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    Event detection and summarization in soccer videos using bayesian network and copula

    , Article IEEE Transactions on Circuits and Systems for Video Technology ; Volume 24, Issue 2 , February , 2014 , Pages 291-304 ; ISSN: 10518215 Tavassolipour, M ; Karimian, M ; Kasaei, S ; Sharif University of Technology
    Abstract
    Semantic video analysis and automatic concept extraction play an important role in several applications; including content-based search engines, video indexing, and video summarization. As the Bayesian network is a powerful tool for learning complex patterns, a novel Bayesian network-based method is proposed for automatic event detection and summarization in soccer videos. The proposed method includes efficient algorithms for shot boundary detection, shot view classification, mid-level visual feature extraction, and construction of the related Bayesian network. The method contains of three main stages. In the first stage, the shot boundaries are detected. Using the hidden Markov model, the... 

    BNQM: A Bayesian Network based QoS Model for Grid service composition

    , Article Expert Systems with Applications ; Volume 42, Issue 20 , 2015 , Pages 6828-6843 ; 09574174 (ISSN) Pourhaji Kazem, A. A ; Pedram, H ; Abolhassani, H ; Sharif University of Technology
    Elsevier Ltd  2015
    Abstract
    The QoS attributes of Grid services play important roles in several tasks in Grid computing such as QoS-aware service composition, service negotiation, resource management, service discovery and scheduling. By considering the dynamic aspects of the Grid environments and also the uncertainty related to Grid services, in this paper, we present BNQM, a Bayesian network based probabilistic QoS Model for Grid service composition. Application of Bayesian network in QoS management makes it possible to indicate the conditional independence relationships among QoS attributes and to provide an effective probabilistic approach to predict new values for some QoS attributes while others are changed.... 

    Cost overrun risk assessment and prediction in construction projects: a bayesian network classifier approach

    , Article Buildings ; Volume 12, Issue 10 , 2022 ; 20755309 (ISSN) Ashtari, M. A ; Ansari, R ; Hassannayebi, E ; Jeong, J ; Sharif University of Technology
    MDPI  2022
    Abstract
    Cost overrun risks are declared to be dynamic and interdependent. Ignoring the relationship between cost overrun risks during the risk assessment process is one of the primary reasons construction projects go over budget. Conversely, recent studies have failed to account for potential interrelationships between risk factors in their machine learning (ML) models. Additionally, the presented ML models are not interpretable. Thus, this study contributes to the entire ML process using a Bayesian network (BN) classifier model by considering the possible interactions between predictors, which are cost overrun risks, to predict cost overrun and assess cost overrun risks. Furthermore, this study... 

    A new real-coded Bayesian optimization algorithm based on a team of learning automata for continuous optimization

    , Article Genetic Programming and Evolvable Machines ; Vol. 15, Issue. 2 , 2014 , pp. 169-193 ; ISSN: 13892576 Moradabadi, B ; Beigy, H ; Sharif University of Technology
    Abstract
    Estimation of distribution algorithms have evolved as a technique for estimating population distribution in evolutionary algorithms. They estimate the distribution of the candidate solutions and then sample the next generation from the estimated distribution. Bayesian optimization algorithm is an estimation of distribution algorithm, which uses a Bayesian network to estimate the distribution of candidate solutions and then generates the next generation by sampling from the constructed network. The experimental results show that the Bayesian optimization algorithms are capable of identifying correct linkage between the variables of optimization problems. Since the problem of finding the... 

    A probabilistic multi-label classifier with missing and noisy labels handling capability

    , Article Pattern Recognition Letters ; Volume 89 , 2017 , Pages 18-24 ; 01678655 (ISSN) Akbarnejad, A ; Soleymani Baghshah, M ; Sharif University of Technology
    Elsevier B.V  2017
    Abstract
    Multi-label classification with a large set of labels is a challenging task. Label-Space Dimension Reduction (LSDR) is the most popular approach that addresses this problem. LSDR methods project the high-dimensional label vectors onto a low-dimensional space that can be predicted from the feature space. Many LSDR methods assume that the training data provide complete label vector for all training samples while this assumption is usually violated particularly when label vectors are high dimensional. In this paper, we propose a probabilistic model that has an effective mechanism to handle missing and noisy labels. In the proposed Bayesian network model, a set of auxiliary random variables,... 

