Dr Frederick Stahl
Areas of interest
Research projects
Background
Professional bodies/affiliations
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Find Dr Stahl on Google Scholar.Publications
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Ashlam, A.
,
Badii, A.
,
Stahl, F.
ORCID: 0000-0002-4860-0203
(2022)
WebAppShield: an approach exploiting machine learning to detect SQLi attacks in an application layer in run-time.
International Journal of Computer and Information Engineering
, 16
(8).
pp.
294-302.
ISSN:
1307-6892
|
doi:
https://dx.doi.org/10.5281/zenodo.6983905
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Prakash, N.
,
Stahl, F.
ORCID: 0000-0002-4860-0203
,
Mueller, C.
,
Ferdinand, O.
,
Zielinski, O.
(2022)
Intelligent Marine Pollution Analysis on Spectral Data.
pp.
1-6.
|
doi:
https://dx.doi.org/10.23919/OCEANS44145.2021.9706056
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Ashlam, A.
,
Badii, A.
,
Stahl, F.
ORCID: 0000-0002-4860-0203
(2022)
A novel approach exploiting machine learning to detect SQLi attacks.
|
doi:
https://dx.doi.org/10.1109/IC_ASET53395.2022.9765948
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Stahl, F.
ORCID: 0000-0002-4860-0203
,
Ferdinand, O.
,
Nolle, L.
,
Pehlken, A.
,
Zielinski, O.
(2021)
AI enabled bio waste contamination-scanner.
Forty-first SGAI International Conference on Artificial Intelligence
pp.
357-363.
|
doi:
https://dx.doi.org/10.1007/978-3-030-91100-3_28
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Lukats, D.
,
Berghöfer, E.
,
Stahl, F.
ORCID: 0000-0002-4860-0203
,
Schneider, J.
,
Pieck, D.
,
Idrees, M.
,
Nolle, L.
,
Zielinski, O.
(2021)
Towards Concept Change Detection in Marine Ecosystems.
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Alzubi, S.
,
Stahl, F.
ORCID: 0000-0002-4860-0203
,
Gaber, M.
(2021)
Towards intrusion detection of previously unknown network attacks.
Communications of the ECMS
, 35
(1).
pp.
35-41.
ISSN:
2522-2414
|
doi:
https://dx.doi.org/10.7148/2021-0035
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Almutairi, M.
,
Stahl, F.
ORCID: 0000-0002-4860-0203
,
Bramer, M.
(2021)
ReG-Rules: an explainable rule-based ensemble learner for classification.
IEEE Access
, 9
pp.
52015-52035.
ISSN:
2169-3536
|
doi:
https://dx.doi.org/10.1109/ACCESS.2021.3062763
- Stahl, F. , Le, T. , Badii, A. , Gaber, M. (2021) A frequent pattern conjunction Heuristic for rule generation in data streams. Information , 12 (1). ISSN: 2078-2489 | doi: https://dx.doi.org/10.3390/info12010024
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Dubuc, T.
,
Stahl, F.
ORCID: 0000-0002-4860-0203
,
Roesch, E.
ORCID: 0000-0002-8913-4173
(2021)
Mapping the big data landscape: technologies, platforms and paradigms for real-time analytics of data streams.
IEEE Access
, 9
pp.
15351-15374.
ISSN:
2169-3536
|
doi:
https://dx.doi.org/10.1109/ACCESS.2020.3046132
- Wolf, M. , van den Berg, K. , Garaba, S. , Gnann, N. , Sattler, K. , Stahl, F. , Zielinski, O. (2020) Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC–Q). Environmental Research Letters , 15 (11). ISSN: 1748-9326 | doi: https://dx.doi.org/10.1088/1748-9326/abbd01
- Stahl, F. and Badii, A. (2020) Building adaptive data mining models on streaming data in real-time. Expert Update , 20 (2). ISSN: 1465-4091 | doi: https://dx.doi.org/10.7148/2018-0008
- Wrench, C. , Stahl, F. , Di Fatta, G. , Karthikeyan, V. , Nauck, D. (2020) A rule induction approach to forecasting critical alarms in a telecommunication network. pp. 480-489.
