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International Journal of Computational Intelligence and Applications最新文献

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Combining Neural Networks with Logic Rules 神经网络与逻辑规则的结合
IF 1.8
International Journal of Computational Intelligence and Applications Pub Date : 2023-03-27 DOI: 10.1142/s1469026823500153
Lujiang Zhang
{"title":"Combining Neural Networks with Logic Rules","authors":"Lujiang Zhang","doi":"10.1142/s1469026823500153","DOIUrl":"https://doi.org/10.1142/s1469026823500153","url":null,"abstract":"How to utilize symbolic knowledge in deep learning is an important problem. Deep neural networks are flexible and powerful, while symbolic knowledge has the virtue of interpretability and intuitiveness. It is necessary to combine the two together to inject symbolic knowledge into neural networks. We propose a novel approach to combine neural networks with logic rules. In this approach, task-specific supervised learning and policy-based reinforcement learning are performed alternately to train a neural model until convergence. The basic idea is to use supervised learning to train a deep model and use reinforcement learning to propel the deep model to meet logic rules. In the process of the policy gradient reinforcement learning, if a predicted output of a deep model meets all logical rules, the deep model is given a positive reward, otherwise, it is given a negative reward. By maximizing the expected rewards, the deep model can be gradually adjusted to meet logical constraints. We conduct experiments on the tasks of named entity recognition. The experimental results demonstrate the effectiveness of our method.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44257933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Instrument Identification Technology Based on Deep Learning 基于深度学习的仪器识别技术
IF 1.8
International Journal of Computational Intelligence and Applications Pub Date : 2021-08-12 DOI: 10.1142/s1469026821500176
Yunhai Song, Zhenzhen Zhou, Hourong Zhang, Haohui Su, Han Zhang, Qi Wang
{"title":"Instrument Identification Technology Based on Deep Learning","authors":"Yunhai Song, Zhenzhen Zhou, Hourong Zhang, Haohui Su, Han Zhang, Qi Wang","doi":"10.1142/s1469026821500176","DOIUrl":"https://doi.org/10.1142/s1469026821500176","url":null,"abstract":"With the continuous improvement of science and technology, the substation remote control system has been constantly improved, which provides the possibility for the complete realization of intelligent and unmanned substation. However, due to the special substation environment, it is easy to cause interference, coupled with the low accuracy of today’s video image processing algorithm, which leads to the frequent occurrence of false alarms and missing alarms. Manual intervention is needed to deal with this, which inhibits the display of automatic intelligent substation processing functions. Therefore, in this paper, the most rapidly developed machine learning algorithm — deep learning is applied to the substation instrument equipment identification processing, in order to improve the accuracy and efficiency of instrument equipment identification, and make due contributions to the full realization of unattended substation.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":"20 1","pages":"2150017:1-2150017:13"},"PeriodicalIF":1.8,"publicationDate":"2021-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64014566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
An Efficient Classification Algorithm Based on T-Cells Maturation with No Parameters 基于无参数t细胞成熟的高效分类算法
IF 1.8
International Journal of Computational Intelligence and Applications Pub Date : 2017-12-17 DOI: 10.1142/S1469026817500249
Chen Jungan, Chen Jinyin, Yang Dongyong
{"title":"An Efficient Classification Algorithm Based on T-Cells Maturation with No Parameters","authors":"Chen Jungan, Chen Jinyin, Yang Dongyong","doi":"10.1142/S1469026817500249","DOIUrl":"https://doi.org/10.1142/S1469026817500249","url":null,"abstract":"In artificial immune system, many algorithms based on negative selection methods have been proposed to achieve satisfying classification performances. However, there are still many problems required to be solved, such as parameters sensibility and computational complexity. In this paper, a novel classification algorithm based on T-cells maturation algorithm was proposed for anomaly detection. Data set from UC Irvine Machine Learning Repository was used for 10-fold cross-validation, and simulation results confirmed its similar performances with AIRS. Compared with other classification algorithms based on negative selection methods, the proposed algorithm has no parameters and lower complexity, and can achieve satisfying classification results.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":"65 1","pages":"1750024"},"PeriodicalIF":1.8,"publicationDate":"2017-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76520601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A New Stochastic Optimization Approach: Dolphin Swarm Optimization Algorithm 一种新的随机优化方法:海豚群优化算法
IF 1.8
International Journal of Computational Intelligence and Applications Pub Date : 2016-06-20 DOI: 10.1142/S1469026816500115
