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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Cao, Chuxue He, Yiming Chen, Yuzhen Song, Chunli Ling, Hao Guan, Renchu Feng, Xiaoyue Jilin University The Key Laboratory for Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Software Changchun China The Second Hospital of Jilin University Changchun China Jilin University The Key Laboratory for Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Changchun China

ISBN: (纸本)9781665401265

Medical scene graph contributes to cognitive tasks such as question answering. An automatic medical scene graph generator can annotate medical images with scene graphs conveniently. An end-to-end model is proposed, which contains an automatic medical scene graph generator based on medical images and surgery records. The novel model can extract symptom information of the images from surgery records via name entity recognition then convert it into scene graphs. The content and format of the scene graph are pre-defined according to the features of the images. A medical scene graph (MSG) dataset is also produced. The dataset contains 116 scene graphs where each graph includes 6 to 30 entities. Experiments show that our model performs well on making scene graphs of CT coronary angiography. Significantly, the scene graphs have the type, state, location, and index of arteries. To verify the practicability of the MSG dataset, We construct an intelligent model that can generate key diagnostic results. In addition, our medical scene graph generator can be applied to other medical areas to annotate different kinds of medical images intelligently. And the MSG dataset can directly be used in other cognitive visual tasks. © 2021 IEEE.

关键词： Graphic methods

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An adaptive fuzzy k-nearest neighbor method based on parallel particle swarm optimization for bankruptcy prediction

An adaptive fuzzy k-nearest neighbor method based on paralle...

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作者： Chen, Hui-Ling Liu, Da-You Yang, Bo Liu, Jie Wang, Gang Wang, Su-Jing Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

ISBN: (纸本)9783642208409

This study proposes an efficient non-parametric classifier for bankruptcy prediction using an adaptive fuzzy k-nearest neighbor (FKNN) method, where the nearest neighbor k and the fuzzy strength parameter m are adaptively specified by the particle swarm optimization (PSO) approach. In addition to performing the parameter optimization for FKNN, PSO is utilized to choose the most discriminative subset of features for prediction as well. Time varying acceleration coefficients (TVAC) and inertia weight (TVIW) are employed to efficiently control the local and global search ability of PSO. Moreover, both the continuous and binary PSO are implemented in parallel on a multi-core platform. The resultant bankruptcy prediction model, named PTVPSO-FKNN, is compared with three classification methods on a real-world case. The obtained results clearly confirm the superiority of the developed model as compared to the other three methods in terms of Classification accuracy, Type I error, Type II error and AUC (area under the receiver operating characteristic (ROC) curve) criterion. It is also observed that the PTVPSO-FKNN is a powerful feature selection tool which has indentified a subset of best discriminative features. Additionally, the proposed model has gained a great deal of efficiency in terms of CPU time owing to the parallel implementation. © 2011 Springer-Verlag.

关键词： Particle swarm optimization (PSO)

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Extracting attributes from deep web interface using instances

Extracting attributes from deep web interface using instance...

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2009 WRI World Congress on Computer Science and Information engineering, CSIE 2009

作者： Hao, Liang Fei, Ren Wanli, Zuo Fengling, He Junhua, Wang College of Computer Science and Technology Jilin University Changchun 130012 China Department of Information Changchun Taxation College Changchun 130117 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 China

ISBN: (纸本)9780769535074

There is myriad high quality information in the Deep Web and the feasible method to access the Deep Web is through the query interface of the Deep Web. It's necessary to extract abundant attributes and semantic relation description from the query interface. Automatic extracting attributes from the query interface and automatically translating a query is a solvable way for addressing the current limitations in accessing Deep Web data sources. We design a framework to automatically extract the attributes and instances from the query interface using the WordNet as a kind of ontology technique to enrich the semantic description of the attributes. Each attribute is extended into a candidate attribute set in the form of a hierarchy tree. At the same time, the hierarchy tree generated by ontology describes the semantic relation of the attributes in the same query interface. We carry out our experiments in the real-world domain. The results of the experiments showed the validation of query translation framework. © 2008 IEEE.

