Daniel Stamate - Publications

Eoin Houstoun, Daniel Stamate, Henry Musto, David Reeves, Catharine Morgan, Roxana Hutanu, Kalliopi Mavromati, Dorina Cadar, Daniel Stahl (2025). Classifying cognitive states of Alzheimer's Disease with machine learning using digital biomarkers from the Bio-Hermes study cohort. To appear in Artificial Intelligence Applications and Innovations, AIAI 2025, Springer, June 2025.

Henry Musto, Daniel Stamate, David Reeves, Catharine Morgan, Roxana Hutanu, Kalliopi Mavromati, Dorina Cadar, Daniel Stahl (2025). Proteomics, Neuropsychological and Demographics Multimodal Machine Learning Approach to Alzheimer's Disease Prediction on the Bio-Hermes Study Cohort. To appear in Artificial Intelligence Applications and Innovations, AIAI 2025, Springer, June 2025.

Diana Shamsutdinova, Daniel Stamate, Daniel Stahl (2025). Balancing accuracy and Interpretability: An R package assessing complex relationships beyond the Cox model and applications to clinical prediction. Int. J. Medical Informatics 194: 105700 (2025), DOI https://doi.org/10.1016/j.ijmedinf.2024.105700

D. Reeves D, C. Morgan, D. Stamate, E. Ford, DM Ashcroft, E. Kontopantelis, et al. (2024). Identifying individuals at high risk for dementia in primary care: Development and validation of the DemRisk risk prediction model using routinely collected patient data. PLoS ONE 19(10): e0310712. DOI https://doi.org/10.1371/journal.pone.0310712

Daniel Stamate, Pradyumna Davuloori, Doina Logofatu, Evelyne Mercure, Caspar Addyman, Mark Tomlinson (2024). Ensembles of Bidirectional LSTM and GRU Neural Nets for Predicting Mother-Infant Synchrony in Videos. In: Engineering Applications of Neural Networks. EANN 2024. Communications in Computer and Information Science, vol 2141. Springer, DOI https://doi.org/10.1007/978-3-031-62495-7_25

Henry Musto, Daniel Stamate, Daniel Stahl (2024). Variational Encoder Based Synthetic Alzheimer's Data Generation for Deep Learning, XGBoost and Statistical Survival Analysis. In 2024 IEEE International Conference on Machine Learning and Applications ICMLA 2024: 1488-1495 DOI: https://doi.org/10.1109/ICMLA61862.2024.00230

Henry Musto, Daniel Stamate, Doina Logofatu, Daniel Stahl (2024). Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling. In: Artificial Neural Networks and Machine Learning, ICANN 2024. Lecture Notes in Computer Science, vol 15023. Springer. DOI https://doi.org/10.1007/978-3-031-72353-7_26

Henry Musto, Daniel Stamate, Doina Logofatu, Lahcen Ouarbya (2023). On a Survival Gradient Boosting, Neural Network and Cox PH based Approach to Predicting Dementia Diagnosis Risk on ADNI, 2023 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM), 2023, DOI 10.1109/BIBM58861.2023.10419115.

Mihaela Breaban, Raluca Necula, Dorel Lucanu, Daniel Stamate (2023). Joint Decision Making in Ant Colony Systems for Solving the Multiple Traveling Salesman Problem. Journal of Procedia Computer Science, Vol 225, Elsevier, December 2023, DOI https://doi.org/10.1016/j.procs.2023.10.345.

Daniel Stamate, Riya Haran, Karolina Rutkowska, Sree Davuloori,  Evelyne Mercure, Caspar Addyman, Mark Tomlinson (2023). Predicting High vs Low Mother-Baby Synchrony with GRU-Based Ensemble Models. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14262. Springer, Cham. DOI https://doi.org/10.1007/978-3-031-44201-8_16.

Henry Musto, Daniel Stamate, Ida Pu, Daniel Stahl (2023). Predicting Alzheimer’s Disease Diagnosis Risk Over Time with Survival Machine Learning on the ADNI Cohort. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2023. Lecture Notes in Computer Science(), vol 14162. Springer, Cham. https://doi.org/10.1007/978-3-031-41456-5_53 .