    Efficient performance monitoring of building central heating system using Bayesian Network method

    , Article Journal of Building Engineering ; Volume 26 , 2019 ; 23527102 (ISSN) Parhizkar, T ; Aramoun, F ; Esbati, S ; Saboohi, Y ; Sharif University of Technology
    Elsevier Ltd  2019
    Abstract
    Central heating system faults affect building energy consumption and indoor thermal comfort significantly. The interdependencies among system components and multiple failure modes present a challenge for system health diagnostics and prognostics. A reliable diagnosis and prognosis can only be ensured when all component conditions are monitored with minimum uncertainty. In this regard, sensors should be selected based on their priority in providing system health information. Currently, most of the research on sensor optimization models optimize sensors position and orientation. However, in this study sensor type is optimized as well. In addition, the proposed method is based on the Bayesian... 

    Extraction of Important Events in Football Videos Using Video Summarization Techniques

    , M.Sc. Thesis Sharif University of Technology Tavassolipour, Mostafa (Author) ; Kasaei, Shohreh (Supervisor)
    Abstract
    Semantic video analysis and automatic concept extraction play an important role in several application including designing content-based search engines, video indexing, and video summarization. Since constructing an appropriate summarized video requires extraction of internal video concepts, video summarization is considered as an application of semantic analysis. The proposed system contains three main stages. In the first stage, the shot boundaries are detected, then using the hidden Markov model (HMM), the video is segmented into larger semantic units, called “play-break” sequences. In the next stage, several features are extracted from each of these units. Finally, in the last stage, in... 

    Designing an Estimation of Distribution Algorithm based on Learning Automata

    , M.Sc. Thesis Sharif University of Technology Moradabadi, Behnaz (Author) ; Beigy, Hamid (Supervisor)
    Abstract
    Evolutionary algorithms are a type of stochastic optimization techniques influenced by genetics and natural evolution. Once the set of candidate solutions has been selected, a new generation is sampled by using recombination (crossover) and mutation operators to the candidate solutions. Public, fixed, problem independent mutation and recombination operators frequently lead to missing building blocks, knowledge of the relationship between variables and result in converging to a local optimum. A method to prevent disruption of building blocks is using the estimation of distribution algorithms (EDAs). The experimental results show that EDAs is capable to identify correct linkage between the... 

    Diagnosis of brucellosis disease using data mining: A case study on patients of a hospital in Tehran

    , Article Journal of Microbiological Methods ; Volume 199 , 2022 ; 01677012 (ISSN) Sebt, M. V ; Jafari, S ; Khavaninzadeh, M ; Shavandi, A ; Sharif University of Technology
    Elsevier B.V  2022
    Abstract
    Background: Brucellosis is a common zoonotic infection of humans from livestock. This bacterial infection is acquired from infected animals and their products. The pathogen of this disease is a genus of bacilli called Brucella, and no effective vaccine has been discovered yet for the prevention of human brucellosis. Objectives: The present study is mainly conducted to diagnose brucellosis accurately and timely, using Data Mining techniques. Based on the knowledge discovered with Data Mining and opinions of specialist physicians, this study aims to propose instructions for diagnosing brucellosis. Materials and methods: The dataset used in this study contains 340 samples and is extracted from... 

    System risk importance analysis using bayesian networks

    , Article International Journal of Reliability, Quality and Safety Engineering ; Volume 25, Issue 1 , 2018 ; 02185393 (ISSN) Noroozian, A ; Baradaran Kazemzade, R. B ; Akhavan Niaki, S. T ; Zio, E ; Sharif University of Technology
    World Scientific Publishing Co. Pte Ltd  2018
    Abstract
    Importance measures (IMs) are used for risk-informed decision making in system operations, safety, and maintenance. Traditionally, they are computed within fault tree (FT) analysis. Although FT analysis is a powerful tool to study the reliability and structural characteristics of systems, Bayesian networks (BNs) have shown explicit advantages in modeling and analytical capabilities. In this paper, the traditional definitions of IMs are extended to BNs in order to have more capability in terms of system risk modeling and analysis. Implementation results on a case study illustrate the capability of finding the most important components in a system. © 2018 World Scientific Publishing Company  

    Dynamic risk assessment of decommissioning offshore jacket structures

    , Article Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, 17 June 2018 through 22 June 2018 ; Volume 3 , 2018 ; 9780791851227 (ISBN) Babaleye, A ; Khorasanchi, M ; Kurt, R. E ; Ocean, Offshore and Arctic Engineering Division ; Sharif University of Technology
    American Society of Mechanical Engineers (ASME)  2018
    Abstract
    The need to develop an integrated dynamic safety and risk analysis model for decommissioning offshore jacket structures is driven by the risky, expensive and complex nature of the operation. Many of the existing risk analysis techniques applicable to offshore assets failed to recognise and capture evolving risks during different stages of the decommissioning operation. This paper describes risk-based safety model to conduct quantitative risk analysis for offshore jacket decommissioning failure. First, a bow-tie technique is developed to model the accident cause-consequence relationship. Subsequently, a Bayesian belief network is used to update the failure probabilities of the contributing... 

