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2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018 -- 28 September 2018 through 30 September 2018, Corporate firms that aim to be permanent in the long term should not lose existing customers and need to win new customers. The increase in the number of firms offering similar services also increases the alternatives for customers. For this reason, there is no guarantee of long-term working with a customer. It has become compulsory to manage customer demands for companies that are in a highly competitive environment. In order to be able to sustain customer loyalty in the long term, they should better recognize them and provide quick returns to their demands. Firms are using help desk applications to manage these demands. Help desk applications are systems that aim to provide information and support to customers or end users about firms' services. In this study, customer demands were analyzed using text mining and machine learning algorithms and classified according to organizational workflow. The data sets used in the study and customer demands were obtained from help desk belonging to Detaysoft which offers live support to over 300 customers. © 2018 IEEE.

2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018 -- 28 September 2018 through 30 September 2018, Today, people often share their ideas, opinions and feelings through forums, social media sites, blogs and similar platforms. For this reason, access to these data has become very easy. Increase in the number of shares makes it possible to analyze and use these data in terms of marketing and politics. However, due to the large number of data, it is impossible that this analysis will be done by humans. Determination of what type of emotion is included automatically is done by sentiment analysis methods. In these methods, the text is defined as a mathematical vector and classified by machine learning methods. Ensemble methods are one of the most important methods used as classifiers in sentiment analysis. In these methods, a classifier error is tried to be solved by another classifier. In sentiment analysis, the feature vector that describes the text is as important as the classifier. Feature vectors obtained using different methods can make mistakes in different places. For this reason, in this study, NSEM is proposed for sentiment analysis, which is a new ensemble method that uses 2 different classifiers and 2 different feature extraction methods. As a result of the analysis, the proposed method is the most successful method with an accuracy rate of 79.1%. © 2018 IEEE.

2-s2.0-85088921078 Athletic performance is considered to be determined by the effect of genetic endowment and environmental factors like nutrition, regular training and psychology. Aim of the present study is to determine the effect of Interleukin-6 (IL-6) rsl800795 polymorphisms in Turkish National Skiing running athletes. A total of 55 individuals, 34 skiers and 21 non-skiers (controls) were enrolled for the study. DNA isolation from buccal samples of athletes was performed by using Invitrogen (Van Allen Way, Carlsbad, CA, USA) commercial kit. Genotyping process was carried out by real-time PCR. GG and GC genotypes were analyzed as equal in athletes. In controls, GG genotype was superior to other genotypes. There was no statistically significant difference in the terms of/L-tf rsl800795 polymorphism between athletes and controls. CC genotype was not detected in both groups. Our results revealed the importance of genetic factors in determining the athlete's sports performance, but there is need for more studies in the terms of IL-6 rsl800795 to clarify their effects on athletic performance. This first report, including the related genetic markers and skiing running athletes will guide scientists for further studies. © 2020 Parlar Scientific Publications. All rights reserved.

Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas, 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018, Three-dimensional structure prediction is one of the important problems in bioinformatics and theoretical chemistry. One of the most important steps in the three-dimensional structure prediction is the estimation of secondary structure. Improving the accuracy rate in protein secondary structure prediction depends on computed attributes as well as the classification algorithms. In multiple alignment methods, which are often used to extract an attribute, the calculated values differ according to the database used for the alignment. For this reason, it is important to use a suitable database against which the target proteins are aligned to compute profile feature vectors. In this study, 5 different datasets are generated for the CB513 benchmark with the aid of two different alignment methods and three different databases. The profile features are fed as input to a two-stage hybrid classifier. According to the experimental results, the highest accuracy rate is obtained when UniClust database is used at the first stage of HHBlits alignment to calculate PSSM values and NR database is used at the first stage of HHBlits alignment to calculate structural profile matrices. © 2018 IEEE.

Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas, 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018, Three-dimensional structure prediction is one of the important problems in bioinformatics and theoretical chemistry. One of the most important steps in the three-dimensional structure prediction is the estimation of secondary structure. Due to rapidly growing databases and recent feature extraction methods datasets used for predicting secondary structure can potentially contain a large number of samples and dimensions. For this reason, it is important to use algorithms that are fast and accurate. In this study, various classification algorithms have been optimized for the second phase of a two-stage classifier on EVAset benchmark both in the original input space and in the space reduced using the information gain metric. The most accurate classifier is obtained as the support vector machine while the extreme learning machine is significantly faster in model training. © 2018 IEEE.

Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas, 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018, Today, with the rapid development of internet and technology, the amount of information in the internet environment is increasing exponentially. Although increasing the number of texts with information is beneficial in terms of diversity, it also brings with problems such as missed research information and inability to access information quickly. To solve this problem, the computer-aided automatic systems that summarize the subject and topic in documents quickly can be useful. However, when looking at the literature, studies on automatic summarization are generally theoretically aimed at increasing summary success. Except a few web applications, the number of applications running as summaries is extremely low. In this study, an easy-to-use mobile application for automatic document summarization methods was developed. The summary of a document can be quickly and easily obtained at the desired time and place thanks to application, thus user needs can be met. In addition, summarizing same document in different forms according to needs is provided with 4 different methods of summarization left to the user's preference. As a result of the experiments on Turkish dataset, the application algorithms achieved a good Rouge-1 score of 0.59 on average. In addition, the application can extract the system summary in an acceptable time, such as an average of 18 seconds. © 2018 IEEE.

25th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017, In changing and constantly evolving information age, together with the developments in computer and internet technology, the production, digitization, storage and sharing of information has become much easier than in the past. The sharing of information via computer networks and the Internet has made information security a vital issue for people, institutions and organizations with critical data. Various information security policies have been established in order to protect the critical preserve data and prevent unauthorized access to this data. Intrusion detection systems which is one of the indispensable elements of information security policies, constantly monitor the network and the system to detect possible unauthorized access and infiltrations. So far, many machine learning methods such as artificial neural networks, support vector machines, decision trees have been used in intrusion detection systems. In this study, differently from other studies, autoencoder based deep learning machines are proposed for intrusion detection. KDDcup99 data set containing 22 attack types has been used in the study and a performance with 99.42% of succes rate has been achieved. © 2017 IEEE.

2nd International Conference on Computer Science and Engineering, UBMK 2017 -- 5 October 2017 through 8 October 2017, In parallel with the rapid development of prospective systems in the last 20 years, many methods have been applied to this field. One of them is the deep learning networks that have attracted the interest of researchers in recent years. The DBN (Deep Belief Network), which trains one layer at a time greedily, uses unsupervised learning for each layer and is composed of RBMs (Restricted Boltzman Machine), has become a turning point in this area. In this study, the deep learning method is applied to the recommender system problem. The Python-based deep learning library, Keras, is used and the existing learning algorithms are compared. © 2017 IEEE.

2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 -- 16 September 2017 through 17 September 2017, In today's technology, people are starting to share their opinions, ideas and feelings through many mediums because the internet is used extensively by every segment. These shares have become an important source of work on sentiment analysis and have led to increased work on this field. The sentiment analysis is simply to determine whether the emotion is included or not, and to determine whether the emotion is positive, negative, or neutral. In this study, chi-square, information gain, gain ratio, gini coefficient, oneR and reliefF methods are applied on the data sets according to the contents of movie comments and the obtained data sets are classified by Support Vector Machines (SVM). As a result of the application, it has been observed that the feature selection methods improve the results of sentiment analysis. © 2017 IEEE.

2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 -- 16 September 2017 through 17 September 2017, Fabric defect detection is vital for fabric quality. In the face of increasing fabric production, the fact that the detection of fabric faults by manpower is insufficient in terms of speed and quality has forced firms to work with automatic systems in this area. Until today, many methods have been developed to automatically detect fabric faults. Common purpose of many of these methods is to find some defective parts in the fabric by making some changes in image processing techniques or using machine learning methods. In this study, data sets obtained by applying local binary pattern and gray level co-occurrence matrix feature extraction methods on Tilda textile data are trained with artificial neural networks and two different models are created and success rates are compared. © 2017 IEEE.

25th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017, Today, with the rapid increase in the use of the internet, thousands of resources can be reached about an information that is interested. However, it is difficult and time consuming to determine which of these sources is useful. Automatic document summarization is a dimension reduction process which remains the important parts of the text. In this study, the TextRank algorithm, which is a graph based summarization approach, is used with 4 different similarity methods. The effect of these methods on the automatically generated summaries is examined. Among the similarity methods, Levenhesiten method was more successful than others with 0,506 Rouge-1 score. © 2017 IEEE.

