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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Huang, Lan Sun, Yinglu Zhao, Ziqi Guo, Chunjie Wang, Yan College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun China The First Hospital of Jilin University Department of Radiology Changchun China

ISBN: (纸本)9798350386226

Segmenting brain white matter hyperintensities (WMH) from 3D Magnetic Resonance (MR) images is crucial for the diagnosis, treatment, and prognosis of Multiple Sclerosis (MS). Unlike common 2D images, this task is more challenging and time-consuming. Classical deep learning methods for 3D image segmentation face two main challenges: 1) Pure 3D networks have more parameters and are prone to overfitting. 2) When using 2D networks to segment slices of 3D images, the lack of 3D structural information results in suboptimal segmentation after reconstruction. To address these difficulties, we propose the 2.5D ASF-UNet, which employs the 2.5D workflow and uses adjacent slices as the input for 2D segmentation network ASF-UNet. In ASF-UNet, separate down-sampling paths are used for the adjacent slices, and the Local Spatial Attention Module (LSAM) is designed to more effectively integrate 3D spatial information into the 2D network. Additionally, the Conv-Spectral-Block (CSB) is designed to extract and integrate local and global features. It allows the model to capture global spatial structures while preserving detailed information. Experimental results on MICCAI MSSEG 2016 and Local MS datasets show that 2.5D ASF-UNet achieve better segmentation performance than other deep learning methods. © 2024 IEEE.

关键词： Image segmentation

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A modified integer-coding genetic algorithm for job shop scheduling problem

A modified integer-coding genetic algorithm for job shop sch...

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8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004

作者： Wu, Chunguo Xiang, Wei Liang, Yanchun Lee, Heow Pueh Zhou, Chunguang College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun130012 China Institute of High Performance Computing Singapore117528 Singapore

ISBN: (纸本)3540228179

An operation template is proposed in this paper for describing the mapping between operations and a subset of natural numbers. With such operation template, a job shop scheduling problem (JSSP) can be transformed into a traveling salesman problem (TSP), hence the integer-coding genetic algorithm for TSP can be easily applied and modified. A decoding strategy, called virtual job shop, is proposed to evaluate the fitness of the individual in GA population. The integration of the operation template and virtual job shop makes the existing integer-coding GA possible for solving an extension of a classical job shop scheduling problem. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Traveling salesman problem

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An effective dimensionality reduction method for text classification based on TFP-tree

An effective dimensionality reduction method for text classi...

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作者： Liu, Lu College of Software Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China College of Computer Science and Technology Jilin University Changchun China

Obtaining interesting and topic-relevant information is a very important task in Web mining. Text classification using a small proportion of labeled data and a large proportion of unlabeled data, also called semi-supervised learning, is a well-known problem. Despite plenty of research on text classification, however, how to effectively and efficiently apply valuable frequent patterns and deal with high-dimensional data in text classification is still an open issue. Due to the increasing data volumes and plenty of high-dimensional data, both distance measures and time complexity could be influenced by the noisy data. This paper targets on this problem and presents a novel method for text classification called CTFP (Classification based on TFP-tree), which uses TFP-tree (Text-Frequent-Pattern-tree) to generate frequent patterns in tremendous amount of texts and conduct text classification in a relatively low dimensional data space. It effectively reduces the data dimensionality during constructing the classifier. Substantial experiments on three datasets (RCV1, SRAA and Reuters-21578) show that our proposed method can achieve better performance than many existing state-of-the-art methods on precision, efficiency and many other evaluation metrics. © 2018 - IOS Press and the authors. All rights reserved.

关键词： Support vector machines

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Meta-path-based outlier detection in heterogeneous information network

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Frontiers of Computer Science 2020年第2期14卷 388-403页

作者： Lu LIU Shang WANG Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012China College of Software Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China College of Communication Engineering Jilin UniversityChangchun 130012China Department of Computer Science New Jersey Institute of TechnologyUniversity HeightsNewark NJ 07102USA

Mining outliers in heterogeneous networks is crucial to many applications,but challenges *** this paper,we focus on identifying meta-path-based outliers in heterogeneous information network(HIN),and calculate the similarity between different types of *** propose a meta-path-based outlier detection method(MPOutliers)in heterogeneous information network to deal with problems in one go under a unified *** calculates the heterogeneous reachable probability by combining different types of objects and their *** discovers the semantic information among nodes in heterogeneous networks,instead of only considering the network *** also computes the closeness degree between nodes with the same type,which extends the whole heterogeneous ***,each node is assigned with a reliable weighting to measure its authority *** experiments on two real datasets(AMiner and Movies dataset)show that our proposed method is very effective and efficient for outlier detection.

