A comparison of feature and semantic-based summarization algorithms for Turkish

Akyokuş, Selim (Dogus Author) -- Conference full title: International Symposium on Innovations in Intelligent Systems and Applicaitons, 21-24June 2010, Kayseri & Cappadocia,TURKEY.In this paper we analyze the performances of a feature-based and two semantic-based text summarization algorithms on a new Turkish corpus. The feature-based algorithm uses the statistical analysis of paragraphs, sentences, words and formal clues found in documents, whereas the two semanticbased algorithms employ Latent Semantic Analysis (LSA) approach which enables the selection of the most important sentences in a semantic way. Performance evaluation is conducted by comparing automatically generated summaries with manual summaries generated by a human summarizer. This is the first study that applies LSA based algorithms to Turkish text summarization and its results are promising

An efficient algorithm for 3D rectangular box packing

Akyokuş, Selim (Dogus Author) -- Conference full title: 9th International Conference, ETAI 2009, Ohrid, September 26-29, Republic of Macedonia, 2009.Getting highest occupancy rate of capacity of a container is very important for the companies, which deals in shipping or has shipping as a part of their main activities. They have to fit 3D boxes in container with optimum or nearest to optimum placement in order to ship more products with a minimum cost. The problem of fitting the boxes which is different from or the same to each other into a big container in optimum level, is called 3-dimensional packing problem. In this problem, the main objective is to minimize used container volume or wasted container space. This provides the reduction of costs in shipments with the use minimum number of containers

Modeling traders' behavior with deep learning and machine learning methods: Evidence from BIST 100 index

Although the vast majority of fundamental analysts believe that technical analysts' estimates and technical indicators used in these analyses are unresponsive, recent research has revealed that both professionals and individual traders are using technical indicators. A correct estimate of the direction of the financial market is a very challenging activity, primarily due to the nonlinear nature of the financial time series. Deep learning and machine learning methods on the other hand have achieved very successful results in many different areas where human beings are challenged. In this study, technical indicators were integrated into the methods of deep learning and machine learning, and the behavior of the traders was modeled in order to increase the accuracy of forecasting of the financial market direction. A set of technical indicators has been examined based on their application in technical analysis as input features to predict the oncoming (one-period-ahead) direction of Istanbul Stock Exchange (BIST100) national index. To predict the direction of the index, Deep Neural Network (DNN), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) classification techniques are used. The performance of these models is evaluated on the basis of various performance metrics such as confusion matrix, compound return, and max drawdown

Document clustering using GIS visualizing and EM clustering method

Full conference title: 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) 19 - 21 June 2013, Albena, BulgariaThis paper uses expectation-maximization clustering algorithm and a simple multidimensional projection method for visualization and data reduction. The multidimensional data is projected into a 2D Cartesian coordinate system. We run EM and K-Means algorithms on the transformed data. The system uses Microsoft Spatial Data Base Engine as a GIS tool for visualization. We used Expectation-Maximization (EM) and K-Means clustering algorithms of the Microsoft Analysis Services. The simple multidimensional projection method used in this paper tries to preserve the similarity relationships in original datasets.IEEE; Bulgarian Sci Acad; Bulgarian Acad Sci, Inst Informat & Commun Technologies; IEEE Bulgarian Sectio

N-gram pattern recognition using multivariate-Bernoulli model with smoothing methods for text classification

