Streaming Intelligence Real Time Fraud Detection in Retail Payments Using Machine Learning

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

Cardiac amyloidosis is associated with poor outcomes and is caused by the interstitial deposition of misfolded proteins, typically ATTR (transthyretin) or AL (light chains). Although specific therapies during early disease stages exist, the diagnosis is often only established at an advanced stage. Cardiovascular magnetic resonance (CMR) is the gold standard for imaging suspected myocardial disease. However, differentiating cardiac amyloidosis from hypertrophic cardiomyopathy may be challenging, and a reliable method for an image-based classification of amyloidosis subtypes is lacking. This study sought to investigate a CMR machine learning (ML) algorithm to identify and distinguish cardiac amyloidosis. This retrospective, multicenter, multivendor feasibility study included consecutive patients diagnosed with hypertrophic cardiomyopathy or AL/ATTR amyloidosis and healthy volunteers. Standard clinical information, semiautomated CMR imaging data, and qualitative CMR features were integrated into a trained ML algorithm. Four hundred participants (95 healthy, 94 hypertrophic cardiomyopathy, 95 AL, and 116 ATTR) from 56 institutions were included (269 men aged 58.5 [48.4-69.4] years). A 3-stage ML screening cascade sequentially differentiated healthy volunteers from patients, then hypertrophic cardiomyopathy from amyloidosis, and then AL from ATTR. The ML algorithm resulted in an accurate differentiation at each step (area under the curve, 1.0, 0.99, and 0.92, respectively). After reducing included data to demographics and imaging data alone, the performance remained excellent (area under the curve, 0.99, 0.98, and 0.88, respectively), even after removing late gadolinium enhancement imaging data from the model (area under the curve, 1.0, 0.95, 0.86, respectively). A trained ML model using semiautomated CMR imaging data and patient demographics can accurately identify cardiac amyloidosis and differentiate subtypes.

Batteries, recognized as effective energy storage solutions, are considered the main facilitators of the world-wide transition towards clean and renewable energy sources. Among different types of batteries, lithium-ion (Li-ion) variants offer higher energy densities and relatively longer life spans when compared to other types. Nonetheless, a primary concern with these batteries is their lifetime. Batteries undergo various degradation mechanisms under storage and use, significantly impacting their lifespan. To this end, it is crucial to predict the degradation and lifetime of Li-ion batteries under given conditions.Researchers use three main methodologies to perform battery health diagnostics and to predict the lifetime of batteries. One approach revolves around using mechanistic, first-principle electrochemical models, also known as physics-based models. If equipped with proper thermodynamic theories, such models can show promising capabilities; however, holistic degradation prediction with these models is still challenging due to the computational complexities and the multitude of parameters that need to be fine-tuned in this approach. The other common strategy is to use empirical models to predict battery degradation. These models generally entail fewer parameters to be identified and are computationally less intensive to solve. Nonetheless, empirical models can suffer from the accuracy point-of-view as they are constrained to predict degradation trends introduced by certain degradation modes. Another common technique in battery life prediction is to utilize purely data-driven methods, such as machine learning (ML) algorithms, which also have shown promising results in the literature on rapid health predictions. However, these methods require large volumes of experimental data for training and testing ML models to ensure accuracy. In addition, data-driven methods are likely to extrapolate poorly to conditions beyond their training data and are indifferent towards the underlying degradation mechanisms. Recently, physics-informed machine learning (PI-ML) methods have garnered significant attention. They integrate physics-based or empirical models (developed based on physics) with a data-driven approach and allow one to train ML models on a smaller set of experimental data.To the best of the authors’ knowledge, the performance comparison between first-principle and empirical models when integrated within PI-ML remains unclear. Therefore, in this work, we aim to compare these two models when applied to prognostics (capacity forecasting and remaining useful life prediction) of a set of Li-ion batteries. To perform this study, we generate aging data for 40 Li-ion coin cells cycled under randomized conditions. Each cell undergoes a three-step charging stage followed by a two-step discharge stage. After obtaining the aging data, we will develop two PI-ML models, one equipped with a physics-based model and another with a set of empirical models. Both PI-ML models in this work will follow the sequential integration approach, where the training data for the final PI-ML model come from both experimental and computational data, the latter of which are obtained from the physics-based or empirical models. The parameters for the physics-based and empirical models are identified from another set of experimental data. Finally, the PI-ML models will be tested with experimental data obtained at different cycling conditions. The data flow for the sequential architecture of PI-ML is shown in the attached figure. This comparative study will help identify the performance of physics-based and empirical models when integrated into PI-ML. The main performance metric considered in this work is each model’s ability to extrapolate beyond the experimental training data set, hence aiding the final PI-ML model in generalizing to conditions not covered by its experimental training data set. Figure 1

