2022

Ashlam, A. A., Badii, A. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (2022) A novel approach exploiting machine learning to detect SQLi attacks. In: 5th International Conference on Advanced Systems and Emergent Technologies, 22-25 Mar 2022, Hammamet, Tunisia. doi: https://doi.org/10.1109/IC_ASET53395.2022.9765948

Prakash, N., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Mueller, C. L., Ferdinand, O. and Zielinski, O. (2022) Intelligent Marine Pollution Analysis on Spectral Data. In: OCEANS 2021, 20-23 SEPT 2021, San Diego, Porto, pp. 1-6. doi: https://doi.org/10.23919/OCEANS44145.2021.9706056

Ashlam, A. A., Badii, A. and Stahl, F. ORCID: https://orcid.org/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://doi.org/10.5281/zenodo.6983905

2021

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Ferdinand, O., Nolle, L., Pehlken, A. and Zielinski, O. (2021) AI enabled bio waste contamination-scanner. In: AI-2021 Forty-first SGAI International Conference on Artificial Intelligence, 14-16 DEC 2021, Cambridge, England, pp. 357-363. doi: https://doi.org/10.1007/978-3-030-91100-3_28

Lukats, D., Berghöfer, E., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Schneider, J., Pieck, D., Idrees, M., Nolle, L. and Zielinski, O. (2021) Towards Concept Change Detection in Marine Ecosystems. In: OCEANS Conference & Exposition, 20-23 SEPT 2021, San Diego - Porto.

Alzubi, S., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gaber, M. M. (2021) Towards intrusion detection of previously unknown network attacks. Communications of the ECMS, 35 (1). pp. 35-41. ISSN 2522-2414 doi: https://doi.org/10.7148/2021-0035

Almutairi, M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Bramer, M. (2021) ReG-Rules: an explainable rule-based ensemble learner for classification. IEEE Access, 9. pp. 52015-52035. ISSN 2169-3536 doi: https://doi.org/10.1109/ACCESS.2021.3062763

Dubuc, T., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Roesch, E. B. ORCID: https://orcid.org/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://doi.org/10.1109/ACCESS.2020.3046132

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Le, T., Badii, A. and Gaber, M. M. (2021) A frequent pattern conjunction Heuristic for rule generation in data streams. Information, 12 (1). 24. ISSN 2078-2489 doi: https://doi.org/10.3390/info12010024

2020

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Badii, A. (2020) Building adaptive data mining models on streaming data in real-time. Expert Update, 20 (2). ISSN 1465-4091 doi: https://doi.org/10.7148/2018-0008

Wolf, M., van den Berg, K., Garaba, S. P., Gnann, N., Sattler, K., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Zielinski, O. (2020) Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC–Q). Environmental Research Letters, 15 (11). 114042. ISSN 1748-9326 doi: https://doi.org/10.1088/1748-9326/abbd01

Idrees, M. M., Minku, L. L., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Badii, A. (2020) A heterogeneous online learning ensemble for non-stationary environments. Knowledge-Based Systems, 188. 104983. ISSN 0950-7051 doi: https://doi.org/10.1016/j.knosys.2019.104983

Wrench, C., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Di Fatta, G., Karthikeyan, V. and Nauck, D. (2020) A rule induction approach to forecasting critical alarms in a telecommunication network. In: 2019 IEEE International Conference on Data Mining Workshops (ICDMW), 8-11 Nov 2019, Beijing, China, pp. 480-489.

2019

Almutairi, M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Bramer, M. (2019) A rule-based classifier with accurate and fast rule term induction for continuous attributes. In: 17th International Conference on Machine Learning and Applications, 17th to 20th of December 2018, Orlando, Florida, pp. 413-420.