Kilimci, Zeynep Hilal (Dogus Author) -- Akyokuş, Selim (Dogus Author) -- Conference full title: 24th Signal Processing and Communication Application Conference, SIU 2016; Zonguldak; Turkey; 16 May 2016 through 19 May 2016.Bu yayında esasen metin sınıflandırma alanında n-gram modeller üzerine odaklandık. N-gram modellerin sınıflandırma başarısı üzerindeki etkisini ölçmek için Naïve Bayes sınıflandırıcıya çeşitli yumuşatma yöntemlerini uyguladık. Naïve Bayes sınıflandırıcısı, metin sınıflandırmada genel olarak Bernoulli ve multinomial olmak üzere iki temel model üzerine inşa edilir. Araştırmacılar, metin sınıflandırma ve benzer alanlarda genellikle multinomial model ve Laplace yumuşatma metodunu birlikte kullanırlar. Bu çalışmanın amacı ise Naïve Bayes sınıflandırma başarısını her iki model için analiz edip n-gram modellerini farklı bir açıdan kullanarak göstermektir. İki model arasındaki çeşitli örüntüleri bulmak için deneylerimizi geniş bir Türkçe veri kümesi üzerinde yürüttük. Deney sonuçları, Bernoulli modelin uygun bir yumuşatma yöntemiyle kullanıldığında n-gram modellerin çoğunda daha iyi bir sonuç verebildiğini gösterdi.In this paper, we mainly study on n-gram models on text classification domain. In order to measure impact of n-gram models on the classification performance, we carry out Naïve Bayes classifier with various smoothing methods. Naïve Bayes classifier has generally used two main event models for text classification which are Bernoulli and multinomial models. Researchers usually address multinomial model and Laplace smoothing on text classification and similar domains. The objective of this study is to demonstrate the classification performance of event models of Naïve Bayes by analyzing both event models with different smoothing methods and using n-gram models from a different perspective. In order to find various patterns between two event models, we carry on experiments a large Turkish dataset. Experiment results indicate that Bernoulli event model with an appropriate smoothing method can outperform on most of the n-gram models

The Analysis of text categorization represented with word embeddings using homogeneous classifiers

Kilimci, Zeynep Hilal (Dogus Author) -- Conference full title: IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019; Sofia; Bulgaria; 3 July 2019 through 5 July 2019.Text data mining is the process of extracting and analyzing valuable information from text. A text data mining process generally consists of lexical and syntax analysis of input text data, the removal of non-informative linguistic features and the representation of text data in appropriate formats, and eventually analysis and interpretation of the output. Text categorization, text clustering, sentiment analysis, and document summarization are some of the important applications of text mining. In this study, we analyze and compare the performance of text categorization by using different single classifiers, an ensemble of classifiers, a neural probabilistic representation model called word2vec on English texts. The neural probabilistic based model namely, word2vec, enables the representation of terms of a text in a new and smaller space with word embedding vectors instead of using original terms. After the representation of text data in new feature space, the training procedure is carried out with the well-known classification algorithms, namely multivariate Bernoulli naïve Bayes, support vector machines and decision trees and an ensemble algorithm such as bagging, random subspace and random forest. A wide range of comparative experiments are conducted on English texts to analyze the effectiveness of word embeddings on text classification. The evaluation of experimental results demonstrates that an ensemble of algorithms models with word embeddings performs better than other classification algorithms that uses traditional methods on English texts