    Improved K2 algorithm for Bayesian network structure learning

    , Article Engineering Applications of Artificial Intelligence ; Volume 91 , 2020 Behjati, S ; Beigy, H ; Sharif University of Technology
    Elsevier Ltd  2020
    Abstract
    In this paper, we study the problem of learning the structure of Bayesian networks from data, which takes a dataset and outputs a directed acyclic graph. This problem is known to be NP-hard. Almost most of the existing algorithms for structure learning can be classified into three categories: constraint-based, score-based, and hybrid methods. The K2 algorithm, as a score-based algorithm, takes a random order of variables as input and its efficiency is strongly dependent on this ordering. Incorrect order of variables can lead to learning an incorrect structure. Therefore, the main challenge of this algorithm is strongly dependency of output quality on the initial order of variables. The main... 

    Inferring Gene Regulatory Networks, Using Machine Learning Approaches

    , M.Sc. Thesis Sharif University of Technology Gheiby, Sanaz (Author) ; Manzuri, Mohammad Taghi (Supervisor)
    Abstract
    Gene regulatory network consists of a set of genes; interacting with each other via their protein products. Such interations lead to the regulation of the genes’ production rate. A breakdown in the regulatory process, may lead to some kinds of diseases. Therefore, understanding the gene regulatory process, is beneficial for both diagnosis and treatment. In this thesis, gene regulatory networks are modeled by the means of dynamic Bayesian networks. We have used sampling based methods, in order to learn the network structure. As these methos have a very high computational cost; we have used a correlation test to prune the search space. This way, an undirected network skeleton is obtained; for... 

    Tracking of Human Sperm Cell using a Dynamic Bayesian Network Based Framework

    , Ph.D. Dissertation Sharif University of Technology Arasteh, Abdollah (Author) ; Vosoughi Vahdat, Bijan (Supervisor) ; Salman Yazdi, Reza (Co-Supervisor)
    Abstract
    Infertility is an important problem to deal in medicine. In every four couples, on average, one couple is affected by infertility in developing countries. In the majority of cases, the infertility of men has a relationship with spermatozoa and semen, and can be measured by semen and spermatozoa analysis for more advanced diagnosis and treatments. Analysis of the movement patterns of spermatozoa were performed by expert screeners earlier, but nowadays, many of these analyzes are performed using computer-based systems called computer assisted sperm analysis (CASA). The benefits of using CASA instead of expert screeners are achieving system-independency and numerical results at the end of the... 

    Probabilistic Reasoning in Collaborative Filtering

    , M.Sc. Thesis Sharif University of Technology Ayati, Behrouz (Author) ; Izadi, Mohammad (Supervisor)
    Abstract
    In this thesis the usage of probabilistic reasoning in collaborative filtering is investigated. The problem of predicting users' rating is formulated as a Bayesian decision problem and a generative probabilistic model is used in order to find the optimal decision. Two different probabilistic models are considered: user based model and rating based model. In user based model prediction of ratings is based on structural learning of Bayesian networks. In rating based model, we assume a predefined Bayesian network represents the joint distribution over model variables and rating prediction is carried out using McMc inference method. MovieLens dataset is chosen to evaluate and compare the results... 

    Determination of a Decision-Making Framework for Environmental Flow Using Bayesian Network Method (Case Study: Shadegan Wetland)

    , M.Sc. Thesis Sharif University of Technology Khorshid, Sepideh (Author) ; Abrishamchi, Ahmad (Supervisor)
    Abstract
    Environmental flows supply critical contributions to wetland health and economic development, while wetlands are one of the richest ecosystems due to diversity of the ecological species. The Shadegan Wetland is the located in the south-west of Iran, at the top of the Persian Gulf. It is the largest wetland of the country with an area of almost 400,000 hectares. The wetland plays a significant role, both hydrological and ecological in the natural functioning of the northern coast of the Gulf. Moreover, Shadegan Protected Zone is of high economic and social importance, as its vegetation is assumed the main source of feeding the wildlife species. On the other hand, the wetland has the potential... 

