An ESR Study on 2,4 Diaminotoluene Exposed to Gamma Rays and Application of Machine Learning

Ceylan, Yusuf; Usta, K.; Aydoğmuş, Hacer Yumurtacı; Usta, Ayhan; Ceylan, N.; Aras, Erdal

doi:10.12693/aphyspola.130.184

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Machine learning is a technique functioning on the basis of logical or binary operations for modeling a problem. It performs data analysis according to automatic calculation procedures [28]. The techniques are divided into supervised and unsupervised.…”

Section: Machine Learningmentioning

confidence: 99%

Integration of Machine Learning Techniques and Control Charts for Multivariate Processes

2019

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Using multivariate control chart instead of univariate control chart for all variables in processes provides more time and labor advantages that are of signi cance in the relations among variables. However, the statistical calculation of the measured values for all variables is regarded as a single value in the control chart. Therefore, it is necessary to determine which variable(s) are the cause of the out-of-control signal. E ective corrective measures can only be developed when the causes of the fault(s) are correctly determined. The present study was aimed at determining the machine learning techniques that could accurately estimate the fault types. Through the Hotelling T 2 chart, out-ofcontrol signals were identi ed and the types of faults a ected by the variables were speci ed. Various machine learning techniques were used to compare classi cation performances. The developed model was employed in the evaluation of paint quality in a painting process. Arti cial Neural Networks (ANNs) was determined as the most successful technique in terms of the performance criteria. The novelty of this study lies in its classi cation of the faults according to their types instead of those of the variables. De ning the faults based on their types facilitates taking e ective and corrective measures when needed.

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Section: Machine Learningmentioning

confidence: 99%

Integration of Machine Learning Techniques and Control Charts for Multivariate Processes

2019

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“…Hence, we consider that the detection of the secondary oscillations or at least finding a relationship between disorders and the secondary oscillation patterns in higher-order Fourier domains can help the early diagnosis of some cardiac disorders. Indeed, the machine learning (ML) algorithms use the features of data to more accurately discriminate them into disordered or non-disordered classes (Boran and Diren 2017;Recioui et al 2017;Edla et al 2016), whereas finding a unique feature which has different values for distinct disorders and non-disorders' cases can be beneficial for such ML algorithms as well as increasing their accuracies in the discrimination of data (Yüksel et al 2017;Cömert and Kocamaz 2017;Ceylan et al 2016;Adar et al 2015;Dekhandji 2017).…”

Section: Introductionmentioning

confidence: 99%

Detection of abnormalities in heart rate using multiple Fourier transforms

Erkuş

Purutçuoğlu

2019

Int. J. Environ. Sci. Technol.

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Fourier transform (FT) is one of the transformation techniques to convert the time-domain signal into the frequency-domain signal. Due to its easy usage, it is applied in many engineering approaches where the data are made of periodic components. Electrocardiography (ECG) is an imaging modality which represents the data of electrophysiological activities of heart. ECG data are gathered from the electrodes that are placed on the specific locations on chest, and electrical activities of heart generally produce periodically shaped time series data. However, this periodicity can be perturbed, or side oscillations can occur due to certain abnormal activities in hearts. From our preliminary studies, we have found that the implementation of the FT multiple times in ECG datasets can be useful in the detection of main and hidden periodicities in autonomous applications. Especially when they are hard to be observed with the first FT. Hereby, this study aims to improve the accuracy of successful detection of such perturbations in ECG data by applying multiple FT and finding the relationship between the side oscillations and also detection of some features of the n th FT in datasets of various cardiac disorders

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“…Therefore, it is important to identify radical structures produced by irradiation. EPR is a powerful spectroscopic method for detecting radicals in the materials [7][8][9][10][11][12][13][14][15][16][17][18]. In this study, Calix [4]arene, labeled calix [4]arene (1), and 25,27-di(4-nitrobenzyl)-26,28-dihydroxycalix [4]arene, labeled calix [4]arene(2), were exposed to gamma rays and then radical structures of these calix [4]arenes were evaluated by EPR method.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Influences of Gamma Rays on Calix[4]Arene and 25,27-di(4-Nitrobenzyl)-26,28-Dihydroxycalix[4]Arene: EPR Study

et al. 2017

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In the present study, calix [4]arene and its derivate 25,27-di(4-nitrobenzyl)-26,28-dihydroxycalix[4]arene were irradiated for 72 hours by 60 Co gamma rays at dose rate of 0.720 kGy/h. These irradiated samples were analyzed by EPR method and it was found that one type of radical was produced in calix[4]arene, and two type of radicals were produced in 25,27-di(4-nitrobenzyl)-26,28-dihydroxycalix[4]arene. The measurements were performed in the temperature range of 273-400 K using x-band EPR spectrometer. It was observed that the spectra, obtained from two samples, have very weak temperature dependence. Hyperfine and g parameters were calculated for two samples.

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An ESR Study on 2,4 Diaminotoluene Exposed to Gamma Rays and Application of Machine Learning

Cited by 4 publications

References 8 publications

Integration of Machine Learning Techniques and Control Charts for Multivariate Processes

Integration of Machine Learning Techniques and Control Charts for Multivariate Processes

Detection of abnormalities in heart rate using multiple Fourier transforms

Comparative Study of Influences of Gamma Rays on Calix[4]Arene and 25,27-di(4-Nitrobenzyl)-26,28-Dihydroxycalix[4]Arene: EPR Study

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