dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018). Synthetic data is artificially created rather than collected from real-world cases and can thus be used to train and validate machine learning models. Although it is an old technique  (Robin, 1993; Drechsler, 2011)  and addresses the problems of data unavailability and low label quality, synthetic data has not been widely used for credit card fraud detection. This method has several advantages, including reducing constraints when using sensitive data, tailoring data needs to certain conditions that cannot be obtained with real datasets, controlling the data distribution utilized in testing, and simplifying the performance comparison  (Ikram Ul Haq et al. , 2016). The main public repositories that provide credit card fraud datasets are the Kaggle datasets 2  , UCI repository 3  , GitHub 4  and the Kilthub repository 5.
Kaggle datasets offers a public platform for publishing datasets and have been a source for several researchers to test their algorithms. Kaggle contains several public and synthetic datasets for credit cards and has been used extensively in the field of credit card fraud detection. The main repositories and public datasets for credit card fraud detection are shown in Table  8. These include PaySim, Credit card fraud detection, and IEEE-CIS. In addition to Kaggle, the UCI repository is a collection of datasets, database and domain theories for machine learning. It is an essential source for analyzing machine learning algorithms. Furthermore, it contains 588 datasets, including some credit card datasets, namely Statlog (German Credit Data) Data Set, and the Credit Approval Data Set (used in  Padmanabhuni et al.
(2019) ,  Han et al. (2021) ,  Zhu et al. (2020) ). Additionally, GitHub contains several synthetic datasets for credit card fraud detection. One of these datasets was produced by Brandon Harris and is used in the Sparcov program 6. It is a synthetic dataset that contains labelled transactions generated using Faker to produce information about merchants and customers. Another synthetic dataset was adapted from a previous one and is used for real-time credit card fraud analysis 7. It was generated between June and October 2021 using Faker to generate the transactions and customer profiles. Finally, the Kilthub repository is an institutional source managed by the library of Carnegie Mellon University. University researchers use this repository to share their materials, including datasets, such as the CERT insider threat dataset used in  Zheng (2020).
Fraud detection testing metrics Choosing the right metric is crucial to assessing machine learning models. Different metrics have been suggested to evaluate ML models in various applications. This section provides the metrics that were used in the surveyed papers and emphasizes the most useful ones that better suit the specificity of the credit card fraud detection problem. Indeed, examining a single metric does not usually provide a full picture of the problem. Meanwhile, the imbalance aspect of the datasets used makes relying on the basic ML metric useless, as illustrated below. In addition, most studies have used misclassification measures to evaluate the various solutions, and thus do not take into account the financial costs of the fraudulent transaction  (Correa Bahnsen et al. , 2016).
Dataset type References Public Real  (Wang et al. , 2018; Babu and Pratap, 2020; Rtayli and Enneya, 2020; El hlouli et al. , 2020; Olowookere and Adewale, 2020; Ingole et al. , 2021; Yang et al. , 2019; Rai and Dwivedi, 2020; Mohammed et al. , 2018; Itoo et al. , 2021; Baabdullah et al. , 2020; Han et al. , 2021; Li et al. , 2021; RB and KR, 2021; Dornadula and Geetha, 2019; Tran and Dang, 2021; Kewei et al. , 2021; Cochrane et al. , 2021; Bagga et al. , 2020; Forough and Momtazi, 2021; Zheng, 2020 ) Synthetic  (Cochrane et al. , 2021; Rtayli and Enneya, 2020 ; Zheng, 2020) Not specified  (Padmanabhuni et al. , 2019; Zhu et al. , 2020).
Classification metrics Accuracy: Accuracy is the most straightforward metric. It is defined as the number of correct predictions divided by the total number of instances, multiplied by 100: Accuracy ¼ 100 Â ðTP þ TNÞ=N In many cases, accuracy is not a good indicator of the performance of a ML model, especially when the class distribution is imbalanced, which is the case for credit card datasets. In this case, even if all samples are predicted as the most frequent class, the model will have a high accuracy rate, which is meaningless since the model is not learning anything. It is instead predicting everything as the majority class. As such, the precision should be considered. Precision: This is one of the most commonly used metrics. It is used to check the positive predictions of the system by finding the ratio of the true positive over the total positive predictions.
AUROC is a performance metric for discrimination; it shows whether the model is able to correctly rank examples and discriminate between cases. However, this metric should not be used when data is heavily imbalanced. Indeed, the false positive rate for highly imbalanced datasets is pulled down due to a large number of true negatives  (Saito and Rehmsmeier, 2015). Area Under the Curve of Precision-Recall (AUC-PR) (aka as AUCPRC): It relies on the Precision-Recall curve, which merges precision and recall in one visualization. For every threshold, precision and recall are calculated and plotted. The higher on the y-axis the curve, the better the model's performance. Knowing at which recall the precision starts to fall fast helps in choosing the threshold and delivering a better model. Like the AUC-ROC score, it is possible to compute the area under the precision-recall curve to obtain one metric describing the model performance.