- Idrees, M. , Minku, L. , Stahl, F. , Badii, A. (2020) A heterogeneous online learning ensemble for non-stationary environments. Knowledge-Based Systems , 188 pp 0. ISSN: 0950-7051 | doi: https://dx.doi.org/10.1016/j.knosys.2019.104983
- Almutairi, M. , Stahl, F. , Bramer, M. (2019) A rule-based classifier with accurate and fast rule term induction for continuous attributes. pp. 413-420.
- Hammoodi, M. , Stahl, F. , Badii, A. (2018) Real-time feature selection technique with concept drift detection using adaptive micro-clusters for data stream mining. Knowledge-Based Systems , 161 pp. 205-239. ISSN: 0950-7051 | doi: https://dx.doi.org/10.1016/j.knosys.2018.08.007
- Almutairi, M. , Stahl, F. , Bramer, M. (2017) Improving modular classification rule induction with G-Prism using dynamic rule term boundaries. In: Bramer, M. and Petridis, M. , (eds.) Artificial Intelligence XXXIV. Lecture Notes in Computer Science Springer (10630). pp. 115-128. ISSN: 0302-9743 ISBN: 9783319710785 | doi: https://dx.doi.org/10.1007/978-3-319-71078-5_9
- Le, T. , Stahl, F. , Gaber, M. , Gomes, J. , Di Fatta, G. (2017) On expressiveness and uncertainty awareness in rule-based classification for data streams. Neurocomputing , 265 pp. 127- 141. ISSN: 0925-2312 | doi: https://dx.doi.org/10.1016/j.neucom.2017.05.081
- Pavlopoulou, N. , Abushwashi, A. , Stahl, F. , Scibetta, V. (2017) A text mining framework for Big Data. Expert Update , 17 (1). ISSN: 1465-4091 (Special Issue on the 1st BCS SGAI Workshop on Data Stream Mining Techniques and Applications)
- Tennant, M. , Stahl, F. , Rana, O. , Gomes, J. (2017) Scalable real-time classification of data streams with concept drift. Future Generation Computer Systems , 75 pp. 187-199. ISSN: 0167-739X | doi: https://dx.doi.org/10.1016/j.future.2017.03.026
- Shakir Hammoodi, M. , Stahl, F. , Tennant, M. , Badii, A. (2017) Towards real-time feature tracking technique using adaptive micro-clusters. Expert Update , 17 (1). ISSN: 1465-4091 (Special Issue on the 1st BCS SGAI Workshop on Data Stream Mining Techniques and Applications)
- Le, T. , Stahl, F. , Wrench, C. , Gaber, M. (2017) A statistical learning method to fast generalised rule induction directly from raw measurements. pp. 935-938.
- Adedoyin-Olowe, M. , Gaber, M. , Dancausa, C. , Stahl, F. , Gomes, J. (2016) A rule dynamics approach to event detection in Twitter with its application to sports and politics. Expert Systems with Applications , 55 pp. 351-360. ISSN: 0957-4174 | doi: https://dx.doi.org/10.1016/j.eswa.2016.02.028
- Hammoodi, M. , Stahl, F. , Tennant, M. (2016) Towards online concept drift detection with feature selection for data stream classification. pp. 1549-1550.
- Wrench, C. , Stahl, F. , Le, T. , Di Fatta, G. , Karthikeyan, V. , Nauck, D. (2016) A method of rule induction for predicting and describing future alarms in a telecommunication network. Springer International Publishing , Cham. pp. 309-323.
- Almutairi, M. , Stahl, F. , Jennings, M. , Le, T. , Bramer, M. (2016) Towards expressive modular rule induction for numerical attributes. Springer International Publishing , Cham. pp. 229-235.
- Wrench, C. , Stahl, F. , Di Fatta, G. , Karthikeyan, V. , Nauck, D. (2016) Data stream mining of event and complex event streams: a survey of existing and future technologies and applications in big data. In: Atzmueller, M. , Oussena, S. , Roth-Berghofer, T. , (eds.) Enterprise Big Data Engineering, Analytics, and Management. Enterprise Big Data Engineering, Analytics, and Management pp. 24-47. ISBN: 9781522502937 | doi: https://dx.doi.org/10.4018/978-1-5225-0293-7
- Al Ghamdi, S. , Di Fatta, G. , Stahl, F. (2015) Optimisation techniques for parallel K-Means on MapReduce. Springer-Verlag New York, Inc. , New York, NY, USA. pp. 193-200.