Asei Akanuma, Daniel Stamate, Mark Bishop (2023). Predicting Colour Reflectance with Gradient Boosting and Deep Learning. The19th IFIP International Conference on Artificial Intelligence Applications and Innovations - AIAI 2023, IFIP Advances in Information and Communication Technology, vol 675, Springer, DOI: https://doi.org/10.1007/978-3-031-34111-3_14.

Asei Akanuma, Daniel Stamate (2023). A Neural Network Approach to Estimating Color Reflectance with Product Independent Models. The European Neural Network Society's 31st International Conference on Artificial Neural Networks, Springer, Lecture Notes in Computer Science, vol 13531, 2022, DOI: https://doi.org/10.1007/978-3-031-15934-3_66.

Daniel Stamate, Henry Musto, Olesya Ajnakina, Daniel Stahl (2023). Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations - AIAI 2022, IFIP Advances in Information and Communication Technology, vol 652, Springer, 2022, DOI: https://doi.org/10.1007/978-3-031-08341-9_35

Diana Shamsutdinova, Daniel Stamate, Angus Roberts, Daniel Stahl (2022). Combining Cox model and tree-based algorithms to boost performance and preserve interpretability for health outcomes. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations - AIAI 2022, IFIP Advances in Information and Communication Technology, vol 652, Springer, 2022. DOI: https://doi.org/10.1007/978-3-031-08337-2_15

John Lanham, Daniel Stamate, Charlotte. A. Wu, Fionn Murtagh, Catharine Morgan, David Reeves, Darren Ashcroft, Evan Kontopantelis, Brian Mcmillan (2021). Predicting risk of dementia with machine learning and survival models using routine primary care records, In: Proceedings of 2021 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM2021), DOI: https://doi.org/10.1109/BIBM52615.2021.9669363.

Henry Musto, Daniel Stamate, Ida Pu, Daniel Stahl (2021). A Machine Learning Approach for Predicting Deterioration in Alzheimer's Disease, In: Proceeding of 20th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), Publisher IEEE, December 2021, DOI: https://doi.org/10.1109/ICMLA52953.2021.00232.

Rapheal Olaniyan, Daniel Stamate, Ida Pu (2021). A two-step optimised BERT-based NLP algorithm for extracting sentiment from financial news, Proceedings of 17th Intl Conference of Artificial Intelligence Applications and Innovations, AIAI 2021, Springer, June 2021, DOI: https://doi.org/10.1007/978-3-030-79150-6_58.

Mihai Ermaliuc, Daniel Stamate, George Magoulas, Ida Pu (2021). Creating Ensembles of Generative Adversarial Network Discriminators for One-class Classification, Proceedings of 22nd Intl Conference of Engineering Applications of Neural Networks, EANN 2021, Springer, June 2021, DOI: https://doi.org/10.1007/978-3-030-80568-5_2.

Particle swarm optimization algorithms for autonomous robots with deterministic leaders using space filling movements, Journal Evolving Systems, Springer, Volume 11, issue 3 (Special Issue: Evolving Intelligent Applications in Engineering), September 2020, pp 383–396, DOI: https://doi.org/10.1007/s12530-018-9245-9. Doina Logofătu, Gil Sobol, Christina Andersson, Daniel Stamate, Kristiyan Balabanov, Tymoteusz Cejrowski

Daniel Stamate, Richard Smith, Ruslan Tsygancov, Rostislav Vorobev, John Langham, Daniel Stahl, David Reeves (2020). Applying Deep Learning to Predicting Dementia and Mild Cognitive Impairment, Proc. of Intl. Conference of Artificial Intelligence Applications and Innovations. AIAI 2020. IFIP Advances in Information and Communication Technology, vol 584. Springer, Cham, DOI:https://doi.org/10.1007/978-3-030-49186-4_26.

