Probabilistic Modeling Approach Research Articles

Applying Bayesian Networks for Detecting Bank Fraudulent Transactions in Nigeria

Bank fraud is an increasingly prevalent issue, causing substantial financial losses for both financial institutions and customers. Also, with the increasing prevalence of bank fraud, especially facilitated by online banking and digital payments, there is a pressing need for potential expertise deployment and advanced fraud detection techniques in real-world banking systems. This study specified the Bayesian Network models for detecting fraudulent bank transactions. Bayesian Networks offer a probabilistic graphical modeling approach that can effectively capture complex relationships and dependencies within financial data. Thus, the research aimed at developing a custom Bayesian network model trained on a large dataset of bank transactions comprising over one million bank transactions to classify instances as fraudulent or non-fraudulent. Python 3.11.10 was used for the graphical representations. Down-sampling was employed to reduce the dataset from its initial size of 1 million observations to 17,650 observations, ensuring a balanced representation of both fraud and non-fraud instances. Various estimation techniques: Maximum Likelihood, Bayesian Networks, and Expectation Maximization were employed and evaluated to learn the model parameters. The model's performance was measured using metrics: accuracy, precision, recall, F1-score, and ROC-AUC. The Bayesian Networks estimator achieved an overall accuracy of 66.18%, precision of 67.73%, F1-score of 64.06%, ROC-AUC of 66.13%, Recall of 60.77%, Sensitivity of 60.77% and Specificity of 71.50%. Also, the Maximum Likelihood achieved an overall Accuracy of 66.77%, Precision of 69.22%, F1-score of 63.96%, ROC-AUC of 66.71%, Recall of 59.45%, Sensitivity of 59.45% and Specificity of 73.97%. Likewise, the Expectation Maximization achieved an overall Accuracy of 66.83%, Precision of 69.31%, F1-score of 64.00%, ROC-AUC of 66.77%, Recall of 59.45%, Sensitivity of 59.45% and Specificity of 74.09%. On the confusion matrix, the model correctly classified 1272 instances as Non-Fraudulent transactions (True Negative). Also, it was observed that the model incorrectly classified 507 instances as fraudulent transactions when they were Non-Fraudulent (False Positive). The model similarly incorrectly classified 687 instances as Non-Fraudulent transactions when they were Fraudulent (False Negative). Finally, it correctly classified 1064 instances as fraudulent transactions (True Positive). These results demonstrated the Bayesian Network's ability to identify fraudulent transactions while minimizing false alarms accurately. The findings highlight the potential of Bayesian Networks as a robust framework for fraud detection in the banking sector, contributing to enhanced security and reduced financial losses. The developed model can be introduced into existing banking systems to strengthen fraud prevention strategies.

Joint probabilistic modeling approach for harmonic and three-phase unbalanced disturbance sources

Harmonics and three-phase imbalance are two common disturbances in power systems that interact with and affect each other, leading to more complex impacts on power system performance. To comprehensively and accurately analyze the distribution and interaction mechanisms of harmonics and three-phase imbalance, this study investigated the joint emission level of harmonic and three-phase unbalanced disturbance sources (H-TU-DS). This paper proposes a joint probabilistic emission level modeling approach. The power, harmonic, and three-phase unbalanced data of H-TU-DS are multidimensional, and the feature dataset is filtered by calculating multiple correlation coefficients to achieve dimensionality reduction without losing data information. Considering the different operating characteristics of the H-TU-DS, an improved fuzzy C-means (FCM) algorithm is applied to classify operating conditions, enabling a comprehensive and accurate analysis of the joint emission level. Based on the nonparametric kernel density function, joint probability modeling of H-TU-DS is performed using an adaptive bandwidth improvement method, effectively enhancing the accuracy of the modeling process. Finally, the effectiveness and accuracy of the proposed methods for feature dataset filtering, operating condition classification, and joint probability modeling are demonstrated through comparisons with both simulated and actual data. The proposed model facilitates the prediction and assessment of the impact of H-TU-DS on power quality after grid connection, enabling the effective prevention of power-quality problems through advanced treatment.

