Pub Date : 2013-07-16DOI: 10.1109/ICCI-CC.2013.6622271
Liang Lei, Jun Peng, Bo Yang
Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. However, how to quickly retrieving images is a challenge because that the speed and efficiency of retrieving image from Internet image is most important. We used genetic algorithm to improve the method based on HSV color space, and optimized the computational workload. First, the paper introduces how to extract dominant color of an image based on HSV color space. Then, it describes how to use genetic algorithm to optimize the algorithm of extracting dominant color. In the end, genetic algorithm is be used for the similarity measure of images. The experiments and results, which based on Corel database, showed that this method has greatly improved the image retrieval in time and precision rates.
{"title":"Image retrieval based on 72-trees and genetic algorithm","authors":"Liang Lei, Jun Peng, Bo Yang","doi":"10.1109/ICCI-CC.2013.6622271","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2013.6622271","url":null,"abstract":"Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. However, how to quickly retrieving images is a challenge because that the speed and efficiency of retrieving image from Internet image is most important. We used genetic algorithm to improve the method based on HSV color space, and optimized the computational workload. First, the paper introduces how to extract dominant color of an image based on HSV color space. Then, it describes how to use genetic algorithm to optimize the algorithm of extracting dominant color. In the end, genetic algorithm is be used for the similarity measure of images. The experiments and results, which based on Corel database, showed that this method has greatly improved the image retrieval in time and precision rates.","PeriodicalId":130244,"journal":{"name":"2013 IEEE 12th International Conference on Cognitive Informatics and Cognitive Computing","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129083490","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}
Pub Date : 2013-07-16DOI: 10.1109/ICCI-CC.2013.6622240
Guihua Wen, Xiaodong Chen, Lijun Jiang, Haisheng Li
The classifiers based on the theory of evidence appear well founded theoretically, however, they have still difficulties to nicely deal with the sparse, the noisy, and the imbalance problems. This paper presents a new general framework to create evidences by defining many kinds of distances between the query and its multiple neighborhoods as the evidences. Particularly, it applies the relative transformation to define the distances. Within the framework, a new classifier called relative evidential classification (REC) is designed, which takes all distances as evidences and combines them using the Dempster'rule of combination. The classifier assigns the class label to the query based on the combined belief. The novel work of this method lies in that a new general framework to create evidences and a new approach to define the distances in the relative space as evidences are presented. Experimental results suggest that the proposed approach often gives the better results in classification.
{"title":"Performing classification using all kinds of distances as evidences","authors":"Guihua Wen, Xiaodong Chen, Lijun Jiang, Haisheng Li","doi":"10.1109/ICCI-CC.2013.6622240","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2013.6622240","url":null,"abstract":"The classifiers based on the theory of evidence appear well founded theoretically, however, they have still difficulties to nicely deal with the sparse, the noisy, and the imbalance problems. This paper presents a new general framework to create evidences by defining many kinds of distances between the query and its multiple neighborhoods as the evidences. Particularly, it applies the relative transformation to define the distances. Within the framework, a new classifier called relative evidential classification (REC) is designed, which takes all distances as evidences and combines them using the Dempster'rule of combination. The classifier assigns the class label to the query based on the combined belief. The novel work of this method lies in that a new general framework to create evidences and a new approach to define the distances in the relative space as evidences are presented. Experimental results suggest that the proposed approach often gives the better results in classification.","PeriodicalId":130244,"journal":{"name":"2013 IEEE 12th International Conference on Cognitive Informatics and Cognitive Computing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129556681","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}
Pub Date : 2013-07-01DOI: 10.1109/ICCI-CC.2013.6622247
Bonnie K. MacKellar, Christina Schweikert, Soon Ae Chun
Patients facing a serious disease often want to be able to search for relevant clinical trials for new or more effective alternative treatments. The NIH makes all of its trials available on a website, in fact, for this purpose. Its search facility, however, is difficult to use and requires the patient to sift through lengthy text descriptions for relevant information. Our overall aim is to build a system that allows for a more patient-focused clinical trial search facility. In this paper, we present a semantic integration approach using RDF triples to develop an integrated clinical trial knowledge representation, by linking different Linked Open Data such as clinical trials provided by NIH as well as the drug side effects dataset SIDER. The integration model uses UMLS to link concepts from different sources with consistent semantics and ontological knowledge. Patient-oriented functions that our prototype system provides include semantic search and query with reasoning ability, and semantic-link browsing where an exploration of one concept leads to other concepts easily via links which can provide visual search for the end users.
