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    Discrimination of target and chaff in marine applications based on symmetry feature

    , Article MRRS 2017 - 2017 IEEE Microwaves, Radar and Remote Sensing Symposium, Proceedings, 29 August 2017 through 31 August 2017 ; 2017 , Pages 131-135 ; 9781509053919 (ISBN) Zarei, N ; Aalami, H ; Nayebi, M. M ; Amin, A. R ; Danaei, M. R ; Yanovsky, F ; Rudiakova, A ; Averyanova, Y ; Sharif University of Technology
    Among the various techniques of electronic warfare in the sea, chaff has particular importance due to its easy application and its impact on the victim radar. Despite the great history of chaff, the significant research has not been published on its recognition. It is difficult to study the behavior of chaff because of the impossibility of chaff tests in an unechoic chamber. Therefore simulation would be a suitable alternative. In this paper, the simulations of symmetric feature of chaff and target are presented and it is used to propose a processing structure, based on various classifiers, for discrimination between chaff and target. Moreover the comparison between discrimination capability... 

    A robust watermarking method for color images using Naive-Bayes classifier

    , Article Proceedings of the Fifth IASTED International Conference on Signal and Image Processing, Honolulu, HI, 13 August 2003 through 15 August 2003 ; Volume 5 , 2003 , Pages 8-12 ; 0889863784 (ISBN) Yaghmaie, F ; Jamzad, M ; Sharif University of Technology
    By watermarking an image, we hide a pattern in it in such a way that the pattern is not visible but can be extracted using a decomposition algorithm and a key in the receiver side. The watermark pattern can be a character string or any small image (pattern). One of the main applications of watermarking is its application in proving digital image ownership in widely used Internet. In this paper, we present a watermarking method for color images which is robust with respect to usual attacks such as noise addition, smoothing, compression and also rotation. The validity of correctness of retrieved watermark is based on the result of a Naive-Bayes classifier. This classier was trained on a set of... 

    Dynamic classifier selection using clustering for spam detection

    , Article 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009, Nashville, TN, 30 March 2009 through 2 April 2009 ; 2009 , Pages 84-88 ; 9781424427659 (ISBN) Famil saeedian, M ; Beigy, H ; Sharif University of Technology
    Most email users have encountered with spam problems, which have been addressed as a text classification or categorization problem. In this paper, we propose a novel spam detection method that uses ensemble of classifiers based on clustering and selection techniques. There is diversity in genre of e-mail's content and this method can find different topics in emails by clustering. It first computes disjoint clusters of emails, and then a classifier is trained on each cluster. When new email arrives, its cluster is identified. The classifier of the identified cluster is selected to classify the new email. Our method can extract many kinds of topics in emails. The evaluation shows that the... 

    A new method for shot classification in soccer sports video based on SVM classifier

    , Article Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation ; 2012 , Pages 109-112 ; 9781467318303 (ISBN) Bagheri Khaligh, A ; Raziperchikolaei, R ; Moghaddam, M. E ; Sharif University of Technology
    Sport video shot classification is a basic step in the sport video processing. For many purposes such as event detection and summarization, shot classification is needed for content filtering. In this paper, we present a new method for soccer video shot classification. At first, in-field and out-of-field frames are separated. In in-field frames three features based on number of connected components and shirt color percent in vertical and horizontal strips are extracted. The features are all new and showed excellent discrimination in the feature space. These features are given to SVM for classifying long, medium and close-up shots. One of the advantages of our method is that, close-ups can be... 

    Incremental RotBoost algorithm: An application for spam filtering

    , Article Intelligent Data Analysis ; Volume 19, Issue 2 , April , 2015 , Pages 449-468 ; 1088467X (ISSN) Ghanbari, E ; Beigy, H ; Sharif University of Technology
    IOS Press  2015
    Incremental learning is a learning algorithm that can get new information from new training sets without forgetting the acquired knowledge from the previously used training sets. In this paper, an incremental learning algorithm based on ensemble learning is proposed. Then, an application of the proposed algorithm for spam filtering is discussed. The proposed algorithm called incremental RotBoost, assumes the environment is stationary. It trains new weak classifiers for newly arriving data, which are added to the ensemble of classifiers. To evaluate the performance of the proposed algorithm, several computer experiments are conducted. The results of computer experiments show the ability of... 

