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作者： Gang Lin Yanchun Liang Xiaoyang Fu Guangshun Chen Shaocan Cai Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Department of Computer Science and TechnologySchool of Computer Science Department of Computer Science and Technology School of Computer ScienceZhuhai College of Jilin University

In order to generate a report for an enterprise where there is neither the API supporting from their existing website systems nor the granted database access rights approval,a daily business report generator system based on web scraping with k nearest neighbor(kNN)classification algorithm is proposed in this *** covers the web crawler technology that is to access existing website system and extract business *** k NN algorithm is applied to identify the verification code on the login page,and the brief daily report generating in a spreadsheet style *** with some OCR engine for image recognition,the system in Python can automatically generate the brief daily business reports by the kNN algorithm,which is better than some library with default training set on validating the verification code.

关键词： image Design of a Daily Brief Business Report Generator based on Web Scraping with KNN Algorithm Web KNN

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Time and Time-Frequency Features Integrated CNN Model for Heart Sound Signals Detection

Time and Time-Frequency Features Integrated CNN Model for He...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhimin Ren Yuheng Qiao Yuping Yuan You Zhou Yanchun Liang Xiaohu Shi College of Computer Science and Technology of Jilin University Changchun China Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China School of Computer Science Zhuhai College of Science and Technology Zhuhai China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Automatic heart sound diagnosis plays an important role in the early detection of cardiovascular diseases. Phonocardiogram (PCG) signals are often used in this field f or its low cost and non-invasive advantages. In this paper, we design a new time and time-frequency features integrated CNN (TTFI-CNN) model for heart sound signals detection. In the model, a 1D CNN and a BiLSTM are combined into 1D CRNN module to extract temporal features from the original PCG signal, and a 2D CNN module is applied to capture high-level features from time-frequency domain MFCCs inputs. The outputs of the two modules are recalibrated by attention mechanism to selectively emphasize informative features and suppress less useful ones. To verify the proposed TTFI-CNN model, it is applied to two public datasets with different classification t asks (binary and multiclassification). The TTFI-CNN model has achieved 97.15% accuracy, 97.13% sensitivity, and 97.17% specificity on physionet/cinc database, and obtained 3.31 and 2.61 precision scores on the PASCAL database A and B, respectively. Compared with the previous state-of-the-art methods, the TTFI-CNN performs best on all the above metrics. https://***/XxxNnnSssWww/TTFI-CNN.

关键词： Heart Time-frequency analysis Sensitivity Costs Databases Feature extraction Time-domain analysis

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Fold-based meta-analysis: a method for identifying differentially expressed genes in microarray data

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Journal of Information and computational Science 2013年第11期10卷 3453-3462页

作者： Tian, Yuan Bai, Tian Liu, Guixia Li, Zhangxiu Wu, Jianan Zhou, Chunguang College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China College of Computer Science and Technology Changchun University Changchun 130022 China

The increase of microarray experiments brings a fresh challenge to analyze microarray data across datasets. Several methods have been developed and implemented. But the current tools were either complicated software with inefficiency methods or web applications, among which some were limited to specific data format. In this paper, we propose Fold-based Meta-analysis (FM), a method to identify Differentially Expressed Genes (DEGs) in microarray data. It could meta-analyze datasets in either the same or different platforms. We test FMT, respectively, on two published Arabidopsis datasets in one platform and three Mouse datasets in three different platforms, and then analyze the Gene Ontology (GO) terms or metabolic pathways of the most DEGs. Our results show that they are highly agreed on DEGs, and many of them are crucial and functional. In summary, our method is fast and practical. © 2013 Binary Information Press.

关键词： Genes

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A hybrid algorithm of minimum spanning tree and nearest neighbor for classifying human cancers

A hybrid algorithm of minimum spanning tree and nearest neig...

