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R&S - Research Studies Anatolia Journal (ISSN: 2630-6441)
Publisher Dr.Arif YILDIZ
ISSN-L 2630-6441
E-ISSN 2630-6441
IF(Impact Factor) 2024 Evaluation Pending
Website http://dergipark.org.tr/rs
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Title proper: R&s - research studies anatolia journal.
Original alphabet of title: Extended roman
Subject: Dewey : 300
Subject: SOCIAL SCIENCES
Publisher: Adıyaman: Arif Yıldız
Dates of publication: 2018- 9999
Frequency: Quarterly
Type of resource: Periodical
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Country: Turkey
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R&S - Research Studies Anatolia Journal R&S - Research Studies Anatolia Journal
Publishing House: Arif YILDIZ Subject(s): Politics / Political Sciences, Social Sciences, Economy, Literary Texts Frequency: 4 issues Online-ISSN: 2630-6441 Status: Active
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R & S - Research Studies Anatolia Journal is an academic published by the NATIONAL, REFEREEDand E-JOURNAL for Social and Humanities.
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Anatolian Studies is the flagship journal of the British Institute at Ankara and is published by Cambridge University Press. It publishes peer-reviewed research articles focused on Türkiye and the Black Sea littoral region in the fields of history, archaeology and related social sciences. The journal accepts new submissions via CUP’s ScholarOne protocol at https://mc.manuscriptcentral.com/ank
Correspondence, including issues relating to submission and requests for permission to reproduce material from volumes 1-61 (1951-2011), should be sent to Janine Su .
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Hard copies of volumes prior to 62 (2012) are available, subject to availability, from Oxbow Books .
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Gnss time series analysis with machine learning algorithms: a case study for anatolia.
1. Introduction
2.1. data acquiring.
Click here to enlarge figure
2.2. Evaluation of GNSS Data
3. methodology, 3.1. data segmentation and feature extraction, 3.2. accuracy and evaluation metrics, 4.1. preprocessing of the time series, 4.2. residual analysis, 5. discussion, 6. conclusions, supplementary materials, author contributions, data availability statement, acknowledgments, conflicts of interest, abbreviations.
GNSS | Global Navigational Satellite System |
TUSAGA | Turkish National Continuous GNSS Network |
RCMT | Regional Centroid Moment Tensor |
Mw | Moment Magnitude |
CME | Common Mode Error |
ML | Machine Learning |
LSTM | Long Short-Term Memory |
NAF | North Anatolian Fault |
EAF | East Anatolian Fault |
IGS | International GNSS Service |
AMLSTM NN | Attention Mechanism with Long Short-Time Memory Neural Network |
TP | True Positive |
TN | True Negative |
FP | False Positive |
FN | False Negative |
TP′ | True Positive Prime |
RNN | Recurrent Neural Network |
MSE | Mean Squared Error |
MAE | Mean Absolute Error |
RMSE | Root Mean Squared Error |
R | R-squared (Coefficient of Determination) |
ROC | Receiver Operating Characteristic |
AUC | Area Under the Curve |
GMT | Generic Mapping Tools |
Hyperparameter | Explanation |
---|---|
Number of trees | Number of boosting rounds |
Learning Rate | Step size shrinkage used to prevent overfitting |
Maximum depth | Maximum depth of a tree |
Minimum Child Weight | Minimum sum of instance weight (hessian) needed in a child |
Subsample | Fraction of observations to be randomly sampled for each tree |
Column sample | Fraction of features to be randomly sampled for each tree |
Hyperparameter | Explanation |
---|---|
Number of Layers | The depth of the network |
Number of Units per Layer | The number of memory cells in each layer |
Dropout Rate | The fraction of input units to drop during training to prevent overfitting |
Learning Rate | The step size for the optimizer |
Batch Size | The number of samples per gradient update |
Number of Epochs | The number of times the entire dataset is passed through the network during training |
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Category | Features |
---|---|
Statistical Features | Mean, Standard Deviation, Skewness, Kurtosis |
Frequency Features | Fourier Transform Coefficients |
