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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 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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2010 3rd International Conference on Advanced Computer Theory and engineering, ICACTE 2010

作者： Zhou, Chunbao Wan, Liming Liang, Yanchun College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 China Research Institute on General Development and Evaluation of Equipment EAAF of PLA Beijing 100076 China

ISBN: (纸本)9781424465408

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 kNN 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: Leukemial, Leukemia2, and Leukemia3 *** human cancers. The MSTNN algorithm improves 5.65% better than k-NN on average LOOCV accuracy and 13.80% better than kNN 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. © 2010 IEEE.

关键词： Classification (of information)

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A novel method for RNA secondary structure prediction

A novel method for RNA secondary structure prediction

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2011 7th International Conference on Natural computation, ICNC 2011

作者： Xing, Chong Ji, Zhaohua Wang, Gang Wang, Yao Shen, Wei Liang, Yanchun Key Laboratory for Symbol Computation and Knowledge Engineering of National Education MinistryCollege of Computer Science and TechnologyJilin University Changchun China College of Information Engineering Guanghua College of Changchun University Changchun China College of Communications Engineering Jilin University Changchun China

ISBN: (纸本)9781424499533

In this paper, we propose PSOfold, a particle swarm optimization for RNA secondary structure prediction. PSOfold is based on the recently published IPSO. We present two strategies to improve the performance of IPSO. Firstly, in order to boost the competence in searching an optimal solution, fuzzy logic control is used to adaptively adjust the parameters in PSO. Accordingly, three fuzzy logic controls are designed by which the inertia weight, learning factors and the number of ants are tuned respectively. Secondly, to further settle the stem permutation problem, we put forward a solution conversion strategy (SCS), which can transform discrete values of stems into an ordered stem combination, thereby supplying an enhanced solution to evaluation of objective function. An evaluation of the performance of PSOfold in terms of prediction accuracy is made via comparison with one dynamic programming algorithm mfold and four metaheuristics, IPSO, ACRNA, RnaPredict, SARNA-Predict and mfold for ten individual known structures. PSOfold is able to predict structures with higher prediction accuracy than the other metaheuristic based methods on certain sequences, and has comparable performance compared with mfold. © 2011 IEEE.

关键词： Particle swarm optimization (PSO)

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Support vector machine regression algorithm based on chunking incremental learning

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ICCS 2006: 6th International Conference on computational Science

作者： Jingqing, Jiang Chuyi, Song Chunguo, Wu Maurizio, Marchese Yangchun, Liang Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Mathematics and Computer Science Inner Mongolia University for Nationalities Tongliao 028043 China Key Laboratory of Information Science and Engineering Beijing Jiaotong University Railway Ministry Beijing 100044 China Italy

ISBN: (纸本)3540343792

On the basis of least squares support vector machine regression (LSSVR), an adaptive and iterative support vector machine regression algorithm based on chunking incremental learning (CISVR) is presented in this paper. CISVR is an iterative algorithm and the samples are added to the working set in batches. The inverse of the matrix of coefficients from previous iteration is used to calculate the regression parameters. Therefore, the proposed approach permits to avoid the calculation of the inverse of a large-scale matrix and improves the learning speed of the algorithm. Support vectors are selected adaptively in the iteration to maintain the sparseness. Experimental results show that the learning speed of CISVR is improved greatly compared with LSSVR for the similar training accuracy. At the same time the number of the support vectors obtained by the presented algorithm is less than that obtained by LSSVR greatly. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Learning systems

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An improved ant colony optimization algorithm based on route optimization and its applications in travelling salesman problem

An improved ant colony optimization algorithm based on route...

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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Zhang, Yi Pei, Zhi-Li Yang, Jin-Hui Liang, Yan-Chun College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Department of Computer Science Jilin Business and Technology College College of Mathematics and Computer Science Inner Mongolia University for Nationalities

ISBN: (纸本)1424415098

In this paper, we introduce two improvements on Ant Colony Optimization (ACO) algorithm: route optimization and individual variation. The first is an optimized implementation of ACO, by which the running time of ants routing is largely reduced. The results of the simulated experiments show that the improved algorithm not only reduces the number of routing in the ACO but also surpasses existing algorithms in performance in solving large-scale TSP problems. In the second improvement, we introduce individual variation to ACO, by which the ants have different routing strategies. Simulation results show that the speed of convergence of ACO algorithm could be enhanced greatly. ©2007 IEEE.

关键词： Ant colony optimization

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A METHOD FOR INCREMENTAL DISCOVERY OF FINANCIAL EVENT TYPES BASED ON ANOMALY DETECTION

arXiv

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arXiv 2023年

作者： Gu, Dianyue Li, Zixu Guan, Zhenhai Zhang, Rui Huang, Lan Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE Jilin University Changchun China

Event datasets in the financial domain are often constructed based on actual application scenarios, and their event types are weakly reusable due to scenario constraints;at the same time, the massive and diverse new financial big data cannot be limited to the event types defined for specific scenarios. This limitation of a small number of event types does not meet our research needs for more complex tasks such as the prediction of major financial events and the analysis of the ripple effects of financial events. In this paper, a three-stage approach is proposed to accomplish incremental discovery of event types. For an existing annotated financial event dataset, the three-stage approach consists of: for a set of financial event data with a mixture of original and unknown event types, a semi-supervised deep clustering model with anomaly detection is first applied to classify the data into normal and abnormal events, where abnormal events are events that do not belong to known types;then normal events are tagged with appropriate event types and abnormal events are reasonably clustered. Finally, a cluster keyword extraction method is used to recommend the type names of events for the new event clusters, thus incrementally discovering new event types. The proposed method is effective in the incremental discovery of new event types on real data sets. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Research on Human Eyesight Tracking Algorithm Based on Monocular Vision 2nd

Research on Human Eyesight Tracking Algorithm Based on Monoc...

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2nd World Conference on Intelligent and 3D Technologies, WCI3DT 2023

作者： Tang, Haochun Zhang, Jindong Yang, Jing College of Software Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789819970100

This paper focuses on the optimization and improvement of the line-of-sight tracking algorithm based on monocular vision, and aims to achieve a series of complex functions through image analysis based on line-of-sight tracking technology and more in-depth analysis of monocular images. Therefore, we use the webgaze framework and use face detection models and feature point recognition libraries to analyze the recognition rate and provide further development of the technology. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Research on the Parsing Algorithm of Monocular Visual Structured Data Based on YOLOv5 2nd

Research on the Parsing Algorithm of Monocular Visual Struct...

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2nd World Conference on Intelligent and 3D Technologies, WCI3DT 2023

作者： Lu, Wanli Zhang, Wen Sun, Mingrui Zhang, Jindong College of Computer Science and Technology Jilin University Changchun130012 China College of Software Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789819721436

Visual perception plays an important role in autonomous driving technology. The two key factors in visual perception tasks are monocular object detection and structured data analysis. In this paper, a structured data parsing algorithm based on monocular vision is proposed. Firstly, using the single-stage YOLOv5 object detection model, the target was located and its kind was identified. Secondly, the 3D position of the object in the camera coordinate system is calculated according to the mapping relationship between the imaging point and the target point. Finally, the distance between the object and the camera is calculated by using the pinhole camera model, and the size of the object and the horizontal declination Angle relative to the optical center of the camera are estimated by geometric derivation. This method not only has a simple principle, but also guarantees the precision of structured data output. Experimental findings support the method's viability and efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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