AIMC Topic: Time Factors

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Stochastic Stability Analysis for Stochastic Coupled Oscillator Networks with Bidirectional Cross-Dispersal.

Computational intelligence and neuroscience
It is well known that stochastic coupled oscillator network (SCON) has been widely applied; however, there are few studies on SCON with bidirectional cross-dispersal (SCONBC). This paper intends to study stochastic stability for SCONBC. A new and sui...

Prediction of Labor Unemployment Based on Time Series Model and Neural Network Model.

Computational intelligence and neuroscience
With the advent of big data, statistical accounting based on artificial intelligence can realistically reflect the dynamics of labor force and market segmentation. Therefore, based on the combination of machine learning algorithm and traditional stat...

Finite-Time H State Estimation for PDT-Switched Genetic Regulatory Networks With Randomly Occurring Uncertainties.

IEEE/ACM transactions on computational biology and bioinformatics
This article is concerned with the problem of finite-time H state estimation for switched genetic regulatory networks with randomly occurring uncertainties. The persistent dwell-time switching rule, as a more versatile class of switching rules, is co...

An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series.

IEEE transactions on neural networks and learning systems
Several techniques for multivariate time series anomaly detection have been proposed recently, but a systematic comparison on a common set of datasets and metrics is lacking. This article presents a systematic and comprehensive evaluation of unsuperv...

Correlation-Based Anomaly Detection Method for Multi-sensor System.

Computational intelligence and neuroscience
In industry, sensor-based monitoring of equipment or environment has become a necessity. Instead of using a single sensor, multi-sensor system is used to fully detect abnormalities in complex scenarios. Recently, physical models, signal processing te...

Deep compartment models: A deep learning approach for the reliable prediction of time-series data in pharmacokinetic modeling.

CPT: pharmacometrics & systems pharmacology
Nonlinear mixed effect (NLME) models are the gold standard for the analysis of patient response following drug exposure. However, these types of models are complex and time-consuming to develop. There is great interest in the adoption of machine-lear...

TimeREISE: Time Series Randomized Evolving Input Sample Explanation.

Sensors (Basel, Switzerland)
Deep neural networks are one of the most successful classifiers across different domains. However, their use is limited in safety-critical areas due to their limitations concerning interpretability. The research field of explainable artificial intell...

Deep Multi-Scale Residual Connected Neural Network Model for Intelligent Athlete Balance Control Ability Evaluation.

Computational intelligence and neuroscience
Athlete balance control ability plays an important role in different types of sports. Accurate and efficient evaluations of the balance control abilities can significantly improve the athlete management performance. With the rapid development of the ...

The Synchronization Analysis of Cohen-Grossberg Stochastic Neural Networks with Inertial Terms.

Computational intelligence and neuroscience
The exponential synchronization (ES) of Cohen-Grossberg stochastic neural networks with inertial terms (CGSNNIs) is studied in this paper. It is investigated in two ways. The first way is using variable substitution to transform the system to another...

Deep learning on time series laboratory test results from electronic health records for early detection of pancreatic cancer.

Journal of biomedical informatics
The multi-modal and unstructured nature of observational data in Electronic Health Records (EHR) is currently a significant obstacle for the application of machine learning towards risk stratification. In this study, we develop a deep learning framew...