Using the Support Vector Regression method for telecommunication networks monitoring

Sheremet, Oleksii; Sadovoi, Oleksandr

doi:10.1109/infocommst.2016.7905320

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Support Vector Regression [73] is an algorithm that allows one to choose a range of acceptable errors, both through an acceptable error rate (ε) and through tuning beyond this acceptable error rate. The operation of the function is depicted as |y i − w i x i | ≤ ε.…”

Section: Activation Functions Used To Train the Neural Networkmentioning

confidence: 99%

Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan

et al. 2023

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The article provides an overview of the accuracy of various yield forecasting algorithms and offers a detailed explanation of the models and machine learning algorithms that are required for crop yield forecasting. A unified crop yield forecasting methodology is developed, which can be adjusted by adding new indicators and extensions. The proposed methodology is based on remote sensing data taken from free sources. Experiments were carried out on crops of cereals, legumes, oilseeds and forage crops in eastern Kazakhstan. Data on agricultural lands of the experimental farms were obtained using processed images from Sentinel-2 and Landsat-8 satellites (EO Browser) for the period of 2017–2022. In total, a dataset of 1600 indicators was collected with NDVI and MSAVI indices recorded at a frequency of once a week. Based on the results of this work, it is found that yields can be predicted from NDVI vegetation index data and meteorological data on average temperature, surface soil moisture and wind speed. A machine learning programming language can calculate the relationship between these indicators and build a neural network that predicts yield. The neural network produces predictions based on the constructed data weights, which are corrected using activation function algorithms. As a result of the research, the functions with the highest prediction accuracy during vegetative development for all crops presented in this paper are multi-layer perceptron, with a prediction accuracy of 66% to 99% (85% on average), and polynomial regression, with a prediction accuracy of 63% to 98% (82% on average). Thus, it is shown that the use of machine learning and neural networks for crop yield prediction has advantages over other mathematical modelling techniques. The use of machine learning (neural network) technologies makes it possible to predict crop yields on the basis of relevant data. The individual approach of machine learning to each crop allows for the determination of the optimal learning algorithms to obtain accurate predictions.

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Section: Activation Functions Used To Train the Neural Networkmentioning

confidence: 99%

Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan

et al. 2023

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Critical power prediction using SVM algorithms

Ryabtseva

Skomorokhov

2020

Procedia Computer Science

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Regression analysis of the performance of asynchronous electric motors on the basis of support vector machine (SVM)

Sheremet,

Sadovoi

2018

Comput. probl. electr. eng.

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The subject of the article is relevant, since the proposed method for performing a regression analysis of the operation of asynchronous motors does not have special requirements to the accuracy of measuring the quantities used in the regression analysis and to the volume of a training sample, so it can be used in modern embedded diagnostic systems. The research methods are based on the use of the support vector machine applied in the regression representation. In this approach, the parameters of a regression model are determined by solving a quadratic programming problem having only one solution. To determine the values of the parameters used to train the model, the general theory of transients in electric machines, methods of mathematical modeling, computational mathematics and methods for determining the symmetric components of generalized vectors are used in the paper. The regression model based on the support vector machine is used to determine the number of damaged rods of the short-circuited rotor of the asynchronous motor. The efficiency of the model has been confirmed by experimental studies. It has been established that a regression model with the radial basis kernel function has the least value of the mean square deviation. Thus, in cases where a regression relation between controlled coordinates must be used, the use of machine learning methods based on the vector space model whose purpose is to find dividing surfaces between classes located as far as possible from all points of the training set has considerable prospects.

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Using the Support Vector Regression method for telecommunication networks monitoring

Cited by 3 publications

References 4 publications

Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan

Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan

Critical power prediction using SVM algorithms

Regression analysis of the performance of asynchronous electric motors on the basis of support vector machine (SVM)

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