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2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)最新文献

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A diversifying hidden units method based on NMF for document representation 一种基于NMF的文档表示的多样化隐藏单元方法
2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803001
X. Jiang, H. Zhang, R. Liu, Y. Zuo
{"title":"A diversifying hidden units method based on NMF for document representation","authors":"X. Jiang, H. Zhang, R. Liu, Y. Zuo","doi":"10.1109/ICKEA.2016.7803001","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7803001","url":null,"abstract":"Document modeling with hidden units as known as topics are very popular. Non-negative matrix factorization(NMF) is one of the most important techniques in document representation, which decomposes a document-term matrix into a document-topic matrix and a topic-term matrix. Since orthogonal constraint would limit terms occur only in one topic, we abandon this strong constraint. Furthermore, in order to represent documents in a certain number of topics with more semantic information, we add diversifying regularization and sparse constraint into NMF, which shows a great improvement in text classification and clustering. In the end, we draw the figure of topics similarities and display the top 20 weighted words in each topic to reveal that diversifying regularization can efficiently reduce the overlapping terms.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123141935","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 novel hybrid method for time series subsequence join 时间序列子序列连接的一种新的混合方法
2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7802985
Vo Duc Vinh, N. Phuc, D. T. Anh
{"title":"A novel hybrid method for time series subsequence join","authors":"Vo Duc Vinh, N. Phuc, D. T. Anh","doi":"10.1109/ICKEA.2016.7802985","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7802985","url":null,"abstract":"The exact method JOCOR, proposed by Mueen et al., is the first method for joining two time series on subsequence correlation. Although JOCOR requires the time complexity O(n2lgn), where n is the length of the time series, it is still time-consuming even for medium-size time series. In this paper, we propose a hybrid method which can run faster than JOCOR. Our method consists of four main steps. First, a list of subsequences is extracted from the raw time series based on important extrema. Second, we apply a nested loop join using a sliding window and Dynamic Time Warping distance to find all the matching subsequences in the two time series. Third, we concatenate all matching subsequences whose indexes are adjacent into longer ones and find the pair of subsequences which has the smallest distance between them. Finally, we apply JOCOR to find the most correlated segments in the two time series. In comparison to JOCOR, our hybrid method performs much faster while high accuracy is guaranteed.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131077929","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 modified Support Vector Clustering method for document categorization 一种改进的支持向量聚类方法用于文档分类
2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7802982
B. Harish, M. Revanasiddappa, S. A. Aruna Kumar
{"title":"A modified Support Vector Clustering method for document categorization","authors":"B. Harish, M. Revanasiddappa, S. A. Aruna Kumar","doi":"10.1109/ICKEA.2016.7802982","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7802982","url":null,"abstract":"In this paper, we propose a novel text categorization method based on modified Support Vector Clustering (SVC). SVC is a density based clustering approach, which handles the arbitrary shape clusters effectively. The main drawback of traditional SVC is that it treats unclassified documents as outliers. To overcome this problem, we employed Fuzzy C-Means (FCM) to cluster unclassified documents. The modified SVC (SVC-FCM) is applied to categorize text documents. The proposed method consists of three steps: In the first step, Regularized Locality Preserving Indexing (RLPI) is applied on Term Document Matrix (TDM) to reduce dimensionality of features. In second step, we use SVC to find base-cluster centers of documents. Finally, we use FCM to cluster unclassified documents. To evaluate the performance of the proposed method, we conducted experiments on standard 20-NewsGroup dataset.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130629898","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
An improved recommendation algorithm based on Bhattacharyya Coefficient 一种基于Bhattacharyya系数的改进推荐算法
2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803027
Huiying Cao, Jiangzhou Deng, Huifang Guo, Bo He, Yong Wang