- Wrench, C. , Stahl, F. , Di Fatta, G. , Karthikeyan, V. , Nauck, D. (2015) Towards expressive rule induction on IP network event streams.
- Tennant, M. , Stahl, F. , Gomes, J. (2015) Fast adaptive real-time classification for data streams with concept drift. Springer International Publishing pp. 265-272.
- Di Fatta, G. , Fortino, G. , Li, W. , Pathan, M. , Stahl, F. , Guerrieri, A. , eds. (2015) Internet and distributed computing systems - 8th international conference, IDCS 2015, Windsor, UK, September 2-4, 2015. Proceedings. Lecture Notes in Computer Science , 9258. Springer ISBN: 9783319232362
- Stahl, F. , May, D. , Mills, H. , Bramer, M. , Gaber, M. (2015) A scalable expressive ensemble learning using Random Prism: a MapReduce approach. Transactions on Large-Scale Data- and Knowledge-Centered Systems , 9070 pp. 90-107. | doi: https://dx.doi.org/10.1007/978-3-662-46703-9_4 (LNCS)
- Zliobaite, I. , Budka, M. , Stahl, F. (2015) Towards cost-sensitive adaptation: when is it worth updating your predictive model?. Neurocomputing , 150 (A). pp. 240-249. ISSN: 0925-2312 | doi: https://dx.doi.org/10.1016/j.neucom.2014.05.084
- Adedoyin-Olowe, M. , Gaber, M. , Stahl, F. (2014) A survey of data mining techniques for social media analysis. Journal of Data Mining & Digital Humanities , 2014 ISSN: 2416-5999
- Adedoyin-Olowe, M. , Gaber, M. , Dancausa, C. , Stahl, F. (2014) Extraction of unexpected rules from Twitter hashtags and its application to sport events. pp. 207-212.
- Tennant, M. , Stahl, F. , Di Fatta, G. , Gomes, J. (2014) Towards a parallel computationally efficient approach to scaling up data stream classification. Springer International Publishing pp. 51-65.
- Le, T. , Stahl, F. , Gomes, J. , Gaber, M. , Di Fatta, G. (2014) Computationally efficient rule-based classification for continuous streaming data. Springer International Publishing pp. 21-34.
- Liu, H. , Gegov, A. , Stahl, F. (2014) Categorization and construction of rule based systems. pp. 183-194. (Engineering Applications of Neural Networks: Mladenov, Valeri, Jayne, Chrisina, Iliadis, Lazaros (eds.) Communications in Computer and Information Science, Vol. 459 Springer)
- Liu, H. , Gegov, A. , Stahl, F. (2014) Unified framework for construction of rule based classification systems. In: Pedrycz, W. and Chen, S. , (eds.) Information Granularity, Big Data and Computational Intelligence. , 8. Springer , Switzerland. pp. 209-230. | doi: https://dx.doi.org/10.1007/978-3-319-08254-7_10
- Gaber, M. , Stahl, F. , Gomes, J. (2014) Pocket data mining - big data on small devices. Studies in big data , 2. Springer International pp 108. ISBN: 9783319027104
- Gaber, M. , Gama, J. , Krishnaswamy, S. , Gomes, J. , Stahl, F. (2014) Data stream mining in ubiquitous environments: state-of-the-art and current directions. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery , 4 (2). pp. 116-138. ISSN: 1942-4795 | doi: https://dx.doi.org/10.1002/widm.1115
- Gaber, M. , Stahl, F. , Gomes, J. (2014) Background. In: Gaber, M. , Stahl, F. , Gomes, J. , (eds.) Pocket Data Mining Big Data on Small Devices. Studies in Big Data Springer International Publishing (2). , Cham. pp. 7-21. ISBN: 9783319027104
- Gaber, M. , Stahl, F. , Gomes, J. (2014) Experimental validation of context-aware PDM. Pocket Data Mining. Studies in Big Data Springer International Publishing (2). pp. 69-80. ISBN: 9783319027111 | doi: https://dx.doi.org/10.1007/978-3-319-02711-1_6
- Gaber, M. , Stahl, F. , Gomes, J. (2014) Pocket data mining framework. Pocket Data Mining. Studies in Big Data Springer International Publishing (2). pp. 23-40. | doi: https://dx.doi.org/10.1007/978-3-319-02711-1_3