Accelerating the design of the effective surface of pressing tools with probabilistic inverse modeling approaches

Accelerating the design of the effective surface of pressing tools with probabilistic inverse modeling approaches

Use of Bayesian probabilistic model approach in common bean varietal recommendation

Use of Bayesian probabilistic model approach in common bean varietal recommendation

Optimizing biomass supply chains: A probabilistic approach to managing uncertainties in southwest Nigeria

Efficient and sustainable use of biomass resources is crucial to meet the increasing demand for bio-based products and renewable energy. The biomass supply chain, which includes harvesting, collecting, logistics, storage, and pre-treatment, faces challenges due to uncertainties such as market fluctuations, equipment availability, weather conditions, and transportation constraints. These uncertainties often hinder the optimisation of the supply chain. This research work explores the performance of the biomass supply chain by optimizing operations while accounting for these uncertainties. Nigeria is faced with power issues and there are resources to combat the problem through generation of cleaner energy from biomass. Using mathematical modelling, the study evaluates the impact of uncertainty on key performance areas like feedstock supply, inventory management, transportation efficiency, and processing capacity. The research demonstrates the importance of incorporating uncertainty-aware solutions to minimize risks and improve the flexibility of the biomass supply chain. Sensitivity analyses and case studies shows that the proposed probabilistic modelling approach provides valuable insights into system vulnerabilities and effective strategies for optimizing operations under uncertain conditions. The findings highlight the potential of this approach to improve decision making, resource allocation, and promote sustainable practices in the biomass sector. Ultimately, the study contributes to advancing biomass supply chain management, paving the way for a more resilient and efficient use of bioresources.

Pricing weekly motor insurance drivers’ with behavioral and contextual telematics data

Telematics boxes integrated into vehicles are instrumental in capturing driving data encompassing behavioral and contextual information, including speed, distance travelled by road type, and time of day. These data can be amalgamated with drivers' individual attributes and reported accident occurrences to their respective insurance providers. Our study analyzes a substantial sample size of 19,214 individual drivers over a span of 55 weeks, covering a cumulative distance of 181.4 million kilometers driven. Utilizing this dataset, we develop predictive models for weekly accident frequency. As anticipated based on prior research with yearly data, our findings affirm that behavioral traits, such as instances of excessive speed, and contextual data pertaining to road type and time of day significantly aid in ratemaking design. The predictive models enable the creation of driving scores and personalized warnings, presenting a potential to enhance traffic safety by alerting drivers to perilous conditions. Our discussion delves into the construction of multiplicative scores derived from Poisson regression, contrasting them with additive scores resulting from a linear probability model approach, which offer greater communicability. Furthermore, we demonstrate that the inclusion of lagged behavioral and contextual factors not only enhances prediction accuracy but also lays the foundation for a diverse range of usage-based insurance schemes for weekly payments.

Microwave-based and convective drying of cabbage (Brassica oleracea L. var capitata L.): Computational intelligence modeling, thermophysical properties, quality and mid-infrared spectrometry

This study assessed and compared the impact of hot air oven drying (HAD) at 50, 60 and 70 °C and microwave drying (MWD) at 195, 307 and 521 W on the kinetics of thermophysical properties and quality of dried cabbage. The thermophysical properties were computed using established model equations. The proximate composition, bioactive compounds, antioxidant capacity (AOC), color and functional groups were analyzed using Association of official analytical chemists (AOAC) test protocol, High performance liquid chromatography (HPLC), Ultraviolet-visible (UV–Vis) spectrometry and Attenuated total reflectance coupled to Fourier transformed infrared spectroscopy (ATR-FTIR), respectively. The moisture ratio was modeled using probabilistic computational intelligence modeling approaches. The results revealed that only density, thermal diffusivity and thermal effusivity appeared to be kinetically controlled by moisture content. Stepwise linear regression (SLR) model and Gaussian process regression (GPR) model were more accurate in simulating the moisture ratio. Increasing HAD temperature preserved all of the proximate compositions except carbohydrates. Similarly, increasing MWD power preserved all the proximate compositions except crude fiber. Increasing HAD temperature from 50 to 60 °C and 60–70 °C decreased the total flavonoid content (TFC) and total phenolic content (TPC) by 21.31 % and 13.60 %, respectively but increased the AOC from 27.2 to 39.6 %. Increasing MWD power levels from 195 to 307 W and 307–521 W decreased TFC by 13.2 % and 0.7 %, respectively but increased the TPC by 9.72 % and 13.14 %, thus AOC increased from 35.2 to 45.7 %. MWD presented higher AOC relative to HAD. Drying at 50 °C and 307 W gave the highest whitening index in HAD and MWD. ATR-FTIR analysis revealed the formation of new compounds by thermal effect. SLR and GPR were more accurate in modeling the drying kinetics. HAD and MWD affected the quality parameters of cabbage differently, thus the choice of the dryer type will depend on the parameters of interest.

