WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653

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Subject: search.filters.subject.Clustering

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Now showing 1 - 10 of 11
  • Conference Object
    Covariance Features for Trajectory Analysis
    (IEEE, 2016) Karadeniz, Talha; Maras, Hadi Hakan
    In this work, we aimed to demonstrate that covariance estimation methods can be used for trajectory classification. We have shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. We have arrived to the conclusion that, when compared to Dynamic Time Warping, the explained technique is faster and may yield more accurate results.
  • Article
    Stylometric Analysis of Sustainable Central Bank Communications: Revealing Authorial Signatures in Monetary Policy Statements
    (MDPI, 2025) Emekci, Hakan; Ozkan, Ibrahim
    Sustainable economic development requires transparent and consistent institutional communication from monetary authorities to maintain long-term financial stability and public trust. This study investigates the latent authorial structure and stylistic heterogeneity of central bank communications by applying stylometric analysis and unsupervised machine learning to official announcements of the Central Bank of the Republic of Turkey (CBRT). Using a dataset of 557 press releases from 2006 to 2017, we extract a range of linguistic features at both sentence and document levels-including sentence length, punctuation density, word length, and type-token ratios. These features are reduced using Principal Component Analysis (PCA) and clustered via Hierarchical Clustering on Principal Components (HCPC), revealing three distinct authorial groups within the CBRT's communications. The robustness of these clusters is validated using multidimensional scaling (MDS) on character-level and word-level n-gram distances. The analysis finds consistent stylistic differences between clusters, with implications for authorship attribution, tone variation, and communication strategy. Notably, sentiment analysis indicates that one authorial cluster tends to exhibit more negative tonal features, suggesting potential bias or divergence in internal communication style. These findings challenge the conventional assumption of institutional homogeneity and highlight the presence of distinct communicative voices within the central bank. Furthermore, the results suggest that stylistic variation-though often subtle-may convey unintended policy signals to markets, especially in contexts where linguistic shifts are closely scrutinized. This research contributes to the emerging intersection of natural language processing, monetary economics, and institutional transparency. It demonstrates the efficacy of stylometric techniques in revealing the hidden structure of policy discourse and suggests that linguistic analytics can offer valuable insights into the internal dynamics, credibility, and effectiveness of monetary authorities. These findings contribute to sustainable financial governance by demonstrating how AI-driven analysis can enhance institutional transparency, promote consistent policy communication, and support long-term economic stability-key pillars of sustainable development.
  • Conference Object
    Covariance Features for Trajectory Analysis
    (Institute of Electrical and Electronics Engineers Inc., 2016) Karadeniz, T.; Maras, H.H.
    In this work, we aimed to demonstrate that covariance estimation methods can be used for trajectory classification. We have shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. We have arrived to the conclusion that, when compared to Dynamic Time Warping, the explained technique is faster and may yield more accurate results. © 2017 Elsevier B.V., All rights reserved.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Computational Structural Analysis for Substituted Nickel Phthalocyanines Preparation and Characterization
    (Revista Chimie Srl, 2016) Youssef, Tamer E.; Baleanu, Dumitru; Al Turaif, Hamad; Aftab, Wasim; Baleanu, Dumitru; Matematik
    Hay synthesis of a novel series of symmetrically substituted nickel phthalocyanine derivatives, [(heterothio)(8)NiPcs] 4(a-e) bearing N-heterocycle moieties, i.e. Pyridine, Thiazoline, Imidazole, Tetrazole rings, was reported. Their novel heterocycle-thio phthalonitrile precursors 3(a-e) were synthesized by the aromatic nucleophilic substitution reaction of 4-nitrorophthalonitrile with N-heterocycle substituted thiols 2(a-e). The structure of the compounds was analyzed by the spectroscopic analysis tools. Besides, the powerful techniques of maximal wavelet discrete wavelet transformation together with boxplot clustering pointed out new aspects of the signals which cannot be detected by the spectroscopic analysis.
  • Article
    Citation - WoS: 16
    Citation - Scopus: 19
    On Comparing and Clustering the Spectral Densities of Several Almost Cyclostationary Processes
    (Elsevier, 2020) Maleki, Mohsen; Borodin, Kirill; Pho, Kim-Hung; Baleanu, Dumitru; Mahmoudi, Mohammad Reza
    In time series analysis, comparing spectral densities of several processes with almost peri-odic spectra is an interested problem. The contribution of this work is to give a technique to com-pare and to cluster the spectral densities of some independent almost periodically correlated (cyclostationary) processes. This approach is based on the limiting distribution for the periodogram and the discrete Fourier transform. The real world examples and simulation results indicate that the approach well acts. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/ 4.0/).
  • Conference Object
    Machine Learning-Based Silence Detection in Call Center Telephone Conversations
    (Ieee, 2019) Iheme, Leonardo O.; Ozan, Sukru; Akagunduz, Erdem
    This study presents the development of a voice activity detection (VAD) system tested on call center telephony data obtained from our local site. The concept of bag of audio words (BoAW) combined with a naive Bayes classifier was applied to achieve the task. It was formulated as a binary classification problem with speech as the positive class and silence/background noise as the negative class. All the processing was performed on the Mel-frequency cepstral coefficients (MFCCs) extracted from the audio recordings. The results which are presented as accuracy score and receiver operating characteristics (ROC) indicate an excellent performance of the developed model. The system is to be deployed within our call center to aid data analysis and improve overall efficiency of the center.
