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Titlebook: Artificial Intelligence: Theories, Models and Applications; 7th Hellenic Confere Ilias Maglogiannis,Vassilis Plagianakos,Ioannis Vl Confere

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發(fā)表于 2025-3-21 19:50:03 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Artificial Intelligence: Theories, Models and Applications
期刊簡(jiǎn)稱7th Hellenic Confere
影響因子2023Ilias Maglogiannis,Vassilis Plagianakos,Ioannis Vl
視頻videohttp://file.papertrans.cn/163/162579/162579.mp4
發(fā)行地址Up-to-date results.Fast-track conference proceedings.State-of-the-art research
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Artificial Intelligence: Theories, Models and Applications; 7th Hellenic Confere Ilias Maglogiannis,Vassilis Plagianakos,Ioannis Vl Confere
影響因子This book constitutes the proceedings of the 7th Hellenic Conference on Artificial Intelligence, SETN 2012, held in Lamia, Greece, in May 2012. The 47 contributions included in this volume were carefully reviewed and selected from 81 submissions. They deal with emergent topics of artificial intelligence and come from the SETN main conference as well as from the following special sessions on advancing translational biological research through the incorporation of artificial intelligence methodologies; artificial intelligence in bioinformatics; intelligent annotation of digital content; intelligent, affective, and natural interfaces; and unified multimedia knowledge representation and processing.
Pindex Conference proceedings 2012
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An AZP-ACO Method for Region-Buildingmatic Zoning Problem (AZP) procedure which is extended to use the Ant Colony Optimization (ACO) technique. The results produced are compared to the original AZP method. Both methods are applied into the classification of economic data in a post code level on the area of Athens.
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Emerge-Sort: Swarm Intelligence Sorting their repeated application ends up in sorted sequences. These are the basic elements of Emerge-Sort, an approach to self-organizing sorting, which we experimentally validate and observe a run-time behavior of .(..).
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Information Sharing and Searching via Collaborative Reinforcement Learningents through collaborative reinforcement learning. This is done by means of local computations performed by agents and payoff propagation. The aim is to ‘tune’ a network of agents for efficient and effective information searching and sharing, without altering the topology or imposing an overlay structure.
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Andrzej H?awiczka,Dariusz Baduraence each other’s decisions in adopting products or behaviors. Therefore, it is essential for companies to identify influential consumers to target, in the hopes that influencing them will lead to a large cascade of further recommendations. Several studies, based on approximation algorithms and assu
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M. Bidjan-Irani,U. Gl?sser,F. J. Rammigprominent methodology, particularly for large instances. However, it lacks uniformity, as it includes many variations accompanied by complex data structures, that cannot be easily brought under the same “umbrella.” In this work we embrace their wide diversity by adopting propagation algorithms. Our
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https://doi.org/10.1007/978-3-642-75002-1pes of cultural institutions, i.e., galleries, libraries, museums, archives and all types of cultural content. Semantic interoperability is a key issue in these developments. Content metadata constitute the main features of cultural items that are analysed and used to interpret users’ queries, so th
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Fehlertolerierende Rechnersystemeelatedness measure, the model assigns to the sentence’s word occurrences the senses that maximize the total pairwise sense relatedness. Experimental results show that our model, with two unsupervised sense relatedness measures, compares well against two other prominent unsupervised word sense disamb
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