To compare the performances of a new fraud detection model with a second one, one concern is to measure how much cost is reduced by the new model through the cost reduction rate Cr d. This is calculated as follows: Cr d ¼ C new d C old d ð2Þ where C new d and C old d are the costs of respectively the new and the old models. The cost metric is a valuable metric that has not been widely used in the credit card fraud detection field. Mean run time: The run time is the duration of the execution of an algorithm. When an algorithm is executed several times on the same data input, the mean over all the obtained run times is computed, giving rise to the mean run time. This metric is important as it compares the reliability of the detection results with the time duration taken to achieve those results.
Discussion Several metrics have been suggested to assess the performance of credit card fraud detection models. Table  10  summarizes the results achieved by the reviewed models. It provides an overview of the testing results obtained for the metrics described for each paper, thus providing a unified general view of the performance of the state-of-the-art techniques. Indeed, different solutions may refer to different terminology for the testing metrics. As shown in Table  10 , for some of these metrics the accuracy was widely used, but not enough to allow the detection system to be evaluated properly. It can be observed from the table that most studies rely on classification metrics, while a few use statistical and cost metrics. It is worth noting that it is important to take into account cost metrics when testing the efficiency of the model, in addition to considering metrics that better fit unbalanced datasets like the MCC.
Big data technologies A huge amount of data is continually being generated and collected. With the increasing use of social media, more and more textual and image data is being collected. Meanwhile, the increase in use of IoT, and the emergence of smart technologies and smart cities has led to a large amount of sensed data that needs to be processed to afford decision-makers clearer insights. Big Data and Data Science describe the application of software and technology integrated with cutting-edge algorithms and techniques to acquire better understandings, produce informed findings, and forecast risks and gains  (Doko and Miskovski, 2019). Big data is characterized by greater variety, rising volumes, and velocity, known as the three vs. More Vs of relevance have been proposed by researchers, such as veracity, which refers to data quality and value, i.
Despite the importance of a distributed approach, this study has shown very few research works have investigated utilizing big data analytics within a distributed architecture to detect and/or predict credit card fraud  (Chen et al. , 2020; Zhou et al. , 2021). Thus, additional research is required to provide real-time systems and investigate the challenges that may impede the effective use of such technologies while preserving the privacy of users. Moreover, it is important to integrate sev-eral data sets from multiple heterogeneous sources to attain more accurate results. Furthermore, there is still a need for open big datasets to support researchers in investigating big data analytic. A possible research direction would be to develop valuable synthetic big datasets.
, 2016). Gartner estimated that global cloud revenue in 2022 to 474 billion, up from 408 billion in 2021  (Gartner, 2021). Moreover, Gartner's analysts predict that in excess of 85% of organizations will have adopted a cloud-first principle by 2025. According to Milind Govekar, a vice president at Gartner, "cloud has enabled new digital experiences such as mobile payment systems where banks have invested in startups, energy companies using the cloud to improve their customers' retail experiences"  (Gartner, 2021). This rapid growth in cloud computing markets has attracted attention from both academia and industry. In particular, cloud computing can offer notable benefits to allow credit card fraud detection from different perspectives, such as cost savings and offering computation power. In cloud computing there are no limitations on memory, computation or storage, as it utilizes large data centers as resources.
The increased usage of small devices for credit card fraud detection raise interesting research questions about the value of migrating computation to the cloud while designing new lightweight solutions and algorithms for remote devices. It is also interesting to adopt a cloud based architecture and reuse cloud intelligent services and infrastructures to empower credit card fraud detection. Very few research studies have investigated cloud computing in the field of credit card detection, and several open research directions can be explored in this field namely, the use of the cloud to collect and store customers' data, along with computation and AI models hosted in the cloud. Moreover, the aggregating of heterogeneous data sources and interoperability between several organizations can assist in enhancing real-time fraud detection.
The concealment of complex networks, can be a breeding ground for fraudulent activities by criminals. Controlling and managing fraud risk is ever more complex, as fraud arises more and more frequently, resulting in high monetary losses for commercial banks and financial institutions. Possible future research directions could include the consideration of hybrid datasets that combine both transactional data and external data collected from IoT devices or the development of new system designs and learning methods involving data collected from IoT devices. Besides, behaviour biometrics collected from IoT devices may be used to authenticate users and avoid fraudulent transactions. Thus, new detection and authentication models should be investigated.
