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    Using Manifold Learning for ECG Processing

    , M.Sc. Thesis Sharif University of Technology Lashgari, Elnaz (Author) ; Jahed, Mehran (Supervisor) ; Hossein Khalaj, Babak (Supervisor)
    Abstract
    The human heart is a complex system that contains many clues about its function in its electrocardiogram (ECG) signal. Due to the high mortality rate of heart diseases, detection and recognition of ECG arrhythmias is essential. The most difficult problem faced by ECG analysis is the vast variations among morphologies of ECG signals. In this study, we propose an approach for y detection of abnormal beats and data visualization with respect to ECG morphologies by using manifold learning. In order to do so, a nonlinear dimensionality reduction method based on the Laplacian Eigenmaps is used to reduce the high dimensions of the ECG signals, followed by the application of Bayesian and FLDA method... 

    Manifold learning for ECG arrhythmia recognition

    , Article 2013 20th Iranian Conference on Biomedical Engineering, ICBME 2013 ; 2013 , Pages 126-131 Lashgari, E ; Jahed, M ; Khalaj, B ; Sharif University of Technology
    IEEE Computer Society  2013
    Abstract
    Heart is a complex system and we can find its function in electrocardiogram (ECG) signal. The records show high mortality rate of heart diseases. So it is essential to detect and recognize ECG arrhythmias. The problem with ECG analysis is the vast variations among morphologies of ECG signals. Premature Ventricular Contractions (PVC) is a common type of arrhythmia which may lead to critical situations and contains risk. This study, proposes a novel approach for detecting PVC and visualizing data with respect to ECG morphologies by using manifold learning. To this end, the Laplacian Eigenmaps - One of the reduction method and it is in the nonlinear category - is used to extract important... 

    Robust fuzzy rough set based dimensionality reduction for big multimedia data hashing and unsupervised generative learning

    , Article Multimedia Tools and Applications ; Volume 80, Issue 12 , 2021 , Pages 17745-17772 ; 13807501 (ISSN) Khanzadi, P ; Majidi, B ; Adabi, S ; Patra, J. C ; Movaghar, A ; Sharif University of Technology
    Springer  2021
    Abstract
    The amount of high dimensional data produced by visual sensors in the smart environments and by autonomous vehicles is increasing exponentially. In order to search and model this data for real-time applications, the dimensionality of the data should be reduced. In this paper, a novel dimensionality reduction algorithm based on fuzzy rough set theory, called Centralized Binary Mapping (CBM), is proposed. The fuzzy CBM kernel is used for extracting the central elements and the memory cells from the blocks of high dimensional data. The proposed applications of CBM in this paper include hashing and generative modelling of multimedia big data. The robustness of the proposed CBM based hashing... 

    Feature Ranking in Text Classification

    , M.Sc. Thesis Sharif University of Technology Sadeghi, Sabereh (Author) ; Beigy, Hamid (Supervisor)
    Abstract
    Text classification is one if the widest and most important applications in data mining. Because of the huge number of features in these applications, a method for dimensionality reduction is needed before applying the classification algorithm. Various number of methods for dimensionality reduction and feature selection are proposed. Feature selection based on feature ranking has received much attention by researchers. The major reasons are their scalability, ease of use, and fast computation. Feature ranking methods are divided to different categories and use different measures for scoring features. Recently ensemble methods have entered the field of ranking, and achieved more accuracy... 

    Comparison of classification and dimensionality reduction methods used in fMRI decoding

    , Article Iranian Conference on Machine Vision and Image Processing, MVIP ; 2013 , Pages 175-179 ; 21666776 (ISSN) ; 9781467361842 (ISBN) Alamdari, N. T ; Fatemizadeh, E ; Sharif University of Technology
    2013
    Abstract
    In the last few years there has been growing interest in the use of functional Magnetic Resonance Imaging (fMRI) for brain mapping. To decode brain patterns in fMRI data, we need reliable and accurate classifiers. Towards this goal, we compared performance of eleven popular pattern recognition methods. Before performing pattern recognition, applying the dimensionality reduction methods can improve the classification performance; therefore, seven methods in region of interest (RDI) have been compared to answer the following question: which dimensionality reduction procedure performs best? In both tasks, in addition to measuring prediction accuracy, we estimated standard deviation of... 