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International Conference on Advanced Computer Theory and engineering, ICACTE

作者： Chunbao Zhou Liming Wan Yanchun Liang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China Research Institute on General Development and Evaluation of Equipment EAAF of PLA Beijing China

ISBN: (纸本)9781424465392

Classification and prediction of different cancers based on gene expression profiles are important for cancer diagnosis, cancer treatment and medication discovery. The k nearest neighbor algorithm (k-NN) is one easy and efficient machine learning method for cancer classification and the parameter k is crucial. In this paper, we integrate minimum spanning tree (MST) and k nearest neighbor algorithm (k-NN) for cancer classification. The MST is designed for the selection of parameter k and the nearest neighbors for k-NN. Firstly we build a minimum spanning tree (MST) based on Euclidean distance between each two samples for gene expression data only including one unknown class sample. Secondly for unknown class sample in the gene expression data, we find the connected samples and then apply majority vote principle. Thirdly if there are tied votes then we expend the connected samples with the nearest neighbors for unknown class sample until all the samples are expended or the class for unknown sample is obtained. This hybrid algorithm is referred to as MSTNN. The hybrid algorithm MSTNN is compared with k-NN and other 3 existing classification algorithms on CNS dataset, Colon dataset and Lung dataset, 3 binary class gene expression datasets and 3 multi-class gene expression datasets: Leukemia1, Leukemia2, and Leukemia3 involving human cancers. The MSTNN algorithm improves 5.65% better than k-NN on average LOOCV accuracy and 13.80% better than k-NN on testing datasets classification average accuracy, and achieves the best performance in all the 5 algorithms. The results demonstrate that the proposed MSTNN algorithm is feasible to classify human cancers.

关键词： Classification algorithms Artificial neural networks Niobium Lungs Colon Support vector machines Cancer

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Content- and semantic-based progressive multimodal image retrieval

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ICIC Express Letters 2011年第1期5卷 201-206页

作者： Song, Haiyu Li, Xiongfei Wang, Pengjie Zhang, Xiaoli Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Qianjin Avenue Changchun 130012 China Institute of Computer Graphics and Image Processing Dalian Nationalities University Economic and Technological Development Zone Liaohe Road Dalian 116600 China

Image retrieval has been an active research topic due to its great importance in the field of image management, digital library and web searching. However, the performances of the existing approaches, including text-based or content-based image retrieval, are often unsatisfactory. Inspired by text-based image retrieval, we propose semantic-based approach, whose similarity computation is semantic relevance rather than word matching. In this paper, a novel approach of progressive image retrieval based on content and semantic is proposed. The proposed retrieval model overcomes the drawbacks of pure content-based and text-based image retrieval, which exploits the strengths of both semantic- and content-based methods, minimizing their weaknesses. Experiments demonstrate the effectiveness and efficiency of the proposed method for image retrieval. © 2011 ISSN.

关键词： Semantics

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Efficient collision detection algorithm based on separating bounding volumes

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Ruan Jian Xue Bao/Journal of Software 2008年第SUPPL.期19卷 143-150页

作者： Wang, Yi Li, Wen-Hui Zhang, Zhen-Hua Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China

An efficient collision detection method based on separating bounding volume (SBV) is proposed. The positions and shapes of SBVs are determined by the optimal separating support hyper planes of two objects. SBVs not only can efficiently detect the separation of models, but have a high culling ratio when models are intersecting. In order to compute SBVs efficiently, an approximate method using SVM is also put forward and tested. At last in penetration region, a method combined with GPU and SBVs is designed to handle the proximity queries. Experimental results illustrate that SBVs based collision detection algorithm is applicable to exact collision detection for 3D models even without topologies and achieves more efficient and balanced performances in separating, colliding and especially puncturing cases. © 2008 by Journal of Software. All rights reserved.

关键词： Support vector machines

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Approximated non-tensor pre-wavelets neural network based on compound training algorithm

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Journal of Information and computational Science 2005年第4期2卷 715-722页

作者： Li, Ying Deng, Zhidong Zou, Xi Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 China Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130021 China Institute of Mathematics Jilin University Changchun 130021 China

In this paper, we propose a new supervised compound learning algorithm for training our constructed approximated bivariate non-tensor product adaptive pre-wavelet neural network (APWNN). On the one hand, the linear weights of APWNN are trained by the self-adaptive learning rate method. On the other hand an extended Kalman filter method is used to update the nonlinear parameters such as dilation parameters and translation parameters. Additionally we demonstrate the efficiency of our proposed method through a concrete example of function approximation.

关键词： Neural networks

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Unsupervised key-phrases extraction from scientific papers using domain and linguistic knowledge

Unsupervised key-phrases extraction from scientific papers u...