Daniel Stamate, Min Kim, Simon Lovestone, Cristina Legido-Quigley et al. (2019). A metabolite-based machine learning approach to diagnose Alzheimer’s-type dementia in blood: Results from the European Medical Information Framework for Alzheimer's Disease biomarker discovery cohort. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, Vol. 5, 2019, pp 933-938, Elsevier, 2019. https://www.sciencedirect.com/science/article/pii/S2352873719300873

Rapheal Olaniyan, Daniel Stamate, Ida Pu, Alexander Zamyatin, Anna Vashkel, Frederic Marechal (2019). Predicting S&P 500 based on its constituents and their social media derived sentiment, Proc. 11th International Conference on Computational Collective Intelligence (ICCCI), 2019, Springer LNCS 11683, DOI: https://doi.org/10.1007/978-3-030-28377-3_12.

Nikolay Nikolaev, Evgueni Smirnov, Daniel Stamate et al. (2019). A Regime-Switching Recurrent Neural Network Model Applied to Wind Time Series, Applied Soft Computing (Journal, Elsevier), 2019, DOI: https://doi.org/10.1016/j.asoc.2019.04.009.

Daniel Stamate, Andrea Katrinecz, Daniel Stah, ESM-MERGE Investigators, Simone J.W. Verhagen, Philippe A.E.G. Delespaul, Jim van Os, Sinan Guloksuz (2019). Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches, J. Schizophrenia Research, Elsevier, 2019, DOI: https://doi.org/10.1016/j.schres.2019.04.028.

Daniel Stamate, Wajdi Alghammdi, Jeremy Ogg, Richard Hoile, Fionn Murtagh (2018). A Machine Learning Framework for Predicting Dementia and Mild Cognitive Impairment, Proc. 17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), 2018. DOI: 10.1109/ICMLA.2018.00107.

Daniel Stahl, Daniel Stamate (2018). Data Science Challenges in Computational Psychiatry and Psychiatric Research. Proc. 5^th IEEE Data Science and Advanced Analytics, 2018. DOI: 10.1109/DSAA.2018.00067.

Frederic Marechal, Daniel Stamate, Rapheal Olaniyan, Jiri Marek (2018). On XLE index constituents’ social media based sentiment informing the index trend and volatility prediction. Proc. 10th International Conference on Computational Collective Intelligence (ICCCI), 2018, Springer LNCS. DOI https://doi.org/10.1007/978-3-319-98446-9_34.

Wajdi Alghamdi, Daniel Stamate, Daniel Stahl, Alexander Zamyatin, Robin Murray, Marta di Forti (2018). A New Machine Learning Framework for Understanding the Link between Cannabis Use and First-Episode Psychosis. Studies in Health Technology and Informatics 2018; 248:9-16, IOS Press, DOI 10.3233/978-1-61499-858-7-9.

Daniel Stamate, Wajdi Alghamdi, Daniel Stahl, Ida Pu, Fionn Murtagh, Danielle Belgrave, Robin Murray and Marta di Forti (2018). Predicting First-Episode Psychosis Associated with Cannabis Use with Artificial Neural Networks and Deep Learning. Proc. 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), 2018, Springer CCIS, DOI: https://doi.org/10.1007/978-3-319-91479-4_57.

Daniel Stamate, Wajdi Alghamdi, Daniel Stahl, Doina Logofatu and Alexander Zamyatin (2018). PIDT: A Novel Decision Tree Algorithm Based on Parameterised Impurities and Statistical Pruning Approaches, Proc. 14th International Conference on Artificial Intelligence Applications and Innovations (AIAI), 2018, Springer IFIP, DOI: https://doi.org/10.1007/978-3-319-92007-8_24.

Daniel Stamate, Wajdi Alghamdi, Daniel Stahl, Alexander Zamyatin, Robin Murray and Marta di Forti (2018). Can Artificial Neural Networks Predict Psychiatric Conditions Associated with Cannabis Use? Proc. 14th International Conference on Artificial Intelligence Applications and Innovations (AIAI), 2018, Springer IFIP, DOI: https://doi.org/10.1007/978-3-319-92007-8_27.