    Robust Design Optimization for Fatigue Life with Geometric and Material Uncertainties of Mechanical Parts Under Random Loading Based on Maximizing Fatigue Life and Minimizing Uncertainty in Fatigue Llife Prediction

    , M.Sc. Thesis Sharif University of Technology Esfahani, Saeed (Author) ; Khodaygan, Saeed (Supervisor)
    Abstract
    Fatigue life prediction of a mechanical part is one of issues which a group of engineers are engaged with it and always they try to design the parts with the maximum of lifetime. Although many researches have been done in this field but yet we can see that predicted life are different from that happens in the reality because there are some uncertainties in the phenomena. Our effort in this project is creating an algorithm design so that the parts are designed by it, have the maximum fatigue life and the minimum uncertainty in prediction. In this project we have considered geometrical, material and random loading uncertainties as error resources. Older methods those are presented in this... 

    Design and Implementation of Distributed Dimensionality Reduction Algorithms under Communication Constraints

    , M.Sc. Thesis Sharif University of Technology Rahmani, Mohammad Reza (Author) ; Maddah Ali, Mohammad Ali (Supervisor) ; Salehkaleybar, Saber (Supervisor)
    Abstract
    Nowadays we are witnessing the emergence of machine learning in various applications. One of the key problems in data science and machine learning is the problem of dimensionality reduction, which deals with finding a mapping that embeds samples to a lower-dimensional space such that, the relationships between the samples and their properties are preserved in the secondary space as much as possible. Obtaining such mapping is essential in today's high-dimensional settings. Moreover, due to the large volume of data and high-dimensional samples, it is infeasible or insecure to process and store all data in a single machine. As a result, we need to process data in a distributed manner.In this... 

    A sensitivity study of FILTERSIM algorithm when applied to DFN modeling

    , Article Journal of Petroleum Exploration and Production Technology ; Vol. 4, issue. 2 , June , 2014 , p. 153-174 ; ISSN: 21900558 Ahmadi, R ; Masihi, M ; Rasaei, M. R ; Eskandaridalvand, K ; Shahalipour, R ; Sharif University of Technology
    Abstract
    Realistic description of fractured reservoirs demands primarily for a comprehensive understanding of fracture networks and their geometry including various individual fracture parameters as well as network connectivities. Newly developed multiple-point geostatistical simulation methods like SIMPAT and FILTERSIM are able to model connectivity and complexity of fracture networks more effectively than traditional variogrambased methods. This approach is therefore adopted to be used in this paper. Among the multiple-point statistics algorithms, FILTERSIM has the priority of less computational effort than does SIMPAT by applying filters and modern dimensionality reduction techniques to the... 

    A novel forensic image analysis tool for discovering double JPEG compression clues

    , Article Multimedia Tools and Applications ; Volume 76, Issue 6 , 2017 , Pages 7749-7783 ; 13807501 (ISSN) Taimori, A ; Razzazi, F ; Behrad, A ; Ahmadi, A ; Babaie Zadeh, M ; Sharif University of Technology
    Springer New York LLC  2017
    Abstract
    This paper presents a novel technique to discover double JPEG compression traces. Existing detectors only operate in a scenario that the image under investigation is explicitly available in JPEG format. Consequently, if quantization information of JPEG files is unknown, their performance dramatically degrades. Our method addresses both forensic scenarios which results in a fresh perceptual detection pipeline. We suggest a dimensionality reduction algorithm to visualize behaviors of a big database including various single and double compressed images. Based on intuitions of visualization, three bottom-up, top-down and combined top-down/bottom-up learning strategies are proposed. Our tool... 

    A novel forensic image analysis tool for discovering double JPEG compression clues

    , Article Multimedia Tools and Applications ; Volume 76, Issue 6 , 2017 , Pages 7749-7783 ; 13807501 (ISSN) Taimori, A ; Razzazi, F ; Behrad, A ; Ahmadi, A ; Babaie Zadeh, M ; Sharif University of Technology
    Abstract
    This paper presents a novel technique to discover double JPEG compression traces. Existing detectors only operate in a scenario that the image under investigation is explicitly available in JPEG format. Consequently, if quantization information of JPEG files is unknown, their performance dramatically degrades. Our method addresses both forensic scenarios which results in a fresh perceptual detection pipeline. We suggest a dimensionality reduction algorithm to visualize behaviors of a big database including various single and double compressed images. Based on intuitions of visualization, three bottom-up, top-down and combined top-down/bottom-up learning strategies are proposed. Our tool... 