关键词： Ontology

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An approach to accelerate diagnosis by isomorphic determining

An approach to accelerate diagnosis by isomorphic determinin...

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2009 International Conference on Information engineering and Computer Science, ICIECS 2009

作者： Wang, Xiaoyu Ouyang, Dantong Feng, Yuxuan Zhao, Jian College of Computer Science and Technology Jilin University Key Laboratory Symbolic Computation and Knowledge Engineering Ministry of Educaion Changchun China

ISBN: (纸本)9781424449941

When diagnosing dynamic system represented as discrete-event systems, it needs to find what happened to the systems from observations. The behavior of system could be represented by automaton model. The diagnostic task consists in determining the models compatible with the observations, but the global computation which is needed in synchronization costs exponential time. In this paper we propose a new method to simplify the automaton by isomorphic determining. The cost of computation will be significantly cut down by merging the automatons with the same structure, and the time-complexity of the diagnosis process could be reduced by the method. ©2009 IEEE.

关键词： Discrete event simulation

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Heterogeneous data mining for planning active surveillance of malaria 15

Heterogeneous data mining for planning active surveillance o...

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ASE BigData and SocialInformatics, ASE BD and SI 2015

作者： Gu, Xiao Chen, Hechang Yang, Bo College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education China

ISBN: (纸本)9781450337359

Malaria is one of the most serious diseases in the world, which is densely distributed in poverty and remote areas. In the prevention and control of malaria, active surveillance is more efficient than passive surveillance to discover the incidences timely and accurately. However, it is always faced with the challenge of how to allocate the limited sources, such as medical staff and medicine, appropriately so as to achieve a maximum infect. In this paper, we propose a novel method to characterize the spatiotemporal patterns of infection risk for active surveillance planning. Specifically, we propose a temporal heterogeneous diffusion network model to discover high risk areas timely, and a mixture optimization method to find high risk areas accurately. The validation on existing real-world data shows that our method outperforms the existing state-of-the-art both in terms of infection risk prediction and planning of active surveillance under different thresholds. © 2015 ACM.

关键词： Mixtures

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A surrogate model based genetic algorithm for complex problem solving 6

A surrogate model based genetic algorithm for complex proble...

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6th Annual International Conference on Network and Information Systems for Computers, ICNISC 2020

作者： Pei, Ying Gao, Hao Han, Xiaosong College of Information Engineering Changchun University of Finance and Economics Changchun130122 China Key Laboratory for Symbol Computation Knowledge Engineering of National Education Ministry College of Computer Science and Technology College of Software Jilin University Changchun130012 China

It is well known that when the fitness function is relatively complex, the optimization time cost of the genetic algorithm will be extremely huge. To address this issue, the surrogate model was employed to predict the fitness value of the optimization problem, to reduce the number of actual calculated fitness values. In this paper, BP neural network, the least square method and support vector machine were fused in the genetic algorithm to evaluate partial individuals' fitness. Sufficient benchmark numerical experiments were conducted, and the results proved that the strategy could reduce the calculating counts of fitness function on similar accuracy basis compared with simple genetic algorithm. © 2020 Journal of Physics: Conference Series.

关键词： Genetic algorithms

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Migration: An Efficient Explorer Operator to Guide Swarm Evolution

Migration: An Efficient Explorer Operator to Guide Swarm Evo...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xiaosong Han Chengyang Zhou Yanchun Liang Renchu Guan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Zhuhai Laboratory of Key Laboratory for Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Zhuhai College of Science and Technology Zhuhai China

Particle Swarm Optimization with Migration (MPSO) is proposed to solve the issue that PSO will encounter unbearable time cost problems when dealing with High-dimension, Expensive and Black-box objective function tasks. The Migration operator is inspired by the migration of Salmon. Salmon will start a dangerous journey from the ocean to the home rivers for reproduction. The process of the entire behavior is similar to the reduction and recovery of dimension. Therefore, we design the Migration operator where a pretrained Wasserstein Autoencoders (WAE) is applied to simulate the migration behavior to accelerate the process of evolution in PSO, and we use Least-Squares Regression in lower space to produce better generation. In comparison with famous baseline methods in some benchmark functions, MPSO converges faster and more accurately, which shows the great potential of migration operations.