{"title":"An improved recommendation algorithm based on Bhattacharyya Coefficient","authors":"Huiying Cao, Jiangzhou Deng, Huifang Guo, Bo He, Yong Wang","doi":"10.1109/ICKEA.2016.7803027","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7803027","url":null,"abstract":"Collaborative Filtering (CF) has become one of the most successful approaches for providing personalized product recommendations to users. Neighborhood-based CF is one of the main forms among all CFs, which is widely used in commercial domain. However, neighborhood-based CF suffers from new user cold-start problem in sparse rating data. In this paper, we propose an improved neighborhood-based CF recommendation algorithm based on Bhattacharyya Coefficient to address the new user cold-start problem. The proposed algorithm combines the item neighborhood information with the user neighborhood information to improve the recommendation precision. Finally, the proposed algorithm is tested on a real dataset and the results show the proposed algorithm has the better recommendation performance.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127237766","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: 6
Category profit optimization in retail industry: An application on haircare products 零售行业品类利润优化:在护发产品上的应用
2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803028
B. Ayan, Alp Ustundag, P. Sarvari
{"title":"Category profit optimization in retail industry: An application on haircare products","authors":"B. Ayan, Alp Ustundag, P. Sarvari","doi":"10.1109/ICKEA.2016.7803028","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7803028","url":null,"abstract":"Based on empirical insights, quantitative decision support systems and the need for more advanced models reflecting category managers' actual decision problems is inevitable. The objective of this paper is to develop retail shelf space management using simulation based optimization. The focus of this research is therefore to examine retail shelf space problems and develop an optimization model to maximize the profitability of a retail haircare products category. The numerical optimization is performed on the category profitability. Exogenous Substitution Model which is one of the assortment models to calculate category profitability is used. The focus point of the model is to decide which product should be listed on the products substitutability. Besides stock levels of products should be calculated. Also, in this model for shelf allocating, an extra decision variable is calculating positions of products. The case study is covering the application of the proposed model in a supermarket.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126051114","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
Demand based blue collared job allocation using multiple priorities and heuristics 基于需求的蓝领工作分配使用多重优先级和启发式
2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803024
Sridhar Amirneni, P. Mukundaram, J. Balaji, N. Nivedha
{"title":"Demand based blue collared job allocation using multiple priorities and heuristics","authors":"Sridhar Amirneni, P. Mukundaram, J. Balaji, N. Nivedha","doi":"10.1109/ICKEA.2016.7803024","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7803024","url":null,"abstract":"With the boom in population, the number of people in the unorganized sector has increased exponentially. The proposed system aims to overcome the problems faced by this strata of society mainly emigration, low salary, seasonal jobs, etc. It works as an extension of the Aadhar Card so that data integrity and reliability is maintained. A basic form is also filled by the user so that the skill set, availability, preference information is gathered. The job allocation is done based on the six priorities which include location, recent work, customer review in addition to the above three factors mentioned. This helps in the periodic allocation of jobs to unorganized workers in an effective way by choosing the most suitable job corresponding to an individual.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127423841","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
Application of Statistical nonparametric tests in Dongting Lake, China: 1961–2012 统计非参数检验在洞庭湖的应用:1961-2012
2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803018
Muhammad Tayyab, Jian-zhong Zhou, X. Zeng, I. Ahmed, R. Adnan
{"title":"Application of Statistical nonparametric tests in Dongting Lake, China: 1961–2012","authors":"Muhammad Tayyab, Jian-zhong Zhou, X. Zeng, I. Ahmed, R. Adnan","doi":"10.1109/ICKEA.2016.7803018","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7803018","url":null,"abstract":"Precise predictions of precipitation trends can play imperative part in economic growth of a state. This study examined precipitation inconsistency for 12 stations at the Dongting Lake, China, over a 52-years study phase (1961-2012). Statistical, nonparametric Mann-Kendall (MK) and Spearman's rho (SR) tests were applied to identify trends seasonal and annual precipitation. The performance of the Mann- Kendall (MK) and Spearman's rho (SR) tests was steady at the tested significance level. The results showed fusion of increasing (positive) and decreasing (negative) trends at different stations within seasonal time scale. Only Yuanjiang River has shown significant trend on seasonal time scale. No significant trends have been exhibited on annual time scale in any case.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131239870","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