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

SummaryAccurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

In recent years, the microbiome has been recognized as an important factor associated with cardiovascular disease (CVD), which is the leading cause of human mortality worldwide. Disparities in gut microbial compositions between individuals with and without CVD were reported, whereby, we hypothesized that utilizing such microbiome-based data for training with supervised machine learning (ML) models could be exploited as a new strategy for evaluation of cardiovascular health. To test our hypothesis, we analyzed the metagenomics data extracted from the American Gut Project. Specifically, 16S rRNA reads from stool samples of 478 CVD and 473 non-CVD control samples were analyzed using five supervised ML algorithms: random forest (RF), support vector machine with radial kernel (svmRadial), decision tree (DT), elastic net (ENet) and neural networks (NN). Thirty-nine differential bacterial taxa (LEfSe: LDA > 2) were identified between CVD and non-CVD groups. ML classifications, using these taxonomic features, achieved an AUC (area under the receiver operating characteristic curve) of ~0.58 (RF). However, by choosing the top 500 high-variance features of operational taxonomic units (OTUs) for training ML models, an improved AUC of ~0.65 (RF) was achieved. Further, by limiting the selection to only the top 25 highly contributing OTU features to reduce the dimensionality of feature space, the AUC was further significantly enhanced to ~0.70 (RF). In summary, this study is the first to demonstrate the successful development of a ML model using microbiome-based datasets for a systematic diagnostic screening of CVD.

The integration of machine learning (ML) algorithms into audit processes represents a significant advancement in the field of auditing, offering substantial benefits in terms of efficiency, accuracy, and risk management. This review examines the transformative potential of ML in auditing, highlighting its key benefits and the challenges that must be addressed to fully leverage its capabilities. Machine learning algorithms, with their ability to analyze large datasets and identify patterns, enhance the accuracy and thoroughness of audits. Traditional auditing methods often rely on sampling and manual checks, which can miss anomalies and fraudulent activities. In contrast, ML algorithms can process entire datasets, uncovering subtle patterns and irregularities that may indicate fraud or errors. This comprehensive analysis reduces the risk of oversight and improves the reliability of audit findings. One of the primary benefits of ML in auditing is its capacity for anomaly detection. ML models can be trained on historical data to understand normal financial behavior and flag deviations that might signify irregularities. This ability to detect anomalies in real-time enables auditors to identify potential issues promptly, reducing the time lag between occurrence and detection of fraud. Predictive analytics, powered by ML, further enhances audit processes by forecasting future risks based on historical data. This proactive approach allows auditors to anticipate and mitigate risks before they materialize, contributing to more robust risk management strategies. Despite these advantages, integrating ML into audit processes presents several challenges. Ensuring data quality and integrity is crucial, as ML algorithms are only as good as the data they analyze. Poor-quality data can lead to inaccurate predictions and conclusions. Additionally, the "black box" nature of some ML algorithms can pose transparency issues, making it difficult for auditors to explain how specific conclusions were reached, which is critical for stakeholder trust and regulatory compliance. Another significant challenge is the potential for algorithmic bias. ML models can inadvertently perpetuate existing biases in the data, leading to unfair or skewed audit outcomes. Continuous monitoring and validation of ML algorithms are necessary to detect and mitigate such biases. In conclusion, while integrating machine learning algorithms into audit processes offers substantial benefits in terms of accuracy, efficiency, and risk management, it also necessitates careful attention to data quality, transparency, and bias mitigation. Addressing these challenges is essential to fully realize the potential of ML in enhancing audit practices. Keywords: Benefits, Challenges, Audit Processes, Algorithms, ML.

This study presents a comparative analysis of the classification performance of facial and emotion recognition systems using Machine Learning (ML) and Deep Learning (DL) algorithms. The primary objective of this work is to evaluate the applicability of emotion recognition in fields such as psychotherapy and crime analysis, using the FER-2013 dataset.The study was conducted with ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Decision Trees, and Gradient Boosting, as well as the DL algorithm, Convolutional Neural Network (CNN). Supported by the application of feature selection, preprocessing, and feature extraction techniques, the model’s performance was measured using standard metrics such as accuracy, precision, F1-score, and AUC-ROC.The experimental results showed that the highest classification accuracy (46.61%) was achieved with the CNN model. While the ML models generally offered lower accuracy, they provided advantages in terms of computational efficiency in specific scenarios.This study aims to contribute to the literature by providing a comparative analysis of ML and DL algorithms and by highlighting the effect of data preprocessing on performance. The findings set targets for future work, such as real-time system integration and hybrid model development.