Urban Infrastructure Vulnerability to Climate-Induced Risks: A Probabilistic Modeling Approach Using Remote Sensing as a Tool in Urban Planning

Urban Infrastructure Vulnerability to Climate-Induced Risks: A Probabilistic Modeling Approach Using Remote Sensing as a Tool in Urban Planning

The impact of the proposed revised Australia's microbiological monitoring programme for beef and sheep meat exported to the EU.

The European Commission asks scientific and technical assistance from EFSA to determine the impact of the revision of the Australian monitoring programme on its ability to detect microbiological contamination. Considering that, in 2010, the European Commission determined the current Australian monitoring programme to be equivalent to the EU requirements for microbiological monitoring further to an EFSA scientific assessment, the current and proposed programmes were described and the total number of alerts was compared using a probabilistic modelling approach. In the current programme, only beef and sheep carcasses are monitored using three-class moving window sampling plans, while in the proposed programme, carcass, bulk meat, primal and offal are monitored using four two-class sampling plans and Salmonella testing is excluded. The models revealed that the current programme provides a higher number of alerts for APC, while the proposed monitoring programme provides a higher number of alerts for E. coli. For APC and E. coli combined, the mean, 5th and 95th centiles of the uncertainty distribution of the total number of alerts in the current and the proposed monitoring programme are 201 [179, 227] and 172 [149, 194] for beef, and 199 [175, 222] and 2897 [2795, 3008] for sheep, respectively. For Salmonella, there are no alerts for the proposed programme since sampling is excluded while for the current programme, the estimated mean, 5th and 95th centiles of the uncertainty distribution of the number of alerts for a 5-year period were 143 [126, 144] for heifer/steer, 1.6 [0, 4] for cow/bull and 0 [0, 0] for lamb/sheep. Overall, for APC and E. coli, the estimated total number of alerts was similar (beef) or higher (sheep) for the proposed compared to the current programme. In contrast, Salmonella sampling is excluded from the proposed programme and thus cannot detect the number of current alerts.

Research on Probabilistic Load Modeling and Simulation Model of Electric Vehicle Charging Station

Abstract The stochastic nature of electric vehicle charging station loads poses certain challenges in establishing relevant load models. This paper introduces a probabilistic load modeling approach that considers factors such as the time electric vehicles begin charging, daily travel distance, charging power, and charging duration. By comprehensively studying the influencing factors of charging station load modeling and combining Monte Carlo simulation with probabilistic statistical analysis, a comprehensive probability load model for typical charging stations is developed. Building upon this, a charging load simulation model is constructed using Simulink. This model can generate daily load curves consistent with the probability load model, making a positive contribution to the study of the impact of electric vehicles on the power grid.

A Data-Driven Model with Hysteresis Compensation for I2RIS Robot.

Retinal microsurgery is a high-precision surgery performed on a delicate tissue requiring the skill of highly trained surgeons. Given the restricted range of instrument motion in the confined intraocular space, snake-like robots may prove to be a promising technology to provide surgeons with greater flexibility, dexterity, and positioning accuracy during retinal procedures such as retinal vein cannulation and epiretinal membrane peeling. Kinematics modeling of these robots is an essential step toward accurate position control. Unlike conventional manipulators, modeling these robots does not follow a straightforward method due to their complex mechanical structure and actuation mechanisms. The hysteresis problem can especially impact the positioning accuracy significantly in wire-driven snake-like robots. In this paper, we propose a data-driven kinematics model using a probabilistic Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) approach with a hysteresis compensation algorithm. Experimental results on the two-degree-of-freedom (DOF) integrated robotic intraocular snake (I2RIS) show that the proposed model with the hysteresis compensation can predict the snake tip bending angle for pitch and yaw with 0.45° and 0.39° root mean square error (RMSE), respectively. This results in overall 60% and 70% improvements of accuracy for yaw and pitch over the same model without the hysteresis compensation.