    The effect of a two steps searching mechanism Using Feature Vectors Related to Image Class in Improving the Performance of CBIR System

    , M.Sc. Thesis Sharif University of Technology Sherafati, Shima (Author) ; Jamzad, Mansoor (Supervisor) ; Manzuri Shalmani, Mohammad Taghi (Co-Advisor)
    Abstract
    Nowadays, retrieval is an inseparable part of user activities and due to growing usage of Content-Based Image Retrieval (CBIR), it has become a hot and challenging research topic specially in the past decade. The most important challenge that retrieval systems (including CBIR systems) are facing is the semantic gap between abstractions in the user’s mind and what is searched. One of the ways of dealing with this challenge is getting more information from the user about what he needs and so decreasing the distance between user’s will and what he gives to search engine as the description of his need. In this research, the class of query image is supposed to be given. For using this... 

    Geometrical Fracture Modeling Within Multiple-Point Statistics Framework

    , M.Sc. Thesis Sharif University of Technology Ahmadi, Rouhollah (Author) ; Masihi, Mohsen (Supervisor) ; Rasaei, Mohammad Reza (Supervisor) ; Eskandaridalvand, Kiomars (Supervisor) ; Shahalipour, Reza (Co-Advisor)
    Abstract
    Majority of the oil and gas reservoirs, in the main hydrocarbon production regions around the world, are naturally fractured reservoirs. Fractures play an important role in reservoir fluid flow either in the form of high permeable complex conduits or strong permeability anisotropies. Realistic characterization of naturally fractured reservoirs requires an exhaustive understanding of fracture connectivity and fracture pattern geometry. These subsequently demand description of many fracture parameters such as density (intensity), spacing, orientation, size and aperture. Therefore, a first step in fractured reservoirs characterization is the static geometric modeling of the subsurface fracture... 

    Optimal supervised feature extraction in internet traffic classification

    , Article IEEE Pacific RIM Conference on Communications, Computers, and Signal Processing - Proceedings ; 2013 , Pages 102-107 ; 1555-5798 (ISSN) ; 9781479915019 (ISBN) Aliakbarian, M. S ; Fanian, A ; Saleh, F. S ; Gulliver, T. A ; Sharif University of Technology
    2013
    Abstract
    Internet traffic classification is important in many aspects of network management such as data exploitation detection, malicious user identification, and restricting application traffic. Previously, features such as port and protocol numbers have been used to classify traffic, but these features can now be changed easily, making their use in traffic classification inadequate. Consequently, traffic classification based on machine learning (ML) is now employed. The number of features used in an ML algorithm has a significant impact on performance, in particular accuracy. In this paper, a minimum best feature set is chosen using a supervised method to obtain uncorrelated features. Outlier... 

    A new ensemble method for feature ranking in text mining

    , Article International Journal on Artificial Intelligence Tools ; Volume 22, Issue 3 , June , 2013 ; 02182130 (ISSN) Sadeghi, S ; Beigy, H ; Sharif University of Technology
    2013
    Abstract
    Dimensionality reduction is a necessary task in data mining when working with high dimensional data. A type of dimensionality reduction is feature selection. Feature selection based on feature ranking has received much attention by researchers. The major reasons are its scalability, ease of use, and fast computation. Feature ranking methods can be divided into different categories and may use different measures for ranking features. Recently, ensemble methods have entered in the field of ranking and achieved more accuracy among others. Accordingly, in this paper a Heterogeneous ensemble based algorithm for feature ranking is proposed. The base ranking methods in this ensemble structure are... 

    Two-dimensional random projection

    , Article Signal Processing ; Volume 91, Issue 7 , 2011 , Pages 1589-1603 ; 01651684 (ISSN) Eftekhari, A ; Babaie-Zadeh, M ; Abrishami Moghaddam, H ; Sharif University of Technology
    2011
    Abstract
    As an alternative to adaptive nonlinear schemes for dimensionality reduction, linear random projection has recently proved to be a reliable means for high-dimensional data processing. Widespread application of conventional random projection in the context of image analysis is, however, mainly impeded by excessive computational and memory requirements. In this paper, a two-dimensional random projection scheme is considered as a remedy to this problem, and the associated key notion of concentration of measure is closely studied. It is then applied in the contexts of image classification and sparse image reconstruction. Finally, theoretical results are validated within a comprehensive set of... 