Predicting financial market in big data: Deep learning

Akyokus, Selim (Dogus Author) -- Conference full title: 2017 International Conference on Computer Science and Engineering (UBMK); Antalya, Turkey; 5 October 2017 through 8 October 2017Derin Öğrenme, büyük miktarda etiketsiz / denetlenmemiş veriden öğrenmeyi başarılı bir şekilde gerçekleştirebildiğinden, büyük verilerden anlamlı gösterimler ve desenler çıkartmayı cazip hale getiriyor. En nasit tanımıyla derin öğrenme, makine öğrenmesi yöntemlerinin büyük verilere uygulanması olarak ifade edilmektedir. Bu çalışmada finansal tahmin ve sınıflama problemlerde derin öğrenme hiyerarşik modellerinin nasıl kullanılabileceği araştırılmıştır. Menkul kıymetleri tasarlama ve fiyatlandırma, portföy oluşturma, risk yönetimi ve hisse senedi piyasası tahmini finans alanındaki önemli tahmin problemlerinden bazılarıdır.. Bu tip problemler, veri ve olaylar arasında karmaşık ilişkiler içeren veri kümelerine sahiptir. Bu karmaşık ilişkilerin tamamıyla ekonomik modelde temsil etmek çok zordur ve bazen imkansızdır. Derin öğrenme metotları veri arasındaki karmaşık ilişkileri temsil ederek, finans alanında standart metotlardan daha faydalı sonuçların elde edilmesine imkan sağlamaktadır. Bu çalışmada, derin öğrenme metotları tanıtılmış, hisse senedi piyasayı tahmin problemine uygulanmış ve başarılı sonuçlar elde edilmiştir.Deep Learning is appealing for learning from large amounts of unlabeled/unsupervised data, making it attractive for extracting meaningful representations and patterns from big data. Deep learning, by its simplest definition, is expressed as the application of machine learning methods to the big data. In this study, it was investigated how to apply hierarchical deep learning models for the problems in finance such as prediction and classification. The Design and pricing of securities, construction of portfolios, risk management and stock market forecasting are some of important prediction problems in finance. These kind of problems include large data sets with complex relationship among data and events. It is very difficult or sometimes impossible to represent these complex relationships in a full economic model. Deep learning methods, by representing complex relationships among data, allows the production of more useful results than standard methods in finance. In this study, we introduced and applied deep learning methods to stock market prediction problem and obtained successful results

Mood detection from physical and neurophysical data using deep learning models

Nowadays, smart devices as a part of daily life collect data about their users with the help of sensors placed on them. Sensor data are usually physical data but mobile applications collect more than physical data like device usage habits and personal interests. Collected data are usually classified as personal, but they contain valuable information about their users when it is analyzed and interpreted. One of the main purposes of personal data analysis is to make predictions about users. Collected data can be divided into two major categories: physical and behavioral data. Behavioral data are also named as neurophysical data. Physical and neurophysical parameters are collected as a part of this study. Physical data contains measurements of the users like heartbeats, sleep quality, energy, movement/mobility parameters. Neurophysical data contain keystroke patterns like typing speed and typing errors. Users' emotional/mood statuses are also investigated by asking daily questions. Six questions are asked to the users daily in order to determine the mood of them. These questions are emotion-attached questions, and depending on the answers, users' emotional states are graded. Our aim is to show that there is a connection between users' physical/neurophysical parameters and mood/emotional conditions. To prove our hypothesis, we collect and measure physical and neurophysical parameters of 15 users for 1 year. The novelty of this work to the literature is the usage of both combinations of physical and neurophysical parameters. Another novelty is that the emotion classification task is performed by both conventional machine learning algorithms and deep learning models. For this purpose, Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural network are employed as deep learning methodologies. Multinomial Naive Bayes (MNB), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and Decision Integration Strategy (DIS) are evaluated as conventional machine learning algorithms. To the best of our knowledge, this is the very first attempt to analyze the neurophysical conditions of the users by evaluating deep learning models for mood analysis and enriching physical characteristics with neurophysical parameters. Experiment results demonstrate that the utilization of deep learning methodologies and the combination of both physical and neurophysical parameters enhances the classification success of the system to interpret the mood of the users. A wide range of comparative and extensive experiments shows that the proposed model exhibits noteworthy results compared to the state-of-art studies

An improved 2D glass cutting solution with genetic algorithms

Akyokuş, Selim (Dogus Author) -- Conference full title: 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA 2011) Istanbul, Turkey, 15 - 18 June 20112D glass cutting is an important problem for glass manufacturers. The objective of 2D glass cutting is to minimize the amount of waste when cutting a whole glass sheet into several pieces according to given cutting orders. In this paper, three different algorithms applied for the solution of 2D glass cutting problem. The performances of solutions are compared. The genetic algorithm with initial population strategy improves the performance of the algorithm and provides better results.TUBITAK, IEEE