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2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)最新文献

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Active learning over evolving data streams using paired ensemble framework 使用配对集成框架对不断变化的数据流进行主动学习
2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449823
Wenhua Xu, Fengfei Zhao, Zhengcai Lu
{"title":"Active learning over evolving data streams using paired ensemble framework","authors":"Wenhua Xu, Fengfei Zhao, Zhengcai Lu","doi":"10.1109/ICACI.2016.7449823","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449823","url":null,"abstract":"Stream data is considered as one of the main sources of big data. The inherent scarcity of labeled instances and the underlying concept drift have posed significant challenges on stream data classification in practice. A paired ensemble active learning framework is proposed to tackle the challenges. First, an ensemble model consists of two base classifiers is exploited to detect the changes over time, as well as to make prediction on new instances. Second, two active learning strategies work alternatively to find out the most informative instances without missing the potential changes happened anywhere in the instance space. Third, the informativeness of an instance is measured by a margin based metric, and it can effectively capture uncertain instances. Experimental results on real-world datasets demonstrate that the proposed approach can achieve good predictive accuracy on data streams.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124905548","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: 15
A new time-dependent algorithm for post enrolment-based course timetabling problem 基于入学后课程排课问题的一种新的时变算法
2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449863
Hongteng Wu, Adi Lin, Defu Zhang, Carine Pierrette Mukamakuza
{"title":"A new time-dependent algorithm for post enrolment-based course timetabling problem","authors":"Hongteng Wu, Adi Lin, Defu Zhang, Carine Pierrette Mukamakuza","doi":"10.1109/ICACI.2016.7449863","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449863","url":null,"abstract":"In this paper, we propose a new time-dependent heu-ristic algorithm for post enrolment-based course timetabling prob-lem. The algorithm operates in two stages: a constructive phase is proposed to insert events into the timetable whilst obeying most hard constraints, and a hill-climbing phase is designed to ensure the timetable meeting all the hard constraints. Each stage is allocated a time limit. The experimental results on instances from the second international timetabling competition show that our algorithm is efficient and competitive with other effective algorithms.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122597707","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
Co-clustering of diseases, genes, and drugs for identification of their related gene modules 疾病、基因和药物的共聚类，用于鉴定其相关基因模块
2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449860
A. Koohi, H. Homayoun, Jie Xu, M. Orooji
{"title":"Co-clustering of diseases, genes, and drugs for identification of their related gene modules","authors":"A. Koohi, H. Homayoun, Jie Xu, M. Orooji","doi":"10.1109/ICACI.2016.7449860","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449860","url":null,"abstract":"Finding gene clusters that can be shared between drugs and diseases plays an important role in drug discovery. Targeting disease causing genes directly in drug development can increase the chance of drug approval through the clinical phase. This paper introduces a new co-clustering approach on the tripartite graph of genes, drugs, and diseases. As a result of co-clustering, gene modules and their related drugs and diseases are identified. It is shown that identified gene modules are functionally related. In addition the resulted gene modules are closely connected to each other in the protein-protein interaction network compared to that of random gene selection. The resulting gene modules can be used for investigating the genes that can be targeted with new drugs for treatment of diseases that are co-clustered with them. The proposed method is scalable and can be used for other multi-view graph co-clustering applications like social networks.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130581719","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
Computational intelligent color normalization for wheat plant images to support precision farming 小麦植物图像的计算智能颜色归一化以支持精准农业
2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449816
S. B. Sulistyo, W. L. Woo, S. Dlay
{"title":"Computational intelligent color normalization for wheat plant images to support precision farming","authors":"S. B. Sulistyo, W. L. Woo, S. Dlay","doi":"10.1109/ICACI.2016.7449816","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449816","url":null,"abstract":"Image colors are considerably affected by the intensity of the light source. In this paper, we propose a color constancy method using neural networks fusion to normalize images captured under sunlight with a variation of light intensities. A genetic algorithm is also applied to optimize the color normalization. A 24-patch Macbeth color checker is utilized as the reference to normalize the images. The results of our proposed method is superior to other methods, i.e. the conventional gray world and scale-by-max methods, as well as linear model and single neural network method. Furthermore, the proposed method can be used to normalize wheat plant images captured under various light intensities.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116028731","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