For example, IoP wearables permit customers to connect with banking apps for better tracking of their financial behaviour, while also performing a wide range of other mobile banking operations. These devices not only facilitate banking operations for the user but can also bestow advanced security features, such as reliance on customer heart rate for biometric authentication. Tokenization can be a sure industry-standard admissible by leading payment networks to guarantee the privacy and security of banking transactions. Furthermore, with card-on-file EMV tokenization, vendors no longer need to store card details in their databases, only tokens. An initial version of the Tokenization framework was published in March 2014 by EMV Co. This framework was intended to promote a new generation of payment products, while empowering the existing payments infrastructure.
Conclusion The rise of IoT technologies and data-driven finance seriously influences the daily life and behaviour of customers. Digital payments are spreading rapidly and gaining in importance, mainly since the COVID-19 pandemic. The use of recent advances, namely AI, big data and cloud computing are attracting huge interest from researchers. Although an abundance of research has been undertaken in the field in recent years, extremely limited comprehensive surveys have also been conducted. Therefore, this research aims to offer the latest and most relevant research on credit card fraud detection, impacting new technologies in the field. Our study analyzed 40 works extracted from the Scopus and Web of Science databases. The literature sample was also explored, focusing on research trends, theoretical achievements, methodology, strengths and weaknesses, modeling and testing.
Evidence shows a few state-of-the-art models have focused on the use of deep learning models, big data analytics and technologies, IoT, real-time and security aspects, whereas the majority of previous works have focused on comparing traditional detection models without a clear effort in providing new designs and models better suited to the credit card fraud detection problem. Besides, it is worth noting that a large majority of the analysed work relies on test metrics that are unsuitable for credit card datasets. Some relevant directions for future work are also proposed. Each topic in this article contains an overview of research done in the area of credit card fraud and key concepts are considered each category. Thus, a thorough analysis of prior findings allows researchers to avoid duplicating empirical studies and to identify relevant research gaps.
Amount is the transaction amount. et al. , 2021; Yang et al. , 2019; Mohammed et al. , 2018; Baabdullah et al. , 2020; Han et al. , 2021; Li et al. , 2021; Dornadula and Geetha, 2019; Tran and Dang, 2021; Forough and Momtazi, 2021) ccFraud Revolution Not 7 attributes: Gender, State, Cardholder, Fraud cases 10 (Rtayli and Enneya, 2020; Mohammed analytics mentioned Balance, NumTrans, NumIntTrans, CreditLine, rate = 5. 96% million et al. , 2018) and fraudRisk, which represent the target variable. IEEE-CIS Kaggle Real IEEE Computational Intelligence Society Two types of features: features related to the 3. 5% 1 (Kewei et al. , 2021) (IEEE-CIS) dataset for fraud detection. This transaction (e. g. , product, card, address) and million dataset includes around 1 million records features related to the identity (e.
Comparison with natural inclusions We used recent datasets on natural inclusions from three different metamorphic suites: 1) blueschists from Syros in garnet formed at ~1. 4-1. 7 GPa and 500-550 °C, and in retrograde epidote grown between ~1. 3-1. 5 GPa 400-500 °C and ~1. 0 GPa and 400 °C  (Cisneros et al. , 2021) ; 2) Holsnøy eclogite from the Bergen Arcs (Norway) where inclusions formed at 1. 4-1. 6 240 GPa, 680-760 °C consistent with previous estimates based on phase equilibria  (Zhong et al. , 2019; Bhowany et al. , 2018) ; 3) gneiss from Papua New Guinea with formation conditions estimated from Ti in quartz and quartz in garnet inclusions as ~1. 0 GPa and 600 °C, which are interpreted as the P-T conditions of garnet growth and entrapment of quartz inclusions  (Gonzalez et al. , 2019).
Organic tissues classi¯cation and visualization based on a regression model on depolarizing data In this section, we used regression models combined with polarimetric data for classi¯cation. 24  This approach is di®erent than the one discussed in Sec. 6. 2, with models based on di®erent classi¯cation algorithms. In particular, we started the study by measuring the Mueller matrix of the four tissues classes described in Sec. 6. 1 (bone, muscle, tendon and myotendinous junction) from a statistical representative collection of ex vivo chicken samples. This was conducted for three di®erent wavelengths (625 nm, 530 nm and 470 nm). From each Mueller matrix measured, for each of the four tissues classes, we calculated a series of representative polarimetric observables, that focuses in the main polarimetric responses of samples (retardance, dichroism and depolarization).
In addition, transforming the polarimetric space to the principal component space allowed us to reduce the initial 27 variables (nine observables for three wavelengths) to a set of independent predictor variables which maintain the information contained in the original metrics describing an adequate proportion of the variance of the initial data. 24  In particular, our PCA analysis reduced the number of variables creating a 10-dimension space explaining more than 90% of the original metrics variance. Before applying the logistic regression on principal components data space, we analyzed the discrimination potential of these particular factors. To do so, the receiver operating characteristic (ROC) curve analysis was used as a criterion to describe the discriminatory potential of each principal component.
