Intensive care unit (ICU) is a very special unit of a hospital, where healthcare professionals provide treatment and, later, close follow-up to the patients. It is crucial to estimate mortality in ICU patients from many viewpoints. The purpose of this study is to classify the status of patients with acute kidney injury (AKI) in ICU as early mortality, late mortality, and survival by the application of Classification and Regression Trees (CART) algorithm to the patients' attributes such as blood urea nitrogen, creatinine, serum and urine neutrophil gelatinase-associated lipocalin (NGAL), alkaline phosphatase, lactate dehydrogenase (LDH), gamma-glutamyl transferase, laboratory electrolytes, blood gas, mean arterial pressure, central venous pressure and demographic details of patients. This study was conducted 50 patients with AKI who were followed up in the ICU. The study also aims to determine the significance of relationship between the attributes used in the prediction of mortality in CART and patients' status by employing the Kruskal-Wallis H test. The classification accuracy, sensitivity, and specificity of CART for the tested attributes for the prediction of early mortality, late mortality, and survival of patients were 90.00%, 83.33%, and 91.67%, respectively. The values of both urine NGAL and LDH on day 7 showed a considerable difference according to the patients' status after being examined by the Kruskal-Wallis H test.

Objective: Almost half of intensive care patients are affected by acute kidney injury (AKI). The purpose of this study is to determine parameters that can be used for predicting of early (within 28 days) and late (within 90 days) mortality in patients who are followed-up with AKI in intensive care units. Materials and Methods: In this study, a dataset that contains 50 patients with AKI in intensive care units was used. This dataset contains blood urea nitrogen, creatinine, plasma and urinary neutrophil gelatinase-associated lipocalin (NGAL), lactate dehydrogenase, alkaline phosphatase and gammaglutamyl transpeptidase values of patients who were admitted to intensive care for various reasons and who developed AKI on the days 1, 3 and 7. In addition to these values, laboratory results such as serum electrolytes on day 1, blood gas; vital signs such as mean arterial pressure, central venous pressure; and demographic data were also recorded. Data mining techniques were applied to determine correlation between all of these data and mortality. Results: The threshold level of urinary NGAL on day 7 was determined to be 69 ng/mL, and strong correlation was found between this threshold level and early mortality. Similarly, the threshold level of plasma NGAL on day 7 was determined to be 150 ng/mL, and this was highly correlated with early mortality. Besides, strong correlation was also found between the difference in the urinary NGAL levels on day 1 and 7, and early mortality. Conclusion: In this study, plasma and urinary NGAL levels were found to be closely related to early mortality in patients who were followed-up with AKI in intensive care units. On the other hand, any parameter associated with late mortality was not found.

Nowadays, one of the most common types of cancer is breast cancer. The early and accurate diagnosis of breast cancer has great importance in the treatment of the disease. In the diagnosis of breast cancer, histopathological analysis of cell and tissue specimens taken by biopsy is considered as the gold standard. Histopathological analysis is a tedious process that is highly dependent on the knowledge and experience of the pathologists. In this study; it is aimed to develop a computer-aided system that can reduce the workload of pathologists and help them in their diagnosis. An image set containing benign and malignant tumor images of breast cancer has been studied. To perform texture analysis on tumor images; first order statistics, Gabor and gray-level co-occurrence matrix (GLCM) feature extraction methods have been applied. Then, various classifiers were applied to the obtained feature matrices and their performances were compared. The highest classification accuracy was achieved 82.06% by Random Forests classifier with feature combination of Gabor and GLCM methods. The results presented here show that computer-assisted diagnosis of breast cancer is a promising field.

Liver biopsy is considered to be the gold standard for analyzing chronic hepatitis and fibrosis; however, it is an invasive and expensive approach, which is also difficult to standardize. Medical imaging techniques such as ultrasonography, computed tomography (CT), and magnetic resonance imaging are non-invasive and helpful methods to interpret liver texture, and may be good alternatives to needle biopsy. Recently, instead of visual inspection of these images, computer-aided image analysis based approaches have become more popular. In this study, a non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images. In this approach, some suitable regions of interests were selected on CT images and a comprehensive set of texture features were obtained from these regions using different methods, such as Gray Level Co-occurrence matrix (GLCM), Laws' method, Discrete Wavelet Transform (DWT), and Gabor filters. Afterwards, sequential floating forward selection and exhaustive search methods were used in various combinations for the selection of most discriminating features. Finally, those selected texture features were classified using two methods, namely, Support Vector Machines (SVM) and k-nearest neighbors (k-NN). The mean classification accuracy in pairwise group comparisons was approximately 95% for both classification methods using only 5 features. Also, performance of our approach in classifying liver fibrosis stage of subjects in the test set into 7 possible stages was investigated. In this case, both SVM and k-NN methods have returned relatively low classification accuracies. Our pairwise group classification results showed that DWT, Gabor, GLCM, and Laws' texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process. Fusing features from these better performing families further improved the classification performance. The results show that our approach can be used as a decision support system in especially pairwise fibrosis stage comparisons. (C) 2014 Elsevier B.V. All rights reserved.