2018

Hammoodi, M. S., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and 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://doi.org/10.1016/j.knosys.2018.08.007

2017

Le, T., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Gaber, M. M., Gomes, J. B. and Di Fatta, G. (2017) On expressiveness and uncertainty awareness in rule-based classification for data streams. Neurocomputing, 265. 127- 141. ISSN 0925-2312 doi: https://doi.org/10.1016/j.neucom.2017.05.081

Tennant, M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Rana, O. and Gomes, J. B. (2017) Scalable real-time classification of data streams with concept drift. Future Generation Computer Systems, 75. pp. 187-199. ISSN 0167-739X doi: https://doi.org/10.1016/j.future.2017.03.026

Shakir Hammoodi, M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Tennant, M. and 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)

Almutairi, M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and 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 (10630). Springer, pp. 115-128. ISBN 9783319710785 doi: https://doi.org/10.1007/978-3-319-71078-5_9

Le, T., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Wrench, C. and Gaber, M. M. (2017) A statistical learning method to fast generalised rule induction directly from raw measurements. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 18-20 Dec 2016, Anaheim, California, USA, pp. 935-938.

Pavlopoulou, N., Abushwashi, A., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and 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)

2016

Adedoyin-Olowe, M., Gaber, M. M., Dancausa, C., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gomes, J. B. (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://doi.org/10.1016/j.eswa.2016.02.028

Wrench, C., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Di Fatta, G., Karthikeyan, V. and Nauck, D. 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. and Roth-Berghofer, T. (eds.) Enterprise Big Data Engineering, Analytics, and Management. IGI Global, pp. 24-47. ISBN 9781522502937 doi: https://doi.org/10.4018/978-1-5225-0293-7

Almutairi, M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Jennings, M., Le, T. and Bramer, M. (2016) Towards expressive modular rule induction for numerical attributes. In: Thirty-sixth SGAI International Conference on Artificial Intelligence, 13-15 DECEMBER 2016, Cambridge, UK, pp. 229-235.

Hammoodi, M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Tennant, M. (2016) Towards online concept drift detection with feature selection for data stream classification. In: 22nd European Conference on Artificial Intelligence, 29th August - 2nd September, The Hague, Holland, pp. 1549-1550.

Wrench, C., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Le, T., Di Fatta, G., Karthikeyan, V. and Nauck, D. (2016) A method of rule induction for predicting and describing future alarms in a telecommunication network. In: Thirty-sixth SGAI International Conference on Artificial Intelligence, 13-15 December 2016, Cambridge, UK, pp. 309-323.

2015

Zliobaite, I., Budka, M. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (2015) Towards cost-sensitive adaptation: when is it worth updating your predictive model? Neurocomputing, 150 (A). pp. 240-249. ISSN 0925-2312 doi: https://doi.org/10.1016/j.neucom.2014.05.084

Tennant, M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gomes, J. (2015) Fast adaptive real-time classification for data streams with concept drift. In: The 8th International Conference on Internet and Distributed Computing Systems, pp. 265-272.

Di Fatta, G., Fortino, G., Li, W., Pathan, M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and 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

Al Ghamdi, S., Di Fatta, G. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (2015) Optimisation techniques for parallel K-Means on MapReduce. In: Proceedings of the 8th International Conference on Internet and Distributed Computing Systems - Volume 9258, pp. 193-200.

Wrench, C., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Di Fatta, G., Karthikeyan, V. and Nauck, D. (2015) Towards expressive rule induction on IP network event streams. In: AI-2015 Thirty-fifth SGAI International Conference on Artificial Intelligence, 15-17 December 2015, Cambridge.