<p><strong>Abstract: </strong></p> <p><strong> </strong>The increasing development of GNSS techniques enables solving geodetic problems on both local and global scales. Parallelly, complex algorithms have been proposed and can also be solved well by Machine Learning (ML). However, ML techniques are sometimes not sensitive enough to gain results with a high probability for some cases, like sparse data or non-stationary GNSS time series. In this study, we use a combination of Human and Machine learning (H&M) to improve the classification performance of continuous GNSS stations. First, 427 permanent GNSS stations are obtained from the EUREF network to train ML models. The models are then applied to classify the quality of 939 continuous observation stations from two projects, EIFEL and IPOC, carried out by the German Research Centre for Geosciences (GFZ), Potsdam, Germany. Next, we independently validate the ML models' reality through a MATLAB program, GNSS metadata, and seismic data. Finally, all data of these 1366 stations are used to re-train the ML models. The main criteria to classify are the number of outliers, jumps in GNSS time series, root mean square errors, observation time-spans, and stability of the crustal motion velocity fields. Applying the approach of the H&M combination improves the performance of the ML models up to 92% while using only ML methods remains ~68%. These ML-based classification models can be applied to estimate the quality of permanent GNSS stations and to manage big databases. The result is the basis for selecting suitable control and monitoring stations in crustal deformation monitoring as well as in civil and industrial applications.</p> <p><strong>Keywords: </strong></p> <p>GNSS station classification, Machine learning, Human & Machine learning combination.</p>

The pooled cohort equations (PCE) predict atherosclerotic cardiovascular disease (ASCVD) risk in patients with characteristics within prespecified ranges and has uncertain performance among Asians or Hispanics. It is unknown if machine learning (ML) models can improve ASCVD risk prediction across broader diverse, real-world populations. We developed ML models for ASCVD risk prediction for multi-ethnic patients using an electronic health record (EHR) database from Northern California. Our cohort included patients aged 18 years or older with no prior CVD and not on statins at baseline (n = 262,923), stratified by PCE-eligible (n = 131,721) or PCE-ineligible patients based on missing or out-of-range variables. We trained ML models [logistic regression with L2 penalty and L1 lasso penalty, random forest, gradient boosting machine (GBM), extreme gradient boosting] and determined 5-year ASCVD risk prediction, including with and without incorporation of additional EHR variables, and in Asian and Hispanic subgroups. A total of 4309 patients had ASCVD events, with 2077 in PCE-ineligible patients. GBM performance in the full cohort, including PCE-ineligible patients (area under receiver-operating characteristic curve (AUC) 0.835, 95% confidence interval (CI): 0.825–0.846), was significantly better than that of the PCE in the PCE-eligible cohort (AUC 0.775, 95% CI: 0.755–0.794). Among patients aged 40–79, GBM performed similarly before (AUC 0.784, 95% CI: 0.759–0.808) and after (AUC 0.790, 95% CI: 0.765–0.814) incorporating additional EHR data. Overall, ML models achieved comparable or improved performance compared to the PCE while allowing risk discrimination in a larger group of patients including PCE-ineligible patients. EHR-trained ML models may help bridge important gaps in ASCVD risk prediction.

The future 6G network will be able to connect massive and various unmanned vehicles (UxVs) into the network, bringing novel requirements toward UxV network security and data privacy. Deploying machine learning (ML)-based intrusion detection on UxVs can be one promising approach. The conventional approach cannot fulfill security and privacy requirements, as the structure can be more flexible, and there may be a lack of a central node in the 6G-based UxV network. Moreover, UxV clients can exhibit high heterogeneity with extremely class-imbalanced training samples. With the presence of federated learning (FL), UxVs can collaborate to train and update ML models while preserving data privacy in the UxV security critical scenario. However, most past works focused on centralized approaches, using cloud servers to assist intrusion detection and service deployments. This article introduces an FL framework with a decentralized design for training ML models on UxVs using intrusion detection as a study case. This scheme can be more flexible, as UxV clients can collaborate to train and synchronize models without the central server. Simulation experiments were conducted to evaluate the efficiency of the proposed approach. Experimental results and theoretical analysis confirm that the proposed method outperforms baseline models with FedAvg and local-trained client models.