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International Conference on Digital Information Management (ICDIM)

作者： Mikalai Krapivin Maurizio Marchese Andrei Yadrantsau Yanchun Liang Department of Information Engineering and Computer Science University of Trento Italy Department of Computer Science Belarusian State University Minsk Belarus College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University China

The domain of Digital Libraries presents specific challenges for unsupervised information extraction to support both the automatic classification of documents and the enhancement of userspsila navigation in the digital content. In this paper, we propose a combined use of machine learning techniques (i.e. Support Vector Machines) and Natural Language Processing techniques (i.e. Stanford NLP parser) to tackle the problem of unsupervised key-phrases extraction from scientific papers. The proposed method strongly depends on the robust structural properties of a scientific paper as well as on the lexical knowledge that we are able to mine from its text. For the experimental assessment we have use a subset of ACM papers in the Computer Science domain containing 400 documents. Preliminary evaluation of the approach shows promising result that improves - on the same data-set - on state-of-the-art Bayesian learning system KEA from a minimum 27% to a maximum 77% depending on KEA parameters tuning and specific evaluation set. Our assessment is performed by comparison with key-phrases assigned by human experts in the specific domain and freely available through ACM portal.

关键词： Data mining Software libraries Navigation Machine learning Support vector machines Support vector machine classification Natural language processing Robustness Computer science Bayesian methods

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Combining Immune with Ant Colony Algorithm for Geometric Constraint Solving

Combining Immune with Ant Colony Algorithm for Geometric Con...

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International Workshop on knowledge Discovery and Data Mining (WKDD)

作者： Hua Yuan Yi Li Wenhui Li Kong Zhao Duo Wang Rongqin Yi Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China School of Computer Science & Engineering Changchun University of Technology China Suzhou Top Institute of Information Technology Suzhou China

Geometric constraint problem can be transformed to an optimization problem which the objective function and constraints are non-convex functions. In this paper an evolutionary algorithm based on ant colony optimization algorithm and the immune system model is proposed to provide solution to the geometric constraints problem. In the new algorithm, affinity calculation process and pheromone trail lying is embedded to maintain diversity and carry out the global search and the local search in many directions rather than one direction around the same individual simultaneously. This new algorithm different with current optimization methods in that it gets the good solution by excluding bad solutions. The experimental results reported here will shed more light into how affects the hybrid algorithm's search power in solving geometric constraint problem.

关键词： Immune system Ant colony optimization Nonlinear equations knowledge engineering Computer science Educational technology Evolutionary computation Data mining Laboratories Computer science education

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Unsupervised and Semi-Supervised Domain Adaptation Networks Based on Instance-Level Contrastive Prototype Matching for Motor Imagery Classification

SSRN

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SSRN 2023年

作者： Zhang, Dongxue Li, Huiying Xie, Jingmeng College of Computer Science and Technology Jilin Province Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun130012 China Xi’an Jiaotong University College of Electronic information Shanxi Province Xi’an China

Due to the convenience and safety of EEG signals, it is widely used in motor imagery (MI) brain-computer interface (BCI). However, there exist distribution differences between different sessions of the same subject. Models trained with data from previous sessions (source domain) often perform poorly on the current sessions (target domain). Domain adaptation techniques have met this challenge to great effect. However, most of these existing studies only consider coarse-grained methods with global information. In this paper, we propose a method for instance-level comparative prototype (ICP) analysis. The approach incorporates global domain alignment and fine-grained local subdomain alignment to simultaneously account for both global and local differences. Depending on the amount of labeled data available in the target domain, the method is implemented in both unsupervised and semi-supervised versions. Specifically, at the domain level, we employ the maximum mean difference (MMD) loss to globally constrain the feature space, achieving comprehensive alignment. In the context of class-level operations, we propose two memory banks designed to house class prototypes within each domain. We meticulously regulate feature embeddings through the application of two contrastive losses based on prototypes. Among them, source contrastive loss is used to spatially organize source features according to categories, thereby mediating inter- and intra-class relationships. At the same time, we propose an entropy-aware interactive contrastive loss to dynamically and personalized apply class information across domains by evaluating the confidence level of target data. To validate our approach, extensive experiments were conducted on a highly regarded public EEG dataset, namely Dataset IIa of the BCI Competition IV, as well as a large-scale EEG dataset called GigaDB. The experiments yielded average classification accuracies of 86.03% and 84.22% respectively. These results demonstrate that o

关键词： Electroencephalography

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