- Gaber, M. , Stahl, F. , Gomes, J. (2014) Conclusions, discussion and future work. Pocket Data Mining. Studies in Big Data Springer International Publishing (2). pp. 95-98. ISBN: 9783319027111 | doi: https://dx.doi.org/10.1007/978-3-319-02711-1_8
- Gaber, M. , Stahl, F. , Gomes, J. (2014) Implementation of pocket data mining. Pocket Data Mining. Studies in Big Data Springer International Publishing (2). pp. 41-59. ISBN: 9783319027111 | doi: https://dx.doi.org/10.1007/978-3-319-02711-1_4
- Gaber, M. , Stahl, F. , Gomes, J. (2014) Potential applications of pocket data mining. Pocket Data Mining. Studies in Big Data Springer International Publishing (2). pp. 81-94. ISBN: 9783319027111 | doi: https://dx.doi.org/10.1007/978-3-319-02711-1_7
- Gaber, M. , Stahl, F. , Gomes, J. (2014) Context-aware PDM (Coll-Stream). Pocket Data Mining. Studies in Big Data Springer International Publishing (2). pp. 61-68. ISBN: 9783319027111 | doi: https://dx.doi.org/10.1007/978-3-319-02711-1_5
- Gaber, M. , Stahl, F. , Gomes, J. (2014) Introduction. Pocket Data Mining. Studies in Big Data Springer International Publishing (2). , Switzerland. pp. 1-5. ISBN: 9783319027111 | doi: https://dx.doi.org/10.1007/978-3-319-02711-1_1
- Roesch, E. , Stahl, F. , Gaber, M. (2014) Bigger data for Big Data: from Twitter to brain-computer interface. Behavioral and Brain Sciences , 37 (1). pp. 97-98. ISSN: 0140-525X | doi: https://dx.doi.org/10.1017/S0140525X13001854
- Stahl, F. and Bramer, M. (2014) Random Prism: a noise-tolerant alternative to Random Forests. Expert Systems , 31 (5). pp. 411-420. ISSN: 1468-0394 | doi: https://dx.doi.org/10.1111/exsy.12032 (special issue on innovative techniques and applications of artificial intelligence)
- Gomes, J. , Adedoyin-Olowe, M. , Gaber, M. , Stahl, F. (2013) Rule Type Identification using TRCM for trend analysis in Twitter. Springer International Publishing pp. 273-278.
- Rausch, P. , Stahl, F. , Stumpf, M. (2013) Efficient interactive budget planning and adjusting under financial stress. Springer International Publishing pp. 375-388.
- Liu, H. , Gegov, A. , Stahl, F. (2013) J-measure based hybrid pruning for complexity reduction in classification rules. WSEAS Transactions on Systems , 12 (9). pp. 433-446. ISSN: 2224-2678
- Stahl, F. , Gabrys, B. , Gaber, M. , Berendsen, M. (2013) An overview of interactive visual data mining techniques for knowledge discovery. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery , 3 (4). pp. 239-256. ISSN: 1942-4795 | doi: https://dx.doi.org/10.1002/widm.1093
- Adedoyin-Olowe, M. , Gaber, M. , Stahl, F. (2013) TRCM: a methodology for temporal analysis of evolving concepts in Twitter. Lecture Notes in Computer Science , 7895 pp. 135-145. ISSN: 0302-9743 | doi: https://dx.doi.org/10.1007/978-3-642-38610-7_13 (Proceedings, Part II. 12th International Conference on Artificial Intelligence and Soft Computing)
- Stahl, F. , Gabber, M. , Max, B. (2013) Scaling up data mining techniques to large datasets using parallel and distributed processing. In: Rausch, P. , Sheta, A. , Ayesh, A. , (eds.) Business Intelligence and Performance Management. Springer pp. 243-259. ISBN: 9781447148654 | doi: https://dx.doi.org/10.1007/978-1-4471-4866-1_16
- Stahl, F. , May, D. , Bramer, M. (2012) Parallel random prism: a computationally efficient ensemble learner for classification. In: Bramer, M. and Petridis, M. , (eds.) Research and Development in Intelligent Systems XXIX. Research and Development in Intelligent Systems XXIX pp. 21-34. ISBN: 9781447147381 | doi: https://dx.doi.org/10.1007/978-1-4471-4739-8_2 (Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence)
