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Probablistic-based Framework For Medical CT Images Segmentation 7

Probablistic-based Framework For Medical CT Images Segmentat...

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IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)

作者： Mohamed, Alaa Salah Ei-Din Salem, Mohammed A. M. Hegazy, Doaa Shedeed, Howida A. Ain Shams Univ Fac Comp & Informat Sci Dept Comp Sci Cairo Egypt

ISBN: (纸本)9781509019496

Liver segmentation is a difficult process due to wide variability of livers shapes and sizes between patients and the intensity similarity between the liver and other organs. Liver segmentation from abdominal Computed Tomography (CT) images is very useful in many diagnostic and surgical processes. It is the essential step in many clinical applications. Medical decisions are rarely taken without the use of imaging technology such as CT, Magnetic Resonance Imaging (MRI), or Ultrasound Imaging (US). In this paper, an automated probabilistic-based framework for liver segmentation from abdominal CT images is presented. The framework consists of four stages;thresholding stage, superpixels construction stage, Bayesian network construction stage and region merging stage. We train and validate our model using 20 clinical volumes. We use the MICCAI dataset (Medical Image Computing and Computer Assisted Intervention for Liver Segmentation). MICCAI dataset is used in more than 90 researches.

关键词： Liver segmentation Computed Tomography Super pixels probabilistic graphical model Bayesian Network Region Merging

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End-to-End Defect Detection in Automated Fiber Placement Based on Artificially Generated Data 14

End-to-End Defect Detection in Automated Fiber Placement Bas...

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14th International Conference on Quality Control by Artificial Vision (QCAV)

作者： Zambal, Sebastian Heindl, Christoph Eitzinger, Christian Scharinger, Josef Profactor GmbH Stadtgut A2 A-4407 Steyr Austria JKU Inst Computat Percept Altenbergerstr 69 A-4040 Linz Austria

ISBN: (数字)9781510630543

ISBN: (纸本)9781510630543

Automated fiber placement (AFP) is an advanced manufacturing technology that increases the rate of production of composite materials. At the same time, the need for adaptable and fast inline control methods of such parts raises. Existing inspection systems make use of handcrafted filter chains and feature detectors, tuned for a specific measurement methods by domain experts. These methods hardly scale to new defects or different measurement devices. In this paper, we propose to formulate AFP defect detection as an image segmentation problem that can be solved in an end-to-end fashion using artificially generated training data. We employ a probabilistic graphical model to generate training images and annotations. We then train a deep neural network based on recent architectures designed for image segmentation. This leads to an appealing method that scales well with new defect types and measurement devices and requires little real world data for training.

关键词： AFP deep learning probabilistic graphical model segmentation end-to-end

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Stratification of Admixture Population: A Bayesian Approach 7

Stratification of Admixture Population: A Bayesian Approach

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7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS)

作者： Tamiji, M. Taheri, S. M. Motahari, S. A. Univ Tehran Coll Engn Tehran Iran Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781728106731

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.

关键词： Population Stratification probabilistic graphical model Bioinformatics Admixture Populations

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Efforts Towards Combining Graphics, Uncertainty, and Semantics: A Survey 15

Efforts Towards Combining Graphics, Uncertainty, and Semanti...

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15th International Conference on Semantics, Knowledge and Grids (SKG) - On Big Data, AI and Future Interconnection Environment

作者： Gao, Yuan Rafi, Muhammad Adnan Chinese Acad Sci Univ Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Aston Univ Dept Comp Sci Birmingham W Midlands England

ISBN: (纸本)9781728158235

modeling the real world is a basic but vital task in computer science. Graphics, uncertainty, and semantics are three key aspects of understanding structure and "why" when handling complex relationships with imperfect or unknown information. The combination of the three aspects can provide a systematic and effective method for modeling the real world. This paper presents a survey of the efforts towards combining these aspects. One branch of the efforts is to combine graphics and uncertainty as probabilistic graphical models (PGMs), and then associate PGMs with semantics. The other branch is to combine graphics and semantics as graph-based knowledge representations, and then add the probability to handle uncertainty. We introduce the models and methods involved in these efforts and discuss the expressiveness, pros and cons of them. Finally, we suggest future work in this domain.