Ajnakina, O., Lally, J., Di Forti, M., Stilo, S.A., Kolliakou, A., Gardner-Sood, P., Dazzan, P., Pariante, C., Marques, T.R., Mondelli, V., MacCabe, J., Gaughran, F., David, A.S., Stamate D., Murray, R.M., & Fisher, H.L (2018). Utilising symptom dimensions with diagnostic categories improves prediction of time to first remission in first-episode psychosis. Schizophrenia Research, DOI: https://doi.org/10.1016/j.schres.2017.07.042 , (Elsevier), 2018, pp. 391-398.

Daniel Stamate, Andrea Katrinecz, Wajdi Alghamdi, Daniel Stahl, ESM-MERGE Group Investigators, Philippe Delespaul, Jim van Os, Sinan Guloksuz (2017). Predicting Psychosis Using the Experience Sampling Method with Mobile Applications. Proc.16th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), 2017, Publisher: IEEE, DOI: 10.1109/ICMLA.2017.00-84

Daniel Stamate, Danielle Belgrave, Rachel Cassidy, Adnan Custovic, Louise Fleming, Andrew Bush and Sejal Saglani (2017).Predictive Modelling Strategies to Understand Heterogeneous Manifestations of Asthma in Early Life. Proc. 16th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), 2017, Publisher: IEEE, DOI 10.1109/ICMLA.2017.0-176.

Doina Logofatu, Gil Sobol, Daniel Stamate (2017). Particle Swarm Optimization Algorithms for Autonomous Robots with Leaders Using Hilbert Curves. Proc 18th International Conference on Engineering Applications of Neural Networks (EANN), 2017, Publisher: Springer, DOI: https://doi.org/10.1007/978-3-319-65172-9_45.

Doina Logofatu, Gil Sobol, Daniel Stamate, Kristiyan Balabanov (2017). A Novel Space Filling Curves Based Approach to PSO Algorithms for Autonomous Agents.
Proc. 9th International Conference on Computational Collective Intelligence (ICCCI), 2017, Springer, DOI: https://doi.org/10.1007/978-3-319-67074-4_35.

Wajdi Alghamdi, Daniel Stamate, Katherine Vang, Daniel Stahl, Marco Colizzi, Giada Tripoli, Diego Quattrone, Olesya Ajnakina, Robin M. Murray and Marta Di Forti (2016). A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use. Proceedings of 15th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), 2016, Publisher: IEEE, DOI: https://doi.org/10.1109/ICMLA.2016.0148 .

Rapheal  Olaniyan, Daniel Stamate, Doina Logofatu, Lahcen Ouarbya (2015). Sentiment and Stock Market Volatility Predictive Modelling - a Hybrid Approach.
Proceedings of the 2nd IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA), 2015, Publisher: IEEE, DOI: https://doi.org/10.1109/DSAA.2015.7344855.

Rapheal Olaniyan, Daniel Stamate, Doina Logofatu (2015). Social Web-Based Anxiety Index’s Predictive Information on S&P 500, Revisited. Proc. of the 3rd International Symposium on Statistical Learning and Data Sciences (SLDS), 2015, Springer LNAI. DOI https://doi.org/10.1007/978-3-319-17091-6_15.

Ida Pu, Daniel Stamate and Yuji Shen (2014). Improving Time-Efficiency in Blocking Expanding Ring Search for Mobile Ad Hoc Networks. Journal of Discrete Algorithms, Volume 24, 2014, pp. 59-67. DOI: https://doi.org/10.1016/j.jda.2013.03.006

Doina Logofatu, Daniel Stamate (2014). Scalable Distributed Genetic Algorithm for Data Ordering Problem with Inversion Using MapReduce. Proc. of the 10th International Conference on Artificial Intelligence Applications and Innovations, IFIP Advances in Information and Communication Technology, vol 436. Springer, Berlin, Heidelberg . DOI https://doi.org/10.1007/978-3-662-44654-6_32.

Daniel Stamate (2014). Quantitative Semantics for Uncertain Knowledge Bases. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 299. Springer, Berlin, Heidelberg. DOI https://doi.org/10.1007/978-3-642-31718-7_21.

