Method of Probability Distribution Fitting for Statistical Data with Small Sample Size

Kuzmin, Valeriyi; Zaliskyi, Maksym; Одарченко, Роман; Polishchuk, O. M.; Ivanets, Olga; Shcherbyna, Olga

doi:10.1109/acit49673.2020.9208842

Cited by 3 publications

(2 citation statements)

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“…The problem of finding the probability distribution for a signal has been tackled thoroughly in the literature [26,27]. Being a topic of interest, multiple frameworks have been developed to handle this problem.…”

Section: Finding the Probability Distribution For A Signalmentioning

confidence: 99%

GeMSyD: Generic Framework for Synthetic Data Generation

Tolas,

Portase,

Potolea

2024

Data

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In the era of data-driven technologies, the need for diverse and high-quality datasets for training and testing machine learning models has become increasingly critical. In this article, we present a versatile methodology, the Generic Methodology for Constructing Synthetic Data Generation (GeMSyD), which addresses the challenge of synthetic data creation in the context of smart devices. GeMSyD provides a framework that enables the generation of synthetic datasets, aligning them closely with real-world data. To demonstrate the utility of GeMSyD, we instantiate the methodology by constructing a synthetic data generation framework tailored to the domain of event-based data modeling, specifically focusing on user interactions with smart devices. Our framework leverages GeMSyD to create synthetic datasets that faithfully emulate the dynamics of human–device interactions, including the temporal dependencies. Furthermore, we showcase how the synthetic data generated using our framework can serve as a valuable resource for machine learning practitioners. By employing these synthetic datasets, we perform a series of experiments to evaluate the performance of a neural-network-based prediction model in the domain of smart device interaction. Our results underscore the potential of synthetic data in facilitating model development and benchmarking.

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Section: Finding the Probability Distribution For A Signalmentioning

confidence: 99%

GeMSyD: Generic Framework for Synthetic Data Generation

Tolas,

Portase,

Potolea

2024

Data

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“…At the beginning of the work you need to clearly determine the number of input parameters -this will be the number of input neurons, and the output parameters will determine the number of output neurons. The number of hidden layers of the neural network can be adjusted, and in this process it is necessary to conduct a series of experiments to determine the best option [12].…”

Section: Fig 1 Ercs Matrixmentioning

confidence: 99%

Model of Decision Making Using Artificial Neural Networks

Khrashchevskyi¹,

Ivanets²,

Нестеренко³

et al. 2022

ECS

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The article makes theoretical generalizations and provides promising solutions to the scientific and theoretical problem of human factor assessment in the safety management system based on predicting the occurrence of an adverse event that may involve risks in aviation activities. The current state and prospects of developing a proactive approach to the safety risk management system and the place of the human factor in identifying sources of danger are analyzed. Generalization and current prospects for the use of artificial neural networks for forecasting tasks and their place in the decision-making system, which allowed to identify unresolved issues, justify appropriate approaches to its solution, in particular to assess the possibility of adverse events of the cardiovascular system . A method for constructing an artificial neural network for forecasting biological risk objects based on a theoretical approach using a decision-making model has been developed. The use of artificial neural networks allowed to develop a model for predicting the occurrence of a negative event of sudden disruption of the functional state of the cardiovascular system of the operator.

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