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, May, D., Mills, H., Bramer, M. and Gaber, M. 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://doi.org/10.1007/978-3-662-46703-9_4 (LNCS)

2014

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 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://doi.org/10.1111/exsy.12032 (special issue on innovative techniques and applications of artificial intelligence)

Liu, H., Gegov, A. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (2014) Unified framework for construction of rule based classification systems. In: Pedrycz, W. and Chen, S. M. (eds.) Information Granularity, Big Data and Computational Intelligence. Springer, Switzerland, pp. 209-230. doi: https://doi.org/10.1007/978-3-319-08254-7_10

Adedoyin-Olowe, M., Gaber, M. M. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (2014) A survey of data mining techniques for social media analysis. Journal of Data Mining & Digital Humanities, 2014. ISSN 2416-5999

Gaber, M. M., Gama, J., Krishnaswamy, S., Gomes, J. B. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (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://doi.org/10.1002/widm.1115

Gaber, M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gomes, J. (2014) Background. In: Gaber, M. M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gomes, J. B. (eds.) Pocket Data Mining Big Data on Small Devices. Studies in Big Data (2). Springer International Publishing, Cham, pp. 7-21. ISBN 9783319027104

Roesch, E. ORCID: https://orcid.org/0000-0002-8913-4173, Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gaber, M. 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://doi.org/10.1017/S0140525X13001854

Liu, H., Gegov, A. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (2014) Categorization and construction of rule based systems. In: 15th International Conference on Engineering Applications of Neural Networks, Sofia, Bulgaria, pp. 183-194. (Engineering Applications of Neural Networks: Mladenov, Valeri, Jayne, Chrisina, Iliadis, Lazaros (eds.) Communications in Computer and Information Science, Vol. 459 Springer)

Le, T., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Gomes, J. B., Gaber, M. M. and Di Fatta, G. (2014) Computationally efficient rule-based classification for continuous streaming data. In: Thirty-fourth SGAI International Conference on Artificial Intelligence, 9-11 Dec 2014, Cambridge, England, pp. 21-34.

Gaber, M. M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gomes, J. B. (2014) Conclusions, discussion and future work. In: Pocket Data Mining. Studies in Big Data (2). Springer International Publishing, pp. 95-98. ISBN 9783319027111 doi: https://doi.org/10.1007/978-3-319-02711-1_8

Gaber, M. M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gomes, J. B. (2014) Context-aware PDM (Coll-Stream). In: Pocket Data Mining. Studies in Big Data (2). Springer International Publishing, pp. 61-68. ISBN 9783319027111 doi: https://doi.org/10.1007/978-3-319-02711-1_5

Gaber, M. M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gomes, J. B. (2014) Experimental validation of context-aware PDM. In: Pocket Data Mining. Studies in Big Data (2). Springer International Publishing, pp. 69-80. ISBN 9783319027111 doi: https://doi.org/10.1007/978-3-319-02711-1_6

Adedoyin-Olowe, M., Gaber, M. M., Dancausa, C. M. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (2014) Extraction of unexpected rules from Twitter hashtags and its application to sport events. In: 13th International Conference on Machine Learning and Applications (ICMLA 2014), 3-5 Dec 2014, Detriot, MI, USA, pp. 207-212.

Gaber, M. M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gomes, J. B. (2014) Implementation of pocket data mining. In: Pocket Data Mining. Studies in Big Data (2). Springer International Publishing, pp. 41-59. ISBN 9783319027111 doi: https://doi.org/10.1007/978-3-319-02711-1_4

Gaber, M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gomes, J. (2014) Introduction. In: Pocket Data Mining. Studies in Big Data (2). Springer International Publishing, Switzerland, pp. 1-5. ISBN 9783319027111 doi: https://doi.org/10.1007/978-3-319-02711-1_1

Gaber, M. M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gomes, J. B. (2014) Pocket data mining - big data on small devices. Studies in big data, 2. Springer International, pp108. ISBN 9783319027104

Gaber, M. M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gomes, J. B. (2014) Pocket data mining framework. In: Pocket Data Mining. Studies in Big Data (2). Springer International Publishing, pp. 23-40. doi: https://doi.org/10.1007/978-3-319-02711-1_3

Gaber, M. M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gomes, J. B. (2014) Potential applications of pocket data mining. In: Pocket Data Mining. Studies in Big Data (2). Springer International Publishing, pp. 81-94. ISBN 9783319027111 doi: https://doi.org/10.1007/978-3-319-02711-1_7

Tennant, M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Di Fatta, G. and Gomes, J. B. (2014) Towards a parallel computationally efficient approach to scaling up data stream classification. In: Thirty-fourth SGAI International Conference on Artificial Intelligence, 9-11 Dec 2014, Cambridge, England, pp. 51-65.

