User profiling and classification for fraud detection in mobile communications networks

The topic of this thesis is fraud detection in mobile communications networks by means of user profiling and classification techniques. The goal is to first identify relevant user groups based on call data and then to assign a user to a relevant group. Fraud may be defined as a dishonest or illegal use of services, with the intention to avoid service charges. Fraud detection is an important application, since network operators lose a relevant portion of their revenue to fraud. Whereas the intentions of the mobile phone users cannot be observed, it is assumed that the intentions are reflected in the call data. The call data is subsequently used in describing behavioral patterns of users. Neural networks and probabilistic models are employed in learning these usage patterns from call data. These models are used either to detect abrupt changes in established usage patterns or to recognize typical usage patterns of fraud. The methods are shown to be effective in detecting fraudulent behavior by empirically testing the methods with data from real mobile communications networks.reviewe

A Web2.0 Strategy for the Collaborative Analysis of Complex Bioimages

Loyek C, Kölling J, Langenkämper D, Niehaus K, Nattkemper TW. A Web2.0 Strategy for the Collaborative Analysis of Complex Bioimages. In: Gama J, Bradley E, Hollmén J, eds. Advances in Intelligent Data Analysis X: 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011. Proceedings. Lecture Notes in Computer Science. Vol 7014. Berlin, Heidelberg: Springer; 2011: 258-269

Proceedings of the ECMLPKDD 2015 Doctoral Consortium

ECMLPKDD 2015 Doctoral Consortium was organized for the second time as part of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), organised in Porto during September 7-11, 2015. The objective of the doctoral consortium is to provide an environment for students to exchange their ideas and experiences with peers in an interactive atmosphere and to get constructive feedback from senior researchers in machine learning, data mining, and related areas. These proceedings collect together and document all the contributions of the ECMLPKDD 2015 Doctoral Consortium

Gaussian process classification for prediction of in-hospital mortality among preterm infants

We present a method for predicting preterm infant in-hospital mortality using Bayesian Gaussian process classification. We combined features extracted from sensor measurements, made during the first 72 h of care for 598 Very Low Birth Weight infants of birth weight <1500 g, with standard clinical features calculated on arrival at the Neonatal Intensive Care Unit. Time periods of 12, 18, 24, 36, 48, and 72 h were evaluated. We achieved a classification result with area under the receiver operating characteristic curve of 0.948, which is in excess of the results achieved by using the clinical standard SNAP-II and SNAPPE-II scores. (C) 2018 Elsevier B.V. All rights reserved.Peer reviewe

Identifying the main drivers for the production and maturation of Scots pine tracheids along a temperature gradient

Even though studies monitoring the phenology and seasonal dynamics of the wood formation have accumulated for several conifer species across the Northern Hemisphere, the environmental control of tracheid production and differentiation is still fragmentary. With microcore and environmental data from six stands in Finland and France, we built auto-calibrated data-driven black box models for analyzing the most important factors controlling the tracheid production and maturation in Scots pine stem. In the best models, estimation was accurate to within a fraction of a tracheid per week. We compared the relative results of models built using different predictors, and found that the rate of tracheid production was partly regular but current and previous air temperature had influence on the sites in the middle of the temperature range and photosynthetic production in the coldest ones. The rate of mature cell production was more difficult to relate to the predictors but recent photosynthetic production was included in all successful models.Peer reviewe

Pathways affected by asbestos exposure in normal and tumour tissue of lung cancer patients

<p>Abstract</p> <p>Background</p> <p>Studies on asbestos-induced tumourigenesis have indicated the role of, e.g., reactive oxygen/nitrogen species, mitochondria, as well as NF-κB and MAPK signalling pathways. The exact molecular mechanisms contributing to asbestos-mediated carcinogenesis are, however, still to be characterized.</p> <p>Methods</p> <p>In this study, gene expression data analyses together with gene annotation data from the Gene Ontology (GO) database were utilized to identify pathways that are differentially regulated in lung and tumour tissues between asbestos-exposed and non-exposed lung cancer patients. Differentially regulated pathways were identified from gene expression data from 14 asbestos-exposed and 14 non-exposed lung cancer patients using custom-made software and Iterative Group Analysis (iGA). Western blotting was used to further characterize the findings, specifically to determine the protein levels of UBA1 and UBA7.</p> <p>Results</p> <p>Differences between asbestos-related and non-related lung tumours were detected in pathways associated with, e.g., ion transport, NF-κB signalling, DNA repair, as well as spliceosome and nucleosome complexes. A notable fraction of the pathways down-regulated in both normal and tumour tissue of the asbestos-exposed patients were related to protein ubiquitination, a versatile process regulating, for instance, DNA repair, cell cycle, and apoptosis, and thus being also a significant contributor of carcinogenesis. Even though UBA1 or UBA7, the early enzymes involved in protein ubiquitination and ubiquitin-like regulation of target proteins, did not underlie the exposure-related deregulation of ubiquitination, a difference was detected in the UBA1 and UBA7 levels between squamous cell carcinomas and respective normal lung tissue (p = 0.02 and p = 0.01) without regard to exposure status.</p> <p>Conclusion</p> <p>Our results indicate alterations in protein ubiquitination related both to cancer type and asbestos. We present for the first time pathway analysis results on asbestos-associated lung cancer, providing important insight into the most relevant targets for future research.</p

BLPA : Bayesian Learn-Predict-Adjust Method for Online Detection of Recurrent Changepoints

Online changepoint detection is an important task for machine learning in changing environments, as it signals when the learning model needs to be updated. Presence of noise that can be mistaken for real changes makes it difficult to develop an effective approach that would have a low false alarm rate and being able to detect all the changes with a minimal delay. In this paper we study how performance of popular Bayesian online detectors can be improved in case of recurrent changes. Modelling recurrence allows us to anticipate future changepoints and predict their locations in time. We propose an approach for inducing and integrating recurrence information in the streaming settings, and demonstrate its effectiveness on synthetic and real-world human activity datasets.Peer reviewe

Gene expression profiles in asbestos-exposed epithelial and mesothelial lung cell lines

BACKGROUND: Asbestos has been shown to cause chromosomal damage and DNA aberrations. Exposure to asbestos causes many lung diseases e.g. asbestosis, malignant mesothelioma, and lung cancer, but the disease-related processes are still largely unknown. We exposed the human cell lines A549, Beas-2B and Met5A to crocidolite asbestos and determined time-dependent gene expression profiles by using Affymetrix arrays. The hybridization data was analyzed by using an algorithm specifically designed for clustering of short time series expression data. A canonical correlation analysis was applied to identify correlations between the cell lines, and a Gene Ontology analysis method for the identification of enriched, differentially expressed biological processes. RESULTS: We recognized a large number of previously known as well as new potential asbestos-associated genes and biological processes, and identified chromosomal regions enriched with genes potentially contributing to common responses to asbestos in these cell lines. These include genes such as the thioredoxin domain containing gene (TXNDC) and the potential tumor suppressor, BCL2/adenovirus E1B 19kD-interacting protein gene (BNIP3L), GO-terms such as "positive regulation of I-kappaB kinase/NF-kappaB cascade" and "positive regulation of transcription, DNA-dependent", and chromosomal regions such as 2p22, 9p13, and 14q21. We present the complete data sets as Additional files. CONCLUSION: This study identifies several interesting targets for further investigation in relation to asbestos-associated diseases