    Structured features in naive bayes classification

    , Article 30th AAAI Conference on Artificial Intelligence, AAAI 2016, 12 February 2016 through 17 February 2016 ; 2016 , Pages 3233-3240 ; 9781577357605 (ISBN) Choi, A ; Tavabi, N ; Darwiche, A ; Artificial Intelligence; Baidu; et al.; IBM; Infosys; NSF ; Sharif University of Technology
    AAAI press  2016
    We propose the structured naive Bayes (SNB) classifier, which augments the ubiquitous naive Bayes classifier with structured features. SNB classifiers facilitate the use of complex features, such as combinatorial objects (e.g., graphs, paths and orders) in a general but systematic way. Underlying the SNB classifier is the recently proposed Probabilistic Sentential Decision Diagram (PSDD), which is a tractable representation of probability distributions over structured spaces. We illustrate the utility and generality of the SNB classifier via case studies. First, we show how we can distinguish players of simple games in terms of play style and skill level based purely on observing the games... 

    A real-time grid-based method for estimating nearest neighbors in euclidean space

    , Article 10th Iranian Conference on Machine Vision and Image Processing, MVIP 2017, 22 November 2017 through 23 November 2017 ; Volume 2017-November , April , 2018 , Pages 176-181 ; 21666776 (ISSN) ; 9781538644041 (ISBN) Zamani, Y ; Shirzad, H ; Kasaei, S ; Sharif University of technology
    IEEE Computer Society  2018
    The problem of finding nearest neighbors in a certain distance is well defined in machine learning area. There are well-known and exact solutions for it. However, in real world problems, especially in machine vision area, where we have a moving sensor and we want to know which objects of the scene are in the measurement range of it, two issues are important. First, usually in these problems, the time cost is more important than accuracy. It means they can tolerate some measurements error if they can do the process in real-time. Second, the location of an object can be described in the three-dimensional space and does not require the higher dimensions. According to these issues, we introduced... 

    Universal adversarial attacks on text classifiers

    , Article 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, 12 May 2019 through 17 May 2019 ; Volume 2019-May , 2019 , Pages 7345-7349 ; 15206149 (ISSN); 9781479981311 (ISBN) Behjati, M ; Moosavi Dezfooli, S. M ; Baghshah, M. S ; Frossard, P ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Despite the vast success neural networks have achieved in different application domains, they have been proven to be vulnerable to adversarial perturbations (small changes in the input), which lead them to produce the wrong output. In this paper, we propose a novel method, based on gradient projection, for generating universal adversarial perturbations for text; namely sequence of words that can be added to any input in order to fool the classifier with high probability. We observed that text classifiers are quite vulnerable to such perturbations: inserting even a single adversarial word to the beginning of every input sequence can drop the accuracy from 93% to 50%. © 2019 IEEE  

    Variant combination of multiple classifiers methods for classifying the EEG signals in brain-computer interface

    , Article 13th International Computer Society of Iran Computer Conference on Advances in Computer Science and Engineering, CSICC 2008, Kish Island, 9 March 2008 through 11 March 2008 ; Volume 6 CCIS , 2008 , Pages 477-484 ; 18650929 (ISSN); 3540899847 (ISBN); 9783540899846 (ISBN) Shoaie Shirehjini, Z ; Bagheri Shouraki, S ; Esmailee, M ; Sharif University of Technology
    Controlling the environment with EEG signals is known as brain computer interface is the new subject researchers are interested in. The aim in such systems is to control the machine without using muscle, and we should control the machine using signals recorded from the surface of the cortex. In this project our focus is on pattern recognition phase in which we use multiple classifier fusion to improve the classification accuracy. We have applied various feature extraction methods and combined their results. Two methods, greedy algorithms and genetic algorithms, are used for selecting the pair feature extractor-classifier (we called expert) between the existed pair. Experiments show that with... 

    Audio classification based on sinusoidal model: a new feature

    , Article 2008 IEEE Region 10 Conference, TENCON 2008, Hyderabad, 19 November 2008 through 21 November 2008 ; 2008 ; 1424424089 (ISBN); 9781424424085 (ISBN) Shirazi, J ; Ghaemmaghami, S ; Sharif University of Technology
    In this paper, a new feature set is presented and evaluated based on sinusoidal modeling of audio signals. Duration of the longest sinusoidal model frequency track, as a measure of the harmony, is used and compared to typical features as input into an audio classifier. The performance of this sinusoidal model feature is evaluated through classification of audio to speech and music using both the GMM and the SVM classifiers. Classification results show the proposed feature, which could be used for the first time in such an audio classification, is quite successful in speech/music classification. Experimental comparisons with popular features for audio classification, such as HZCRR and LSTER,... 