Daniel Stamate, Ida Pu (2012). Imperfect Information Fusion using Rules with Bilattice based Fixpoint Semantics. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 299. Springer, Berlin, Heidelberg. DOI https://doi.org/10.1007/978-3-642-31718-7_19.

Daniel Stamate, Ida Pu (2012). Fixpoint Semantics for Extended Logic Programs on Bilattice based Multivalued Logics, and Applications. Proceedings of ManyVal Conference, 2012. http://www.dicom.uninsubria.it/~brunella.gerla/manyval/stamate.pdf

Daniel Stamate (2010). Queries with Multivalued Logic based Semantics for Imperfect Information Fusion. 40th IEEE International Symposium on Multiple-Valued Logic , 2010. https://doi.org/10.1109/ISMVL.2010.62

Daniel Stamate (2009). A Bilattice based Fixed Point Semantics for Integrating Imperfect Information. Proceedings of the 6th Workshop on Fixed Points in Computer Science (FICS), 2009. https://cs.ioc.ee/fics09/proceedings/contrib12.pdf

Daniel Stamate (2008). Default Reasoning with Imperfect Information in Multivalued Logics. Proc. 38th IEEE International Symposium on Multiple-Valued Logics (IEEE ISMVL), 2008. DOI https://doi.org/10.1109/ISMVL.2008.45

Daniel Stamate (2008). Imperfect Information Representation through Extended Logic Programs in Bilattices. Uncertainty and Intelligent Information Systems, B.Bouchon-Meunier, R.R. Yager, C. Marsala, and M. Rifqi (Eds.), World Scientific, ISBN 978-981-279-234-1, 2008. https://doi.org/10.1142/9789812792358_0030

S.W. Qaiyumi, D. Stamate (2007). Reduction in Dimensions and Clustering using Risk and Return Model.
In Proc 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07) , 2007. DOI https://doi.org/10.1109/AINAW.2007.308

Daniel Stamate (2006). Representing Imperfect Information through Extended Logic Programs in Multivalued Logics. Proceedings of the 11th biennial Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), 2006. https://www.math.s.chiba-u.ac.jp/~yasuda/open2all/Paris06/IPMU2006/HTML/FINALPAPERS/P222.PDF

Daniel Stamate (2006). Assumption based Multiple-Valued Semantics for Extended Logic Programs. Proc. 36th IEEE International Symposium on Multiple-Valued Logic (ISMVL'06). https://doi.org/10.1109/ISMVL.2006.13

Yann Loyer, Nicolas Spyratos, Daniel Stamate (2004). Hypothesis-based Semantics of Logic Programs in Multivalued Logics. ACM Transactions on Computational Logic (TOCL), Vol 5(3), 2004, pp. 508-527. DOI https://doi.org/10.1145/1013560.1013565

Yann Loyer, Nicolas Spyratos, Daniel Stamate (2003). Parameterized Semantics for Logic Programs - a Unifying Framework. Theoretical Computer Science Vol. 308(1-3), Elsevier, 2003, pp. 429-447. DOI https://doi.org/10.1016/S0304-3975(03)00047-1

Yann Loyer, Nicilas Spyratos, Daniel Stamate (2000). Hypothesis Support for Information Integration in Four-Valued Logics. In: Theoretical Computer Science: Exploring New Frontiers of Theoretical Informatics. TCS 2000. Lecture Notes in Computer Science, vol 1872. Springer. DOI https://doi.org/10.1007/3-540-44929-9_37.