关键词： probabilistic graphical model Markov logic network probabilistic soft logic graph-based knowledge representation semantic network semantic link network

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modeling Truth Existence in Truth Discovery 15

Modeling Truth Existence in Truth Discovery

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21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Zhi, Shi Zhao, Bo Tong, Wenzhu Gao, Jing Yu, Dian Ji, Heng Han, Jiawei Univ Illinois Urbana IL 61801 USA LinkedIn Mountain View CA USA SUNY Buffalo Buffalo NY USA Rensselaer Polytech Inst Troy NY USA

ISBN: (纸本)9781450336642

When integrating information from multiple sources, it is common to encounter conflicting answers to the same question. Truth discovery is to infer the most accurate and complete integrated answers from conflicting sources. In some cases, there exist questions for which the true answers are excluded from the candidate answers provided by all sources. Without any prior knowledge, these questions, named no-truth questions, are difficult to be distinguished from the questions that have true answers, named has truth questions. In particular, these no-truth questions degrade the precision of the answer integration system. We address such a challenge by introducing source quality, which is made up of three fine-grained measures: silent rate, false spoken rate and true spoken rate. By incorporating these three measures, we propose a probabilistic graphical model, which simultaneously infers truth as well as source quality without any a priori training involving ground truth answers. Moreover, since inferring this graphical model requires parameter tuning of the prior of truth, we propose an initialization scheme based upon a quantity named truth existence score, which synthesizes two indicators, namely, participation rate and consistency rate. Compared with existing methods, our method can effectively filter out no-truth questions, which results in more accurate source quality estimation. Consequently, our method provides more accurate and complete answers to both has-truth and no-truth questions. Experiments on three real-world datasets illustrate the notable advantage of our method over existing state-of-the-art truth discovery methods.

关键词： information integration system knowledge base truth existence knowledge graph truth finding probabilistic graphical model truth discovery source quality information extraction

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PRomodel: A model-Driven Software Environment that Facilitates and Expedites the Development of Systems that Handle Uncertainty

<i>PRoModel</i>: A Model-Driven Software Environment that Fa...

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Theory of modeling and Simulation - DEVS Integrative M&S Symposium (DEVS 2012)

作者： Lopez-Landa, Rosa Noguez, Julieta Tecnol Monterrey Campus Ciudad Mexico Monterrey Mexico

ISBN: (纸本)9781618397867

PRomodel is a software environment that facilitates and expedites the development of systems that handle uncertainty. Uncertainty arises when information is incomplete or incorrect or if we have deficient models that limit the way in which knowledge can be represented. probabilistic Relational models (PRMs) are structures that merge, in the same model: (1) the entities and their relationships from an Entity Relationship model, and (2) the random variables and conditional dependence associations that represent the uncertainty in the domain. PRomodel use PRMs to generate, automatically, the code artifacts and the database to produce a functional prototype of a Web application. In the same way, PRomodel generates the uncertainty model to allow the propagation of evidence and inference of knowledge. The evaluation of PRomodel was made through the development of an Intelligent Tutoring System with a simulator for training operators of power plants. Initial tests show that PRomodel expedited the development of systems that handle uncertainty, providing significant savings in time and effort.

关键词： Active learning simulation Bayesian network model-driven software engineering probabilistic graphical model probabilistic relational model service-oriented architecture uncertainty

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A Multi-hop Link Prediction Approach Based on Reinforcement Learning in Knowledge Graphs 11

A Multi-hop Link Prediction Approach Based on Reinforcement ...

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11th International Symposium on Computational Intelligence and Design (ISCID)

作者： Chen, Heng Li, Guanyu Sun, Yunhao Jiang, Wei Chen, Xinying Dalian Maritime Univ Fac Informat Sci & Technol Dalian 116026 Peoples R China DaLian Univ Foreign Languages Sch Software Dalian 116044 Peoples R China Dalian Jiao Tong Univ Sch Software Dalian 116028 Peoples R China

ISBN: (纸本)9781538685273

Knowledge Graphs (KGs), both manually and automatically constructed, are far from complete. Recently, Knowledge Graph Completion (KGC) has been receiving a great deal of attention. Link prediction is a key task in KGC. In this work, we research multi-hop link prediction based on reinforcement learning (RL) in KGs. More specifically, we propose a novel RL framework for learning more accurate link prediction models: we frame link prediction problem in KGs as an inference problem in probabilistic graphical model (PGM) and use maximum entropy RL to maximize the expected return. Our experimental evaluations show that our proposed method performs well on FB15K-237 and NELL-995 datasets.

关键词： Reinforcement learning multi-hop link prediction knowledge graph completion probabilistic graphical model

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Interpretable and Scalable graphical models for Complex Spatio-Temporal Processes

Interpretable and Scalable Graphical Models for Complex Spat...