Trend Features | Linear Regression Coefficients, Polynomial Regression Coefficients |
Metric Features | Maximum Displacement, Minimum Displacement, Range |
Threshold Features | Threshold features for discontinuity detection |
Actual | Predicted | |
---|---|---|
TP and (TP′) | TN | |
FP | FN |
Model | Hyperparameter | Initial Value |
---|---|---|
XGBoost | Number of trees | 200 |
XGBoost, LSTM | Learning Rate | 0.1 |
XGBoost | Maximum depth | 7 |
XGBoost | Minimum Child Weight | 3 |
XGBoost | Subsample | 0.9 |
XGBoost | Column sample | 0.8 |
LSTM | Number of Layers | 2 |
LSTM | Number of Units per Layer | 100 |
LSTM | Dropout Rate | 0.3 |
LSTM | Batch Size | 64 |
Hyperparameter | Tested Values | Selected Value |
---|---|---|
Number of trees | [100, 200, 300, 500, 1000] | 200 |
Learning Rate | [0.1, 0.2, 0.3] | 0.2 |
Maximum depth | [3, 5, 7, 9] | 7 |
Minimum Child Weight | [1, 3, 5, 7, 10] | 5 |
Subsample | [0.5, 0.7, 0.9, 1.0] | 0.9 |
Column sample | [0.3, 0.5, 0.7, 0.8, 1.0] | 0.8 |
Number of Layers | [1, 2, 3] | 2 |
Number of Units per Layer | [50, 100, 150] | 100 |
Dropout Rate | [0.2, 0.3, 0.4] | 0.3 |
Batch Size | [16, 32, 64, 128] | 64 |
Metric | XGBoost | LSTM |
---|---|---|
MAE | 1.8 | 2.0 |
RMSE | 2.2 | 2.5 |
0.84 | 0.81 |
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Özbey, V.; Ergintav, S.; Tarı, E. GNSS Time Series Analysis with Machine Learning Algorithms: A Case Study for Anatolia. Remote Sens. 2024 , 16 , 3309. https://doi.org/10.3390/rs16173309
Özbey V, Ergintav S, Tarı E. GNSS Time Series Analysis with Machine Learning Algorithms: A Case Study for Anatolia. Remote Sensing . 2024; 16(17):3309. https://doi.org/10.3390/rs16173309
Özbey, Volkan, Semih Ergintav, and Ergin Tarı. 2024. "GNSS Time Series Analysis with Machine Learning Algorithms: A Case Study for Anatolia" Remote Sensing 16, no. 17: 3309. https://doi.org/10.3390/rs16173309
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Clinical Polymorphism of Shereshevsky–Turner Syndrome in Rostov Oblast and the Northern Caucasus in 1978–1998
- Published: June 2001
- Volume 37 , pages 671–677, ( 2001 )
Cite this article
- E. P. Guskov 1 ,
- T. P. Shkurat 1 ,
- A. Ya. Polienko 1 ,
- S. S. Amelina 2 ,
- E. K. Timolyanova 2 ,
- S. V. Lomteva 2 ,
- B. V. Vladimirsky 3 &
- B. B. Vladimirsky 3
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Polymorphism of Shereshevsky–Turner syndrome (STS) was studied in 233 patients who were examined at medical genetic services of the Research Institute of Obstetrics and Pediatrics (Rostov-on-Don) and Rostov Regional Hospital from 1978 to 1998. The subjects examined were residents of the Rostov oblast (administrative region) (RO) and some settlements in the northern Caucasus (NC). The mean incidence rate of STS was 3.8 per 10 000 newborns in this region in the period studied. Most STS cases were accounted for by the X trisomy (60 and 66.6% in the RO and NC, respectively). The mosaic form of STS was found in 25% of cases in both RO and NC. Other cytogenetic forms were found in 13.5 and 8.33% of patients from the RO and NC, respectively. The clinical polymorphism of STS, dynamics of its manifestation during ontogeny, and anthropometric parameters of the patients were studied. The effects of the age of parents, the season and month of conception, occupational hazards at the parents' workplaces, and the place of residence on the risk of STS were analyzed. Factor analysis was used to determine the sets of the main clinical signs characteristic of different STS cytogenetic forms in the RO and NC populations.
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Guskov, E.P., Shkurat, T.P., Polienko, A.Y. et al. Clinical Polymorphism of Shereshevsky–Turner Syndrome in Rostov Oblast and the Northern Caucasus in 1978–1998. Russian Journal of Genetics 37 , 671–677 (2001). https://doi.org/10.1023/A:1016681426123
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Polymorphism of Shereshevsky-Turner syndrome (STS) was studied in 233 patients who were examined at medical genetic services of the Research Institute of Obstetrics and Pediatrics (Rostov-on-Don) and Rostov Regional Hospital from 1978 to 1998. The subjects examined were residents of the Rostov oblast (administrative region) (RO) and some settlements in the northern Caucasus (NC). The mean ...
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