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Full-Text Articles in Physical Sciences and Mathematics
On The Feasibility Of Profiling, Forecasting And Authenticating Internet Usage Based On Privacy Preserving Netflow Logs, Soheil Sarmadi
On The Feasibility Of Profiling, Forecasting And Authenticating Internet Usage Based On Privacy Preserving Netflow Logs, Soheil Sarmadi
USF Tampa Graduate Theses and Dissertations
Understanding Internet user behavior and Internet usage patterns is fundamental in developing future access networks and services that meet technical as well as Internet user needs. User behavior is routinely studied and measured, but with different methods depending on the research discipline of the investigator, and these disciplines rarely cross. We tackle this challenge by developing frameworks that the Internet usage statistics used as the main features in understanding Internet user behaviors, with the purpose of finding a complete picture of the user behavior and working towards a unified analysis methodology. In this dissertation we collected Internet usage statistics via …
Effect Of Intuitionistic Fuzzy Normalization In Microarray Gene Selection, Prema Ramasamy, Premalatha Kandhasamy
Effect Of Intuitionistic Fuzzy Normalization In Microarray Gene Selection, Prema Ramasamy, Premalatha Kandhasamy
Turkish Journal of Electrical Engineering and Computer Sciences
Analysis of gene expression data is essential in microarray gene expression in order to retrieve the required information. Gene expression data generally contain a large number of genes but a small number of samples. The complicated relations among the different genes make analysis more difficult, and removing irrelevant genes improves the quality of results. This paper presents two fuzzy preprocessing techniques, using a fuzzy set (FS) and intuitionistic fuzzy set (IFS), to normalize datasets. In the feature selection part, four statistical methods were used. Using three publicly available gene expression datasets, the fuzzy normalization techniques were compared with two standard …
Modified Stacking Ensemble Approach To Detect Network Intrusion, Necati̇ Demi̇r, Gökhan Dalkiliç
Modified Stacking Ensemble Approach To Detect Network Intrusion, Necati̇ Demi̇r, Gökhan Dalkiliç
Turkish Journal of Electrical Engineering and Computer Sciences
Detecting intrusions in a network traffic has remained an issue for researchers over the years. Advances in the area of machine learning provide opportunities to researchers to detect network intrusion without using a signature database. We studied and analyzed the performance of a stacking technique, which is an ensemble method that is used to combine different classification models to create a better classifier, on the KDD'99 dataset. In this study, the stacking method is improved by modifying the model generation and selection techniques and by using different classifications algorithms as a combiner method. Model generation is performed using subsets of …
Last Level Cache Partitioning Via Multiverse Thread Classification, Burak Sezi̇n Ovant, İsa Ahmet Güney, Muhammed Emi̇n Savaş, Gürhan Küçük
Last Level Cache Partitioning Via Multiverse Thread Classification, Burak Sezi̇n Ovant, İsa Ahmet Güney, Muhammed Emi̇n Savaş, Gürhan Küçük
Turkish Journal of Electrical Engineering and Computer Sciences
Last level caches (LLCs) are part of the last line of defense against the famous memory wall problem. Today, almost all processors utilize a LLC for the same reason. This study extends our previous work, which proposed a cache-partitioning mechanism using thread classification. Here, we propose three enhancements to the existing system: 1) an adaptive traffic threshold mechanism for more portable classification hardware, 2) a new method for classifying way-hungry threads, and finally, 3) a thorough comparison of two design alternatives. Compared to the original way- partitioning mechanism, we show that the proposed mechanism's performance improved by around 6%, on …
Topological Feature Extraction Of Nonlinear Signals And Trajectories And Its Application In Eeg Signals Classification, Saleh Lashkari, Ali Sheikhani, Mohammad Reza Hashemi Golpayegani, Ali Moghimi, Hamid Reza Kobravi
Topological Feature Extraction Of Nonlinear Signals And Trajectories And Its Application In Eeg Signals Classification, Saleh Lashkari, Ali Sheikhani, Mohammad Reza Hashemi Golpayegani, Ali Moghimi, Hamid Reza Kobravi
Turkish Journal of Electrical Engineering and Computer Sciences
This study introduces seven topological features that characterize attractor dynamic of nonlinear and chaotic trajectories in a phase space. These features quantify volume, occupied space, nonuniformity, and curvature of trajectory. The features are evaluated as initial point invariant measures by a practical approach, which means that a feature is only sensitive to dynamic changes. The Lorenz and Rossler system trajectories are employed in this evaluation. Moreover, the proposed features are used in a real world application, i.e. epileptic seizure electroencephalogram signal classification. As the result shows, these features are efficient in this task in comparison with others studies that used …
