Social computational systems

Agarwal, Nitin; Xu, Xiaowei

doi:10.1016/j.jocs.2011.07.006

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…Both the analysis of complex networks and tasks related to human-like vision systems can be successfully tackled with nature-inspired algorithms, as shown here. Needless to say, there are many other complex domains such as cellular automata [23] or social computational systems [24], just to mention a few, in which this kind of techniques can be deployed, thus bringing many interesting avenues for further research.…”

Section: Perspectivesmentioning

confidence: 99%

Complex metaheuristics

Cotta

Schaefer

2016

Journal of Computational Science

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a b s t r a c tComplexity is a prevalent feature of numerous natural and artificial systems and as such has attracted much scientific interest in the last decades. The pursuit of computational tools capable of analyzing, modeling or designing systems exhibiting this complex nature -in which the properties of the system are not evident at the bottom level but emerge from its global structure -is a major issue. Metaheuristics can play here an important role due to its intrinsic adaptability and powerful optimization capabilities. In many regards, metaheuristics are also examples of complex systems since their behavior emanates from the orchestrated interplay of simpler algorithmic components. This bidirectional connection between metaheuristics and complex systems offers numerous avenues for fruitful research.

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Section: Perspectivesmentioning

confidence: 99%

Complex metaheuristics

Cotta

Schaefer

2016

Journal of Computational Science

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“…Well-defined áreas of artificial intelligence (genetic algorithms, neural networks), mathematics, and theoretical computer science (L systems, DNA computing) are massively influenced by the behaviour of various biological entities and phenomena [1]. In the last decades, new emerging fields of so-called natural computing identify new (unconventional) computational paradigms in different forms [2].…”

Section: Introductionmentioning

confidence: 99%

The optimal combination: Grammatical swarm, particle swarm optimization and neural networks

López

Blas

Arteta

2012

Journal of Computational Science

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Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuáis to get optimal solutions. Such co-operative model differs from competitive models in the way that individuáis die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks.

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“…Socially intelligent computing systems have recently emerged as a new interdisciplinary research area. In socially intelligent computing systems "social refers to the interactions among people and increasingly more sophisticated computing technologies; intelligent refers to the emerging intelligence exhibited by such systems as well as their increasing knowledge about people and their interactions with one another and with computers; computing refers to the computation technologies that act as mediators among people, as tools used by people, and as equal or complementary participants with people" [Maher, 2009], [Agarwal and Xu, 2011]. Research in socially intelligent computing broadens the scope and coverage of social computing [Wang, et al 2005] and spans a variety of issues ranging from intelligent collaborative systems [Prinz, et al 2010] to the use of semantic technologies and the web of linked open data in order to support computational sociality [Sheth and Nagarajan 2009], while related applications use intelligent computing infrastructures that exploit social media on the web [Bothos, et al 2010].…”

Section: Introductionmentioning

confidence: 99%

A socially intelligent approach for enterprise information search and recommendation

Apostolou¹,

Mentzas²

2012

2012 18th International ICE Conference on Engineering, Technology and Innovation

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This paper focuses on the development of socially intelligent computing systems at the enterprise level. Specifically, in order to improve the information search and recommendation functionalities of social business software, we extend corporate knowledge structuring approaches, such as folksonomies and taxonomies, with the addition of statistical topic models. We use probabilistic models in order to uncover hidden topics in the corporate 'knowledge base' and hence add an intelligent perspective in social collaboration. Probabilistic topic models are based upon the idea that documents are mixtures of topics, where a topic is defined as a probability distribution over words. We apply our approach in the Organik social business software platform and deploy it in five companies. Our results showed enhanced recommendations and improved search efficiency, while our approach effectively addresses problems in query expansion and recommends relevant resources and tags which in turn can leverage the creation and evolution of social knowledge structures like folksonomies.

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Social computational systems

Cited by 7 publications

References 18 publications

Complex metaheuristics

Complex metaheuristics

The optimal combination: Grammatical swarm, particle swarm optimization and neural networks

A socially intelligent approach for enterprise information search and recommendation

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