Wang Yong, Wang Tao, Zhang Cheng-zhi, Huang Hua-Juan
{"title":"A New Stochastic Optimization Approach: Dolphin Swarm Optimization Algorithm","authors":"Wang Yong, Wang Tao, Zhang Cheng-zhi, Huang Hua-Juan","doi":"10.1142/S1469026816500115","DOIUrl":"https://doi.org/10.1142/S1469026816500115","url":null,"abstract":"A novel nature-inspired swarm intelligence (SI) optimization is proposed called dolphin swarm optimization algorithm (DSOA), which is based on mimicking the mechanism of dolphins in detecting, chasing after, and preying on swarms of sardines to perform optimization. In order to test the performance, the DSOA is evaluated against the corresponding results of three existing well-known SI optimization algorithms, namely, particle swarm optimization (PSO), bat algorithm (BA), and artificial bee colony (ABC), in the terms of the ability to find the global optimum of a range of the popular benchmark functions. The experimental results show that the proposed optimization seems superior to the other three algorithms, and the proposed algorithm has the performance of fast convergence rate, and high local optimal avoidance.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":"15 1","pages":"1650011"},"PeriodicalIF":1.8,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1142/S1469026816500115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64014167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 16
EFFICIENT DNA MOTIF DISCOVERY USING MODIFIED GENETIC ALGORITHM 基于改进遗传算法的高效DNA基序发现
IF 1.8
International Journal of Computational Intelligence and Applications Pub Date : 2013-09-24 DOI: 10.1142/S146902681350017X
E. A. Daoud
{"title":"EFFICIENT DNA MOTIF DISCOVERY USING MODIFIED GENETIC ALGORITHM","authors":"E. A. Daoud","doi":"10.1142/S146902681350017X","DOIUrl":"https://doi.org/10.1142/S146902681350017X","url":null,"abstract":"In this study, a new genetic algorithm was developed to discover the best motifs in a set of DNA sequences. The main steps were: finding the potential positions in each sequence by using few voters (1–5 sequences), constructing the chromosomes from the potential positions, evaluating the fitness for each gene (position) and for each chromosome, calculating the new random distribution, and using the new distribution to generate the next generation. To verify the effectiveness of the proposed algorithm, several real and artificial datasets were used; the results are compared to the standard genetic algorithm, and Gibbs, MEME, and consensus algorithms. Although all the algorithms have low correlation with the correct motifs, the new algorithm exhibits higher accuracy, without sacrificing implementation time.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":"12 1","pages":"1350017"},"PeriodicalIF":1.8,"publicationDate":"2013-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1142/S146902681350017X","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64014113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Combining Statistical and Geometric Features for Colonic Polyp Detection in CTC Based on Multiple Kernel Learning. 基于多核学习的CTC结肠息肉检测中统计特征与几何特征的结合。
IF 1.8
International Journal of Computational Intelligence and Applications Pub Date : 2010-01-01 DOI: 10.1142/S1469026810002744
Shijun Wang, Jianhua Yao, Nicholas Petrick, Ronald M Summers
{"title":"Combining Statistical and Geometric Features for Colonic Polyp Detection in CTC Based on Multiple Kernel Learning.","authors":"Shijun Wang,&nbsp;Jianhua Yao,&nbsp;Nicholas Petrick,&nbsp;Ronald M Summers","doi":"10.1142/S1469026810002744","DOIUrl":"https://doi.org/10.1142/S1469026810002744","url":null,"abstract":"<p><p>Colon cancer is the second leading cause of cancer-related deaths in the United States. Computed tomographic colonography (CTC) combined with a computer aided detection system provides a feasible approach for improving colonic polyps detection and increasing the use of CTC for colon cancer screening. To distinguish true polyps from false positives, various features extracted from polyp candidates have been proposed. Most of these traditional features try to capture the shape information of polyp candidates or neighborhood knowledge about the surrounding structures (fold, colon wall, etc.). In this paper, we propose a new set of shape descriptors for polyp candidates based on statistical curvature information. These features called histograms of curvature features are rotation, translation and scale invariant and can be treated as complementing existing feature set. Then in order to make full use of the traditional geometric features (defined as group A) and the new statistical features (group B) which are highly heterogeneous, we employed a multiple kernel learning method based on semi-definite programming to learn an optimized classification kernel from the two groups of features. We conducted leave-one-patient-out test on a CTC dataset which contained scans from 66 patients. Experimental results show that a support vector machine (SVM) based on the combined feature set and the semi-definite optimization kernel achieved higher FROC performance compared to SVMs using the two groups of features separately. At a false positive per scan rate of 5, the sensitivity of the SVM using the combined features improved from 0.77 (Group A) and 0.73 (Group B) to 0.83 (p ≤ 0.01).</p>","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":"9 1","pages":"1-15"},"PeriodicalIF":1.8,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1142/S1469026810002744","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29359860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 20