Feature Selection Algorithm For No-Reference Image Quality Assessment Using Natural Scene Statistics, Imran Fareed Nizami, Muhammad Majid, Khawar Khurshid
Feature Selection Algorithm For No-Reference Image Quality Assessment Using Natural Scene Statistics, Imran Fareed Nizami, Muhammad Majid, Khawar Khurshid
Turkish Journal of Electrical Engineering and Computer Sciences
Images play an essential part in our daily lives and the performance of various imaging applications is dependent on the user?s quality of experience. No-reference image quality assessment (NR-IQA) has gained importance to assess the perceived quality, without using any prior information of the nondistorted version of the image. Different NR-IQA techniques that utilize natural scene statistics classify the distortion type based on groups of features and then these features are used for estimating the image quality score. However, every type of distortion has a different impact on certain sets of features. In this paper, a new feature selection algorithm …
Enlarging Multiword Expression Dataset By Co-Training, Senem Kumova Meti̇n
Enlarging Multiword Expression Dataset By Co-Training, Senem Kumova Meti̇n
Turkish Journal of Electrical Engineering and Computer Sciences
In multiword expressions (MWEs), multiple words unite to build a new unit in language. When MWE identification is accepted as a binary classification task, one of the most important factors in performance is to train the classifier with enough number of labelled samples. Since manual labelling is a time-consuming task, the performances of MWE recognition studies are limited with the size of the training sets. In this study, we propose the comparison-based and common-decision co-training approaches in order to enlarge the MWE dataset. In the experiments, the performances of the proposed approaches were compared to those of the standard co-training …
Artificial Immune System Based Wastewater Parameter Estimation, Cengi̇z Sertkaya, Ni̇lüfer Yurtay
Artificial Immune System Based Wastewater Parameter Estimation, Cengi̇z Sertkaya, Ni̇lüfer Yurtay
Turkish Journal of Electrical Engineering and Computer Sciences
The basis of a wastewater treatment system is to achieve the desired characteristics of the wastewater treatment process. An estimation of the obtained wastewater treatment characteristics provides the information needed to set up the current process steps, and it is important to have an optimum treatment. In this study, an artificial immune system (AIS) structure is developed to estimate important wastewater output parameters such as pH, DBO, DQO, and SS for the first time. The proposed AIS models are based on the clonal selection principle, and the dataset is provided from the University of California Irvine (UCI) Machine Learning Library. …
Classification Of Surface Electromyogram Signals Based On Directed Acyclic Graphs And Support Vector Machines, Xinhui Hu, Jiangming Kan, Wenbin Li
Classification Of Surface Electromyogram Signals Based On Directed Acyclic Graphs And Support Vector Machines, Xinhui Hu, Jiangming Kan, Wenbin Li
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a novel classification approach for surface electromyogram (sEMG) signals. The proposed classification approach involves two steps: (1) feature extraction from an sEMG, in which a 7-dimensional feature vector is extracted from 27 types of features of the sEMG by linear discriminant analysis (LDA), and (2) a novel classifier, DAGSVMerr, based on a directed acyclic graph (DAG) and support vector machine (SVM), in which a separability measure function based on erroneous recognition rates (ERRs) is defined to determine the initial operation list. The proposed approach takes advantage of the feedback idea to improve the performance of the classification. …
Improved Method Of Heuristic Classification Of Vowels From An Acoustic Signal, Josef Krocil, Zdenek Machacek, Jiri Koziorek, Radek Martinek, Jan Nedoma, Marcel Fajkus
Improved Method Of Heuristic Classification Of Vowels From An Acoustic Signal, Josef Krocil, Zdenek Machacek, Jiri Koziorek, Radek Martinek, Jan Nedoma, Marcel Fajkus
Turkish Journal of Electrical Engineering and Computer Sciences
This paper describes research in the field of the improved methodology of the classification of vowels /a, a:/, /$\varepsilon$, $\varepsilon$:/, /ı, i:/, /o, o:/, and /u, u:/ (vowel symbols according to IPA, i.e. International Phonetic Alphabet). The aim is to develop an improved method enabling the automatic allocation of vowel symbols to the corresponding time segments of acoustic recordings of an undisturbed speech signal. The combined classification method is based on finding frequencies of the first two local maxims (formants) in a smoothed linear predictive amplitude spectrum (LPC, linear predictive coding) and zero-crossing values of each speech active voiced short-term …