Titlebook: Artificial Intelligence Applications and Innovations; AIAI 2018 IFIP WG 12 Lazaros Iliadis,Ilias Maglogiannis,Vassilis Plagia Conference pr

記憶

sed in the exploitation of the inter-documents similarities (lexical and/or semantics) after a sophisticated preprocessing. The experimental evaluation demonstrates that the proposed algorithm has the potential to improve the quality of the retrieved results.

Antarctic

myocardium

放氣

偏離

Detecting Question Intention Using a?K-Nearest Neighbor Based Approachuestion intention. In addition, a k-nearest neighbor based approach has been developed for question classification. Experiments show that our approach has a good level of accuracy in identifying different question types.

Condescending

aplomb

Query Disambiguation Based on Clustering Techniquessed in the exploitation of the inter-documents similarities (lexical and/or semantics) after a sophisticated preprocessing. The experimental evaluation demonstrates that the proposed algorithm has the potential to improve the quality of the retrieved results.

尖酸一點

The Biomolecular Computation Paradigm: A Survey in Massive Biological?Computationtion additionally paves the way for two- and three-dimensional self assemblying biological tiles which are closely linked at a theoretical level to a Turing machine, establishing thus its computational power. Applications include medium sized instances of TSP and the evaluation of the output of bounded fan-out Boolean circuits.

configuration

Conference proceedings 2018ations and Innovations, AIAI 2018, held in Rhodes, Greece, in May 2018.?The workshops were the Workshop on Semantics in the Deep: Semantic Analytics for Big Data, SEDSEAL 2018;? the Third Workshop on 5G - Putting Intelligence to the Network Edge, 5G-PINE 2018; the 7th Mining Humanistic Data Workshop

Ardent

Towards Big Data Analytics in?Large-Scale Federations of Semantically Heterogeneous IoT Platformsusing semantic mappings between them and a unified ontology. Data queries to the SIL and result collection is handled by a cloud-based data management layer, namely the Data Lake, along with storage of metadata needed by data analytics methods. Based on this infrastructure, web-based data analytics