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probabilistic-graphical-models
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Stratification of admixture population:A bayesian approach
, Article 7th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2019, 29 January 2019 through 31 January 2019 ; 2019 ; 9781728106731 (ISBN) ; Taheri, S. M ; Motahari, S. A ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
A statistical algorithm is introduced to improve the false inference of active loci, in the population in which members are admixture. The algorithm uses an advanced clustering algorithm based on a Bayesian approach. The proposed algorithm simultaneously infers the hidden structure of the population. In this regard, the Monte Carlo Markov Chain (MCMC) algorithm has been used to evaluate the posterior probability distribution of the model parameters. The proposed algorithm is implemented in a bundle, and then its performance is widely evaluated in a number of artificial databases. The accuracy of the clustering algorithm is compared with the STRUCTURE method based on certain criterion. © 2019...
Improving Sampling Efficiency of Probabilistic Graphical Models
, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
Deep learning methods have become more popular in the past years. These methods use complex network architectures to model rich, hierarchical datasets. Although most of the research has been centered around Discriminative models, however, recently a lot of research is focused on Deep Generative Models. Two of the pioneering models in this field are Generative Adversarial Networks and Variational Auto-Encoders. In addition, knowing the structure of data helps models to search in a narrower hypothesis space. Most of the structure in datasets are models using Probabilistic Graphical Models. Using this structural information, one can achieve better parameter estimations. In the case of...
Management of Classifiers Pool in Data Stream Classification Using Probabilistic Graphical Models
, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
Concept drift is a common situation in data streams where distribution which data is generated from, changes over time due to various reasons like environmental changes. This phenomenon challenges classification process strongly. Recent studies on keeping a pool of classifiers each modeling one of the concepts, have achieved promising results. Storing used classifiers in a pool enables us to exploit prior knowledge of concepts in the future occurrence of them. Most of the methods presented so far, introduce a similarity measure between current and past concepts and select the closest stored concept as current one. These methods don’t consider possible relations and dependenies between...
Analysing Purchase Satisfaction Using Opinion Mining
, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
Opinions and experiences of others give us valuable information in making decisions. Recently, with the expansion of using social networks and websites, people can easily share their opinions about miscellaneous things. This huge amount of information cannot be analyzed by individuals, so a system that automatically analyzes opinions is needed. This need invokes new field of research that is called opinion mining. User’s viewpoints could change during the time, and this is an important issue for companies. One of the most challenging sub-problems of opinion mining is model-based opinion mining, which aims to model the generation of words by modeling their probabilities. In this thesis, we...
Learning Deep Generative Models for Structured Data
,
Ph.D. Dissertation
Sharif University of Technology
;
Beigy, Hamid
(Supervisor)
Abstract
Recently, a new generation of machine learning tasks, namely data generation, was born by emerging deep networks and modern methods for training neural networks on one hand, and the growth of available training data for training these networks on the other hand. Although distribution estimation and sampling were well-known problems in the science of statics, deep generative models can properly generate samples from real world distributions that common statistical methods fail in them e.g., image and music generation.Due to these improvements in deep generative models, researchers have recently tried to propose deep generative models for datasets with complex structures. These structured...
Statistical association mapping of population-structured genetic data
, Article IEEE/ACM Transactions on Computational Biology and Bioinformatics ; 2017 ; 15455963 (ISSN) ; Janghorbani, S ; Motahari, S. A ; Fatemizadeh, E ; Sharif University of Technology
Abstract
Association mapping of genetic diseases has attracted extensive research interest during the recent years. However, most of the methodologies introduced so far suffer from spurious inference of the associated sites due to population inhomogeneities. In this paper, we introduce a statistical framework to compensate for this shortcoming by equipping the current methodologies with a state-of-the-art clustering algorithm being widely used in population genetics applications. The proposed framework jointly infers the disease-associated factors and the hidden population structures. In this regard, a Markov Chain-Monte Carlo (MCMC) procedure has been employed to assess the posterior probability...
Glioma Tumor Segmentation in Brain MRI Using Atlas-based Learning and Graph Structures
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor) ; Beigy, Hamid (Co-Supervisor)
Abstract
Brain cancer is a lump or tumor in the brain caused by abnormal growth of cells. Glioma is a common type of tumor that develops in the brain. In order to plan precise treatment or accurate tumor removal surgery, brain tumor segmentation is critical for detecting all parts of tumor and its surrounding tissues. To visualize the brain anatomy and detect its abnormalities, we use Magnetic Resonance Imaging (MRI) as an input. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in cancer detection is a challenging task. Moreover, due to the intensity inhomogeneity existing in brain MRI and gray...
Statistical association mapping of population-structured genetic data
, Article IEEE/ACM Transactions on Computational Biology and Bioinformatics ; Volume 16, Issue 2 , 2019 , Pages 636-649 ; 15455963 (ISSN) ; Janghorbani, S ; Motahari, A ; Fatemizadeh, E ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
Association mapping of genetic diseases has attracted extensive research interest during the recent years. However, most of the methodologies introduced so far suffer from spurious inference of the associated sites due to population inhomogeneities. In this paper, we introduce a statistical framework to compensate for this shortcoming by equipping the current methodologies with a state-of-the-art clustering algorithm being widely used in population genetics applications. The proposed framework jointly infers the disease-associated factors and the hidden population structures. In this regard, a Markov Chain-Monte Carlo (MCMC) procedure has been employed to assess the posterior probability...
WLFS: Weighted label fusion learning framework for glioma tumor segmentation in brain MRI
, Article Biomedical Signal Processing and Control ; Volume 68 , 2021 ; 17468094 (ISSN) ; Jamzad, M ; Sharif University of Technology
Elsevier Ltd
2021
Abstract
Glioma is a common type of tumor that develops in the brain. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in cancer detection is a challenging task in cancer detection. In recent researches, the combination of atlas-based segmentation and machine learning methods have presented superior performance over other automatic brain MRI segmentation algorithms. To overcome the side effects of limited existing information on atlas-based segmentation, and the long training and the time consuming phase of learning methods, we proposed a semi-supervised learning framework by introducing a...