Pub Date : 1900-01-01DOI: 10.1109/ICCMA.2013.6506163
Y. Karam, T. Baker, A. Taleb-Bendiab
Businesses started to exploit the forthcoming value from deployment of cloud computing as a new caterpillar paradigm to reach out more diversified customer slices. Although the general concepts they practically focus on are: viability, survivability, adaptability, etc., however, on the ground, there is still a lack for forming mechanisms to sustain viability with adaptation of new types of requirements that pertain to other un-tackled aspects of the echosystem. Such aspects like social intentionality are of actors and their goals. This paper introduces modern dynamic software programming environment aided with modelling support to achieve operationalization and adaptation of abstract object; goals and their properties as formation of new type of requirements into service based applications distributed over the cloud. This will in turn provide system runtime components to interactively confer to guarantee self-adaptive behaviour with respect to its functional and non-functional characteristics.
{"title":"Intention-oriented modelling support for GORE in elastic cloud applications","authors":"Y. Karam, T. Baker, A. Taleb-Bendiab","doi":"10.1109/ICCMA.2013.6506163","DOIUrl":"https://doi.org/10.1109/ICCMA.2013.6506163","url":null,"abstract":"Businesses started to exploit the forthcoming value from deployment of cloud computing as a new caterpillar paradigm to reach out more diversified customer slices. Although the general concepts they practically focus on are: viability, survivability, adaptability, etc., however, on the ground, there is still a lack for forming mechanisms to sustain viability with adaptation of new types of requirements that pertain to other un-tackled aspects of the echosystem. Such aspects like social intentionality are of actors and their goals. This paper introduces modern dynamic software programming environment aided with modelling support to achieve operationalization and adaptation of abstract object; goals and their properties as formation of new type of requirements into service based applications distributed over the cloud. This will in turn provide system runtime components to interactively confer to guarantee self-adaptive behaviour with respect to its functional and non-functional characteristics.","PeriodicalId":187834,"journal":{"name":"2013 International Conference on Computer Medical Applications (ICCMA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133934619","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 : 1900-01-01DOI: 10.1109/ICCMA.2013.6506186
Loay E. George, Esraa Z. Mohammed
The research presented in this paper was aimed to improve the retrieval performance of an images retrieval system in medical applications based on texture features. In general, the work consists of two phases: (1) enrollment phase, which consist of feature extraction based on Co-occurrence matrix and run length matrix features combined with developed method to measure the roughness, (2) retrieving phase, which use the artificial neural network and similarity measurement. The conducted tests were carried on 600 medical images from four types of tissues (i.e., blood cells, breast tissues, GI tissues, liver tissues) and give very high precision and recall rates (100,98).
{"title":"Tissues image retrieval system based on co-occuerrence, run length and roughness features","authors":"Loay E. George, Esraa Z. Mohammed","doi":"10.1109/ICCMA.2013.6506186","DOIUrl":"https://doi.org/10.1109/ICCMA.2013.6506186","url":null,"abstract":"The research presented in this paper was aimed to improve the retrieval performance of an images retrieval system in medical applications based on texture features. In general, the work consists of two phases: (1) enrollment phase, which consist of feature extraction based on Co-occurrence matrix and run length matrix features combined with developed method to measure the roughness, (2) retrieving phase, which use the artificial neural network and similarity measurement. The conducted tests were carried on 600 medical images from four types of tissues (i.e., blood cells, breast tissues, GI tissues, liver tissues) and give very high precision and recall rates (100,98).","PeriodicalId":187834,"journal":{"name":"2013 International Conference on Computer Medical Applications (ICCMA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117059763","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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