2013

Liu, H., Gegov, A. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (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. ORCID: https://orcid.org/0000-0002-4860-0203, Gabrys, B., Gaber, M. M. and 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://doi.org/10.1002/widm.1093

Adedoyin-Olowe, M., Gaber, M. M. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (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://doi.org/10.1007/978-3-642-38610-7_13 (Proceedings, Part II. 12th International Conference on Artificial Intelligence and Soft Computing)

Rausch, P., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Stumpf, M. (2013) Efficient interactive budget planning and adjusting under financial stress. In: Thirty-Third SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, 10-12 DECEMBER 2013, Cambridge UK, pp. 375-388.

Gomes, J., Adedoyin-Olowe, M., Gaber, M. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (2013) Rule Type Identification using TRCM for trend analysis in Twitter. In: Thirty-Third SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, 10-12 DECEMBER 2013, Cambridge UK, pp. 273-278.

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Gabber, M. M. and Max, B. (2013) Scaling up data mining techniques to large datasets using parallel and distributed processing. In: Rausch, P., Sheta, A. F. and Ayesh, A. (eds.) Business Intelligence and Performance Management. Springer, pp. 243-259. ISBN 9781447148654 doi: https://doi.org/10.1007/978-1-4471-4866-1_16

2012

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, May, D. and 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. Springer, London, pp. 21-34. ISBN 9781447147381 doi: https://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. ORCID: https://orcid.org/0000-0002-4860-0203 and Bramer, M. (2012) Scaling up classification rule induction through parallel processing. Knowledge Engineering Review. pp. 243-259. ISSN 1469-8005 doi: https://doi.org/10.1017/S0269888912000355

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 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://doi.org/10.1016/j.knosys.2012.04.014

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 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://doi.org/10.1002/widm.1052

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 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://doi.org/10.1016/j.knosys.2011.06.016

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Gaber, M. M., Aldridge, P., May, D., Liu, H., Bramer, M. and Yu, P. S. (2012) Homogeneous and heterogeneous distributed classification for pocket data mining. In: Hameurlain, A., Küng, J. and Wagner, R. (eds.) Transactions on large-scale data and knowledge-centered systems V. Lecture Notes in Computer Science (7100). Springer, pp. 183-205. ISBN 9783642281471

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Gaber, M. M. and Salvador, M. 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://doi.org/10.1007/978-1-4471-4739-8_5

2011

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Gaber, M. M., Liu, H., Bramer, M. and Yu, P. S. (2011) Distributed classification for pocket data mining. In: Kryszkiewicz, M., Rybinski, H., Skowron, A. and Ras, Z. W. (eds.) Foundations of Intelligent Systems, Proc. of ISMIS 20111, the 19th Int. Symposium on Methodologies for Intelligent Systems. Lecture Notes in Computer Science (6804). Springer, pp. 336-345. ISBN 9783642219153 doi: https://doi.org/10.1007/978-3-642-21916-0_37

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Gaber, M. M., Bramer, M. and Yu, P. S. (2011) Distributed hoeffding trees for pocket data mining. In: International Conferance on High Performance Computing and Simulation (HPCS), 2011. IEEE, pp. 686-692. ISBN 9781612843803 doi: https://doi.org/10.1109/HPCSim.2011.5999893

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Bramer, M. (2011) Induction of modular classification rules: using Jmax-pruning. In: Bramer, M., Petridis, M. and Hopgood, A. (eds.) Research and Development in Intelligent Systems XXVII. Springer, London, pp. 79-92. ISBN 9780857291295 doi: https://doi.org/10.1007/978-0-85729-130-1_6

