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Receiver function analysis is perhaps the most popular tool amongst seismologists for estimating the receiver-side crustal structure using three-component recordings of teleseismic P-wave coda. Several methods have been suggested for successful construction of receiver function time series, and almost all of them involve deconvolution, the inverse of the convolution operation, in either the time or frequency domain. This paper presents an automated time-domain deconvolution approach employing truncated singular value decomposition iteratively during the computations. This theoretically straightforward approach does not need any regularization parameter, and results from its application to noise-containing synthetic seismograms demonstrate its effectiveness. A high level of compatibility between the receiver functions calculated using the recordings of the Lodumlu (LOD) station, located in central Anatolia, and results from previous seismological studies provides further evidence for the functionality of this new algorithm. Based on hypothetical experiments and real data applications, iteratively truncated singular value decomposition may be considered a reliable alternative to conventional receiver function calculation schemes. Simplicity, stability, and the need for minimal human interaction are the main advantages of the proposed technique. [ABSTRACT FROM AUTHOR]

In a non-line-of-sight (NLOS) environment, ultra-wide band (UWB) high accuracy positioning has been one of the hot topics in studying indoor positioning. In this paper, a factor graph-based UWB positioning algorithm has been proposed based on an improved Turkey robust kernel. It has overcome not only the defect of the least squares algorithm for UWB positioning against non-Gaussian noise but also eliminated the shortcoming of Turkey robust kernel against over-optimization. Aiming at the character of UWB data generally larger than its true value due to barriers, robust kernel will be added into merely big ranging data. However, due to the presence of small ranging data possibly caused by error positioning, the squares of residuals will be taken as the optimized objective function. The experimental result proves that UWB positioning algorithm based on the improved Turkey robust kernel outperforms ordinary UWB positioning algorithms in NLOS environments, with the average positioning accuracy improved by around 20–30%. [ABSTRACT FROM AUTHOR]

Ensemble learning methods are one of the most powerful tools for the pattern classification problems. In this paper, the effects of ensemble learning methods and some physical bone densitometry parameters on osteoporotic fracture detection were investigated. Six feature set models were constructed including different physical parameters and they fed into the ensemble classifiers as input features. As ensemble learning techniques, bagging, gradient boosting and random subspace (RSM) were used. Instance based learning (IBk) and random forest (RF) classifiers applied to six feature set models. The patients were classified into three groups such as osteoporosis, osteopenia and control (healthy), using ensemble classifiers. Total classification accuracy and f-measure were also used to evaluate diagnostic performance of the proposed ensemble classification system. The classification accuracy has reached to 98.85 % by the combination of model 6 ( five BMD + five T-score values) using RSM-RF classifier. The findings of this paper suggest that the patients will be able to be warned before a bone fracture occurred, by just examining some physical parameters that can easily be measured without invasive operations. [ABSTRACT FROM AUTHOR]

In these days, there are many various diseases, whose diagnosis is very hardly. Breast cancer is one of these type diseases. In this paper, accuracy diagnosis of normal, benign, and malign breast cancer cell were found by combining mean success rates Jensen Shannon, Hellinger, and Triangle measure which connected with each other. In this article, an diagnostic method based on feature extraction Discrete Wavelet Entropy Energy (DWEE) and Jensen Shannon, Hellinger, Triangle Measure (JHT) Classifier for diagnosis of breast cancer. This diagnosis method is called as DWEE-JHT this paper. With this diagnosis method have found optimal feature subset using discrete wavelet transform feature extraction. Then these convenient features are given to Jensen Shannon, Hellinger, Triangle Measure (JHT) classifier. Then, between classifiers which are Jensen Shannon, Hellinger, and triangle distance have been validated the measures via relationships. Afterwards, breast cancer cells are classified using Jensen Shannon, Hellinger, and Triangle distance. Mean success rate of 16 feature vector with Jensen Shannon classifier is found % 97.81. Mean success rate of 16 feature vector with Hellinger classifier is found % 97.75. Mean success rate of 16 feature vector with Triangle classifier is found % 97.87. By averaging of results obtained from these 3 classifiers are found as 97.81 % average of accuracy diagnosis. [ABSTRACT FROM AUTHOR]

