Serdar Korukoğlu

Effective RED: An algorithm to improve RED's performance by reducing packet loss rate

Random early detection (RED) is an effective congestion control mechanism acting on the intermediate gateways. We describe a new active queue management scheme, Effective RED (ERED) that aims to reduce packet loss rates in a simple and scalable manner. We made a few change to packet drop function of existing RED scheme. The rest of the original RED remains unchanged. We achieve this by making several refinements and by controlling packet dropping function both with average queue size and instantaneous queue size. Simulations demonstrate that ERED achieves a highest throughput and lowest packet drops than RED, Blue, REM, FRED, LDC and SRED. Since ERED is fully compatible with RED, we can easily upgrade/replace the existing RED implementations by ERED.

Ensemble of keyword extraction methods and classifiers in text classification

Text classification is a domain with high dimensional feature space.Extracting the keywords as the features can be extremely useful in text classification.An empirical analysis of five statistical keyword extraction methods.A comprehensive analysis of classifier and keyword extraction ensembles.For ACM collection, a classification accuracy of 93.80% with Bagging ensemble of Random Forest. Automatic keyword extraction is an important research direction in text mining, natural language processing and information retrieval. Keyword extraction enables us to represent text documents in a condensed way. The compact representation of documents can be helpful in several applications, such as automatic indexing, automatic summarization, automatic classification, clustering and filtering. For instance, text classification is a domain with high dimensional feature space challenge. Hence, extracting the most important/relevant words about the content of the document and using these keywords as the features can be extremely useful. In this regard, this study examines the predictive performance of five statistical keyword extraction methods (most frequent measure based keyword extraction, term frequency-inverse sentence frequency based keyword extraction, co-occurrence statistical information based keyword extraction, eccentricity-based keyword extraction and TextRank algorithm) on classification algorithms and ensemble methods for scientific text document classification (categorization). In the study, a comprehensive study of comparing base learning algorithms (Naive Bayes, support vector machines, logistic regression and Random Forest) with five widely utilized ensemble methods (AdaBoost, Bagging, Dagging, Random Subspace and Majority Voting) is conducted. To the best of our knowledge, this is the first empirical analysis, which evaluates the effectiveness of statistical keyword extraction methods in conjunction with ensemble learning algorithms. The classification schemes are compared in terms of classification accuracy, F-measure and area under curve values. To validate the empirical analysis, two-way ANOVA test is employed. The experimental analysis indicates that Bagging ensemble of Random Forest with the most-frequent based keyword extraction method yields promising results for text classification. For ACM document collection, the highest average predictive performance (93.80%) is obtained with the utilization of the most frequent based keyword extraction method with Bagging ensemble of Random Forest algorithm. In general, Bagging and Random Subspace ensembles of Random Forest yield promising results. The empirical analysis indicates that the utilization of keyword-based representation of text documents in conjunction with ensemble learning can enhance the predictive performance and scalability of text classification schemes, which is of practical importance in the application fields of text classification.

Moving object detection and tracking by using annealed background subtraction method in videos: Performance optimization

In computer vision, moving object detection and tracking methods are the most important preliminary steps for higher-level video analysis applications. In this frame, background subtraction (BS) method is a well-known method in video processing and it is based on frame differencing. The basic idea is to subtract the current frame from a background image and to classify each pixel either as foreground or background by comparing the difference with a threshold. Therefore, the moving object is detected and tracked by using frame differencing and by learning an updated background model. In addition, simulated annealing (SA) is an optimization technique for soft computing in the artificial intelligence area. The p-median problem is a basic model of discrete location theory of operational research (OR) area. It is a NP-hard combinatorial optimization problem. The main aim in the p-median problem is to find p number facility locations, minimize the total weighted distance between demand points (nodes) and the closest facilities to demand points. The SA method is used to solve the p-median problem as a probabilistic metaheuristic. In this paper, an SA-based hybrid method called entropy-based SA (EbSA) is developed for performance optimization of BS, which is used to detect and track object(s) in videos. The SA modification to the BS method (SA-BS) is proposed in this study to determine the optimal threshold for the foreground-background (i.e., bi-level) segmentation and to learn background model for object detection. At these segmentation and learning stages, all of the optimization problems considered in this study are taken as p-median problems. Performances of SA-BS and regular BS methods are measured using four videoclips. Therefore, these results are evaluated quantitatively as the overall results of the given method. The obtained performance results and statistical analysis (i.e., Wilcoxon median test) show that our proposed method is more preferable than regular BS method. Meanwhile, the contribution of this study is discussed.