Short term traffic flow prediction based on on-line sequential extreme learning machine 基于在线顺序极值学习机的短期交通流预测
2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449818
Zhiyuan Ma, Guangchun Luo, Dijiang Huang
{"title":"Short term traffic flow prediction based on on-line sequential extreme learning machine","authors":"Zhiyuan Ma, Guangchun Luo, Dijiang Huang","doi":"10.1109/ICACI.2016.7449818","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449818","url":null,"abstract":"Traffic flow cannot be predicted solely based on historical data due to its high dynamics and sensitivity to emergency situations. In this paper, a real traffic data collected from 2011 to 2014 is used, and an adaptive prediction model based on a variant of Extreme Learning Machine (ELM), namely On-line Sequential ELM with forgetting mechanism, is built. The model has the capability of updating itself using incoming data, and adapts to the changes in real time. However, limitations, such as the requirements of large number of neurons and dataset size for initialization, are discovered in practice. To improve the applicability, another scheme involving sequential updating and network reconstruction is proposed. The experimental results show that, compared with the previous method, the proposed one has better performance in time while achieving the similar accuracy.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114461828","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: 36
Subspace video stabilization based on matrix transformation and Bezier curve 基于矩阵变换和Bezier曲线的子空间视频稳像
2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449837
Zheng Zhao, Xiaohong Ma
{"title":"Subspace video stabilization based on matrix transformation and Bezier curve","authors":"Zheng Zhao, Xiaohong Ma","doi":"10.1109/ICACI.2016.7449837","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449837","url":null,"abstract":"Video stabilization improves video quality by removing undesirable jitter to receive stable and comfortable video sequences. This paper proposes a new approach for subspace video stabilization. To make feature trajectories factorization more accurate, we segment feature trajectories into fragments to construct local trajectory matrices, then obtain smooth trajectories based on subspace constraint, matrix transformation and Bezier curve. Finally, according to the original feature points and the final corresponding stable feature points, we use mesh warp to receive high-quality and plausible videos. Experiments show that our method can generate comparable results with regard to some other state-of-the-art video stabilization methods, furthermore in some scenes our results are better than theirs.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127134561","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
A novel approach based on differential evolution for blind deconvolution 一种基于差分进化的盲反卷积新方法
2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449813
Kai Kang, Yang Cao, Zengfu Wang
{"title":"A novel approach based on differential evolution for blind deconvolution","authors":"Kai Kang, Yang Cao, Zengfu Wang","doi":"10.1109/ICACI.2016.7449813","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449813","url":null,"abstract":"Blind deconvolution refers to a class of problems of recovering a sharp version of a blurred image without any information about the blur kernel. In this paper, we propose a novel approach for blind deconvolution based on differential evolution (DE) algorithm, which is arguably one of the most powerful stochastic real-parameter optimization algorithms. Thanks to DE algorithm, various non-conjugate kernel priors, which can be used to effectively restrain the estimated kernel from unexpected situations such as delta kernel, are prone to be introduced to the proposed approach. In order to accelerate the computation speed, we relax the image prior, utilizing the Gaussian prior instead of the well-known sparse prior. Then the optimization problem turns to be convex, what's more, the optimal solution can be effectively solved in frequency domain. In addition, we use the kernel prior cost to propose candidate solutions to speed up the computation further. Finally, given the estimated kernel, we estimate the sharp image by sparse prior. Experimental results and comparisons demonstrate the effectiveness of our method.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127165994","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
Improvement of spatial data clustering algorithm in city location 城市定位空间数据聚类算法的改进
2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449812
Qibing Zhu