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作者： Wang, Yu University of Michigan

学位级别：Ph.D., Doctor of Philosophy

This thesis focuses on data that has complex spatio-temporal structure and on probabilistic graphical models that learn the structure in an interpretable and scalable manner. We target two research areas of interest: Gaussian graphical models for tensor-variate data and summarization of complex time-varying texts using topic models. This work advances the state-of-the-art in several directions. First, it introduces a new class of tensor-variate Gaussian graphical models via the Sylvester tensor equation. Second, it develops an optimization technique based on a fast-converging proximal alternating linearized minimization method, which scales tensor-variate Gaussian graphical model estimations to modern big-data settings. Third, it connects Kronecker-structured (inverse) covariance models with spatio-temporal partial differential equations (PDEs) and introduces a new framework for ensemble Kalman filtering that is capable of tracking chaotic physical systems. Fourth, it proposes a modular and interpretable framework for unsupervised and weakly-supervised probabilistic topic modeling of time-varying data that combines generative statistical models with computational geometric methods. Throughout, practical applications of the methodology are considered using real datasets. This includes brain-connectivity analysis using EEG data, space weather forecasting using solar imaging data, longitudinal analysis of public opinions using Twitter data, and mining of mental health related issues using TalkLife data. We show in each case that the graphical modeling framework introduced here leads to improved interpretability, accuracy, and scalability.

关键词： Spatio-temporal modeling High-dimensional statistics Machine learning Covariance estimation probabilistic graphical model

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Exploiting non-redundant local patterns and probabilistic models for analyzing structured and semi-structured data

Exploiting non-redundant local patterns and probabilistic mo...

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作者： Wang, Chao The Ohio State University

学位级别：Ph.D.

This work seeks to develop a probabilistic framework for modeling, querying and analyzing large-scale structured and semi-structured data. The framework has three components: (1) Mining non-redundant local patterns from data; (2) Gluing these local patterns together by employing probabilistic models (e.g., Markov random field (MRF), Bayesian network); and (3) Reasoning (making inference) over the data for solving various data analysis tasks. In more detail, our contributions are as follows:@pqdt@break@Mining non-redundant frequent itemset patterns on large transactional data. Often times in many real-world problems frequent pattern mining algorithms yield so many frequent patterns that the end-user is swamped when it comes to interpreting the results. We present an approach of employing probabilistic models to identify non-redundant itemset patterns from a large collection of frequent itemsets on transactional data. We show that our approach can effectively eliminate a large amount of redundancy from a large collection of itemset patterns.@pqdt@break@Employing local probabilistic models to glue non-redundant itemset patterns on large transactional or network data. We propose a technique of employing local probabilistic models to glue non-redundant itemset patterns together in tackling the link prediction task in co-authorship network analysis. The new technique effectively combines topology analysis on network structure data and frequency analysis on network event log data. The main idea is to consider the co-occurrence probability of two end nodes associated with a candidate link. We propose a method of building MRFs over local data regions to compute this co-occurrence probability. Experimental results demonstrate that the co-occurrence probability inferred from the local probabilistic models is very useful for link prediction.@pqdt@break@Employing global probabilistic models to glue non-redundant itemset patterns on large transactional data. We explore employing

关键词： Computer Science probabilistic graphical model Undirected graphical model Markov Random Field Maximum Entropy Feature Selection Frequent Pattern Mining Non-Redundant Frequent Pattern Structured and Semi-Structure Data Social Network Analysis Link Prediction

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Prescribed Fire modeling using Knowledge-Guided Machine Learning for Land Management

Prescribed Fire Modeling using Knowledge-Guided Machine Lear...

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SIAM International Conference on Data Mining (SDM)

作者： Chatterjee, Somya Sharma Lindsay, Kelly Chatterjee, Neel Patil, Rohan De Callafon, Ilkay Altintas Steinbach, Michael Giron, Daniel Nguyen, Mai H. Kumar, Vipin Univ Minnesota Twin Cities Minneapolis MN 55455 USA Univ Calif San Diego La Jolla CA USA Colorado State Univ Ft Collins CO USA

ISBN: (纸本)9781611978032

In recent years, the increasing threat of devastating wildfires has underscored the need for effective prescribed fire management. Process-based computer simulations have traditionally been employed to plan prescribed fires for wildfire prevention. However, even simplified process models are too compute-intensive to be used for real-time decision-making. Traditional ML methods used for fire modeling offer computational speedup but struggle with physically inconsistent predictions, biased predictions due to class imbalance, biased estimates for fire spread metrics (e.g., burned area, rate of spread), and limited generalizability in out-of-distribution wind conditions. This paper introduces a novel machine learning (ML) framework that enables rapid emulation of prescribed fires while addressing these concerns. To overcome these challenges, the framework incorporates domain knowledge in the form of physical constraints, a hierarchical modeling structure to capture the interdependence among variables of interest, and also leverages pre-existing source domain data to augment training data and learn the spread of fire more effectively. Notably, improvement in fire metric (e.g., burned area) estimates offered by our framework makes it useful for fire managers, who often rely on these estimates to make decisions about prescribed burn management. Furthermore, our framework exhibits better generalization capabilities than the other ML-based fire modeling methods across diverse wind conditions and ignition patterns.

关键词： prescribed fire modeling knowledge guided machine learning probabilistic graphical model surrogate model

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