Even though liver biopsy is critical for evaluating chronic hepatitis and fibrosis, it is an invasive, costly, and difficult to standardize approach. The developments in medical image processing and artificial intelligence methods have advanced the potential of using computer-aided diagnosis techniques in the classification of liver tissues. The aim of this study was to develop a non-invasive, cost-effective, and fast approach to specify fibrosis stage using the texture properties of computed tomography images of liver. Gray level co-occurrence matrix, discrete wavelet transform, and discrete Fourier transform were the image analysis tools in the feature extraction phase. Following dimension reduction of the texture features support vector machines and k-nearest neighbor methods were used in the classification phase of this study. Our results showed that our approach is feasible in fibrosis staging especially in pairwise stage comparisons with success rate of approximately 90%.

Current hand prostheses are mostly driven by electromyography (EMG) signal, and existing experiments have proved that multichannel EMG signal controls are not suitable due to early fatigue problems and high effort, requirements to perform even simple activities. Therefore, in this study we present a new voice-controlled active hand prosthesis to perform several basic tasks.We first designed a novel multifingered prosthetic hand with the ability of picking up and releasing objects. The prosthetic hand employs 3 DC motors and gears to transfer motion to the linked parts of the fingers. We used flexible thin-film, resistive force sensors at the fingertips of the prosthetic hand to adjust the grip force at the fingers. The second part of the study involves the use of speech recognition to control the prosthetic hand. The control circuit that we designed consisted of an HM2007 speech recognition IC and a PIC microcontroller to drive the DC motors moving the fingers.We implemented both the prosthetic hand and its speech recognition-based control electronics. As of now, we have programmed the control hardware to recognize simple pick up and release operations and have successfully tested them. In a future study, we will include more voice commands for the operation of the hand, such as a realistic handshake, and improve the cosmetics of the hand in order to make it look more natural.

Pulpa ve periradikuler doku hastaliklarinin cogu hem aerop hem de anaerop mikroorganizmalari iceren karisik bir mikroflora ile iliskilidir. Hekim tedavi ile ilgili etkili bir yontem belirleyebilmek icin mikroorganizma varligi ile endodontik hastalik arasindaki yakin iliskiyi kavramalidir. Endodontik enfeksiyonlardaki bakteri turlerini ortaya cikarmak kullanilmaktadir. Son yillarda molekuler yontemlerdeki gelismelere ragmen her iki metodun da bir takim dezavantajlari bulunmaktadir. Elektronik burun (EB) terimi, insan koku duyusunu taklit edebilen bir elektronik sistemi tanimlar. Elektronik burun sistemleri tip alaninda ve kullanilmaktadir. Bu sistemlerinin en onemli ozelligi, koku cesitlerini cok kisa bir sure icerisinde insan burnundaki hassasiyet derecesinde algilayip ayristirabilmesi ve sonucu objektif olarak sunmasidir. Bu calismada, kok kanal mikroorganizma turunun saf kulturunden elektronik burun sistemi kullanilarak koku verileri alinmistir. Bu veriler Ayirma siniflandirilmis, Mahalanobis ve ikinci dereceden fonksiyonlar (quadratic) olmak uzere 3 ayri metodun performansi incelenmistir. Mahalanobis ve ikinci dereceden fonksiyonlar kullanarak gerceklestirilen AA ile siniflandirmada % 100 basari elde edilmistir. Bu calisma ile EB cihazinin, mikroorganizma suspansiyonlarinin arastirilmistir

Background Root canal treatment is a debridement process which disrupts and removes entire microorganisms from the root canal system. Identification of microorganisms may help clinicians decide on treatment alternatives such as using different irrigants, intracanal medicaments and antibiotics. However, the difficulty in cultivation and the complexity in isolation of predominant anaerobic microorganisms make clinicians resort to empirical medical treatments. For this reason, identification of microorganisms is not a routinely used procedure in root canal treatment. In this study, we aimed at classifying 7 different standard microorganism strains which are frequently seen in root canal infections, using odor data collected using an electronic nose instrument. Method Our microorganism odor data set consisted of 5 repeated samples from 7 different classes at 4 concentration levels. For each concentration, 35 samples were classified using 3 different discriminant analysis methods. In order to determine an optimal setting for using electronic-nose in such an application, we have tried 3 different approaches in evaluating sensor responses. Moreover, we have used 3 different sensor baseline values in normalizing sensor responses. Since the number of sensors is relatively large compared to sample size, we have also investigated the influence of two different dimension reduction methods on classification performance. Results We have found that quadratic type dicriminant analysis outperforms other varieties of this method. We have also observed that classification performance decreases as the concentration decreases. Among different baseline values used for pre-processing the sensor responses, the model where the minimum values of sensor readings in the sample were accepted as the baseline yields better classification performance. Corresponding to this optimal choice of baseline value, we have noted that among different sensor response model and feature reduction method combinations, the difference model with standard deviation based dimension reduction or normalized fractional difference model with principal component analysis based dimension reduction results in the best overall performance across different concentrations. Conclusion Our results reveal that the electronic nose technology is a promising and convenient alternative for classifying microorganisms that cause root canal infections. With our comprehensive approach, we have also determined optimal settings to obtain higher classification performance using this technology and discriminant analysis.

