Development of the method of forecasting the atmospheric air pollution parameters based on error correction by neural-like structures of the model of successive geometric transformations

Mishchuk, Oleksandra

doi:10.15587/2312-8372.2019.188743

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…The latter confirms the existence of the function of output signals and depends on a variety of system states and input signals -parameters of the patient's state. As in other monitoring devices, data gaps still remain a significant problem [18]. In [18], algorithms for eliminating gaps are presented and investigated.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…As in other monitoring devices, data gaps still remain a significant problem [18]. In [18], algorithms for eliminating gaps are presented and investigated. However, the proposed methods do not exclude an error, and the method of accelerating the process of data loss recovery, proposed [19], concerns only one parameter and does not apply to multiparameter time series.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Methods and tools of formation of general indexes for automation of devices in rehabilitative medicine for post-stroke patients

Trunov

Beglytsia

Gryshchenko

et al. 2021

EEJET

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The current processes of recovery of post-infarction and post-stroke patients in the context of the establishment of the institution of family doctors and insurance medicine are considered. It was proposed to introduce modules for automation of recovery process (MARP) to ensure procedures, quality of life and reduce labor costs during the period of long-term recovery. The forms of presentation of the model of the integral indicator are substantiated, which, in accordance with the requirements of the Ministry of Health, assesses the generalized indicator of the patient's condition (GPC), the quality of medical services and increases the efficiency of data compression. A consistent application of two Euclidean norms is proposed, which leads indicators of dissimilar physical nature to a limited metric space. The relationship between the lower and upper bounds of the GPC, the error, the width of the sliding window, and the values of the derivatives was established on the basis of the Taylor series expansion, geometric inequality and limited space. The model for evaluating the GPS as a lower bound and the method for generating information about its properties are substantiated. A three-level comparator is applied and an indicator vector (IV) is introduced as an informational addition to the time series. Additional capabilities for intelligent analysis are demonstrated. The model of GPC through IV is presented. The examples of IV values are used to demonstrate its applicability to the intelligent analysis of the recovery process. Openness, accessibility, transparency of GPC and IV as tools of KIT is implemented by the princes of public administration (PA) by reducing it to quantitative control and comparison if there are quantitative and qualitative indicators in the list. IV, sliding windows, as PA and KIT tools in software (SW) for a diagnostic conclusion and correction of the course of procedures, are numerically investigated. It is demonstrated on examples of a numerical experiment with software how the combined application of the method for calculating the GPC and IV effectively affects the compression ratio, increasing it to 60–75 %

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Methods and tools of formation of general indexes for automation of devices in rehabilitative medicine for post-stroke patients

Trunov

Beglytsia

Gryshchenko

et al. 2021

EEJET

View full text Add to dashboard Cite

show abstract

“…Ref. [13] proposes a method for predicting atmospheric air pollution based on neural networks or also the case of [14] using a similar method for fruit detection and classification.…”

Section: Introductionmentioning

confidence: 99%

Digitalization, Circular Economy and Environmental Sustainability: The Application of Artificial Intelligence in the Efficient Self-Management of Waste

Alonso

Forradellas

Morell³

et al. 2021

Sustainability

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The great advances produced in the field of artificial intelligence and, more specifically, in deep learning allow us to classify images automatically with a great margin of reliability. This research consists of the validation and development of a methodology that allows, through the use of convolutional neural networks and image identification, the automatic recycling of materials such as paper, plastic, glass, and organic material. The validity of the study is based on the development of a methodology capable of implementing a convolutional neural network to validate a reliability in the recycling process that is much higher than simple human interaction would have. The method used to obtain this better precision will be transfer learning through a dataset using the pre-trained networks Visual Geometric Group 16 (VGG16), Visual Geometric Group 19 (VGG19), and ResNet15V2. To implement the model, the Keras framework is used. The results conclude that by using a small set of images, and thanks to the later help of the transfer learning method, it is possible to classify each of the materials with a 90% reliability rate. As a conclusion, a model is obtained with a performance much higher than the performance that would be reached if this type of technique were not used, with the classification of a 100% reusable material such as organic material.

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Development of the method of forecasting the atmospheric air pollution parameters based on error correction by neural-like structures of the model of successive geometric transformations

Cited by 2 publications

References 18 publications

Methods and tools of formation of general indexes for automation of devices in rehabilitative medicine for post-stroke patients

Methods and tools of formation of general indexes for automation of devices in rehabilitative medicine for post-stroke patients

Digitalization, Circular Economy and Environmental Sustainability: The Application of Artificial Intelligence in the Efficient Self-Management of Waste

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