Multistage SVR-RBF-Based Model for Heart Rate Prediction of Individuals

Izonin, Ivan; Tkachenko, Roman; Holoven, Rostyslav; Shavarskyi, Maksym; Bukin, Serhii; Shevchuk, Ihor

doi:10.1007/978-3-031-24468-1_19

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…The developed method in this paper is based on an approach that allows a smart IoT device to process only that part of the data it collects and transfer the predicted value as an additional attribute to the next one smart IoT device from the developed network. This approach is based on the ensemble of machine learning methods (Li et al, 2019) and is justified by the principles of linearization of the response surface (Izonin et al, 2023). It is the basis for the effective implementation of Edge computing.…”

Section: Modeling and Resultsmentioning

confidence: 99%

“…Also, we have compared our cascade with the existing SVR (with RBF kernel)-based cascade scheme (Izonin et al, 2023). The last method was developed using similar principles but without an additional module of non-linear input extension because RBF functions did the same.…”

Section: Discussionmentioning

confidence: 99%

“…It is explained by a significant increase in the number of independent attributes due to the application of Ito decomposition. The existing SVR (with RBF kernel)-based cascade scheme (Izonin et al, 2023) provides a good performance indicator (Figure 4). However, due to the principles of its training and test algorithms, this method cannot be implemented at the Edge computing.…”

Section: Discussionmentioning

confidence: 99%

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A cascade ensemble-learning model for the deployment at the edge: case on missing IoT data recovery in environmental monitoring systems

Izonin,

Tkachenko,

Krak

et al. 2023

Front. Environ. Sci.

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In recent years, more and more applied industries have relied on data collection by IoT devices. Various IoT devices generate vast volumes of data that require efficient processing. Usually, the intellectual analysis of such data takes place in data centers in cloud environments. However, the problems of transferring large volumes of data and the long wait for a response from the data center for further corrective actions in the system led to the search for new processing methods. One possible option is Edge computing. Intelligent data analysis in the places of their collection eliminates the disadvantages mentioned above, revealing many advantages of using such an approach in practice. However, the Edge computing approach is challenging to implement when different IoT devices collect the independent attributes required for classification/regression. In order to overcome this limitation, the authors developed a new cascade ensemble-learning model for the deployment at the Edge. It is based on the principles of cascading machine learning methods, where each IoT device that collects data performs its analysis based on the attributes it contains. The results of its work are transmitted to the next IoT device, which analyzes the attributes it collects, taking into account the output of the previous device. All independent at-tributes are taken into account in this way. Because of this, the proposed approach provides: 1) The possibility of effective implementation of Edge computing for intelligent data analysis, that is, even before their transmission to the data center; 2) increasing, and in some cases maintaining, classification/regression accuracy at the same level that can be achieved in the data center; 3) significantly reducing the duration of training procedures due to the processing of a smaller number of attributes by each of the IoT devices. The simulation of the proposed approach was performed on a real-world set of IoT data. The missing data recovery task in the atmospheric air state data was solved. The authors selected the optimal parameters of the proposed approach. It was established that the developed model provides a slight increase in prediction accuracy while significantly reducing the duration of the training procedure. However, in this case, the main advantage is that all this happens within the bounds of Edge computing, which opens up several benefits of using the developed model in practice.

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Section: Modeling and Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A cascade ensemble-learning model for the deployment at the edge: case on missing IoT data recovery in environmental monitoring systems

Izonin,

Tkachenko,

Krak

et al. 2023

Front. Environ. Sci.

Self Cite

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“…Kooh et al [13] employ supervised ML algorithms to model MB dye adsorption by Azolla [14] pinnata, aiming for accurate predictions of adsorption capacity across various conditions. SVR-RBF [15] emerges as the top-performing algorithm, achieving an R value of 0.994 with minimal error.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Neural Networks for Chemical Reaction Prediction: Insights from Methylene Blue Reduction Reactions

Malashin,

Tynchenko,

Gantimurov

et al. 2024

IJMS

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This paper offers a thorough investigation of hyperparameter tuning for neural network architectures using datasets encompassing various combinations of Methylene Blue (MB) Reduction by Ascorbic Acid (AA) reactions with different solvents and concentrations. The aim is to predict coefficients of decay plots for MB absorbance, shedding light on the complex dynamics of chemical reactions. Our findings reveal that the optimal model, determined through our investigation, consists of five hidden layers, each with sixteen neurons and employing the Swish activation function. This model yields an NMSE of 0.05, 0.03, and 0.04 for predicting the coefficients A, B, and C, respectively, in the exponential decay equation A + B · e−x/C. These findings contribute to the realm of drug design based on machine learning, providing valuable insights into optimizing chemical reaction predictions.

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Improved Architecture and the Synthesis Algorithm for Bithreshold Neural Network Classifier

Kotsovsky,

Batyuk

2023

2023 IEEE 18th International Conference on Computer Science and Information Technologies (CSIT)

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Multistage SVR-RBF-Based Model for Heart Rate Prediction of Individuals

Cited by 6 publications

References 23 publications

A cascade ensemble-learning model for the deployment at the edge: case on missing IoT data recovery in environmental monitoring systems

A cascade ensemble-learning model for the deployment at the edge: case on missing IoT data recovery in environmental monitoring systems

Optimizing Neural Networks for Chemical Reaction Prediction: Insights from Methylene Blue Reduction Reactions

Improved Architecture and the Synthesis Algorithm for Bithreshold Neural Network Classifier

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