Liver with cirrhosis emerges when the cells of liver begin to die and the tissues become a functional knot from these. In the diagnosis of fibrosis, the needle biopsy is a golden standard. Although this technique is a good technique in reaching accurate diagnosis, its being an invasive method arises disadvantage. The developments in medical image processing and artificial intelligence techniques have advanced the potential of using diagnosis system in classification of liver tissues. In this study, we have aimed at producing some objective measures using image analysis, which will be of assistance in the diagnosis of cirrhosis. In order to differentiate between regions of liver with cirrhosis and healthy parenchymal tissues, we have used first order statistical texture features and second order texture features computed from gray level cooccurrence matrix of liver computerized tomography (CT) images. Then liver CT images of healthy people and people with cirrhosis have been classified with support vector machines (SVM) by using all these acquired features. The most successful classification has been calculated as 85.19% with the method of 10 fold cross-validation.

In this paper, we have made medical application of a new artificial immune system named the information gain-based artificial immune recognition system (IG-AIRS) which is minimized the negative effects of taking into account all attributes in calculating Euclidean distance in shape-space representation which is used in many artificial immune systems. For medical data, microorganism dataset was applied in the performance analysis of our proposed system. Microorganism dataset was obtained using Cyranose 320 electronic nose. Our proposed system reached 92.35% classification accuracy with five-fold cross validation method. This result ensured that IG-AIRS would be helpful in classification of microorganism species based on laboratory tests, and would open the way to various microorganism species determine support by using electronic nose.

Identification of microorganisms causing root canal infections is an important step in the treatment of these infections. Cultivating the microorganism involved is a relatively difficult and time consuming process. Therefore, clinicians prefer to follow a treatment method based on their prior experience, rather than identifying the related pathogen microorganism and choosing a treatment strategy accordingly. In this study, we have acquired odor data using an electronic-nose equipment with 32 carbon polymer sensors, from pure cultures of 7 microorganisms which are typical causes of root canals infections. We have worked on 28 specimens that are prepared at the Microbiology Laboratory of Pharmacy Faculty. Therefore, there were 4 odor data samples for each of the 7 microorganism types. We have then processed odor data using different pre-processing and dimensions reduction methods and obtained 18 different datasets. We have finally classified these datasets into 7 groups using Discriminant Analysis (DA) and investigated performance of several subtypes of DA algorithm, namely linear, Mahalanobis and quadratic. We have observed that the quadratic approach produces relatively better classification performance. Besides, we have figured out the impact of different pre-processing methods on the classification accuracy.

In this paper, a new method is proposed for digital hardware implementation of Petri net-based specifications. The purpose of this paper is to introduce a new discrete event control system paradigm, where the control system is modeled with extended Petri nets and implemented as an asynchronous controller using circuit elements. The applicability of the proposed method is demonstrated by an asynchronous implementation of a Petri net-based discrete event control system (DECS) for an experimental manufacturing system using a Xilinx field programmable gate array (FPGA). Unlike microprocessor, microcontroller or programmable logic controller (PLC)-based software implementations or hardware-based synchronous implementations, the implementation method used in this paper is asynchronous and based on hardware offering very high speed to control fast plants at low cost. This paper is expected to serve as a guideline to show how to obtain very high speed, concurrent and asynchronous Petri net-based controllers.

This paper presents an asynchronous implementation of a Petri net based discrete event control system (DECS) using a Xilinx field programmable gate array (FPGA). Unlike microprocessor, microcontroller or programmable logic controller (PLC)-based software implementations, and hardwarebased synchronous implementations, the implementation method used in this paper is asynchronous and based on hardware offering very high speed to control fast plants at low cost. This paper reports on the successful implementation of a Petri net based DECS for an experimental manufacturing system by using the method proposed in (Uzam et al, 2001). The implementation of the controller is accomplished by using a Xilinx FPGA. This paper, therefore, is expected to serve as a guideline to show how to obtain very high speed, concurrent and asynchronous Petri net based controllers.

This paper shows, how Simple Automation Petri Nets (SAPN) can be used for modelling of Discrete Event Control Systems (DECS). The design of DECS consist of two steps namely modelling and implementation. At the first step, a model of a DECS is obtained to perform desired control tasks. Then the obtained model can be implemented by means of a microprocessor, microcontroller, PLC or FPGA. Finally the obtained controller can be used to control a DECS. The aim of this paper is to shed light on the use of SAPN as a formalism to control DES.