A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification

Abstract Typically performed by supervised machine learning algorithms, sentiment analysis is highly useful for extracting subjective information from text documents online. Most approaches that use ensemble learning paradigms toward sentiment analysis involve feature engineering in order to enhance the predictive performance. In response, we sought to develop a paradigm of a multiobjective, optimization-based weighted voting scheme to assign appropriate weight values to classifiers and each output class based on the predictive performance of classification algorithms, all to enhance the predictive performance of sentiment classification. The proposed ensemble method is based on static classifier selection involving majority voting error and forward search, as well as a multiobjective differential evolution algorithm. Based on the static classifier selection scheme, our proposed ensemble method incorporates Bayesian logistic regression, naive Bayes, linear discriminant analysis, logistic regression, and support vector machines as base learners, whose performance in terms of precision and recall values determines weight adjustment. Our experimental analysis of classification tasks, including sentiment analysis, software defect prediction, credit risk modeling, spam filtering, and semantic mapping, suggests that the proposed classification scheme can predict better than conventional ensemble learning methods such as AdaBoost, bagging, random subspace, and majority voting. Of all datasets examined, the laptop dataset showed the best classification accuracy (98.86%).

A feature selection model based on genetic rank aggregation for text sentiment classification

Sentiment analysis is an important research direction of natural language processing, text mining and web mining which aims to extract subjective information in source materials. The main challenge encountered in machine learning method-based sentiment classification is the abundant amount of data available. This amount makes it difficult to train the learning algorithms in a feasible time and degrades the classification accuracy of the built model. Hence, feature selection becomes an essential task in developing robust and efficient classification models whilst reducing the training time. In text mining applications, individual filter-based feature selection methods have been widely utilized owing to their simplicity and relatively high performance. This paper presents an ensemble approach for feature selection, which aggregates the several individual feature lists obtained by the different feature selection methods so that a more robust and efficient feature subset can be obtained. In order to aggregate the individual feature lists, a genetic algorithm has been utilized. Experimental evaluations indicated that the proposed aggregation model is an efficient method and it outperforms individual filter-based feature selection methods on sentiment classification.

A simulated annealing-based optimal threshold determining method in edge-based segmentation of grayscale images

Image segmentation is a significant low-level method of the image processing area. As the matter of the fact that there is no selected certainty in interpreting the computer vision problems, there are many likely solutions. Some morphological methods used in image segmentation cause over-segmentation problems. Region merging, the usage of markers and the usage of multi-scale are the solutions for the over-segmentation problems found in the literature. However, these approaches give rise to under-segmentation problem. Simulated annealing (SA) is an optimization technique for soft computing. In our study, the problem of image segmentation is treated as a p-median (i.e., combinatorial optimization) problem. Therefore, the SA is used to solve p-median problem as a probabilistic metaheuristic. In the optimization method that is introduced in this paper, optimal threshold has been obtained for bi-level segmentation of grayscale images using our entropy-based simulated annealing (ESA) method. In addition, this threshold is used in determining optimal contour for edge-based image segmentation of grayscale images. Compared to the available methods (i.e., Otsu, only-entropy and Snake method) in the literature, our ESA method is more feasible in terms of performance measurements, threshold values and coverage area ratio of the region of interest (ROI).