    Solving MEC and MEC/GI problem models, using information fusion and multiple classifiers

    , Article Innovations'07: 4th International Conference on Innovations in Information Technology, IIT, Dubai, 18 November 2007 through 20 November 2007 ; 2007 , Pages 397-401 ; 9781424418411 (ISBN) Asgarian, E ; Moeinzadeh, M. H ; Mohammadzadeht, J ; Ghazinezhad, A ; Habibi, J ; Najafi Ardabili, A ; Sharif University of Technology
    IEEE Computer Society  2007
    Mutations in Single Nucleotide Polymorphisms (SNPs - different variant positions (1%) from human genomes) are responsible for some genetic diseases. As a consequence, obtaining all SNPs from human populations is one of the primary goals of recent studies in human genomics. Two sequences of mentioned SNPs in diploid human organisms are called haplotypes. In this paper, we study haplotype reconstruction from SNP-fragments with and without genotype information, problems. Designing serial and parallel classifiers was center of our research. Genetic algorithm and K-means were two components of our approaches. This combination helps us to cover the single classifier's weaknesses. ©2008 IEEE  

    Evolving fuzzy classifiers using a symbiotic approach

    , Article 2007 IEEE Congress on Evolutionary Computation, CEC 2007; Singapore, 25 September 2007 through 28 September 2007 ; 2007 , Pages 1601-1607 ; 1424413400 (ISBN); 9781424413409 (ISBN) Soleymani Baghshah, M ; Bagheri Shouraki, S ; Halavati, R ; Lucas, C ; Sharif University of Technology
    Fuzzy rule-based classifiers are one of the famous forms of the classification systems particularly in the data mining field. Genetic algorithm is a useful technique for discovering this kind of classifiers and it has been used for this purpose in some studies. In this paper, we propose a new symbiotic evolutionary approach to find desired fuzzy rulebased classifiers. For this purpose, a symbiotic combination operator has been designed as an alternative to the recombination operator (crossover) in the genetic algorithms. In the proposed approach, the evolution starts from simple chromosomes and the structure of chromosomes gets complex gradually during the evolutionary process. Experimental... 

    Combination of multiple classifiers with fuzzy integral method for classifying the EEG signals in brain-computer interface

    , Article ICBPE 2006 - 2006 International Conference on Biomedical and Pharmaceutical Engineering, Singapore, 11 December 2006 through 14 December 2006 ; 2006 , Pages 157-161 ; 8190426249 (ISBN); 9788190426244 (ISBN) Shoaie, Z ; Esmaeeli, M ; Shouraki, S. B ; Sharif University of Technology
    In this paper we study the effectiveness of using multiple classifier combination for EEG signal classification aiming to obtain more accurate results than it possible from each of the constituent classifiers. The developed system employs two linear classifiers (SVM,LDA) fused at the abstract and measurement levels for integrating information to reach a collective decision. For making decision, the majority voting scheme has been used. While at the measurement level, two types of combination methods have been investigated: one used fixed combination rules that don't require prior training and a trainable combination method. For the second type, the fuzzy integral method was used. The... 

    Music emotion recognition using two level classification

    , Article 2014 Iranian Conference on Intelligent Systems, ICIS 2014 ; Feb , 2014 ; 9781479933501 Pouyanfar, S ; Sameti, H ; Sharif University of Technology
    Rapid growth of digital music data in the Internet during the recent years has led to increase of user demands for search based on different types of meta data. One kind of meta data that we focused in this paper is the emotion or mood of music. Music emotion recognition is a prevalent research topic today. We collected a database including 280 pieces of popular music with four basic emotions of Thayer's two Dimensional model. We used a two level classifier the process of which could be briefly summarized in three steps: 1) Extracting most suitable features from pieces of music in the database to describe each music song; 2) Applying feature selection approaches to decrease correlations... 

    Analysis, interpretation, and recognition of facial action units and expressions using neuro-fuzzy modeling

    , Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11 April 2010 through 13 April 2010 ; Volume 5998 LNAI , April , 2010 , Pages 161-172 ; 03029743 (ISSN) ; 9783642121586 (ISBN) Khademi, M ; Kiapour, M. H ; Manzuri Shalmani, M. T ; Kiaei, A. A ; Sharif University of Technology
    In this paper an accurate real-time sequence-based system for representation, recognition, interpretation, and analysis of the facial action units (AUs) and expressions is presented. Our system has the following characteristics: 1) employing adaptive-network-based fuzzy inference systems (ANFIS) and temporal information, we developed a classification scheme based on neuro-fuzzy modeling of the AU intensity, which is robust to intensity variations, 2) using both geometric and appearance-based features, and applying efficient dimension reduction techniques, our system is robust to illumination changes and it can represent the subtle changes as well as temporal information involved in formation... 

