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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]

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]