Seasonal affective disorder in eight groups in Turkey: a cross-national perspective

OBJECTIVE Previous estimates of the prevalence of seasonal affective disorder (SAD) in community-based samples generally originated from western countries. We report prevalence rates in eight groups from four latitudes in Turkey. METHOD Seasonal Pattern Assessment Questionnaire (SPAQ) was distributed to the community-based samples from eight different locations at four latitudes in Turkey. The prevalence rates of winter SAD and subsyndromal SAD (S-SAD) were estimated for the four groups at the same latitudes by using SPAQ responses. RESULTS We distributed 3229 SPAQs, had an overall response rate of 54.16% and 1749 SPAQs were included in the analyses. Seasonality was reported as a problem by 549 subjects (31.57%) of our 1749 respondents. Prevalence of winter SAD and S-SAD are estimated as 4.86 and 8.35%, respectively, for the whole group. Prevalence rates were determined for each center and for four latitudes (two centers at the same latitude were grouped as one). In Adana-Gaziantep (lt. 37), Izmir-Elaziğ (lt. 38), Eskişehir-Ankara (lt. 39) and Trabzon-Edirne (lt. 41), the prevalence rates for winter SAD were 6.66, 2.25, 8.00 and 3.76%, respectively. CONCLUSIONS Our prevalence estimates of winter SAD are similar to those found in previous community-based studies at the same latitudes; no correlation was found between latitude and prevalence of winter SAD, which could be related to the sampling methodology or to the fact that there were only 5 degrees of difference between the latitudes.

A software for performance evaluation and comparison of people detection and tracking methods in video processing

Digital video content analysis is an important item for multimedia content-based indexing (MCBI), content-based video retrieval (CBVR) and visual surveillance systems. There are some frequently-used generic object detection and/or tracking (D&T) algorithms in the literature, such as Background Subtraction (BS), Continuously Adaptive Mean Shift (CMS), Optical Flow (OF) and etc. An important problem for performance evaluation is the absence of stable and flexible software for comparison of different algorithms. This software is able to compare them with the same metrics in real-time and at the same platform. In this paper, we have designed and implemented the software for the performance comparison and the evaluation of well-known video object D&T algorithms (for people D&T) at the same platform. The software works as an automatic and/or semi-automatic test environment in real-time, which uses the image and video processing essentials, e.g. morphological operations and filters, and ground-truth (GT) XML data files, charting/plotting capabilities and etc.

An improved ant algorithm with LDA-based representation for text document clustering

Document clustering can be applied in document organisation and browsing, document summarisation and classification. The identification of an appropriate representation for textual documents is extremely important for the performance of clustering or classification algorithms. Textual documents suffer from the high dimensionality and irrelevancy of text features. Besides, conventional clustering algorithms suffer from several shortcomings, such as slow convergence and sensitivity to the initial value. To tackle the problems of conventional clustering algorithms, metaheuristic algorithms are frequently applied to clustering. In this paper, an improved ant clustering algorithm is presented, where two novel heuristic methods are proposed to enhance the clustering quality of ant-based clustering. In addition, the latent Dirichlet allocation (LDA) is used to represent textual documents in a compact and efficient way. The clustering quality of the proposed ant clustering algorithm is compared to the conventional clustering algorithms using 25 text benchmarks in terms of F-measure values. The experimental results indicate that the proposed clustering scheme outperforms the compared conventional and metaheuristic clustering methods for textual documents.

Moving Object Detection and Tracking in Videos

This chapter provides four sections. The first section introduces the moving object D&T infrastructure and basis of some methods for object detection and tracking (D&T) in videos. In object D&T applications, there is manual or automatic D&T process. Also, the image features, such as color, shape, texture, contours, and motion can be used to track the moving object(s) in videos. The detailed information for moving object detection and well-known trackers are presented in this section as well. In second section, the background subtraction (BS) method and its applications are given in details. The third section declares the details for Mean-shift (MS), Mean-shift filtering (MSF), and continuously adaptive Mean-shift (CMS or CAMShift) methods and their applications. In fourth section, the details for the optical flow (OF), the corner detection through feature points, and OF-based trackers are given in details.