Learning style recognition using Artificial Neural Network for adaptive user interface in e-learning

Kolekar, Sucheta V.; Sanjeevi, Sriram G.; Bormane, D. S.

doi:10.1109/iccic.2010.5705768

Cited by 44 publications

(17 citation statements)

References 4 publications

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“…However, the model has been evaluated with simulation data which did not represent natural attitudes of learners. In [95], Feed Forward Neural Network also proposed to classify learners to their corresponding LS of FSLSM by monitoring their actions with an e-learning system. This model was chosen due to two reasons.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

State of The Art of Learning Styles-Based Adaptive Educational Hypermedia Systems (Ls-Baehss)

Al-Azawei

Badii

2014

IJCSIT

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

State of The Art of Learning Styles-Based Adaptive Educational Hypermedia Systems (Ls-Baehss)

Al-Azawei

Badii

2014

IJCSIT

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“…In results, decision making can be developed in many problems. A scale is large because in many countries over 70% population lives in cities [8].…”

Section: City Tasks In Social Mediamentioning

confidence: 99%

Social media and efficient computer infrastructure in smart city

et al. 2018

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Social media require an efficient infrastructures of computer and communication systems to support a smart city. In a big city, there are several crucial dilemmas with a home and public space planning, a growing population, a global warming, carbon emissions, a lack of key resources like water and energy, and a traffic congestion. In a smart city, we expect an efficient and sustainable transportation, efficient management of resources and a better urban planning. In this paper, social media are proposed to support smart city with efficient computer infrastructure. Moreover, some methods are described to increase the availability and efficiency of an information infrastructure. Two criteria have been formulated to assign some key resources in a smart city system. The process of finding some compromise solutions from Pareto-optimal solutions has been illustrated. Metaheuristics of collective intelligence, including particle swarm optimization PSO, ant colony optimization ACO, an algorithm of bee colony ABC, and differential evolution DE have been described due to smart city infrastructure improving. Other application of above metaheuristics in smart city have been also presented.

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“…In the case of consortia, access to documents should be done through virtual offices, which are a kind of middleware between the system and the system of management and the execution of a task. Because it is crucial the training of employees, the number of courses can be done online [9].…”

Section: The Role Of E-learning In a Sma Citymentioning

confidence: 99%

Social media for e-learning of citizens in smart city

et al. 2018

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The rapid development of social media can be applied for citizens' e-learning in a smart city. Big cities have to cope with several open issues like a growing population or a traffic congestion. Especially, a home and public space is supposed to be used in more efficient way. Sustainable homes and buildings can be planned with using some modern techniques. Even currently, there is a huge problem with a lack of key resources like water and energy. So, an efficient management of resources is strongly required. What is more, a global warming and carbon emissions are considered as some critical factors for living conditions in many cities, too. A vision of a smart city is related to a better protection of a natural environment and a more efficient use of it. Moreover, citizens expect an efficient and sustainable transportation in livable city. To present some solutions on above issues, this paper outlines the methodology of using social media to provide necessary knowledge by citizenship training systems. In particular, some selected e-learning applications have been characterized. They are related to pedagogical agents in e-learning. In addition, some advanced meta-heuristics have been proposed with particular emphasis on genetic programming, artificial neural networks, neuro-evolution algorithms, support vector machines, and some collective intelligence algorithms. Finally, cloud services are discussed regarding a smart city management and training.

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Learning style recognition using Artificial Neural Network for adaptive user interface in e-learning

Cited by 44 publications

References 4 publications

State of The Art of Learning Styles-Based Adaptive Educational Hypermedia Systems (Ls-Baehss)

State of The Art of Learning Styles-Based Adaptive Educational Hypermedia Systems (Ls-Baehss)

Social media and efficient computer infrastructure in smart city

Social media for e-learning of citizens in smart city

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