An agent-based approach for integrating user profile into a knowledge management process

Pierre, Samuel; Kacan, C.; Probst, Wilfried

doi:10.1016/s0950-7051(00)00090-3

Cited by 10 publications

(3 citation statements)

References 2 publications

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“…Quite some literature can be found on the incorporation of user models in various knowledge management applications (e.g. [1,18,20,21]), mostly concentrating on information filtering tasks. Similar techniques as adopted in these information retrieval applications can be used to create expertise profiles, thereby incorporating tacit knowledge into a knowledge management system.…”

Section: User Modeling and Knowledge Managementmentioning

confidence: 99%

Multiple-vector user profiles in support of knowledge sharing

Vertommen

Janssens

Moor

et al. 2008

Information Sciences

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Section: User Modeling and Knowledge Managementmentioning

confidence: 99%

Multiple-vector user profiles in support of knowledge sharing

Vertommen

Janssens

Moor

et al. 2008

Information Sciences

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“…As shown in (4), T S comprises the accumulated time delay for processing the data request of each client and the elapsed time to activate the mobile agents for carrying out the data distribution management (Tan et al, 2001;Pierre et al, 2000). After each FS propagates the data of objects to the mobile agents, these agents take their data filtering policy to reject some junk data and send the results back to the mobile clients via the wireless network.…”

Section: Simulation Environment Managermentioning

confidence: 99%

Mobile agents for information retrieval in hybrid simulation environment

Lü

Hsu

2007

Journal of Network and Computer Applications

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“…The accuracy of a classifier for a given set of test samples is the percentage of test set samples that are correctly classified by the classifier. The associated class label of each test sample is compared with the learned classifier's class prediction for that sample[16][17][18][19][20][21][22][23][24]. A brief working insight of few prominent classifiers are presented below.…”

mentioning

confidence: 99%

A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks

Kumar

Mazzara

Messina

et al. 2019

Advances in Intelligent Systems and Computing

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Terrorism has become one of the most tedious problems to deal with and a prominent threat to mankind. To enhance counter-terrorism, several research works are developing efficient and precise systems, data mining is not an exception. Immense data is floating in our lives, though the scarce availability of authentic terrorist attack data in the public domain makes it complicated to fight terrorism. This manuscript focuses on data mining classification techniques and discusses the role of United Nations in counter-terrorism. It analyzes the performance of classifiers such as Lazy Tree, Multilayer Perceptron, Multiclass and Naïve Bayes classifiers for observing the trends for terrorist attacks around the world. The database for experiment purpose is created from different public and open access sources for years 1970-2015 comprising of 156,772 reported attacks causing massive losses of lives and property. This work enumerates the losses occurred, trends in attack frequency and places more prone to it, by considering the attack responsibilities taken as evaluation class.

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An agent-based approach for integrating user profile into a knowledge management process

Cited by 10 publications

References 2 publications

Multiple-vector user profiles in support of knowledge sharing

Multiple-vector user profiles in support of knowledge sharing

Mobile agents for information retrieval in hybrid simulation environment

A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks

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