Multi-disciplinary seismic resilience modeling for developing mitigation policies and recovery planning

The multi-disciplinary data and information available at a community level comprise the foundation of natural hazard resilience modeling. These data enable and inform mitigation and recovery planning decisions prior to and following damaging events such as earthquakes. This paper presents a multi-disciplinary seismic resilience modeling methodology to assess the vulnerability of the built environment and economic systems. This methodology can assist decision-makers with developing effective mitigation policies to improve the seismic resilience of communities. Two complementary modeling strategies are designed to examine the impacts of scenario earthquakes from a combined engineering and economic perspective. The engineering model is developed using a probabilistic fragility-based modeling approach and is analyzed using Monte Carlo (MC) simulations subject to seismic multi-hazard, including simulated ground shaking and resulting liquefaction of the soil, to quantify the physical damage to buildings and electric power substations (EPS). The outcome of the analysis is subsequently used as input to repair and recovery models to quantify repair cost and recovery time metrics for buildings and as input to functionality models to estimate the functionality of individual buildings and substations by accounting for their interdependency. The economic model consists of a spatial computable general equilibrium (SCGE) model that aggregates commercial buildings into sectors for retail, manufacturing, services, etc., and aggregates residential buildings into a wide range of household groups. The SCGE model employs building functionality estimates to quantify the economic losses. The outcomes of this integrated modeling consist of engineering and economic impact metrics, which are used to investigate mitigation actions to help inform a community on approaches to achieve its resilience goals. An illustrative case study of Salt Lake County (SLC), Utah, developed through an extensive collaborative partnership and engagement with SLC officials, is presented. The results demonstrate the effectiveness of the proposed methodology in quantifying the loss and functional recovery of infrastructure systems, the impacts on capital stock, employment, and household income and the effect of various mitigation strategies in reducing the losses and functional recovery time subject to earthquakes with varying intensities.

Human-Robot Task Handoff: A Probabilistic Modeling Approach Explored through Cooperative Drawing

Recent research in human-robot interaction explores the potential for human-machine collaboration in surgical procedures, dividing tasks into manual and automatable subtasks. This paper investigates the task of handoff detection, crucial for the success of robot-assisted surgery, focusing on the creation of a synthetic dataset which can be used for training and benchmarking models for this task. We present a dataset of parabolas, simulating cooperative drawing between a human and a robot, with variations in drawing rates and added noise. The study demonstrates the applicability of HMMs in determining handoff points, laying the groundwork for future research in human-machine collaborative surgery. The dataset, along with the provided code and raw data, are provided as a resource for future research. Finally, we discuss the limitations of the dataset and suggest directions for future research, emphasizing the need for higher-dimensional and real-world datasets.

Development and validation of a biomechanically fidelic surgical training knee model.

Knee arthroplasty technique is constantly evolving and the opportunity for surgeons to practice new techniques is currently highly dependent on the availability of cadaveric specimens requiring certified facilities. The high cost, limited supply, and heterogeneity of cadaveric specimens has increased the demand for synthetic training models, which are currently limited by a lack of biomechanical fidelity. Here, we aimed to design, manufacture, and experimentally validate a synthetic knee surgical training model which reproduces the flexion dependent varus-valgus (VV) and anterior-posterior (AP) mechanics of cadaveric knees, while maintaining anatomic accuracy. A probabilistic finite element modeling approach was employed to design physical models to exhibit passive cadaveric VV and AP mechanics. Seven synthetic models were manufactured and tested in a six-degree-of-freedom hexapod robot. Overall, the synthetic models exhibited cadaver-like VV and AP mechanics across a wide range of flexion angles with little variation between models. In the extended position, two models showed increased valgus rotation (<0.5°), and three models showed increased posterior tibial translation (<1.7 mm) when compared to the 95% confidence interval (CI) of cadaveric measurements. At full flexion, all models showed VV and AP mechanics within the 95% CI of cadaveric measurements. Given the repeatable mechanics exhibited, the knee models developed in this study can be used to reduce the current reliance on cadaveric specimens in surgical training.

