AIMC Topic: Models, Theoretical

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Weighted Incremental-Decremental Support Vector Machines for concept drift with shifting window.

Neural networks : the official journal of the International Neural Network Society
We study the problem of learning the data samples' distribution as it changes in time. This change, known as concept drift, complicates the task of training a model, as the predictions become less and less accurate. It is known that Support Vector Ma...

Statistical methods for validation of predictive models.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Predictive models are widely used in clinical practice. Despite of the increasing number of published AI systems, recent systematic reviews have identified lack of statistical rigor in the development and validation of predictive models. This work re...

An ASIP for Neural Network Inference on Embedded Devices with 99% PE Utilization and 100% Memory Hidden under Low Silicon Cost.

Sensors (Basel, Switzerland)
The computation efficiency and flexibility of the accelerator hinder deep neural network (DNN) implementation in embedded applications. Although there are many publications on deep neural network (DNN) processors, there is still much room for deep op...

Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models.

IEEE transactions on cybernetics
Fuzzy-rough cognitive networks (FRCNs) are recurrent neural networks (RNNs) intended for structured classification purposes in which the problem is described by an explicit set of features. The advantage of this granular neural system relies on its t...

Target Convergence Analysis of Cancer-Inspired Swarms for Early Disease Diagnosis and Targeted Collective Therapy.

IEEE transactions on neural networks and learning systems
Sensing and perception is generally a challenging aspect of decision-making. In the nanoscale, however, these processes face further complications due to the physical limitations of devising the nanomachines with more limited perception, more noise, ...

Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review.

International journal of environmental research and public health
COVID-19 is a disease caused by SARS-CoV-2 and has been declared a worldwide pandemic by the World Health Organization due to its rapid spread. Since the first case was identified in Wuhan, China, the battle against this deadly disease started and ha...

Data Pre-Processing Using Neural Processes for Modeling Personalized Vital-Sign Time-Series Data.

IEEE journal of biomedical and health informatics
Clinical time-series data retrieved from electronic medical records are widely used to build predictive models of adverse events to support resource management. Such data is often sparse and irregularly-sampled, which makes it challenging to use many...

Neural Networks Application for the Data of PID Controller for Acrobot.

TheScientificWorldJournal
Acrobots are a system that has levels of operating states in many investigated cases, and they are subjects to many events during operation due to the mechanisms of locomotion processes. These states have been investigated in specific situations. Due...

Machine learning to predict effective reaction rates in 3D porous media from pore structural features.

Scientific reports
Large discrepancies between well-mixed reaction rates and effective reactions rates estimated under fluid flow conditions have been a major issue for predicting reactive transport in porous media systems. In this study, we introduce a framework that ...

Formulating Convolutional Neural Network for mapping total aquifer vulnerability to pollution.

Environmental pollution (Barking, Essex : 1987)
Aquifer vulnerability mapping to pollution is topical research activity, and common frameworks such as the basic DRASTIC framework (BDF) suffer from the inherent subjectivity. This paper formulates an artificial intelligence modeling strategy based o...