{"title":"Patient-oriented clinical trials search through semantic integration of Linked Open Data","authors":"Bonnie K. MacKellar, Christina Schweikert, Soon Ae Chun","doi":"10.1109/ICCI-CC.2013.6622247","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2013.6622247","url":null,"abstract":"Patients facing a serious disease often want to be able to search for relevant clinical trials for new or more effective alternative treatments. The NIH makes all of its trials available on a website, in fact, for this purpose. Its search facility, however, is difficult to use and requires the patient to sift through lengthy text descriptions for relevant information. Our overall aim is to build a system that allows for a more patient-focused clinical trial search facility. In this paper, we present a semantic integration approach using RDF triples to develop an integrated clinical trial knowledge representation, by linking different Linked Open Data such as clinical trials provided by NIH as well as the drug side effects dataset SIDER. The integration model uses UMLS to link concepts from different sources with consistent semantics and ontological knowledge. Patient-oriented functions that our prototype system provides include semantic search and query with reasoning ability, and semantic-link browsing where an exploration of one concept leads to other concepts easily via links which can provide visual search for the end users.","PeriodicalId":130244,"journal":{"name":"2013 IEEE 12th International Conference on Cognitive Informatics and Cognitive Computing","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115181072","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}
Pub Date : 2013-07-01DOI: 10.4018/ijssci.2013070105
M. V. Egmond, Shushma Patel, D. Patel
Global information systems (IS) are often being designed and implemented without due consideration or management of the human aspect of information systems. The lack of acknowledgement of human factors generates cost overruns, time delays and ultimately could lead to a partial failure of the system or even an aborted implementation. In this paper we present the concept of the information system implementation transformation (ISIT) cloud that covers dynamics of global information system implementations. We have depicted these dynamics as interpretative readiness curves in relation to IS implementation phases. We argue that human elements are impacting the overall level of implementation readiness. We support our argument by discussing the role of attitudes towards IS implementations, after which we break it down into on the role culture link our ISIT concept to the layered reference model of the brain (LRMB) to understand the role cognitive elements within IS implementations. The related charts that we present are serving as the framework our research. The results of our approach provide improved understanding of the human elements of global information system implementations and its organizational readiness.
{"title":"Exploring human dynamics in global information system implementations Culture, attitudes and cognitive elements","authors":"M. V. Egmond, Shushma Patel, D. Patel","doi":"10.4018/ijssci.2013070105","DOIUrl":"https://doi.org/10.4018/ijssci.2013070105","url":null,"abstract":"Global information systems (IS) are often being designed and implemented without due consideration or management of the human aspect of information systems. The lack of acknowledgement of human factors generates cost overruns, time delays and ultimately could lead to a partial failure of the system or even an aborted implementation. In this paper we present the concept of the information system implementation transformation (ISIT) cloud that covers dynamics of global information system implementations. We have depicted these dynamics as interpretative readiness curves in relation to IS implementation phases. We argue that human elements are impacting the overall level of implementation readiness. We support our argument by discussing the role of attitudes towards IS implementations, after which we break it down into on the role culture link our ISIT concept to the layered reference model of the brain (LRMB) to understand the role cognitive elements within IS implementations. The related charts that we present are serving as the framework our research. The results of our approach provide improved understanding of the human elements of global information system implementations and its organizational readiness.","PeriodicalId":130244,"journal":{"name":"2013 IEEE 12th International Conference on Cognitive Informatics and Cognitive Computing","volume":"520 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127619849","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}
Pub Date : 2012-08-12DOI: 10.1109/ICCI-CC.2013.6622216
E. Brown
Summary form only given. Watson, named after IBM founder Thomas J. Watson, was built by a team of IBM researchers who set out to accomplish a grand challenge - build a computing system that rivals a human's ability to answer questions posed in natural language with speed, accuracy and confidence. The quiz show Jeopardy! provided the ultimate test of this technology because the game's clues involve analyzing subtle meaning, irony, riddles and other complexities of natural language in which humans excel and computers traditionally fail. Watson passed its first test on Jeopardy!, beating the show's two greatest champions in a televised exhibition match, but the real test will be in applying the underlying natural language processing and analytics technology in business and across industries. In this talk I will introduce the Jeopardy! grand challenge, present an overview of Watson and the DeepQA technology upon which Watson is built, and explore future applications of this technology.
{"title":"Watson: The Jeopardy! Challenge and beyond","authors":"E. Brown","doi":"10.1109/ICCI-CC.2013.6622216","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2013.6622216","url":null,"abstract":"Summary form only given. Watson, named after IBM founder Thomas J. Watson, was built by a team of IBM researchers who set out to accomplish a grand challenge - build a computing system that rivals a human's ability to answer questions posed in natural language with speed, accuracy and confidence. The quiz show Jeopardy! provided the ultimate test of this technology because the game's clues involve analyzing subtle meaning, irony, riddles and other complexities of natural language in which humans excel and computers traditionally fail. Watson passed its first test on Jeopardy!, beating the show's two greatest champions in a televised exhibition match, but the real test will be in applying the underlying natural language processing and analytics technology in business and across industries. In this talk I will introduce the Jeopardy! grand challenge, present an overview of Watson and the DeepQA technology upon which Watson is built, and explore future applications of this technology.","PeriodicalId":130244,"journal":{"name":"2013 IEEE 12th International Conference on Cognitive Informatics and Cognitive Computing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116090524","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}
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