- Stahl, F. , Gaber, M. , Salvador, M. (2012) eRules: a modular adaptive classification rule learning algorithm for data streams. In: Bramer, M. and Petridis, M. , (eds.) Research and Development in Intelligent Systems XXIX. Springer , London. pp. 65-78. ISBN: 9781447147381 | doi: https://dx.doi.org/10.1007/978-1-4471-4739-8_5
- Stahl, F. and Bramer, M. (2012) Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks. Knowledge-Based Systems , 35 pp. 49-63. ISSN: 0950-7051 | doi: https://dx.doi.org/10.1016/j.knosys.2012.04.014
- Stahl, F. and Jordanov, I. (2012) An overview of the use of neural networks for data mining tasks. WIREs: Data Mining and Knowledge Discovery , 2 (3). pp. 193-208. ISSN: 1942-4795 | doi: https://dx.doi.org/10.1002/widm.1052
- Stahl, F. , Gaber, M. , Aldridge, P. , May, D. , Liu, H. , Bramer, M. , Yu, P. (2012) Homogeneous and heterogeneous distributed classification for pocket data mining. In: Hameurlain, A. , Küng, J. , Wagner, R. , (eds.) Transactions on large-scale data and knowledge-centered systems V. Lecture Notes in Computer Science Transactions on Large-Scale Data-and Knowledge-Centered Systems V (7100). pp. 183-205. ISBN: 9783642281471
- Stahl, F. and Bramer, M. (2012) Scaling up classification rule induction through parallel processing. Knowledge Engineering Review pp. 243-259. ISSN: 1469-8005 | doi: https://dx.doi.org/10.1017/S0269888912000355
- Stahl, F. and Bramer, M. (2012) Jmax-pruning: a facility for the information theoretic pruning of modular classification rules. Knowledge-Based Systems , 29 pp. 12-19. ISSN: 0950-7051 | doi: https://dx.doi.org/10.1016/j.knosys.2011.06.016
- Stahl, F. and Bramer, M. (2011) Random prism: an alternative to random forests. In: Bramer, M. , Petridis, M. , Nolle, L. , (eds.) Research and Development in Intelligent Systems XXVIII. Proceedings of Ai-2011: Research and Development in Intelligent Systems Xxviii Incorporating Applications and Innovations in Intelligent Systems XIX pp. 5-18. ISBN: 9781447123170 | doi: https://dx.doi.org/10.1007/978-1-4471-2318-7_1
- Stahl, F. , Gaber, M. , Liu, H. , Bramer, M. , Yu, P. (2011) Distributed classification for pocket data mining. In: Kryszkiewicz, M. , Rybinski, H. , Skowron, A. , Ras, . , (eds.) Foundations of Intelligent Systems, Proc. of ISMIS 20111, the 19th Int. Symposium on Methodologies for Intelligent Systems. Lecture Notes in Computer Science Foundations of Intelligent Systems (6804). pp. 336-345. ISBN: 9783642219153 | doi: https://dx.doi.org/10.1007/978-3-642-21916-0_37
- Stahl, F. , Gaber, M. , Bramer, M. , Yu, P. (2011) Distributed hoeffding trees for pocket data mining. International Conferance on High Performance Computing and Simulation (HPCS), 2011 . IEEE pp. 686-692. ISBN: 9781612843803 | doi: https://dx.doi.org/10.1109/HPCSim.2011.5999893
- Stahl, F. and Bramer, M. (2011) Induction of modular classification rules: using Jmax-pruning. In: Bramer, M. , Petridis, M. , Hopgood, A. , (eds.) Research and Development in Intelligent Systems XXVII. Springer , London. pp. 79-92. ISBN: 9780857291295 | doi: https://dx.doi.org/10.1007/978-0-85729-130-1_6
- Swain, M. , Silva, C. , Loureiro-Ferreira, N. , Ostropytskyy, V. , Brito, J. , Riche, O. , Stahl, F. , Dubitzky, W. , Brito, R. (2010) P-found: grid-enabling distributed repositories of protein folding and unfolding simulations for data mining. Future Generation Computer Systems , 26 (3). pp. 424-433. ISSN: 0167-739X | doi: https://dx.doi.org/10.1016/j.future.2009.08.008
