AIMC Topic: Models, Theoretical

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An ecologically constrained procedure for sensitivity analysis of Artificial Neural Networks and other empirical models.

PloS one
Sensitivity analysis applied to Artificial Neural Networks (ANNs) as well as to other types of empirical ecological models allows assessing the importance of environmental predictive variables in affecting species distribution or other target variabl...

Disturbance and uncertainty rejection performance for fractional-order complex dynamical networks.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the synchronization issue for a family of time-delayed fractional-order complex dynamical networks (FCDNs) with time delay, unknown bounded uncertainty and disturbance. A novel fractional uncertainty and disturbance estimator ...

Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks.

International journal of environmental research and public health
Diabetes is a chronic and noncommunicable but preventable disease that is affecting the Mexican population at worrying levels, being the first place in prevalence worldwide. Early diabetes detection has become important to prevent other health condit...

Theories of Error Back-Propagation in the Brain.

Trends in cognitive sciences
This review article summarises recently proposed theories on how neural circuits in the brain could approximate the error back-propagation algorithm used by artificial neural networks. Computational models implementing these theories achieve learning...

A fast kernel extreme learning machine based on conjugate gradient.

Network (Bristol, England)
Kernel extreme learning machine (KELM) introduces kernel leaning into extreme learning machine (ELM) in order to improve the generalization ability and stability. But the Penalty parameter in KELM is randomly set and it has a strong impact on the per...

Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework.

International journal of environmental research and public health
The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing r...

tRNA-DL: A Deep Learning Approach to Improve tRNAscan-SE Prediction Results.

Human heredity
BACKGROUND: tRNAscan-SE is the leading tool for transfer RNA (tRNA) annotation, which has been widely used in the field. However, tRNAscan-SE can return a significant number of false positives when applied to large sequences. Recently, conventional m...

Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space.

Journal of chemical information and modeling
Acute toxicity is one of the most challenging properties to predict purely with computational methods due to its direct relationship to biological interactions. Moreover, toxicity can be represented by different end points: it can be measured for dif...

Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting.

Magnetic resonance in medicine
PURPOSE: MRSI has shown great promise in the detection and monitoring of neurologic pathologies such as tumor. A necessary component of data processing includes the quantitation of each metabolite, typically done through fitting a model of the spectr...

A Deep Neural Network-Based Pain Classifier Using a Photoplethysmography Signal.

Sensors (Basel, Switzerland)
Side effects occur when excessive or low doses of analgesics are administered compared to the required amount to mediate the pain induced during surgery. It is important to accurately assess the pain level of the patient during surgery. We proposed a...