Evolutionary Algorithms for the Design of Neural Network Classifiers for the Classification of Pain Intensity

Mamontov, Danila; Polonskaia, Iana S.; Skorokhod, Alina; Semenkin, ‪Eugene; Kessler, Viktor; Schwenker, Friedhelm

doi:10.1007/978-3-030-20984-1_8

Cited by 8 publications

(6 citation statements)

References 28 publications

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“…In order to select the best suitable architecture of the CNN-based model reservoir detection without the direct involvement of the expert, we used the extension of the Fedot framework -the evolutionary NAS tool 3 that allows identifying the optimal architecture of the convolution neutral classifier [31].…”

Section: Deep Learning For the Seismic Slices Classificationmentioning

confidence: 99%

Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea

Nikitin,

Revin,

Hvatov

et al. 2021

Preprint

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The paper describes the usage of intelligent approaches for field development tasks that may assist a decision-making process. We focused on the problem of wells location optimization and two tasks within it: improving the quality of oil production estimation and estimation of reservoir characteristics for appropriate wells allocation and parametrization, using machine learning methods. For oil production estimation, we implemented and investigated the quality of forecasting models: physics-based, pure data-driven, and hybrid one. The CRMIP model was chosen as a physics-based approach. We compare it with the machine learning and hybrid methods in a frame of oil production forecasting task. In the investigation of reservoir characteristics for wells location choice, we automated the seismic analysis using evolutionary identification of convolutional neural network for the reservoir detection. The Volve oil field dataset was used as a case study to conduct the experiments. The implemented approaches can be used to analyze different oil fields or adapted to similar physics-related problems.

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Section: Deep Learning For the Seismic Slices Classificationmentioning

confidence: 99%

Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea

Nikitin,

Revin,

Hvatov

et al. 2021

Preprint

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“…Artificial neural networks (ANNs) are currently considered a powerful method for data analysis and pattern recognition [324]. In particular, the application of so-called deep learning neural networks have grown with great intensity in recent years [325].…”

Section: Future Trends and Conclusionmentioning

confidence: 99%

“…In particular, the application of so-called deep learning neural networks have grown with great intensity in recent years [325]. The design of the architecture of these networks remains an open area of study and their application to the objective evaluation of pain is clearly justified when physiological signals of different etiology are evaluated [324]. The objective measurement of pain through biomedical sensors is not ready for clinical use, hence there are enormous research and innovation opportunities in the field of the application of sensor technologies combined with artificial intelligence and machine learning techniques [319].…”

Section: Future Trends and Conclusionmentioning

confidence: 99%

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Naranjo-Hernández

Reina‐Tosina

Roa

2020

Sensors

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Non-oncologic chronic pain is a common high-morbidity impairment worldwide and acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely perceived as a subjective experience, what makes challenging its objective measurement. However, the physiological traces of pain make possible its correlation with vital signs, such as heart rate variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily activity monitoring or facial expressions, which can be acquired with diverse sensor technologies and multisensory approaches. As the assessment and management of pain are essential issues for a wide range of clinical disorders and treatments, this paper reviews different sensor-based approaches applied to the objective evaluation of non-oncological chronic pain. The space of available technologies and resources aimed at pain assessment represent a diversified set of alternatives that can be exploited to address the multidimensional nature of pain.

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Section: Introductionmentioning

confidence: 99%

“…Critically, however, there is no apparent consensus on the optimal set of features to use: the pioneering work on this question has explored a highly diverse field of potential physiological parameters. [14][15][16]24,39,40,48,49,59,65,81,82,84,90,94,95,99 In this study, we report preliminary pain prediction results using physiological data collected from chronic pain sufferers on our new Pain Meter. We investigated both individual-level models and an overall, population-level model, spanning various combinations of pulse and temperature features.…”

Section: Introductionmentioning

confidence: 99%

Preliminary study: quantification of chronic pain from physiological data

Cheng¹,

Santander

et al. 2022

PR9

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Evolutionary Algorithms for the Design of Neural Network Classifiers for the Classification of Pain Intensity

Cited by 8 publications

References 28 publications

Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea

Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Preliminary study: quantification of chronic pain from physiological data

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