An evapotranspiration method comparison was carried out by the International Water Management Institute (IWMI, Sri Lanka), at two locations in the Gediz Basin, Turkey, in the period from May to September 1998. In the IWMI study a number of ground-based techniques were compared with results obtained by remote sensing methods. Recently, a search of the satellite active archive yielded over 70 high quality level 1b images from NOAA/AVHRR over the same time period. The processing of these images with the SEBAL algorithm enabled us to build up a detailed time series of sensible and latent heat fluxes for a period of 120 days. In this paper a comparison is made between the sensible and latent heat fluxes determined from the present series of NOAA-14/AVHRR images and the results obtained earlier from various other prediction methods applied during the 1998 IWMI project. Specifically, the NOAA/SEBAL results are assessed against the scintillometer and temperature fluctuation methods. The results show that the NOAA derived evapotranspiration values follow the seasonal irrigation cycle quite well and correspond closely to the Landsat derived values, although they are lower than the results obtained with the traditional crop factor and Penman–Monteith methods. [ABSTRACT FROM AUTHOR]

The predictive ability of a hybrid model integrating the Firefly Algorithm (FFA), as a heuristic optimization tool with the Multilayer Perceptron (MLP-FFA) algorithm for the prediction of water level in Lake Egirdir, Turkey, is investigated. The accuracy of the hybrid MLP-FFA model is then evaluated against the standalone MLP-based model developed with the Levenberg-Marquadt optimization scheme applied for in the backpropagation-based learning process. To develop and investigate the veracity of the proposed hybrid MLP-FFA model, monthly time scale water level data for 56 years (1961-2016) are applied to train and test the hybrid model. The input combinations of the standalone and the hybrid predictive models are determined in accordance with the Average Mutual Information computed from the historical water level (training) data; generating four statistically significant lagged combinations of historical data to be adopted for the 1-month forecasting of lake water level. The proposed hybrid MLP-FFA model is evaluated with statistical score metrics: Nash-Sutcliffe efficiency, root mean square and mean absolute error, Wilmott’s Index and Taylor diagram developed in the testing phase. The analysis of the results showed that the hybrid MLP-FFA4 model (where 4 months of lagged combinations of lake water level data are utilized) performed more accurately than the standalone MLP4 model. For the fully optimized hybrid (MLP-FFA4) model evaluated in the testing phase, the Willmott’s Index was approximately 0.999 relative to 0.988 (MLP 4) and the root mean square error was approximately 0.029 m and compared to 0.102 m. Moreover, the inter-comparison of the forecasted and the observed data with various other performance metrics (including the Taylor diagram) verified the robustness of the proposed hybrid MLP-FFA4 model over the standalone MLP4 model applied in the problem of forecasting lake water level prediction in the current semi-arid region in Turkey. [ABSTRACT FROM AUTHOR]

Stereopsis of X-ray images can produce 3D tree of coronary arteries up to a certain accuracy level with a lower dose of radiation when compared to computer tomography (CT). In this study, a novel and complete automatic system is designed that covers preprocessing, segmentation, matching and reconstruction steps for that purpose. First, an automatic and novel pattern recognition technique is applied for extraction of the bifurcation points with their diameters recorded in a map. Then, a novel optimization algorithm is run for matching the branches efficiently which is based on that map and the epipolar geometry of stereopsis. Finally, cut branches are fixed one by one at the bifurcations for completing the 3D reconstruction. This method prevails the similar ones in the literature with this novelty since it automatically and inherently prevents the wrong overlapping of branches. Other essential problems like correct detection of the bifurcations and accurate calibration parameters and fast overlapping of matched branches are addressed at acceptable levels. The accuracy of bifurcation extraction is high at 90 % with 96 % sensitivity. Accuracy of vessel centerlines has rootmean-square (rms) error smaller than 0.57 mm for 20 different patients. For phantom model, rms error is 0.75 ± 0.8 mm in 3D localization. [ABSTRACT FROM AUTHOR]

With the increase in accumulated data and usage of the Internet, social media such as Twitter has become a fundamental tool to access all kinds of information. Therefore, it can be expressed that processing, preparing data, and eliminating unnecessary information on Twitter gains its importance rapidly. In particular, it is very important to analyze the information and make it available in emergencies such as disasters. In the proposed study, an earthquake with the magnitude of Mw = 6.8 on the Richter scale that occurred on January 24, 2020, in Elazig province, Turkey, is analyzed in detail. Tweets under twelve hashtags are clustered separately by utilizing the Social Spider Optimization (SSO) algorithm with some modifications. The sum-of intra-cluster distances (SICD) is utilized to measure the performance of the proposed clustering algorithm. In addition, SICD, which works in a way of assigning a new solution to its nearest node, is used as an integer programming model to be solved with the GUROBI package program on the test data-sets. Optimal results are gathered and compared with the proposed SSO results. In the study, center tweets with optimal results are found by utilizing modified SSO. Moreover, results of the proposed SSO algorithm are compared with the K-means clustering technique which is the most popular clustering technique. The proposed SSO algorithm gives better results. Hereby, the general situation of society after an earthquake is deduced to provide moral and material supports. [ABSTRACT FROM AUTHOR]

