References of "Aleksandrova, Marharyta 50030301"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailSecurity and Performance Implications of BGP Rerouting-resistant Guard Selection Algorithms for Tor
Mitseva, Asya UL; Aleksandrova, Marharyta UL; Engel, Thomas UL et al

in Security and Performance Implications of BGP Rerouting-resistant Guard Selection Algorithms for Tor (2020, May)

Detailed reference viewed: 43 (6 UL)
Full Text
Peer Reviewed
See detailBacAnalytics: A Tool to Support Secondary School Examination in France
Roussanaly, Azim; Aleksandrova, Marharyta UL; Boyer, Anne

in 25th International Symposium on Intelligent Systems (ISMIS 2020) (2020, May)

Students who failed the final examination in the secondary school in France (known as baccalauréat or baccalaureate) can improve their scores by passing a remedial test. This test consists of two oral ... [more ▼]

Students who failed the final examination in the secondary school in France (known as baccalauréat or baccalaureate) can improve their scores by passing a remedial test. This test consists of two oral examinations in two subjects of the student's choice. Students announce their choice on the day of the remedial test. Additionally, the secondary education system in France is quite complex. There exist several types of baccalaureate consisting of various streams. Depending upon the stream students belong to, they have different subjects allowed to be taken during the remedial test and different coefficients associated with each of them. In this context, it becomes difficult to estimate the number of professors of each subject required for the examination. Thereby, the general practice of remedial test organization is to mobilize a large number of professors. In this paper, we present BacAnalytics - a tool that was developed to assist the rectorate of secondary schools with the organization of remedial tests for the baccalaureate. Given profiles of students and their choices of subjects for previous years, this tool builds a predictive model and estimates the number of required professors for the current year. In the paper, we present the architecture of the tool, analyze its performance, and describe its usage by the rectorate of the Academy of Nancy-Metz in Grand Est region of France in the years 2018 and 2019. BacAnalytics achieves almost 100% of prediction accuracy with approximately 25% of redundancy and was awarded a French national prize Impulsions 2018. [less ▲]

Detailed reference viewed: 37 (2 UL)
Peer Reviewed
See detailContrast classification rules for mining local differences in medical data
Aleksandrova, Marharyta UL; Chertov, Oleg; Brun, Armelle et al

in Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2017 9th IEEE International Conference on (2017)

Detailed reference viewed: 103 (4 UL)
Peer Reviewed
See detailSets of Contrasting Rules: A Supervised Descriptive Rule Induction Pattern for Identification of Trigger Factors
Aleksandrova, Marharyta UL; Brun, Armelle; Chertov, Oleg et al

in Tools with Artificial Intelligence (ICTAI), 2016 IEEE 28th International Conference on (2016)

Detailed reference viewed: 92 (3 UL)
Peer Reviewed
See detailPrediction of nocturnal hypoglycemia by an aggregation of previously known prediction approaches: proof of concept for clinical application
Tkachenko, Pavlo; Kriukova, Galyna; Aleksandrova, Marharyta UL et al

in Computer Methods & Programs in Biomedicine (2016), 134

Detailed reference viewed: 102 (1 UL)
Peer Reviewed
See detailSets of Contrasting Rules to Identify Trigger Factors.
Aleksandrova, Marharyta UL; Brun, Armelle; Chertov, Oleg et al

in ECAI (2016)

Detailed reference viewed: 44 (1 UL)
Peer Reviewed
See detailAutomatic Identification of Trigger Factors: a Possibility for Chance Discovery
Aleksandrova, Marharyta UL; Brun, Armelle; Chertov, Oleg et al

in 2nd European Workshop on Chance Discovery and Data Synthesis (EWCDDS16) (2016)

Detailed reference viewed: 44 (1 UL)
Peer Reviewed
See detailIdentifying representative users in matrix factorization-based recommender systems: application to solving the content-less new item cold-start problem
Aleksandrova, Marharyta UL; Brun, Armelle; Boyer, Anne et al

in Journal of Intelligent Information Systems (2016)

Detailed reference viewed: 149 (15 UL)
Peer Reviewed
See detailCan Latent Features Be Interpreted as Users in Matrix Factorization-Based Recommender Systems?
Brun, Armelle; Aleksandrova, Marharyta UL; Boyer, Anne

in Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)-Volume 02 (2014)

Detailed reference viewed: 100 (0 UL)
Peer Reviewed
See detailSearch for user-related features in matrix factorization-based recommender systems
Aleksandrova, Marharyta UL; Brun, Armelle; Boyer, Anne et al

in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2014), PhD Session Proceedings (2014)

Detailed reference viewed: 53 (0 UL)
Peer Reviewed
See detailFuzzy clustering with prototype extraction for census data analysis
Chertov, Oleg; Aleksandrova, Marharyta UL

in Soft Computing: State of the Art Theory and Novel Applications (2013)

Detailed reference viewed: 100 (6 UL)
Peer Reviewed
See detailUsing association rules for searching levers of influence in census data
Chertov, Oleg; Aleksandrova, Marharyta UL

in Procedia Social and Behavioral Sciences (2013), 73

Detailed reference viewed: 87 (1 UL)
See detailGroup methods of data processing
Chertov, Oleg; Tavrov, Dan; Pavlov, Dmytro et al

Book published by Lulu. com (2010)

Detailed reference viewed: 107 (0 UL)