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Integration of scanning and image processing algorithms for lane detection based on fuzzy method

Integration of scanning and image processing algorithms for ...

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作者： Zhang, Jindong Jia, Xiaoyan Li, Jinfeng College of Computer Science and Technology Jilin University Changchun China Department of Hand Surgery First Hospital of Jilin University Changchun130021 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China

In order to detect lane rapidly and accurately, the integration of scanning and image processing algorithms (SIP) based on the fuzzy method is proposed. Further, combination of the proposed algorithm with an adaptive threshold value for image binarization, the least-square method and Bessel curve algorithm are proposed for detection and to fit the lane. The proposed SIP algorithm was evaluated by various tests. The experimental results indicate that the average time consumed for the detection of lane in each frame is 16.7639ms, and the accuracy of lane detection is 95. The proposed algorithm demonstrates good robustness, and can be used as the core algorithm for further application in lane departure warning systems. © 2015 - IOS Press and the authors. All rights reserved.

关键词： Computer vision

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A pseudo label based dataless naive bayes algorithm for text classification with seed words 27

A pseudo label based dataless naive bayes algorithm for text...

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27th International Conference on computational Linguistics, COLING 2018

作者： Li, Ximing Yang, Bo College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

ISBN: (纸本)9781948087506

Traditional supervised text classifiers require a large number of manually labeled documents, which are often expensive to obtain. Recently, dataless text classification has attracted more attention, since it only requires very few seed words of categories that are much cheaper. In this paper, we develop a pseudo-label based dataless Naive Bayes (PL-DNB) classifier with seed words. We initialize pseudo-labels for each document using seed word occurrences, and employ the expectation maximization algorithm to train PL-DNB in a semi-supervised manner. The pseudo-labels are iteratively updated using a mixture of seed word occurrences and estimations of label posteriors. To avoid noisy pseudo-labels, we also consider the information of nearest neighboring documents in the pseudo-label update step, i.e., preserving local neighborhood structure of documents. We empirically show that PL-DNB outperforms traditional dataless text classification algorithms with seed words. Especially, PL-DNB performs well on the imbalanced dataset. © 2018 COLING 2018 - 27th International Conference on computational Linguistics, Proceedings. All rights reserved.

关键词： Iterative methods

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An Improved Particle Swarm Optimization for Feature Selection

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Journal of Bionic engineering 2011年第2期8卷 191-200页

作者： Yuanning Liu College of Computer Science and Technology Jilin University Changchun 130012 P. R. China Key Laboratory of S.vmbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 P. R. China

Particle Swarm Optimization （PSO） is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation or destruction of a swarm, has not been considered further. In this paper, we formulate four rules by introducing the mechanism for survival of the fittest, which simulates the competition among the swarms. Based on the mechanism, we design a modified Multi-Swarm PSO （MSPSO） to solve discrete problems, which consists of a number of sub-swarms and a multi-swarm scheduler that can monitor and control each sub-swarm using the rules. To further settle the feature selection problems, we propose an Improved Feature Selection （1FS） method by integrating MSPSO, Support Vector Machines （SVM） with F-score method. The IFS method aims to achieve higher generalization capa- bility through performing kernel parameter optimization and feature selection simultaneously. The performance of the proposed method is compared with that of the standard PSO based, Genetic Algorithm （GA） based and the grid search based mcthods on 10 benchmark datasets, taken from UCI machine learning and StatLog databases. The numerical results and statistical analysis show that the proposed IFS method performs significantly better than the other three methods in terms of prediction accuracy with smaller subset of features.

关键词： particle swarm optimization feature selection data mining support vector machines

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