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Bramer, M. (2011) Random prism: an alternative to random forests. In: Bramer, M., Petridis, M. and Nolle, L. (eds.) Research and Development in Intelligent Systems XXVIII. Springer, London, pp. 5-18. ISBN 9781447123170 doi: https://doi.org/10.1007/978-1-4471-2318-7_1

2010

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Gaber, M. M., Bramer, M. and Yu, P. S. (2010) Pocket data mining: towards collaborative data mining in mobile computing environments. In: Proc. ICTAI 2010, the 22nd IEEE Int. Conf. on Tools with Artificial Intelligence. IEEE, pp. 323-330. ISBN 9781424488179 doi: https://doi.org/10.1109/ICTAI.2010.118

Swain, M., Silva, C. G., Loureiro-Ferreira, N., Ostropytskyy, V., Brito, J., Riche, O., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Dubitzky, W. and Brito, R. M. M. (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://doi.org/10.1016/j.future.2009.08.008

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Bramer, M. and Adda, M. (2010) J-PMCRI: a methodology for inducing pre-pruned modular classification rules. In: Bramer, M. (ed.) Artificial Intelligence in Theory and Practice III. IFIP Advances in Information and Communication Technology (331). Springer, Berlin, pp. 47-56. ISBN 9783642152856 doi: https://doi.org/10.1007/978-3-642-15286-3_5

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Bramer, M. and Adda, M. (2010) Parallel rule induction with information theoretic pre-pruning. In: Research and Development in Intelligent Systems XXVI. Springer, London, pp. 151-164. ISBN 9781848829824 doi: https://doi.org/10.1007/978-1-84882-983-1_11

2009

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Bramer, M. and Adda, M. (2009) PMCRI: a parallel modular classification rule induction framework. In: Machine Learning and Data Mining in Pattern Recognition. Lecture Notes in Computer Science (5632). Springer, pp. 148-162. ISBN 9783642030697 doi: https://doi.org/10.1007/978-3-642-03070-3_12

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Bramer, M. and Adda, M. (2009) Parallel induction of modular classification rules. In: Bramer, M., Petridis, M. and Coenen, F. (eds.) Research and Development in Intelligent Systems XXV. London, Berlin, pp. 343-348. ISBN 9781848821705 doi: https://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)

2008

Swain, M., Ostropytskyy, V., Silva, C. G., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Riche, O., Brito, R. M. M. and Dubitzky, W. (2008) Grid computing solutions for distributed repositories of protein folding and unfolding simulations. In: Computational Science – ICCS 2008. Lecture Notes in Computer Science (5103). Springer, Berlin, pp. 70-79. ISBN 9783540693888 doi: https://doi.org/10.1007/978-3-540-69389-5_10

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Bramer, M. and Adda, M. (2008) P-Prism: a computationally efficient approach to scaling up classification rule induction. In: Bramer, M. (ed.) Artificial Intelligence in Theory and Practice II. IFIP – The International Federation for Information Processing (276). Springer, USA, pp. 77-86. ISBN 9780387096940 doi: https://doi.org/10.1007/978-0-387-09695-7_8

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Bramer, M. (2008) Towards a computationally efficient approach to modular classification rule induction. In: Bramer, M., Coenen, F. and Petridis, M. (eds.) Research and Development in Intelligent Systems XXIV. Springer, London, pp. 357-362. ISBN 9781848000933 doi: https://doi.org/10.1007/978-1-84800-094-0_27

2005

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Berrar, D., Silva, C., Rodrigues, R., Brito, R.M.M. and Dubitzky, W. (2005) Grid warehousing of molecular dynamics protein unfolding data. In: Proceedings of CCGrid2005, the IEEE 2005 International Symposium on Cluster Computing and the Grid. IEEE, pp. 496-503. ISBN 0780390741 doi: https://doi.org/10.1109/CCGRID.2005.1558594

Berrar, D., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Silva, C., Rui Rodrigues, J., Brito, R. M. M. and 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://doi.org/10.1007/s10877-005-0676-z