2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 -- 16 September 2017 through 17 September 2017, Adaptive Network Based Fuzzy Inference Systems (ANFIS) is a hybrid artificial intelligence method that uses artificial neural network models with parallel computing and learning features and fuzzy logic extraction. The creation of models with more input parameter counts with ANFIS is not very convenient for applications. Dimension reduction methods are shown as a solution to this problem. Dimensional Reduction is the method used to represent the data in a lower dimensional space. Reduction of the number of input parameters by using Auto-Encoder and Principle Component Analysis and reduction of the number of input parameters and formation of the optimal solution of probing with ANFIS model constitute the framework of this work. © 2017 IEEE.

2nd International Conference on Computer Science and Engineering, UBMK 2017 -- 5 October 2017 through 8 October 2017, Today, with the development of the internet, documents containing information such as articles, news, web pages are produced and stored in digital environment. However, the increase in the number of media where people are able to add new contents such as social media, Twitter, and blog has increased the amount of information on the internet to enormous size. However, it is very difficult and time-consuming to determine whether or not information under research is reached. Automated document summarization systems can reduce the size of the text while keeping the important part of the text and present quickly whether the text contains the desired information. In this study, graph based document summarization methods are discussed. Besides the LexRank method, TextRank algorithm is used with 4 different similarity methods. Unlike other studies, Longest Common Subsequence (LCS), a similarity measure method, is used as a measure of similarity between nodes in the TextRank algorithm. Among the similarity measurement methods used, the longest subset achieved the best success by taking 0,510 Roguel and 0,266 Rouge-2 scores in English dataset. Similarly, the same method yields 0,742 Rouge-1 and 0,676 Rouge-2 scores in Turkish data set, which are better than other methods. © 2017 IEEE.

25th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017, Users have to require authentication with many times different a set of username and password which access various service providers and applications in their daily task and on social life. In this case, the user must need to memorize many pair of a set of username and password. This one is then to enforce the users using ordinary/same passwords or to keep note of passwords somewhere. It is a problem as a secret of private user information on social life which to generate more crucial problems for a business applications. To get rid of this problem, a single sign on (SSO) is suggested. SSO describing a set of username and password maintain multiply passwords to access for different service providers and applications. In this study, we argued out the prevalent and the current issue of SSO protocols in literature and CAS which is one of the SSO protocols, is used to examine a model of business application. © 2017 IEEE.

This paper provides a case study on application of wavelet techniques to analyze wind speed and energy (renewable and environmental friendly energy). Solar and wind are main sources of energy that allows farmers to have the potential for transferring kinetic energy captured by the wind mill for pumping water, drying crops, heating systems of green houses, rural electrification’s or cooking. Larger wind turbines (over 1 MW) can pump enough water for small–scale irrigation. This study tried to initiate data gathering process for wavelet analyses, different scale effects and their role on wind speed and direction variations. The wind data gathering system is mounted at latitudes: 37º 50″ N; longitude 30º 33″ E and height: 1200 m above mean sea level at a hill near Süleyman Demirel University campus. 10 minutes average values of two levels wind speed and direction (10m and 30m above ground level) have been recorded by a data logger between July 2001 and February 2002. Wind speed values changed between the range of 0 m/s and 54 m/s. Annual mean speed value is 4.5 m/s at 10 m ground level. Prevalent wind direction is south west. Artificial Neural Network analyses are also applied to predict wind speed values in winter and spring.

This study was aimed to investigate the possible effects of acute training on hypofyse hormone levels and some biochemical analytes in kick-boxers. The study was included 23 healthy professional male kick-boxers (age range: from 17 to 32 years and BMI range: from 19.0 to 24.2). Sportsmen were exposed to a total of 95-minute training program including training and a kick-boxing match. Venous blood samples and urine were taken from sportsmen just before and immediately after training and some biochemical analytes were analyzed. The evaluation of pre- and post-training serum levels of analytes have shown that growth hormone, adrenocorticotropic hormone, prolactine, throide stimulating hormone, adrenalin and noradrenalin levels were significantly increased and serum insulin-like growth factor I and free thyroxine levels were significantly decreased by training. There were no significant differences between pre- and post-training serum levels of other biochemical analytes; luteinizing hormone, follicle-stimulating hormone, free triiodothyronine, cortisole and dopamin. Our results have revealed the fact that exercise alters pituitary functions and this increment is associated with response to exercise stress. "We are in agree with the previous recommendations about regulary screening of pituitary functions and total hormonal status of competing and retired kick-boxers".

BackgroundDespite being used commonly in bovine medicine, information on reliability of point-of-care (POC) lactate meters is limited.

