Multiple perspectives HMM-based feature engineering for credit card fraud detection

Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this article, we model a sequence of credit card transactions from three different perspectives, namely (i) does the sequence contain a Fraud? (ii) Is the sequence obtained by fixing the card-holder or the payment terminal? (iii) Is it a sequence of spent amount or of elapsed time between the current and previous transactions? Combinations of the three binary perspectives give eight sets of sequences from the (training) set of transactions. Each one of these sets is modelled with a Hidden Markov Model (HMM). Each HMM associates a likelihood to a transaction given its sequence of previous transactions. These likelihoods are used as additional features in a Random Forest classifier for fraud detection. This multiple perspectives HMM-based approach enables an automatic feature engineering in order to model the sequential properties of the dataset with respect to the classification task. This strategy allows for a 15% increase in the precision-recall AUC compared to the state of the art feature engineering strategy for credit card fraud detection.Comment: Presented as a poster in the conference SAC 2019: 34th ACM/SIGAPP Symposium on Applied Computing in April 201

An Integrative Approach for Estimating Cutting Tools Wear in Turning

Cutting tools replacement is an important industrial concern because of the costs that stem from them. In many cases, cutting inserts are replaced either too late or too early. Consequently, either workpieces are scraped or cutting inserts are wasted. That is why there is a need for on-line evaluation of cutting tool wear, hence their residual lifetime before replacement. This estimate may be achieved through condition monitoring, either using direct measurements on the tool, or indirect measurement through machining variables or production quality. The condition monitoring data may in turn be used in statistical or probabilistic models, which are built with help of experimental data or information from numerical models. In the case of this work, the Cox Proportional Hazards Model is used with additional information from numerical models and cutting forces monitoring

Design of Experiment: Wear Indicators for Cutting Tools: Estimate of Tool Remaining Useful Life

Remplacement optimal des outils coupants Simulation de la dégradation des outils de coupe par un procédé gamma Utilisation et ajustement d'un modèle de Cox aux risques proportionnels pour prédire la durée de vie d'un outil coupant Vérification de la prédiction face à une nouvelle campagne expérimentale Conception de la campagne expérimentale Analyse de la qualité de prédiction et de ses facteurs d'influenc

Time2Go4.0 - Collaborative Projects for Top Indstrial Managers in Sustainable Industry 4.0 - How to ensure quality of preparation and execution of collaborative blended design projects including 4.0 and sustainability aspects?

4. Quality education9. Industry, innovation and infrastructur

Experimental Measurement of Wear Indicators for Estimating the Tool Remaining Useful Life

Remplacement opitmal des outils coupants ; Simulation de la dégradation des outils de coupe par un procédé gamma ; Utilisation et ajustement d'un modèle de Cox aux risques proportionnels pour prédire la durée de vie d'un outil coupant ; Vérification de la prédiction face à une nouvelle campagne expérimentale ; Conception de la campagne expérimentale ; Analyse de la qualité de la prédiction et de ses facteurs d'influence

Imaging dynamics beneath turbid media via parallelized single-photon detection

Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard methods aim to form images based upon optical absorption or fluorescent emission, it is also well-established that the temporal correlation of scattered coherent light diffuses through tissue much like optical intensity. Few works to date, however, have aimed to experimentally measure and process such data to demonstrate deep-tissue imaging of decorrelation dynamics. In this work, we take advantage of a single-photon avalanche diode (SPAD) array camera, with over one thousand detectors, to simultaneously detect speckle fluctuations at the single-photon level from 12 different phantom tissue surface locations delivered via a customized fiber bundle array. We then apply a deep neural network to convert the acquired single-photon measurements into video of scattering dynamics beneath rapidly decorrelating liquid tissue phantoms. We demonstrate the ability to record video of dynamic events occurring 5-8 mm beneath a decorrelating tissue phantom with mm-scale resolution and at a 2.5-10 Hz frame rate

Transient motion classification through turbid volumes via parallelized single-photon detection and deep contrastive embedding

Fast noninvasive probing of spatially varying decorrelating events, such as cerebral blood flow beneath the human skull, is an essential task in various scientific and clinical settings. One of the primary optical techniques used is diffuse correlation spectroscopy (DCS), whose classical implementation uses a single or few single-photon detectors, resulting in poor spatial localization accuracy and relatively low temporal resolution. Here, we propose a technique termed Classifying Rapid decorrelation Events via Parallelized single photon dEtection (CREPE)}, a new form of DCS that can probe and classify different decorrelating movements hidden underneath turbid volume with high sensitivity using parallelized speckle detection from a $32\times32$ pixel SPAD array. We evaluate our setup by classifying different spatiotemporal-decorrelating patterns hidden beneath a 5mm tissue-like phantom made with rapidly decorrelating dynamic scattering media. Twelve multi-mode fibers are used to collect scattered light from different positions on the surface of the tissue phantom. To validate our setup, we generate perturbed decorrelation patterns by both a digital micromirror device (DMD) modulated at multi-kilo-hertz rates, as well as a vessel phantom containing flowing fluid. Along with a deep contrastive learning algorithm that outperforms classic unsupervised learning methods, we demonstrate our approach can accurately detect and classify different transient decorrelation events (happening in 0.1-0.4s) underneath turbid scattering media, without any data labeling. This has the potential to be applied to noninvasively monitor deep tissue motion patterns, for example identifying normal or abnormal cerebral blood flow events, at multi-Hertz rates within a compact and static detection probe.Comment: Journal submissio