Oguz Kaynar

Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

Research highlights? The use of multiple regression (MR), artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) models, for the prediction of swell percent of soils, was described and compared. ? However the accuracies of ANN and ANFIS models may be evaluated relatively similar, it is shown that the constructed ANN models of RBF and MLP exhibit a high performance than ANFIS and multiple regression for predicting swell percent of clays. ? The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. ? The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations. In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use of MLP and RBF functions of ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil was described, and compared with the traditional statistical model of MR (multiple regression). However the accuracies of ANN and ANFIS models may be evaluated relatively similar. It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations.

Neural computing models for prediction of permeability coefficient of coarse-grained soils

Correlations are very significant from the earliest days; in some cases, it is essential as it is difficult to measure the amount directly, and in other cases it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternate statistical tool, and new techniques such as artificial neural networks, fuzzy inference systems, genetic algorithms, and their hybrids were employed for developing the predictive models to estimate the needed parameters, in the recent years. Determination of permeability coefficient (k) of soils is very important for the definition of hydraulic conductivity and is difficult, expensive, time-consuming, and involves destructive tests. In this paper, use of some soft computing techniques such as ANNs (MLP, RBF, etc.) and ANFIS (adaptive neuro-fuzzy inference system) for prediction of permeability of coarse-grained soils was described and compared. As a result of this paper, it was obtained that the all constructed soft computing models exhibited high performance for predicting k. In order to predict the permeability coefficient, ANN models having three inputs, one output were applied successfully and exhibited reliable predictions. However, all four different algorithms of ANN have almost the same prediction capability, and accuracy of MLP was relatively higher than RBF models. The ANFIS model for prediction of permeability coefficient revealed the most reliable prediction when compared with the ANN models, and the use of soft computing techniques will provide new approaches and methodologies in prediction of some parameters in soil mechanics.

Soft Computing Approach in Modeling Energy Consumption

In this chapter, we build an intelligent model based on soft computing technologies to improve the prediction accuracy of Energy Consumption in Greece. The model is developed based on Genetic Algorithm and Co-Active Neuro Fuzzy Inference System (GACANFIS) for the prediction of Energy Consumption. For verification of the performance accuracy, the results of the propose GACANFIS model were compared with the performance of Backpropagation Neural network (BP-NN), Fuzzy Neural Network (FNN), and Co-Active Neuro Fuzzy Inference System (CANFIS). Performance analysis shows that the propose GACANFIS improve the prediction accuracy of Energy Consumption as well as CPU time. Comparison of the results with previous literature further proved the effectiveness of the proposed approach. The prediction of Energy Consumption is required for expanding capacity, strategy in Energy supply, investment in capital, analysis of revenue, and management of market research.

Intrusion detection with autoencoder based deep learning machine

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.

Deep learning based recommender systems

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.

Examining of single sign on protocols and a model of business application

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.

Graph based automatic document summarization with different similarity methods

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.

Comparison of graph based document summarization method

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.

Feature selection methods in sentiment analaysis

In todays 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.

Fabric defect detection with LBP-GLMC

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.