{"title":"Improvement of spatial data clustering algorithm in city location","authors":"Qibing Zhu","doi":"10.1109/ICACI.2016.7449812","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449812","url":null,"abstract":"Spatial data mining is a new research direction in the field of Data Mining. In recent years, with the continuous development of data mining technology, spatial data attracts more and more attentions of scholars and experts. Spatial clustering analysis is an important part of spatial data mining. Nowadays, spatial clustering analysis has become more and more mature, widely used in various fields. Spatial clustering analysis algorithm can deeply discover the knowledge which hidden in the geospatial information, find out the representative node of one or a number of spatial data collection, discovery the law of the spatial distribution. Classic clustering algorithm basing on partition widely used in the field of cities planning and provide valuable reference. This paper is based on the spatial data mining method, analysis and optimize the spatial data clustering algorithm in the Location Problem in the city, providing scientific location decisions.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"383 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122858924","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
Adaptive CDN-based bandwidth conserving algorithm for Mobile IPTV 基于cdn的移动IPTV自适应带宽节约算法
2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449859
B. Abubakar, M. Petridis, D. Gill, Saeed Malekshahi Gheytassi
{"title":"Adaptive CDN-based bandwidth conserving algorithm for Mobile IPTV","authors":"B. Abubakar, M. Petridis, D. Gill, Saeed Malekshahi Gheytassi","doi":"10.1109/ICACI.2016.7449859","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449859","url":null,"abstract":"Network bandwidth and server capacity are gradually becoming overloaded due to high demand and rapid evolution of high quality multimedia services over the Internet. Internet Protocol Television (IPTV) is among the multimedia services that demand more of network and server resources, especially with the emergence of Mobile IPTV. It is imperative for service providers to maintain good quality management services in order to satisfy their clients. To improve the required Quality of Service (QoS) and Quality of Experience (QoE), a Content Distribution Network (CDN) approach is being adopted and used by service providers, where contents are replicated over multiple distributed servers with the best server selected to serve an incoming request. In this paper, we propose an Adaptive CDN-Based Bandwidth Conserving Algorithm for Mobile IPTV that adapts to different server bandwidth capacity in order to improve the QoS, which in turn will provide the required QoE. Results from the simulation tests show that the proposed algorithm performed well in adapting to different server bandwidth level to switch between using the server or client to serve an incoming service requests. It also confirmed that the proposed algorithm outperformed the normal CDN-based IPTV system in server load reduction, high throughput and low end-to-end delay.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"376 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132290710","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
A new improved fruit fly optimization algorithm for traveling salesman problem 旅行商问题的一种改进果蝇优化算法
2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449797
Lvjiang Yin, Xinyu Li, Liang Gao, Chao Lu
{"title":"A new improved fruit fly optimization algorithm for traveling salesman problem","authors":"Lvjiang Yin, Xinyu Li, Liang Gao, Chao Lu","doi":"10.1109/ICACI.2016.7449797","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449797","url":null,"abstract":"Traveling salesman problem (TSP) which is a classic combinational optimization problem has a wide range of applications in many areas. Many researchers focus on this problem and propose several algorithms. However, it was proved to be NP-hard, which is very difficult to be solved. No algorithm can solve any types of this problem effectively. In order to propose an effective algorithm for TSP, this paper improves the fruit fly optimization algorithm (FOA) proposed recently. As far as we know, the FOA has not yet been applied to solve TSP. Therefore, several modifications of FOA have to be made to meet the characteristics of TSP. Based on the whole search framework and the essence of FOA, some operations of particle swarm optimization (PSO) have been introduced into this method. In the smell search phase, the cluster mechanism of the fruit flies has been used to copy flies to one point and the mutation operation of genetic algorithm is used as the method of information exchanging among fruit flies for random search. In the visual search phase, the generalized PSO is applied to balance the global search and local search abilities of proposed algorithm. To evaluate the performance of proposed algorithm, some experiments and comparisons with other reported algorithms have been conducted. The results show the feasibility and effectiveness of proposed algorithm in solving TSP.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128510843","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: 9
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