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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3

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Department: search.filters.department.Department of Industrial EngineeringSubject: search.filters.subject.Electrical electronics engineering

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Now showing 1 - 4 of 4
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    A binarization strategy for modelling mixed data in multigroup classification
    (Institute of Electrical and Electronics Engineers (IEEE), 2013) Masmoudi, Youssef; Chabchoub, Habib; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956
    This paper presents a binarization pre-processing strategy for mixed datasets. We propose that the use of binary attributes for representing nominal and integer data is beneficial for classification accuracy. We also describe a procedure to convert integer and nominal data into binary attributes. Expectation-Maximization (EM) clustering algorithms was applied to classify the values of the attributes with a wide range to use a small number of binary attributes. Once the data set is pre-processed, we use the Support Vector Machine (LibSVM) for classification. The proposed method was tested on datasets from the literature. We demonstrate the improved accuracy and efficiency of presented binarization strategy for modelling mixed and complex data in comparison to the classification of the original dataset, nominal dataset and binary dataset.
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    Route balancing vehicle routing problem with time windows for urban logistics
    (IEEE, 2019) N/A; Department of Industrial Engineering; Ulusoy, Banu; Türkay, Metin; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956
    The vehicle routing problem (VRP) has been widely studied in operations research literature with many extensions. This paper studies VRP with time windows and route balance. The objective is to minimize the total number of routes, total cost, total distance, and total time while providing a balance between the routes. We develop a mathematical model to solve small instances of problems. For large instances of problems, we develop a heuristics algorithm. We validate the heuristic algorithm on Solomon benchmark problems. The heuristic algorithm decreases the total number of routes in the solutions by 14%, and total distance of the routes by 12%. We show that the algorithm gives successful results and can be applicable in various areas of logistics.
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    Introduction to the special issue on fuzzy analytics and stochastic methods in neurosciences
    (IEEE-Inst Electrical Electronics Engineers Inc, 2020) Kropat, Erik; Weber, Gerhard-Wilhelm; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956
    The papers in this special section examine the use of fuzzy analytics and stochastic methods in the field of neuroscience. Recent theoretical and technological advancements provide new and deeper insights into the fundamental mechanisms of information processing in the neural system. This important process is accompanied by the tremendous rise of experimental data, which are waiting for further exploration. Modern methodologies and tools from neuroimaging, brain imaging, optogenetic devices, and in vitro and in vivo multielectrode recordings today generate high-quality neurophysiological data with a resolution quality that has never been reached before. These accelerating developments offer promising pathways to enhance our comprehension of the nervous system. Most innovative approaches of computational neuroscience lead to more realistic biophysical models that provide amazing chances for refined analyses of intracellular signaling and dynamics in heterogeneous neural networks, intrinsic connections of space-time processes, multisensory integration, and conditional behavior or links between brain regions in economic and daily-life decision making. Significant computational challenges arise from the high complexity of neural systems and the large number of constituents with yet unknownfunctional interconnections.
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    System optimization for peer-to-peer multi hop video broadcasting in wireless ad Hoc networks
    (IEEE, 2008) N/A; N/A; Department of Electrical and Electronics Engineering; Department of Industrial Engineering; Dedeoğlu, Volkan; Atıcı, Çağdaş; Sunay, Mehmet Oğuz; Salman, Fatma Sibel; Master Student; PhD Student; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Department of Industrial Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; 178838
    We consider peer-to-peer video broadcasting using cooperation among peers in an ad hoc wireless network. As opposed to the traditional single hop broadcasting, multiple hops cause an increase in broadcast video quality while creating interference and increasing transmission delay. We develop heuristics for the NP-complete problem of finding the subset of cooperating peers and the number of hops to maximize the data rate and to minimize the maximum observed delay in the system. Simulations show that using the proposed heuristics, different video sequences can be viewed at high qualities with acceptable delay among all peers.