Yasin Gormez

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.

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.