In particular, for a given unknown tissue image, the four models can be applied pixel-to-pixel, assigning in each case a certain probability belonging to a certain tissue class. As an example, in the following, we show this particular situation applied to the study of a tendon. An arbitrary tendon image, not belonging to the database used to train the models, was tested. In particular, from the Mueller matrix of an arbitrary chicken tendon, we extracted all the polarimetric observables above-described and they were transformed to the principal components space. With the i coe±cients corresponding to the already constructed models, and using the principal components related to the new tendon, the function shown in Eq. (  17 ) was implemented for each tissue class. This was conducted pixel-to-pixel, so four images were obtained, each one providing the probability of each pixel to belong to a certain tissue class.
Therefore, for our coarsening study of wet foams in the present paper, we study the extent to which surface bubble size distributions and average radii are representative of the foam bulk structure. We present the samples and the experimental setup used in the ISS and on ground in section 2; then we describe in section 3 three different image analysis procedures that we used for the planimetric measurements to determine the average bubble size and distribution at the surface. We present in section 4, a ground experiment performed to compare the initial bubble size distribution measured at the surface of a wet foam right after its production to that measured for a bubble monolayer of the same foam. In the case of such a layer, the bubble sizes can be determined without any ambiguity. Then we turn to the analysis of the DTS data that yield an average of the bubble size.
Previous expressions relating the average bubble size to the transmitted intensity published in the literature  Durian et al. [1991]  could not be used, due to the unusual geometry of our ISS sample cells. We have therefore derived the required analytical relation starting from the light diffusion equation, and taking into account the cuboid shape of the cell and its specific optical boundary conditions. This calculation is described in section 5. The analysis of the ISS experimental data using this model is performed all along the coarsening process and presented in section 6.
Manual image analysis, using ellipses For every liquid fraction, raw images over the coarsening process have been analyzed "by hand" using the software ImageJ. The contour of each bubble taken at the middle of the dark ring outlining the bubble is manually fitted by an ellipse (with small ellipticity, i. e. between 1 and 1. 15), and its area A is measured. Then, the equivalent radius of a circular bubble of the same area is calculated as R = A/π. The systematic error that affects the measure of R from the ISS images is estimated to be of the order of one pixel, i. e. about 6 µm. This image analysis allows the determination of the bubble radius distribution, since the earliest age just after the end of the foaming process until the end of the coarsening duration where there remains about 50 bubbles at the sample surface, for all investigated liquid fractions.
From these effective radius distributions, several different moments are evaluated: the mean radius, the second and third moments R 2 = R 2 1/2 and R 3 = R 3 1/3 respectively, and the Sauter mean radius R 32 = R 3 / R 2. 3. 2 Automatic measurement by standard image analysis, using circles The image analysis has been performed using a Python script. The protocol for automated image analysis in Python is illustrated in Figure  2 : (a) raw image, (b) image whose brightness and contrast have been increased using the PIL library, (c) image whose contrast has again been improved and this time locally (using the equalize adapthist function of Scikit-image), (d) binarized image using a local thresholding method (adaptive threshold function of the CV2 library) (e) image having undergone morphological transformations (erosion / dilation) using the CV2 library in order to reduce the noise due to bubbles on the lower planes, (f) image on which the area A inside the black contours is measured (using regionprops from the Skimage library) and circles of radius R = A/π are shown, (g) original image on which the detected bubbles are represented as well as the size of the "crop" in which the analysis was performed (red rectangle).
Automatic image analysis with machine learning The two previous subsections describe different methods used to analyze images of the surface bubbles. While both methods make accurate measurements the "manual" elliptical fitting is time consuming and the "automatic" circular fits work best for high liquid fraction foams; for both cases some bubbles are not well described by one single convex shape. To bypass these issues we turn to a machine learning algorithm for image segmentation called Bellybutton [https://pypi. org/project/bellybuttonseg/] that is capable of handling a multiplicity of non-circular / non-ellipsoidal bubble shapes at any liquid fraction. This algorithm uses a convolutional neural network trained on pixel-by-pixel inside vs outside classification of hand-segmented images; further details of the algorithm itself are beyond the scope of this paper.
1 cannot be used to deduce the evolution of ℓ * from our experimental data quantitatively. for these two ℓ * values, predicted by Eq. 17. It is plotted versus the number of terms i max taken into account in Eq. 17. The extrapolation length is z e = 1. 8 for the investigated liquid fraction. The sample dimensions a, b, L considered in the calculation are those of our experimental configuration given in the text. The plot shows that the convergence is almost complete for i max = 15. Similar convergence results are obtained for other liquid fractions and transport mean free path evolutions encountered in our coarsening experiments.