The present article aims to investigate a comparative effect of the mechanical properties and tribological operating variables (applied load and sliding distance) on the tribo‐performance of graphene incorporated woven glass fabric reinforced epoxy (GFRE) composites. Computational and data‐driven machine learning (ML) approach has been extensively applied to examine the advancement of the tribological systems. For the study, tribo‐mechanical data, gathered from previous investigations by the present authors, have been used as well. Accordingly, a predictive model, based on the ML algorithms for predicting specific wear rates (SWR), has been developed. Further, a co‐relation between the SWR and the mechanical properties of materials has been determined. For the investigation process, three ML approaches, have been applied, viz., artificial neural network (ANN), Random Forest (RF), and Gradient Boosting Machine (GBM). Feature score analysis has been done as well revealing that variation of the sliding distance, interlaminar shear strength (ILSS) and product of tensile strength (s) and elongation (e) significantly influence the SWR prediction. The trained ML models can predict the tribo‐performance from mechanical properties variables and tribological test conditions, which is impossible with conventional two‐parameter correlations. During the present study, the coefficient of determination ( R 2 value) in different models was found to be 0.9883, 0.9884, and 0.9762 for the ANN, RF, and GBM, respectively. The best‐performing model was RF.

The majority of ungauged regions around the world are in protected areas and rivers with non-perennial flow regimes, which are vital to water security and conservation. There is a limited amount of ground data available in such regions, making it difficult to obtain streamflow information. This study examines how in situ streamflow datasets in data- rich regions can be used to extrapolate streamflow information into regions with poor data availability. These data-rich regions include North America (987 catchments), South America (813 catchments), and Western Europe (457 catchments). South Africa and Central Asia are defined as data-poor regions. We obtained 81 catchments and 133 catchments for these two data-poor regions, respectively, and assumed they are pseudo ungauged regions for our analysis. We trained machine learning (ML) algorithms using climate and catchments attributes input variables in data-rich (i.e., source) regions and analyzed the possibility of using these pre-trained ML models to estimate climatological monthly streamflow over data-poor (i.e., target) regions. We found that including diverse climate and catchment attributes in training data sets can greatly improve ML algorithms' performance regardless of significant geographical distance between input datasets. The pre-trained ML models over North America and South America could be used effectively to estimate streamflow over data-poor regions. This study provides insight into the selection of input datasets and ML algorithms with different sets of hyperparameters for a geographic streamflow extrapolation.

Accurate estimation of reference evapotranspiration (ETo) plays a vital role in irrigation and water resource planning. The Penman–Monteith method recommended by the Food and Agriculture Organization (FAO PM56) is widely used and considered a standard to calculate ETo. However, FAO PM56 cannot be used with limited meteorological variables, so it is compulsory to choose an alternative model for ETo estimation, which requires fewer variables. This study built ten machine learning (ML) models based on multi-function, neural network, and tree-based structure against the FAO PM56 method. For this purpose, time series temperature data on a monthly scale are only used to train ML models. The developed ML models were applied to estimate ETo at different test stations and the obtained results were compared with the FAO PM56 method to verify and validate their performance in ETo estimation for the selected stations. In addition, multiple statistical indicators, including root-mean-square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (r) were calculated to compare the performance of each ML model on ETo estimation. Among the applied ML models, the ETo tree boost (TB) ML model outperformed the other ML models in estimating ETo in diverse climatic conditions based on statistical indicators (R2, NSE, r, RMSE, and MAE). Moreover, the observed R2, NSE, and r were the highest for the TB ML model, while RMSE and MAE were found to be the lowest at the study sites compared to other applied ML models. Lastly, ETo point data yielded from the TB ML model was used in an interpolation process to create monthly and annual ETo maps. Based on the ETo maps, this study suggests mainly a focus on areas with high ETo values and proper irrigation scheduling of crops to ensure water sustainability.

The purpose of this analysis is to assess the use of machine learning (ML) algorithms in the prediction of postoperative outcomes, including complications, recurrence, and death in transsphenoidal surgery. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically reviewed all papers that used at least one ML algorithm to predict outcomes after transsphenoidal surgery. We searched Scopus, PubMed, and Web of Science databases for studies published prior to May 12, 2021. We identified 13 studies enrolling 5,048 patients. We extracted the general characteristics of each study; the sensitivity, specificity, area under the curve (AUC) of the ML models developed as well as the features identified as important by the ML models. We identified 12 studies with 5,048 patients that included ML algorithms for adenomas, three with 1807 patients specifically for acromegaly, and five with 2105 patients specifically for Cushing's disease. Nearly all were single-institution studies. The studies used a heterogeneous mix of ML algorithms and features to build predictive models. All papers reported an AUC greater than 0.7, which indicates clinical utility. ML algorithms have the potential to predict postoperative outcomes of transsphenoidal surgery and can improve patient care. Ensemble algorithms and neural networks were often top performers when compared with other ML algorithms. Biochemical and preoperative features were most likely to be selected as important by ML models. Inexplicability remains a challenge, but algorithms such as local interpretable model–agnostic explanation or Shapley value can increase explainability of ML algorithms. Our analysis shows that ML algorithms have the potential to greatly assist surgeons in clinical decision making.