Design of Kansei baby bags by using Fuzzy Linguistic principles 运用模糊语言学原理设计感性婴儿袋
2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803012
A. H. Soewardi, B. Nasution
{"title":"Design of Kansei baby bags by using Fuzzy Linguistic principles","authors":"A. H. Soewardi, B. Nasution","doi":"10.1109/ICKEA.2016.7803012","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7803012","url":null,"abstract":"One of consumers' considerations in buying a product is that it can satisfy their feeling and emotion because such product can improve happiness, satisfaction and the standard of better living. However, most of the existing products, such as baby bags, are still designed based on companies' perspective that they only have minimal function, uncomfortable, unattractive and not robust. Thus, the objective of this research is to develop an innovative baby bag that meets consumers' kansei. Kansei Engineering (KE) method and Fuzzy Linguistic principle were used to design. 115 respondents were involved in this study to identify the Kansei words. Meanwhile, two experts participated as the reference to determine the rules of fuzzy attributes. Orthogonal array and conjoint analysis were conducted to incorporate all attribute potentials in developing a new single concept design. Statistical analysis was also done to test the hypothesis. The result of this study is an innovative design of baby bags which is valid to meet consumers' needs such as comfortable, attractive color, multifunction, durable, affordable, safe, simple, attractive design and practical at 5% of significant level.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133067828","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
Efficient sentiment classification of Twitter feeds Twitter feed的高效情感分类
2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7802996
Nicholas Chamansingh, Patrick Hosein
{"title":"Efficient sentiment classification of Twitter feeds","authors":"Nicholas Chamansingh, Patrick Hosein","doi":"10.1109/ICKEA.2016.7802996","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7802996","url":null,"abstract":"Sentiment Analysis encompasses the use of Natural Language Processing together with statistics and machine learning methods for the identification, extraction and characterization of sentiment elements from a body of text. Micro-blog platforms, such as Twitter, allows for the sharing of real-time comments and opinions from millions of users on various topics. This research presents an experiment to determine an efficient sentiment classifier of real-time Twitter feeds. Naive Bayes, Support Vector Machine (SVM) and Maximum Entropy (MaxEnt) classification methods were compared. For each approach we used the same pre-processing and feature selection methods. Chi-Square feature selection was used to determine the smallest feature set and training data size needed for a classifier with a given accuracy level, storage requirements and classification time. Results show that, when compared to previous work, a significant reduction in data input and processing can be achieve while maintaining an acceptable level of accuracy.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127360280","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: 6
Situation reasoning framework for the Internet of Things environments using deep learning results 使用深度学习结果的物联网环境情境推理框架
2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI: 10.1109/ICKEA.2016.7803006
Seyoung Park, Mye Sohn, Haeran Jin, Hyun-Jung Lee
{"title":"Situation reasoning framework for the Internet of Things environments using deep learning results","authors":"Seyoung Park, Mye Sohn, Haeran Jin, Hyun-Jung Lee","doi":"10.1109/ICKEA.2016.7803006","DOIUrl":"https://doi.org/10.1109/ICKEA.2016.7803006","url":null,"abstract":"A goal of this paper is to suggest a framework to infer the situation using IOT sensor data. To do so, the framework adopts contexts which were derived from the learning results of multiple deep neural networks for IOT sensor data and carries out hierarchical clustering of contexts in terms of the spatio-temporality. With the learned dendrogram, the most appropriate situation is inferred from case-based reasoning depending on the similar time and location. The result of reasoning is stored in a case memory and this can contribute to learning of a case base. The primary contribution of this paper is the situation reasoning under consideration for spatio-temporality that is a characteristic of IOT sensor data. Also, we performed experiments to show the superiority of our framework. The experimental results are not bad for a first attempt. In further research, if the algorithms are improved, we can expect better results.","PeriodicalId":241850,"journal":{"name":"2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125344396","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: 5
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