    Nonlinear Dimensionality Reduction via Path-Based Isometric Mapping

    , Article IEEE Transactions on Pattern Analysis and Machine Intelligence ; Volume 38, Issue 7 , 2016 , Pages 1452-1464 ; 01628828 (ISSN) Najafi, A ; Joudaki, A ; Fatemizadeh, E ; Sharif University of Technology
    IEEE Computer Society 
    Abstract
    Nonlinear dimensionality reduction methods have demonstrated top-notch performance in many pattern recognition and image classification tasks. Despite their popularity, they suffer from highly expensive time and memory requirements, which render them inapplicable to large-scale datasets. To leverage such cases we propose a new method called "Path-Based Isomap". Similar to Isomap, we exploit geodesic paths to find the low-dimensional embedding. However, instead of preserving pairwise geodesic distances, the low-dimensional embedding is computed via a path-mapping algorithm. Due to the much fewer number of paths compared to number of data points, a significant improvement in time and memory... 

    An Efficient semi-supervised multi-label classifier capable of handling missing labels

    , Article IEEE Transactions on Knowledge and Data Engineering ; Volume 31, Issue 2 , 2019 , Pages 229-242 ; 10414347 (ISSN) Hosseini Akbarnejad, A ; Soleymani Baghshah, M ; Sharif University of Technology
    IEEE Computer Society  2019
    Abstract
    Multi-label classification has received considerable interest in recent years. Multi-label classifiers usually need to address many issues including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels, as well as exploiting unlabeled data to improve prediction performance. To tackle datasets with a large set of labels, embedding-based methods represent the label assignments in a low-dimensional space. Many state-of-the-art embedding-based methods use a linear dimensionality reduction to map the label assignments to a low-dimensional space. However, by doing so, these... 

    Variants of vector space reductions for predicting the compositionality of English noun compounds

    , Article 12th International Conference on Language Resources and Evaluation, LREC 2020, 11 May 2020 through 16 May 2020 ; 2020 , Pages 4379-4387 Alipoor, P ; Schulte im Walde, S ; Sharif University of Technology
    European Language Resources Association (ELRA)  2020
    Abstract
    Predicting the degree of compositionality of noun compounds such as snowball and butterfly is a crucial ingredient for lexicography and Natural Language Processing applications, to know whether the compound should be treated as a whole, or through its constituents, and what it means. Computational approaches for an automatic prediction typically represent and compare compounds and their constituents within a vector space and use distributional similarity as a proxy to predict the semantic relatedness between the compounds and their constituents as the compound's degree of compositionality. This paper provides a systematic evaluation of vector-space reduction variants across kinds, exploring... 

    Various reduced-order surrogate models for fluid flow and mass transfer in human bronchial tree

    , Article Biomechanics and Modeling in Mechanobiology ; Volume 20, Issue 6 , 2021 , Pages 2203-2226 ; 16177959 (ISSN) Abbasi, Z ; Bozorgmehry Boozarjomehry, R ; Sharif University of Technology
    Springer Science and Business Media Deutschland GmbH  2021
    Abstract
    The bronchial tree plays a main role in the human respiratory system because the air distribution throughout the lungs and gas exchange with blood occur in the airways whose dimensions vary from several centimeters to micrometers. Organization of about 60,000 conducting airways and 33 million respiratory airways in a limited space results in a complex structure. Due to this inherent complexity and a high number of airways, using target-oriented dimensional reduction is inevitable. In addition, there is no general reduced-order model for various types of problems. This necessitates coming up with an appropriate model from a variety of different reduced-order models to solve the desired... 

    Various reduced-order surrogate models for fluid flow and mass transfer in human bronchial tree

    , Article Biomechanics and Modeling in Mechanobiology ; Volume 20, Issue 6 , 2021 , Pages 2203-2226 ; 16177959 (ISSN) Abbasi, Z ; Bozorgmehry Boozarjomehry, R ; Sharif University of Technology
    Springer Science and Business Media Deutschland GmbH  2021
    Abstract
    The bronchial tree plays a main role in the human respiratory system because the air distribution throughout the lungs and gas exchange with blood occur in the airways whose dimensions vary from several centimeters to micrometers. Organization of about 60,000 conducting airways and 33 million respiratory airways in a limited space results in a complex structure. Due to this inherent complexity and a high number of airways, using target-oriented dimensional reduction is inevitable. In addition, there is no general reduced-order model for various types of problems. This necessitates coming up with an appropriate model from a variety of different reduced-order models to solve the desired...