Brucellosis is an important zoonotic disease and the prevention is made by vaccination of animals. For ewes Brucella melitensis REV-I live attenuated vaccine is widely used but because of some risks such as being infected during the vaccination, re-infection of animals, a new vaccine with less side effect is need to be improved. In this Study it was aimed to determine a strong humoral immune response by encapsulating the Brucella outer membrane antigens with the optimum liposome formulation and the statistical analyses showed that the Brucella outer membrane extract encapsulated with liposome can evoke humoral immune response better than live attenuated B. melitensis REV-1 vaccine in mice.

The increase in the amount of content shared on social media makes it difficult to extract meaningful information from scientific studies. Accordingly, in recent years, researchers have been working extensively on sentiment analysis studies for the automatic evaluation of social media data. One of the focuses of these studies is sentiment analysis on tweets. The more tweets are available, the more features in terms of words exist. This leads to the curse of dimensionality and sparsity, resulting in a decrease in the success of the classification. In this study, Gini Index, Information Gain and Genetic Algorithm (GA) are used for feature selection and Support Vector Machines (SVMs), Artificial Neural Networks (ANN) and Centroid Based classification algorithms are used for the classification of Turkish tweets obtained from 3 different GSM operators. The feature selection methods are combined with the classification methods to investigate the effect on the success rate of analysis. Especially, when the SVMs are used with the GA as a hybrid, 96.8% success has been achieved for the classification of the tweets as positive or negative. © 2018, North Atlantic University Union. All rights reserved., We would like to express our special appreciation and thanks to Turkish Airlines for the financial support.

Asian Research Publishing Network (ARPN), In this study, an analysis was performed by examining the wind power potential of Ki{dotless}rklareli province which is in the west of Turkey. Statistical data between 2001 and 2007 was used in this study. The data was obtained from Ki{dotless}rklareli branch of State Meteorological Service. In Ki{dotless}rklareli region, wind speed forecasts regarding the year 2013 were made for windpower plants that are supposed to be built. WEKA tool was used for the performed analyzes. Algorithm which was used for forecasting is REPTree which is decision tree algorithm. There are two basic reasons to use REPTree algorithm. First, it produces better results compared to other machine learning methods, and secondly, the model produced with REPTree has a clear content. For this reason, new information can be gathered by using the tree model. This advantage of REPTree algorithm is combined with Bagging method and average model is generated by using the models produced by new training sets that are derived from the original training set. In this way, the model that will provide the highest accuracy rate is produced. The correlation coefficient value between the real and estimated values is obtained as 0,8154 by applying cross-validation method on the training set. This shows that REPTree can be used along with Bagging method for the wind speed forecasting of the year 2013. © 2005 - 2013 JATIT & LLS. All rights reserved.

Aytekin, İsmail (Balıkesir Author), The objective of the present study was to investigate the relationship between body condition score (BCS) and some metabolic blood parameters in postpartum (60 +/- 10 days) non-pregnant dairy cows. The animals stemming from 2 farms in Erzurum province were assigned into two groups according to their BCS as thin cows (group I, n = 6, BCS: 2.29 +/- 0.04 units) and normal cows (group II, n = 8, BCS: 2.81 +/- 0.05 units) and in parallel, serum biochemical parameters were determined. BCS has significantly differed between the 2 groups and was significantly correlated with AST (aspartate aminotransferase) activity and BUN (blood urea nitrogen) concentration. In cows with medium BCS, the mean glycaemia was significantly depressed compared to the thin animals whereas significant increases in mean BUN and AST activity were observed. In addition, although the mean values have not significantly differed between the 2 groups, it was also recorded that BHBA (beta-hydroxy butyric acid), triglyceride and VLDL (very low density lipoproteins) concentrations and the proportions of free fatty acids tended to slightly increased in the group II, suggesting strengthened lipomobilisation whereas the LDL (low density lipoproteins) concentrations and cholesterolemia at a lesser extent tended to decrease. Slight increases in proteinemia and albuminemia noted in the group II were coupled with mild decreases in minerals, indicating low perturbations in the hydro-electrolytic equilibrium. The fT(3) (free triiodothyronine) concentrations remained similar between the 2 groups. These results suggest that the negative energy balance was more pronounced in cows with medium BCS, leading to a higher risk for metabolic disorders and that thyroid hormones appeared to be weakly involved in the regulation of lipid metabolism in the early lactation period., Commission for Ataturk University Scientific Research 2009/287