Current Issue : April - June Volume : 2012 Issue Number : 2 Articles : 3 Articles
Ambient Intelligence (AmI) joins together the fields of ubiquitous computing and communications, context awareness, and\r\nintelligent user interfaces. Energy, fault-tolerance, and mobility are newly added dimensions of AmI.Within the context of AmI the\r\nconcept of mobile ad hoc networks (MANETs) for ââ?¬Å?anytime and anywhereââ?¬Â is likely to play larger roles in the future in which people\r\nare surrounded and supported by small context-aware, cooperative, and nonobtrusive devices that will aid our everyday life. The\r\nconnection between knowledge generation and communication ad hoc networking is symbioticââ?¬â?knowledge generation utilizes\r\nad hoc networking to perform their communication needs, and MANETs will utilize the knowledge generation to enhance their\r\nnetwork services. The contribution of the present study is a distributed evolving fuzzy modeling framework (EFMF) to observe and\r\ncategorize relationships and activities in the user and application level and based on that social context to take intelligent decisions\r\nabout MANETs service management. EFMF employs unsupervised online one-pass fuzzy clustering method to recognize nodesââ?¬â?¢\r\nmobility context from social scenario traces and ubiquitously learn ââ?¬Å?friendsââ?¬Â and ââ?¬Å?strangersââ?¬Â indirectly and anonymously....
A new method called mutable smart bee (MSB) algorithm proposed for cooperative optimizing of the maximum power output\n(MPO) and minimum entropy generation (MEG) of an Atkinson cycle as a multiobjective, multi-modal mechanical problem.\nThis method utilizes mutable smart bee instead of classical bees. The results have been checked with some of the most common\noptimizing algorithms like Karaboga�s original artificial bee colony, bees algorithm (BA), improved particle swarm optimization\n(IPSO), Lukasik firefly algorithm (LFFA), and self-adaptive penalty function genetic algorithm (SAPF-GA). According to obtained\nresults, it can be concluded thatMutable Smart Bee (MSB) is capable to maintain its historical memory for the location and quality\nof food sources and also a little chance of mutation is considered for this bee. These features were found as strong elements for\nmining data in constraint areas and the results will prove this claim....
Extractive multidocument summarization is modeled as a modified p-median problem. The problem is formulated with taking\ninto account four basic requirements, namely, relevance, information coverage, diversity, and length limit that should satisfy\nsummaries. To solve the optimization problem a self-adaptive differential evolution algorithm is created. Differential evolution has\nbeen proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge toward\nlocal optimum solutions, need to manually adjust the parameters, and finding the best values for the control parameters is a consuming\ntask. In the paper is proposed a self-adaptive scaling factor in original DE to increase the exploration and exploitation ability.\nThis paper has found that self-adaptive differential evolution can efficiently find the best solution in comparison with the canonical\ndifferential evolution. We implemented our model on multi-document summarization task. Experiments have shown that\nthe proposed model is competitive on the DUC2006 dataset....
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