Yann Loyer, Niclas Spyratos, Daniel Stamate (2000). Integration of Information in Four-Valued Logics under Non-Uniform Assumptions. Proceedings of the 30th IEEE International Symposium on Multiple-Valued Logics (ISMVL), 2000, pp. 185-191. DOI https://doi.org/10.1109/ISMVL.2000.848618

Yann Loyer, Nicolas Spyratos, Daniel Stamate (2000). Interfacing Decision Support Systems under Incomplete Information. International Journal Information Theories and Applications 7(1), 2000, pp. 38-48. http://www.foibg.com/ijita/vol01-09/ijita-fv07.htm

Y. Loyer, N. Spyratos, D. Stamate (2000). Test d'Hypothèses pour l'Intégration d'Informations en Logique à Quatre Valeurs. Proceedings of JFPLC'2000 (Journées Francophones de Programmation Logique et Programmation par Contraintes), Hermes, 2000, pp. 265-278.

Yann Loyer, Nicolas Spyratos, Daniel Stamate (2000). Hypotheses Based Semantics for Information Integration in Four-valued Logics. Proceedings of the Fixed Points in Computer Science Workshop (FICS), 2000.

Yann Loyer,  Nicolas Spyratos, Daniel Stamate (2000). Interfacing Decision Support Systems under Incomplete Information. Proceedings of the 25th International Conference on Information and Communication Technologies and Programming, 2000, pp. 21-31.

Yann Loyer, Nicolas Spyratos, Daniel Stamate (1999). Computing and Comparing Semantics of Programs in Four-valued Logics. Proceedings of the 24th International Symposium on Mathematical  Foundations of Computer Science (MFCS), 1999, LNCS No. 1672, Springer, pp. 59-69.

Dominique Laurent, Nicolas Spyratos, Daniel Stamate (1998). Deterministic Enforcement of Constraints. Journal of Programming and Computer Software 24, 1998, pp. 71-83.

Y. Loyer, N. Spyratos, D. Stamate (1998). Unification des Semantiques Usuelles de Programmes Logiques. Proceedings of JFPLC'98 (Journées Francophones de Programmation Logique et Programmation par Contraintes), Hermes, 1998, pp. 135-150.

Gosta Grahne,  Nicolas Spyratos, Daniel Stamate (1997). Semantics and Containment of Queries with Internal and External conjunctions.  Proceedings of the 6th International Conference on Database Theory (ICDT), 1997, LNCS  No. 1186  Springer, pp. 71-82.

Gosta Grahne, Nicolas Spyratos, Daniel Stamate (1997). Semantics and Containment of Queries in Multimedia Information Systems. Proceedings of the 2nd International Workshop on Multimedia Information Systems, 1997, pp. 82-87.

Nicolas Spyratos, Daniel Stamate (1997). Multivalued Stable Semantics for Updating Databases with Uncertain Information. Frontiers in Artificial Intelligence and Applications: Information Modelling and Knowledge Bases VIII, H. Kangassalo et al. (eds), IOS Press, pp. 129-144, ISBN 905199334X, 1997.

N. Spyratos, D. Stamate (1996). Bases de Données avec Informations Incertaines. Sémantique et Mises à Jour. Proceedings of JFPLC'96 (Journées Francophones de Programmation Logique et Programmation
par Contraintes), Hermes, 1996,  pp. 49-63.

M. Halfeld Ferrari Alves, D. Laurent, N. Spyratos, D. Stamate (1996). A Class of Active Database Constraints. Proceedings of the International Conference on Information Technology, 1996. 

Daniel Stamate, Henri Luchian (1995). Answer-Perturbation Techniques for the Protection of Statistical Databases. Statistics and Computing 5, Springer, 1995, pp. 203-213.

Daniel Stamate, Henri Luchian, Ben Paechter (1994). A general Model for the Answer-Perturbation Techniques. Proceedings of the 7th International Working Conference on Scientific and Statistical Database Management (SSDBM), 1994, Charlottesville, USA,  pp. 90-96.

Henri Luchian, Daniel Stamate (1992). Statistical Protection for Statistical Databases. Proceedings of the 6th International Working Conference on Scientific and Statistical Database Management (SSDBM), 1992, Ascona, Switzerland, pp. 160-177.

Henri Luchian and Daniel Stamate (1992). User-Oriented Approach for the Protection of Statistical Databases. Scientific Annals of "Alexandru Ioan Cuza" University of Iasi, vol. 1, Computer Science, 1992, pp. 41-55.