ANFIS is an artificial intelligence technique which is composed of a combination of artificial neural networks and fuzzy inference system. Due to its structure, it is used in modeling and identifying numerous systems in various fields. The training process of ANFIS is important to obtain effective results with it. So, a successful training algorithm should be used. In this study, the ANFIS is trained by using ABC algorithm for the solution of the real-world problem. For this purpose, the number of foreign visitors coming to Turkey from the USA, Germany, Bulgaria, France, Georgia, the Netherlands, England, Iran and Russia is estimated. In addition, total number of visitors coming to Turkey is also predicted. In applications, 150 months of data between July 2002 and December 2014 are utilized and a time series is created using these data. The results obtained using ABC algorithm are compared with GA, DE, HS and PSO. As a conclusion, it is seen that the results obtained using ABC algorithm for estimation of number of foreign visitors are more successful than other optimization methods. [ABSTRACT FROM AUTHOR]

Premise and consequent parameters of ANFIS are optimized by an optimization algorithm in its training process. A successful optimization algorithm should be utilized for an effective training process. In this study, an adaptive and hybrid artificial bee colony (aABC) algorithm, which is one of the variants of ABC algorithm, is employed in ANFIS training. aABC algorithm uses arithmetic crossover and adaptive neighborhood radius in the solution generating mechanism. aABC algorithm has gained the ability to obtain fast convergence and quality solution with these two control parameters. ANFIS is trained using aABC algorithm to obtain better solutions according to standard ABC algorithm. Firstly, five nonlinear static test systems are utilized for performance analysis of aABC algorithm. With aABC algorithm, performance increases up to about 16% compared to standard ABC algorithm. At the same time, better convergence is obtained in all examples. Wilcoxon signed rank test is applied to determine significance of the results. In addition, the results reached by aABC algorithm are compared with GA, PSO, HS algorithms and more effective results are found with aABC algorithm. As a result, it is seen that aABC algorithm is more successful than ABC, GA, PSO and HS in ANFIS training for identification of nonlinear static systems. Secondly, ANFIS is also trained by utilizing aABC algorithm for solving a real-world problem. Estimating number of foreign visitors coming to Turkey is selected as a real-world problem. The results obtained are compared standard with standard ABC algorithm, and more successful results are found by aABC algorithm. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the agreement between integrated management of childhood illness (IMCI) and final diagnosis in patients presenting with cough at the second and third level health institutions.Methods: This cross-sectional study included 373 children aged 2-60 mo who presented with cough at the pediatric emergency and outpatient clinics in the Department of Pediatrics. After clinical examination of children, body temperature, respiratory rate, saturation, presence or absence of the chest indrawing, rales, wheezing and laryngeal stridor were recorded. Cases were categorized according to IMCI algorithm regarding the severity using the color code, such as red (urgent treatment), yellow (treatment in the hospital), or green (treatment at home). Final diagnosis after physical examination, laboratory analysis and chest X-ray was compared with the IMCI algorithm.Results: Study agreement between IMCI classification and final diagnosis was 74.3% with kappa value 0.55 (moderate agreement). Similar agreement values were detected in both the second and third level health institutions. Health condition and gender did not affect agreement value. Agreement were found to be high in patients <24 mo of age (ĸ = 0.67), presence of fever and cough (ĸ = 0.54), tachypnea (ĸ = 0.93), chest indrawing (ĸ = 1.00) and oxygen saturation of <94%(ĸ = 0.90).Conclusions: Adding saturation level to the IMCI algorithmic diagnosis may increase agreement between IMCI classification and final diagnosis. [ABSTRACT FROM AUTHOR]