  • Article
    Citation - WoS: 21
    Citation - Scopus: 27
    Reporting and Analyzing Alternative Clustering Solutions by Employing Multi-Objective Genetic Algorithm and Conducting Experiments on Cancer Data
    (Elsevier, 2014) Peng, Peter; Addam, Omer; Ozyer, Sibel T.; Elzohbi, Mohamad; Elhajj, Ahmad; Gao, Shang; Alhajj, Reda
    Clustering is an essential research problem which has received considerable attention in the research community for decades. It is a challenge because there is no unique solution that fits all problems and satisfies all applications. We target to get the most appropriate clustering solution for a given application domain. In other words, clustering algorithms in general need prior specification of the number of clusters, and this is hard even for domain experts to estimate especially in a dynamic environment where the data changes and/or become available incrementally. In this paper, we described and analyze the effectiveness of a robust clustering algorithm which integrates multi-objective genetic algorithm into a framework capable of producing alternative clustering solutions; it is called Multi-objective K-Means Genetic Algorithm (MOKGA). We investigate its application for clustering a variety of datasets, including microarray gene expression data. The reported results are promising. Though we concentrate on gene expression and mostly cancer data, the proposed approach is general enough and works equally to cluster other datasets as demonstrated by the two datasets Iris and Ruspini. After running MOKGA, a pareto-optimal front is obtained, and gives the optimal number of clusters as a solution set. The achieved clustering results are then analyzed and validated under several cluster validity techniques proposed in the literature. As a result, the optimal clusters are ranked for each validity index. We apply majority voting to decide on the most appropriate set of validity indexes applicable to every tested dataset. The proposed clustering approach is tested by conducting experiments using seven well cited benchmark data sets. The obtained results are compared with those reported in the literature to demonstrate the applicability and effectiveness of the proposed approach. (C) 2013 Elsevier B.V. All rights reserved.
  • Conference Object
    Evaluation of Clustering Performance of Hyperspectral Bands
    (Ieee, 2015) Sakarya, Ufuk; Toreyin, Behcet Ugur; Haliloglu, Onur; Haliloʇlu, Onur
    Hyperspectral images have huge data volume that contains spectral and spatial information. This high data volume leads to processing, storage, and transmission problems. Moreover, insufficient training data results in Hughes phenomenon. It is possible to solve these problems with the help of feature selection. In this paper, a method that evaluates the clustering performance of spectral bands is proposed as a pre-processing operation in order to realize feature selection. This method is clustering each spectral band based on "dominant sets" technique and it evaluates the clustering performance of each band. The proposed method is time efficient since it works on a small set of training data instead of the whole hyperspectral data. In this study, "dominant sets" technique is first applied to hyperspectral image processing as a clustering method.
  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 3
    Classification of Fmri Data by Using Clustering
    (Ieee, 2015) Mogultay, Hazal; Alkan, Sarper; Yarman-Vural, Fatos T.; Moʇultay, Hazal
    Recognition of the the cognitive states by using functional Magnetic Rezonans Imaging (fMRI) data is a challenging problem that has been a focus of scientific research for a long time. In this study the effectiveness of clustering and the ensemble learning techniques on fMRI dataset is investigated and different paramaters are compared. Moreover, the performance of these techniques are tested on both raw voxel intensity values and meshes formed by multiple voxels. Clusters are compared to the functional brain regions, however higher performances are obtained when the number of clusters is higher than the number of functional brain regions.
  • Article
    Citation - WoS: 8
    Citation - Scopus: 9
    Power Aware Routing Protocols in Wireless Sensor Network
    (Ieice-inst Electronics information Communications Eng, 2016) Oztoprak, Kasim; Hassanpour, Reza; Alsultan, Mohammed
    Wireless Sensor Networks (WSNs) have gained importance with a rapid growth in their applications during the past decades. There has also been a rise in the need for energy-efficient and scalable routing along with the data aggregation protocols for the large scale deployments of sensor networks. The traditional routing algorithms suffer from drawbacks such as the presence of one hop long distance data transmissions, very large or very small clusters within a network at the same moment, over-accumulated energy consumption within the cluster-heads (CHs) etc. The lifetime of WSNs is also decreased due to these drawbacks. To overcome them, we have proposed a new method for the Multi Hop, Far-Zone and Load-Balancing Hierarchical-Based Routing Algorithm for Wireless Sensor Network (MFLHA). Various improvements have been brought forward by MFLHA. The first contribution of the proposed method is the existence of a large probability for the nodes with higher energy to become the CH through the introduction of the energy decision condition and energy-weighted factor within the electing threshold of the CH. Secondly, MFLHA forms a Far-Zone, which is defined as a locus where the sensors can reach the CH with an energy less than a threshold. Finally, the energy consumption by CHs is reduced by the introduction of a minimum energy cost method called the Multi-Hop Inter-Cluster routing algorithm. Our experimental results indicate that MFLHA has the ability to balance the network energy consumption effectively as well as extend the lifetime of the networks. The proposed method outperforms the competitors especially in the middle range distances.