    Symbiotic evolution of rule based classifier systems

    , Article International Journal on Artificial Intelligence Tools ; Volume 18, Issue 1 , 2009 , Pages 1-16 ; 02182130 (ISSN) Halavati, R ; Bagheri Shouraki, S ; Lotfi, S ; Esfandiar, P ; Sharif University of Technology
    Evolutionary Algorithms are vastly used in development of rule based classifier systems in data mining where the rule base is usually a set of If-Then rules and an evolutionary trait develops and optimizes these rules. Genetic Algorithm is usually a favorite solution for such tasks as it globally searches for good rule-sets without any prior bias or greedy force, but it is usually slow. Also, designing a good genetic algorithm for rule base evolution requires the design of a recombination operator that merges two rule bases without disrupting the functionalities of each of them. To overcome the speed problem and the need to design recombination operator, this paper presents a novel algorithm... 

    An exploratory study on application of various classification models to distinguish switchable-hydrophilicity solvents based on 3D-descriptors

    , Article Separation Science and Technology (Philadelphia) ; 2020 Shiri, M ; Shiri, F ; Sharif University of Technology
    Taylor and Francis Inc  2020
    A set of solvents were classified into the switchable-hydrophilicity solvents (SHSs) and non-switchable-hydrophilicity solvents based on forming or not forming a biphasic mixture with water. SHSs have been developed to make the reaction and product separation processes easier. Herein, three classifier algorithms and various feature selection techniques relay on 3D-molecular descriptors to characterize chemicals and forecast their classes were employed. Cfs-SVM method was employed to perform a classification study. The importance of this study helps to understand more about the presence of hydrophobic groups, their position, and their shape in the molecule. © 2020, © 2020 Taylor & Francis... 

    Improvements in audio classification based on sinusoidal modeling

    , Article 2008 IEEE International Conference on Multimedia and Expo, ICME 2008, Hannover, 23 June 2008 through 26 June 2008 ; 2008 , Pages 1485-1488 ; 9781424425716 (ISBN) Shirazi, J ; Ghaemmaghami, S ; Razzazi, F ; Sharif University of Technology
    In this paper, a set of features is presented and evaluated based on sinusoidal modeling of audio signals. Amplitude, frequency, and phase parameters of the sinusoidal model are used and compared as input features into an audio classifier system. The performance of sinusoidal model features is evaluated for classification of audio into speech and music classes using both the Gaussian and the GMM (Gaussian Mixture Model) classifiers. Experimental results show superiority of the amplitude parameters of the sinusoidal model, which could be used for the first time for such an audio classification, as compared to the popular cepstral features. By using a set of 40 sinusoidal features, we achieved... 

    A new Bayesian classifier for skin detection

    , Article 3rd International Conference on Innovative Computing Information and Control, ICICIC'08, Dalian, Liaoning, 18 June 2008 through 20 June 2008 ; 2008 ; 9780769531618 (ISBN) Shirali Shahreza, S ; Mousavi, M. E ; Sharif University of Technology
    Skin detection has different applications in computer vision such as face detection, human tracking and adult content filtering. One of the major approaches in pixel based skin detection is using Bayesian classifiers. Bayesian classifiers performance is highly related to their training set. In this paper, we introduce a new Bayesian classifier skin detection method. The main contribution of this paper is creating a huge database to create color probability tables and new method for creating skin pixels data set. Our database consists of about 80000 images containing more than 5 billions pixels. Our tests shows that the performance of Bayesian classifier trained on our data set is better than... 

    Birth-death frequencies variance of sinusoidal model a new feature for audio classification

    , Article SIGMAP 2008 - International Conference on Signal Processing and Multimedia Applications, Porto, 26 July 2008 through 29 July 2008 ; 2008 , Pages 139-144 ; 9789898111609 (ISBN) Ghaemmaghami, S ; Shirazi, J ; Sharif University of Technology
    In this paper, a new feature set for audio classification is presented and evaluated based on sinusoidal modeling of audio signals. Variance of the birth-death frequencies in sinusoidal model of signal, as a measure of harmony, is used and compared to typical features as the input into an audio classifier. The performance of this sinusoidal model feature is evaluated through classification of audio to speech and music using both the GMM and the SVM classifiers. Classification results show that the proposed feature is quite successful in speech/music classification. Experimental comparisons with popular features for audio classification, such as HZCRR and LSTER, are presented and discussed....