Neonatal jaundice is a common condition that occurs in newborn infants in the first week of life. Today, techniques used for detection are required blood samples and other clinical testing with special equipment. The aim of this study is creating a non-invasive system to control and to detect the jaundice periodically and helping doctors for early diagnosis. In this work, first, a patient group which is consisted from jaundiced babies and a control group which is consisted from healthy babies are prepared, then between 24 and 48 h after birth, 40 jaundiced and 40 healthy newborns are chosen. Second, advanced image processing techniques are used on the images which are taken with a standard smartphone and the color calibration card. Segmentation, pixel similarity and white balancing methods are used as image processing techniques and RGB values and pixels' important information are obtained exactly. Third, during feature extraction stage, with using colormap transformations and feature calculation, comparisons are done in RGB plane between color change values and the 8-color calibration card which is specially designed. Finally, in the bilirubin level estimation stage, kNN and SVR machine learning regressions are used on the dataset which are obtained from feature extraction. At the end of the process, when the control group is based on for comparisons, jaundice is succesfully detected for 40 jaundiced infants and the success rate is 85 %. Obtained bilirubin estimation results are consisted with bilirubin results which are obtained from the standard blood test and the compliance rate is 85 %. [ABSTRACT FROM AUTHOR]

The ensemble method has long been used to reduce the errors that are caused by initial conditions and/or parameterizations of models in forecasting problems. In this study, neural network (NN) simulations are applied to ensemble weather forecasting. Temperature forecasts averaged over 2 weeks from four different forecasts are used to develop the NN model. Additionally, an ensemble mean of bias-corrected data is used as the control experiment. Overall, ensemble forecasts weighted by NN with feed forward backpropagation algorithm gave better root mean square error, mean absolute error, and same sign percent skills compared to those of the control experiment in most stations and produced more accurate weather forecasts. [ABSTRACT FROM AUTHOR]

Group A rotaviruses are regarded as major enteric pathogens of large ruminants, while their impact on the health of small ruminants is not well documented. We report the detection of group A rotavirus from a large outbreak of enteritis that occurred in two goat flocks in the town of Kırklareli, Marmara Region, Turkey, in 2007. The disease was observed in young kids, with high morbidity and mortality rates, but not in adult animals. Rotavirus antigen was detected in the stools of the examined animals, and rotaviruses were isolated in MA104 cells. Upon sequencing of the VP4, VP6, VP7 and NSP4 genes, the strain (RVA/goat-tc/TUR/Kirklareli/2007/G8P[1]) was characterized as G8P[1], with E2 NSP4 and VP6 I2 genotype. These findings indicate that group A rotavirus should be included in the diagnostic algorithms for enteric disease in small ruminants. [ABSTRACT FROM AUTHOR]

Summary: The incidence of hip fractures in Turkey increased markedly from that reported in 1988/1989 so that FRAX® models for Turkey should be revised. Introduction: The MEDOS study in 1988/1989 reported that men and women from Turkey had exceptionally low rates of hip fracture. The aim of the FRACTURK study was to estimate current and future hip fracture risks and the prevalence of osteoporosis in Turkey. Methods: Hip fracture cases in 2009 were identified from interviews of a population-based sample of 26,424 residents aged 50 years or more in 12 different regions of Turkey and in two hospital surveys. Bone mineral density was evaluated by DXA in an age-stratified sample of 1,965 men and women. Results: Hip fracture incidence in the community-based survey was similar to that in the hospital survey. The age-specific incidence in men and women was substantially higher than that reported for 1988/1989. At the age of 50 years, the remaining lifetime probability of a hip fracture was 3.5% in men and 14.6% in women. In 2009, there were approximately 24,000 hip fractures estimated in Turkey, 73% of which were found in women. Assuming no change in the age- and sex-specific incidence, the number of hip fractures was expected to increase to nearly 64,000 in 2035. The prevalence of osteoporosis at the femoral neck was 7.5% and 33.3% in men and women, respectively, aged 50 years or more. Conclusion: Although Turkey is still among the countries with low hip fracture rates in Europe, the incidence has increased markedly in the last 20 years. This finding can be used to recalibrate fracture risk assessment models for Turkey. [ABSTRACT FROM AUTHOR]

The article presents the study results on the use of multilayer neural networks (MLNNs) with genetic algorithm on the diagnosis of tuberculosis disease. Topics discussed include the reference to the mycobacterium tuberculosis with its limited treatment capacities in developing countries, the two hidden layers with the training algorithm and the success of classification in comparison with reference studies. Also mentioned was the Levenberg-Marquardt training algorithm.