关键词： data mining heterogeneous information network outlier detection short text similarity

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Strategically migrating agents in itinerary graph

Strategically migrating agents in itinerary graph

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Bo Yang Da-You Liu Kun Yang Sheng-Sheng Wang College of Computer Science and Technology Open Symbol Computation and Knowledge Engineering Laboratory of State Education Department Jilin University China

ISBN: (纸本)0780378652

Mobile agent need migrate to several hosts for accomplishing its task. Migration strategy is responsible for planning out an optimal migration path, which ensures mobile agent to complete its task correctly and efficiently at the minimum cost. The paper introduces the concept of itinerary graph and presents three migration strategies based on it. Itinerary graph not only describes the migration semantics of mobile agent but also reflects the changes of software and hardware environment where mobile agent lives. During its travel, mobile agent equipped with itinerary graph perceives these changes, react quickly, modify its migration path autonomously and accomplish its task according to certain specified criterions, which embody the reactivity and autonomy of agent. Besides improving the efficiency, these migration strategies enhances the performance of mobile agent system because they avoid migration failure resulted from network disconnection or node crashing.

关键词： Mobile agents File servers Path planning Strategic planning Cost function Distributed computing Educational institutions Computer science Educational technology knowledge engineering

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An Ontology-based Method in Information Translation

An Ontology-based Method in Information Translation

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International Conference on Semantics, knowledge and Grid (SKG)

作者： Bai Yan Bai Yan Liu Dayou Liu Dayou Open Symbol Computation and Knowledge Engineering Laboratory of State Education Ministry College of Computer Science and Technology Jilin University China

An ontology-based method named AOBM is proposed in this paper. It fully takes into account the factors that will afect the communication, and using ontology can be represented in agent's knowledge base. Pmvided ontology did not belong to the public data or have any public ontology, AOBM establishes a layering algorithm, which is able to avoid logical exception handling policies, ensure the right understanding of the communication entity concept, and resolve the consistency problem of the concept translation. Experimental results show that AOBM is superior to other methods, and can effectively improve the communication consistency of a mobile agent system under certain conditions.

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Mining data records based on ontology evolution for deep web

Mining data records based on ontology evolution for deep web

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2010 2nd International Conference on Computer engineering and Technology, ICCET 2010

作者： Wen, Zhang Chen, Kerui Fan, Zhang College of Information Engineering Zhongzhou University Zhengzhou 450044 China College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9781424463503

Currently, the research for the extraction of information in deep web is pretty active. Although many researchers already adopted ontology in the data extraction, many problems still exist. This paper proposed an ontology evolution based method for mining in the data area. Not only will this method solve the problem when the website only consists of one record, but it also can identify he meaning of data that has no labels. With the evolution of ontology, the extraction of data records is being more accurate. Experiments indicate that this method could improve the accuracy and efficiency of data extraction. © 2010 IEEE.

关键词： Ontology

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Central Author Mining from Co-authorship Network

Central Author Mining from Co-authorship Network

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International Symposium on computational Intelligence and Design, ISCID

作者： Tao Peng Delong Zhang Xiaoming Liu Shang Wang Wanli Zuo Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China

Most researches on co-authorship network analyze the author's information globally according to the overall network topology structure, instead of analyzing the author's local network. Therefore, this paper presents a community mining algorithm and divides big co-authorship network into small communities, in which entities' relationship is closer. Then we mine central authors in community by three different centrality standards including closeness centrality, eigenvector centrality and a new proposed measure termed extensity degree centrality. We choose the SIGMOD data as datasets and measure the centrality from different views. And experiments in co-authorship network achieve many interesting results, which indicate our technique is efficient and feasible, and also have reference value for scientific evaluation.

关键词： Communities Collaboration Social network services Corporate acquisitions Clustering algorithms Correlation Network topology

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A diversity-guided heuristic-based genetic algorithm for triangulation of Bayesian networks

A diversity-guided heuristic-based genetic algorithm for tri...

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作者： Dong, Xuchu Yu, Haihong Ouyang, Dantong Ye, Yuxin Zhang, Yonggang Department of Computer Science and Technology Jilin University Changchun Jilin 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun Jilin 130012 China

ISBN: (纸本)9788988678251

For the optimization problem about triangulation of Bayesian networks, a novel genetic algorithm, DHGA, is proposed in this paper. DHGA employs a heuristic-based mutation operation. Moreover, it uses population diversity to identify stagnation and convergence as well as to guide the search procedure. Experiments on representative benchmarks show that DHGA posses better performance and robustness than other swarm intelligence methods.

关键词： Bayesian networks

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An analysis of temporal and spatial relations

An analysis of temporal and spatial relations

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： He Hu Da-You Liu Sheng-Sheng Wang Open Symbol Computation and Knowledge Engineering Laboratory of State Education Committee College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)0780378652

In this paper, we explore the links between two well-known models in spatial and temporal reasoning - Allen's interval algebra in temporal reasoning and the RCC model in spatial reasoning. We show that the temporal relations identified by interval algebra are actually a combination of RCC-8 relations with directional relations. The work divides RCC-8 relations into two distinct sets with respect to directional factors. This result can be utilized to combine topology and directional relations in particular applications.

关键词： Algebra Calculus knowledge engineering Laboratories Computer science education Educational institutions Computer science Educational technology Topology Rivers

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