting models to operate, which takes a long time and has higher requirements for the ...

AI-Driven sleep staging from actigraphy and heart rate.

PloS one
Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). H...

Design of continuous-time recurrent neural networks with piecewise-linear activation function for generation of prescribed sequences of bipolar vectors.

Neural networks : the official journal of the International Neural Network Society
A recurrent neural network (RNN) can generate a sequence of patterns as the temporal evolution of the output vector. This paper focuses on a continuous-time RNN model with a piecewise-linear activation function that has neither external inputs nor hi...

Input-to-state stability of positive delayed neural networks via impulsive control.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with the positivity and impulsive stabilization of equilibrium points of delayed neural networks (DNNs) subject to bounded disturbances. With the aid of the continuous dependence theorem for impulsive delay differential equati...

Finite-time cluster synchronization for complex dynamical networks under FDI attack: A periodic control approach.

Neural networks : the official journal of the International Neural Network Society
In this paper, the finite-time cluster synchronization problem is addressed for complex dynamical networks (CDNs) with cluster characteristics under false data injection (FDI) attacks. A type of FDI attack is taken into consideration to reflect the d...

Prediction meets time series with gaps: User clusters with specific usage behavior patterns.

Artificial intelligence in medicine
With mHealth apps, data can be recorded in real life, which makes them useful, for example, as an accompanying tool in treatments. However, such datasets, especially those based on apps with usage on a voluntary basis, are often affected by fluctuati...

An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury.

AMIA ... Annual Symposium proceedings. AMIA Symposium
A longstanding challenge surrounding deep learning algorithms is unpacking and understanding how they make their decisions. Explainable Artificial Intelligence (XAI) offers methods to provide explanations of internal functions of algorithms and reaso...

Aperiodic switching event-triggered stabilization of continuous memristive neural networks with interval delays.

Neural networks : the official journal of the International Neural Network Society
The stabilization problem is studied for memristive neural networks with interval delays under aperiodic switching event-triggered control. Note that, most of delayed memristive neural networks models studied are discontinuous, which are not the real...