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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.

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Subject: SOCIAL SCIENCES

Publisher: Adıyaman: Arif Yıldız

Dates of publication: 2018- 9999

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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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JANUARY, APRIL, JULY, OCTOBER

WRITING LANGUAGE

TURKISH- ENGLISH

SUBJECTS OF WORKING

SOCIAL and HUMAN SCIENTIFIC

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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 .

Requests for permission to reproduce material from volume 62 (2012) onwards should be sent to Cambridge Journals: UK/ROW Permissions Manager: [email protected] ; US Rights and Permissions Manager: [email protected] .

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Electronic back-copies of Anatolian Studies are available for purchase from Cambridge Core and can be accessed by members.

Hard-copies of volumes 62 (2012) onwards are available, subject to availability, from Cambridge Journals: for the United Kingdom, Europe and Rest of the World contact [email protected] ; for the United States, Canada and Mexico contact [email protected] .

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.

research studies anatolia journal

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.

GNSSGlobal Navigational Satellite System
TUSAGATurkish National Continuous GNSS Network
RCMTRegional Centroid Moment Tensor
MwMoment Magnitude
CMECommon Mode Error
MLMachine Learning
LSTMLong Short-Term Memory
NAFNorth Anatolian Fault
EAFEast Anatolian Fault
IGSInternational GNSS Service
AMLSTM NNAttention Mechanism with Long Short-Time Memory Neural Network
TPTrue Positive
TNTrue Negative
FPFalse Positive
FNFalse Negative
TP′True Positive Prime
RNNRecurrent Neural Network
MSEMean Squared Error
MAEMean Absolute Error
RMSERoot Mean Squared Error
R R-squared (Coefficient of Determination)
ROCReceiver Operating Characteristic
AUCArea Under the Curve
GMTGeneric Mapping Tools
HyperparameterExplanation
Number of treesNumber of boosting rounds
Learning RateStep size shrinkage used to prevent overfitting
Maximum depthMaximum depth of a tree
Minimum Child WeightMinimum sum of instance weight (hessian) needed in a child
SubsampleFraction of observations to be randomly sampled for each tree
Column sampleFraction of features to be randomly sampled for each tree
HyperparameterExplanation
Number of LayersThe depth of the network
Number of Units per LayerThe number of memory cells in each layer
Dropout RateThe fraction of input units to drop during training to prevent overfitting
Learning RateThe step size for the optimizer
Batch SizeThe number of samples per gradient update
Number of EpochsThe number of times the entire dataset is passed through the network during training
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CategoryFeatures
Statistical FeaturesMean, Standard Deviation, Skewness, Kurtosis
Frequency FeaturesFourier Transform Coefficients
Trend FeaturesLinear Regression Coefficients, Polynomial Regression Coefficients
Metric FeaturesMaximum Displacement, Minimum Displacement, Range
Threshold FeaturesThreshold features for discontinuity detection
ActualPredicted
TP and (TP′)TN
FPFN
ModelHyperparameterInitial Value
XGBoostNumber of trees200
XGBoost, LSTMLearning Rate0.1
XGBoostMaximum depth7
XGBoostMinimum Child Weight3
XGBoostSubsample0.9
XGBoostColumn sample0.8
LSTMNumber of Layers2
LSTMNumber of Units per Layer100
LSTMDropout Rate0.3
LSTMBatch Size64
HyperparameterTested ValuesSelected 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
MetricXGBoostLSTM
MAE1.82.0
RMSE2.22.5
0.840.81
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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

research studies anatolia journal

  • 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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