    Applications of Hidden Markov Models in Activity Recognition in an Ambient Intelligent Environment

    , M.Sc. Thesis Sharif University of Technology Mirarmandehi, Nasim (Author) ; Rabiei, Hamid Reza (Supervisor)
    Abstract
    Ambient Intelligence (AmI) is an environment in which devices are embedded and connected to each other with a communication network, working in concert to predict users’ wishes according to the context of the environment (devices and people) to help them with their everyday activities. An ambient intelligent environment should be context-aware. One of the most complicated problems in context-aware computations is recognition of the activities in which users of the environment are engaged. These activities could be recognized by means of the information hidden in communication networks of the devices, especially different sensors embedded in the environment to ease up the process. Most of... 

    Credit Scoring of Commercial Loan Applicants in Iranian Banking Industry, A Comparative Analysis of Bayesian Approach, Logit, and Neural Networks

    , M.Sc. Thesis Sharif University of Technology Ghanbari, Hamed (Author) ; Zamani, Shiva (Supervisor) ; Bahramgiri, Mohsen (Supervisor)
    Abstract
    The development of effective models for classification problems, such as the problem of selecting which credit applicants to accept, has been the subject of intense research for decades. Many static and dynamic methods, ranging from statistical classifiers to decision trees, nearest-neighbor methods, and neural networks, have already been proposed to tackle this problem and to assist decision making in the area of consumer and commercial credit. Given the profusion of modeling and data management techniques, it is often the case that which model has the more appropriate outputs in classification of the same problem. Among the stated methods although the latter, Neural Networks, is powerful... 

    The transiting system GJ1214: High-precision defocused transit observations and a search for evidence of transit timing variation

    , Article Astronomy and Astrophysics ; Volume 549 , 2012 ; 00046361 (ISSN) Harpsøe, K. B. W ; Hardis, S ; Hinse, T. C ; Jørgensen, U. G ; Mancini, L ; Southworth, J ; Alsubai, K. A ; Bozza, V ; Browne, P ; Burgdorf, M. J ; Calchi Novati, S ; Dodds, P ; Dominik, M ; Fang, X. S ; Finet, F ; Gerner, T ; Gu, S. H ; Hundertmark, M ; Jessen Hansen, J ; Kains, N ; Kerins, E ; Kjeldsen, H ; Liebig, C ; Lund, M. N ; Lundkvist, M ; Mathiasen, M ; Nesvorný, D ; Nikolov, N ; Penny, M. T ; Proft, S ; Rahvar, S ; Ricci, D ; Sahu, K. C ; Scarpetta, G ; Schäfer, S ; Schönebeck, F ; Snodgrass, C ; Skottfelt, J ; Surdej, J ; Tregloan Reed, J ; Wertz, O ; Sharif University of Technology
    2012
    Abstract
    Aims. We present 11 high-precision photometric transitobservations of the transiting super-Earth planet GJ≠1214≠b. Combining these data with observations from other authors, we investigate the ephemeris for possible signs of transit timing variations (TTVs) using a Bayesian approach. Methods. The observations were obtained using telescope-defocusing techniques, and achieve a high precision with random errors in the photometry as low as 1 mmag per point. To investigate the possibility of TTVs in the light curve, we calculate the overall probability of a TTV signal using Bayesian methods. Results. The observations are used to determine the photometric parameters and the physical properties... 

    Distributed binary majority voting via exponential distribution

    , Article IET Signal Processing ; Volume 10, Issue 5 , 2016 , Pages 532-542 ; 17519675 (ISSN) Salehkaleybar, S ; Golestani, S. J ; Sharif University of Technology
    Institution of Engineering and Technology 
    Abstract
    In the binary majority voting problem, each node initially chooses between two alternative choices. The goal is to design a distributed algorithm that informs nodes which choice is in majority. In this study, the authors formulate this problem as a hypothesis testing problem and propose fixed-size and sequential solutions using classical and Bayesian approaches. In the sequential version, the proposed mechanism enables nodes to test which choice is in majority, successively in time. Hence, termination of the algorithm is embedded within it, contrary to the existing approaches which require a monitoring algorithm to indicate the termination. This property makes the algorithm more efficient in...