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1

Kernel function based regression approaches for estimating the oxygen transfer performance of plunging hollow jet aerator

Kumar M., Tiwari N. K., Ranjan S.

Journal of Achievements in Materials and Manufacturing Engineering

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2019

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Vol. 95, nr 2

74--84

EN

Purpose: To evaluate the capability of various kernels employed with support vector regression (SVR) and Gaussian process regression (GPR) techniques in estimating the volumetric oxygen transfer coefficient of plunging hollow jets. Design/methodology/approach: In this study, a data set of 81 observations is acquired from laboratory experiments of hollow jets plunging on the surface of water in the tank. The jet variables: jet velocity, jet thickness, jet length, and water depth are varied accordingly and the values of volumetric oxygen transfer coefficient is computed. An empirical relationship expressing the oxygenation performance of plunging hollow jet aerator in terms of jet variables is formulated using multiple nonlinear regression. The performance of this nonlinear relationship is compared with various kernel function based SVR and GPR models. Models developed with the training data set (51 observations) are checked on testing data set (24 observations) for performance comparison. Sensitivity analysis is carried out to examine the influence of jet variables in effecting the oxygen transfer capabilities of plunging hollow jet aerator. Findings: The overall comparison of kernels yielded good estimation performance of Radial Basis Function kernel (RBF) and Pearson VII Function kernel (PUK) using the SVR technique which is followed by nonlinear regression, and other kernel function based regression models. Research limitations/implications: The results of the study pertaining to the performance of kernels are based on the current experimental conditions and the estimation potential of the regression models may fluctuate beyond the selection of current data range due to datadependant learning of the soft computing models. Practical implications: Volumetric oxygen transfer coefficient of plunging hollow jets can be predicted precisely using SVR model by employing RBF as kernel function as compared to empirical correlation and other kernel function based regression models. Originality/value: The comparative analysis of kernel functions is conducted in this study. In previous studies, the predictive modelling approaches are implemented in simulating the aeration properties of cylindrical solid jets only, while this paper simulates the volumetric oxygen transfer coefficient of diverging hollow jets with the jet variables by utilizing polynomial, normalized polynomial, PUK, and RBF kernels in SVR and GPR.

2

Accurate prediction of continuous blood glucose based on support vector regression and differential evolution algorithm

Hamdi T., Ben Ali J., Di Costanzo V., Fnaiech F., Moreau E., Ginoux J. M.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 2

362--372

EN

Type 1 diabetes (T1D) is a chronic disease requiring patients to know their blood glucose values in order to ensure blood glucose levels as close to normal as possible. Hence, the ability to predict blood glucose levels is of a great interest for clinical researchers. In this sense, the literature is rich with several solutions that can predict blood glucose levels. Unfortunately, these methods require the patient to specific their daily activities: meal intake, insulin injection and emotional factors, which can be error prone. To reduce this burden on the patent, this work proposes to use only continuous glucose monitoring (CGM) data to predict blood glucose levels independently of other factors. To support this, support vector regression (SVR) and differential evolution (DE) algorithms were investigated. The proposed method is validated using real CGM data of 12 patients. The obtained average of root mean square error (RMSE) was 9.44, 10.78, 11.82 and 12.95 mg/dL for prediction horizon (PH) respectively equal to 15, 30, 45 and 60 min. The results of the present study and comparison with some previous works show that the proposed method holds promise. The SVR based on DE algorithm achieved high prediction accuracy while being robustness, automatic, and requiring no human intervention.

3

Using support vector regression in gene selection and fuzzy rule generation for relapse time prediction of breast cancer

Mahmoodian H., Ebrahimian L.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 3

466--472

EN

Gene expression profiles have been recently used in survival analysis, tumor classification and ER status identification. The prediction of breast cancer recurrence based on gene expression profile has been regarded in some previous studies in which the procedures were based on the concept of regression functions and fuzzy systems. In this study, a method based on the combination of these two concepts is presented; not only a method for gene selection, but also a systematic way to create fuzzy rules are going to be offered. Due to the ability of type-2 fuzzy systems in handling of uncertain systems, the proposed model is developed to type-2. The results show that this model has been improved in comparison to previous ones.

4

Comparison of machine learning methods for runoff forecasting in mountainous watersheds with limited data

Adamowski J., Prasher S. O.

Journal of Water and Land Development

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2012

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no. 17 [VII-XII]

89-97

EN

Runoff forecasting in mountainous regions with processed based models is often difficult and inaccurate due to the complexity of the rainfall-runoff relationships and difficulties involved in obtaining the required data. Machine learning models offer an alternative for runoff forecasting in these regions. This paper explores and compares two machine learning methods, support vector regression (SVR) and wavelet networks (WN) for daily runoff forecasting in the mountainous Sianji watershed located in the Himalayan region of India. The models were based on runoff, antecedent precipitation index, rainfall, and day of the year data collected over the three year period from July 1, 2001 and June 30, 2004. It was found that both the methods provided accurate results, with the best WN model slightly outperforming the best SVR model in accuracy. Both the WN and SVR methods should be tested in other mountainous watershed with limited data to further assess their suitability in forecasting.

PL

Prognozowanie spływu z obszarów górskich z użyciem programowanych modeli jest często trudne i niedokładne z powodu złożonych zależności między opadem a spływem i problemów związanych z pozyskaniem niezbędnych danych. Modele uczenia maszynowego stwarzają alternatywę dla prognozowania spływu z takich regionów. W pracy analizowano i porównano dwie metody uczenia maszynowego - metodę regresji wektorów nośnych (SVR) i sieci falkowych (WN) do dobowego prognozowania spływu w górskiej zlewni Sianji, usytuowanej w indyjskiej części Himalajów. Modele opracowano na podstawie danych o spływie, wskaźniku poprzednich opadów, opadzie i kolejnym dniu roku za trzyletni okres od 1 lipca 2001 r. do 30 czerwca 2004 r. Stwierdzono, że obie metody zapewniają dokładne wyniki, przy czym najlepszy model WN nieco przewyższa najlepszy model SVR pod względem dokładności. Obie metody powinny być testowane w innych zlewniach górskich o ograniczonej liczbie danych, aby lepiej ocenić ich przydatność do prognozowania.

5

Analytical study of different approaches to determine optimal cutting force model

Krizek Z., Jurkovic Z., Brezocnik M.

Archives of Materials Science

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2007

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Vol. 28, nr 1-4

69-74

EN

Determination of optimal machining parameters is an engineering task with aim to reduce the production cost and achieve desired product quality. Such exercise can be tackled on many different ways. The goal of this work is to present some of the possible approaches and to benchmark them among each other. These principles are analyzed: response surface methodology (RSM), evolutionary algorithms (GA & GP), support vector regression (SVR) and artificial neural networks (ANN). All methods implement completely different data handling philosophies with the same goal, to build the model which is able to predict cutting force in satisfying manner. Those aspects are chosen to be evaluated and compared: average percentage deviation of all data, ability to find generalized model and minimize the risk of over fitting and at least the runtime of each single model determination. Average percentage deviation is one of the best indicators of the quality of model. The ability to find generalized model is good indicator of the flexibility of model, and shows how model deals with unknown data. The runtime is important in a real time environment or in scenarios where conditions change frequently. Cutting force data used in this benchmark comes from experimental research of longitudinal turning process.