REALIZATION PROBLEM FOR POSITIVE CONTINUOUS-TIME SYSTEMS WITH DELAYS 具有时滞的正连续系统的实现问题
IF 1.8
International Journal of Computational Intelligence and Applications Pub Date : 2006-06-01 DOI: 10.1142/S1469026806002003
Kaczorek Tadeusz
{"title":"REALIZATION PROBLEM FOR POSITIVE CONTINUOUS-TIME SYSTEMS WITH DELAYS","authors":"Kaczorek Tadeusz","doi":"10.1142/S1469026806002003","DOIUrl":"https://doi.org/10.1142/S1469026806002003","url":null,"abstract":"The realization problem for positive, continuous-time linear single-input, single-output systems with delays is formulated and solved. Sufficient conditions for the existence of positive realizations of a given proper transfer function are established. A procedure for computation of positive minimal realizations is presented and illustrated by an example.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":"06 1","pages":"289-298"},"PeriodicalIF":1.8,"publicationDate":"2006-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1142/S1469026806002003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64014100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
A NOVEL MECHANISM BASED ON ARTIFICIAL LOGICAL SPIDER WEB FOR REROUTING IN MPLS NETWORKS 一种基于人工逻辑蜘蛛网的MPLS网络重路由机制
IF 1.8
International Journal of Computational Intelligence and Applications Pub Date : 2005-06-01 DOI: 10.1142/S1469026805001520
Xinyu Yang, Yi Shi
{"title":"A NOVEL MECHANISM BASED ON ARTIFICIAL LOGICAL SPIDER WEB FOR REROUTING IN MPLS NETWORKS","authors":"Xinyu Yang, Yi Shi","doi":"10.1142/S1469026805001520","DOIUrl":"https://doi.org/10.1142/S1469026805001520","url":null,"abstract":"Multiprotocol label switching (MPLS) is a hybrid solution that combines the advantages of easy forwarding with the ability of guaranteeing quality-of-service (QoS). To deliver reliable service, MPLS requires traffic protection and recovery. Rerouting is one such recovery mechanism. In this paper, we propose a novel rerouting model called DDRAAS that is inspired by the spider and its web in nature. We try to establish an artificial logical spider web in the MPLS network to reorganise it into a structure that is more regular and simple. Based on this, we give the definition of the reroute area. Artificial spiders are then used to explore recovery paths dynamically in the reroute area. DDRAAS can be used to calculate the recovery paths in advance in order to protect the work path, while the improved DDRAAS can be a fast rerouting algorithm to calculate and establish recovery paths when faults occur. We have simulated our mechanism using the MPLS network simulator (MNS) and the performance metrics were compared to those of other proposals. The simulation results show that our mechanism is better in reducing packet loss, disorder and has faster rerouting speed. These improvements help to minimise the effects of link failure and/or congestion.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":"05 1","pages":"119-139"},"PeriodicalIF":1.8,"publicationDate":"2005-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1142/S1469026805001520","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64014045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Book Review: "Biorobotics: Methods and Applications", Barbara Webb and Thomas R. Consi 书评:《生物机器人:方法与应用》，芭芭拉·韦伯和托马斯·康西著
IF 1.8
International Journal of Computational Intelligence and Applications Pub Date : 2003-09-01 DOI: 10.1142/S1469026803000951
P. Chandana
{"title":"Book Review: \"Biorobotics: Methods and Applications\", Barbara Webb and Thomas R. Consi","authors":"P. Chandana","doi":"10.1142/S1469026803000951","DOIUrl":"https://doi.org/10.1142/S1469026803000951","url":null,"abstract":"","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":"03 1","pages":"309-310"},"PeriodicalIF":1.8,"publicationDate":"2003-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1142/S1469026803000951","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64014036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
BOOK REVIEW: "SWARM INTELLIGENCE," J. KENNEDY, R. C. EBERHART and Y. SHI 书评:《群体智慧》，J. KENNEDY, R. C. EBERHART, Y. SHI
IF 1.8
International Journal of Computational Intelligence and Applications Pub Date : 2003-03-01 DOI: 10.1142/S1469026803000513
A. T. Hayes
{"title":"BOOK REVIEW: \"SWARM INTELLIGENCE,\" J. KENNEDY, R. C. EBERHART and Y. SHI","authors":"A. T. Hayes","doi":"10.1142/S1469026803000513","DOIUrl":"https://doi.org/10.1142/S1469026803000513","url":null,"abstract":"","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":"13 1","pages":"135-139"},"PeriodicalIF":1.8,"publicationDate":"2003-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1142/S1469026803000513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64013707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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