- Stahl, F. , Bramer, M. , Adda, M. (2010) J-PMCRI: a methodology for inducing pre-pruned modular classification rules. In: Bramer, M. , (eds.) Artificial Intelligence in Theory and Practice III. IFIP Advances in Information and Communication Technology Artificial intelligence in theory and practice III (331). pp. 47-56. ISBN: 9783642152856 | doi: https://dx.doi.org/10.1007/978-3-642-15286-3_5
- Stahl, F. , Bramer, M. , Adda, M. (2010) Parallel rule induction with information theoretic pre-pruning. Research and Development in Intelligent Systems XXVI. Research and Development in Intelligent Systems XXVI pp. 151-164. ISBN: 9781848829824 | doi: https://dx.doi.org/10.1007/978-1-84882-983-1_11
- Stahl, F. , Gaber, M. , Bramer, M. , Yu, P. (2010) Pocket data mining: towards collaborative data mining in mobile computing environments. Proc. ICTAI 2010, the 22nd IEEE Int. Conf. on Tools with Artificial Intelligence. , 2. IEEE pp. 323-330. ISBN: 9781424488179 | doi: https://dx.doi.org/10.1109/ICTAI.2010.118
- Stahl, F. , Bramer, M. , Adda, M. (2009) Parallel induction of modular classification rules. In: Bramer, M. , Petridis, M. , Coenen, F. , (eds.) Research and Development in Intelligent Systems XXV. Research and Development in Intelligent Systems XXV pp. 343-348. ISBN: 9781848821705 | doi: https://dx.doi.org/10.1007/978-1-84882-171-2_25 (Proceedings of AI-2008, the Twenty-eighth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence)
- Stahl, F. , Bramer, M. , Adda, M. (2009) PMCRI: a parallel modular classification rule induction framework. Machine Learning and Data Mining in Pattern Recognition . Lecture Notes in Computer Science LNCS: Machine Learning and Data Mining in Pattern Recognition (5632). pp. 148-162. ISSN: 0302-9743 ISBN: 9783642030697 | doi: https://dx.doi.org/10.1007/978-3-642-03070-3_12
- Swain, M. , Ostropytskyy, V. , Silva, C. , Stahl, F. , Riche, O. , Brito, R. , Dubitzky, W. (2008) Grid computing solutions for distributed repositories of protein folding and unfolding simulations. Computational Science – ICCS 2008. Lecture Notes in Computer Science Computational Science--ICCS 2008 (5103). pp. 70-79. ISBN: 9783540693888 | doi: https://dx.doi.org/10.1007/978-3-540-69389-5_10
- Stahl, F. and Bramer, M. (2008) Towards a computationally efficient approach to modular classification rule induction. In: Bramer, M. , Coenen, F. , Petridis, M. , (eds.) Research and Development in Intelligent Systems XXIV. Research and Development in Intelligent Systems XXIV pp. 357-362. ISBN: 9781848000933 | doi: https://dx.doi.org/10.1007/978-1-84800-094-0_27
- Stahl, F. , Bramer, M. , Adda, M. (2008) P-Prism: a computationally efficient approach to scaling up classification rule induction. In: Bramer, M. , (eds.) Artificial Intelligence in Theory and Practice II. IFIP – The International Federation for Information Processing Artificial Intelligence in Theory and Practice II (276). pp. 77-86. ISBN: 9780387096940 | doi: https://dx.doi.org/10.1007/978-0-387-09695-7_8
- Stahl, F. , Berrar, D. , Silva, C. , Rodrigues, R. , Brito, R. , Dubitzky, W. (2005) Grid warehousing of molecular dynamics protein unfolding data. Proceedings of CCGrid2005, the IEEE 2005 International Symposium on Cluster Computing and the Grid. , 1. IEEE pp. 496-503. ISBN: 780390741 | doi: https://dx.doi.org/10.1109/CCGRID.2005.1558594
- Berrar, D. , Stahl, F. , Silva, C. , Rui Rodrigues, J. , Brito, R. , Dubitzky, W. (2005) Towards data warehousing and mining of protein unfolding simulation data. Journal of Clinical Monitoring and Computing , 19 (4-5). pp. 307-317. ISSN: 1573-2614 | doi: https://dx.doi.org/10.1007/s10877-005-0676-z