Enhancing medical image classification with generative AI using latent denoising diffusion probabilistic model and wiener filtering approach

In the evolving landscape of medical diagnostics, a paradigm shift is being catalysed by the advent of generative artificial intelligence. Medical X-ray, CT and MRI images are essential diagnostic tools used by healthcare professionals to assess various musculoskeletal conditions. However, obtaining a sufficient number of medical images for training deep learning models can be challenging due to limited access to labelled data. Hence, the authors propose a novel Latent diffusion process for synthesizing medical images that closely resemble real patient images, aiming to address the challenge of limited access to labelled data in medical diagnostics. Leveraging deep learning and generative modelling techniques, this method synthesizes high-fidelity images that closely mimic real patient scans. By introducing noise and subsequently training the model to denoise, the approach captures intricate patterns inherent in authentic medical images. Among the four datasets utilized, the Diabetes Retinopathy dataset demonstrates superior performance, achieving the highest Mean Structural Similarity Index (MSSIM) score of 0.57 (compared to the dataset baseline of 0.62) and an accuracy of 93.75% when passed through the proposed pipeline. The Cataract dataset, registered a MSSIM score of 0.51 (versus the dataset baseline of 0.53) and an accuracy score of 97.52%, while the Knee OA dataset follows closely with MSSIM and accuracy scores of 0.65 (in contrast to the dataset baseline of 0.63) and 68.66% respectively. The results obtained are then compared with the results generated by the other state of the art models.

A market driven identification of current and emerging skills requiring an MBA degree: a topic modelling approach

ABSTRACT The emerging field of Labour Market Intelligence focuses on extracting real-time information for professional skills. Online job posting websites provide unique opportunities to gather such intelligence, useful for job seekers and academic administrators in charge of preparing the future labour force. We propose a probabilistic topic modelling approach, incorporating occupational ontologies, to extract skillsets required for jobs needing a Master of Business Administration (MBA) degree. Analysing LinkedIn job postings in Pennsylvania, USA, we identified 28 job functions and associated skillsets confirming known demands and revealing new ones not yet identified in government and private labour market reports. By aggregating these skills, we can align MBA curricula with market needs, offering a superior alternative to conventional surveys. Our results are comparable with other popular neural models. Our approach provides improved computational efficiency with simpler methods Additionally, this methodology can be applied globally in other fields with existing occupational ontologies.

Assessment on fast simulation of wind-driven pollutant dispersion around a street canyon with regime-switching Markov chain

High-density cities shorten commutes but also create proximity to air pollution sources. Sensors coupled with simulations can achieve real-time monitoring and risk assessment, and the recently emerging data-driven models provide opportunities that allow minimizing the computation burden of physical models. This study investigates the use of a time inhomogeneous Markov chain (CLF-MC) to fast simulation of wind-driven pollutant dispersion around a street canyon. The CLF-MC model is trained and tested using synthetic datasets obtained from large eddy simulations (LES). The LES model is validated using a benchmark wind tunnel test, which yields correlation coefficients above 0.95. The results show that the CLF-MC can faithfully reproduce the cumulative distribution of air pollution concentrations at the receptor site using the optimal model configuration identified in this study. For the hold-out test dataset, the release condition is a time-varying release strength, and the CLF-MC shows improved prediction results compared to the baseline models, i.e. the weighted average deviation is 15 % and 51 % smaller, respectively. In comparison to LES, the CLF-MC model is 4.8 × 106 times faster. These findings provide scientific references for fast and accurate wind-driven pollutant dispersion simulation using the probabilistic modelling approach.

A novel probabilistic modeling for multilateral random attacks in cyber‐physical system reliability analysis

AbstractCyber‐Physical Systems (CPS) are at the forefront of the intersection between information technology and physical operations, revolutionizing industries such as communication, automation, control, and intelligent transportation. However, evaluating the reliability of CPS, especially in the presence of multilateral random attacks, poses a complex challenge. This paper introduces an inventive probabilistic modeling approach to enhance the analysis of CPS reliability under the influence of multilateral random attacks. We provide an overview of fundamental CPS concepts and their significant role in modern society, followed by a comprehensive exploration of multilateral random attacks. Our novel probabilistic modeling approach is presented as a means to improve the accuracy and robustness of CPS reliability assessments when faced with multilateral random attacks. Numerical experiments and simulations validate the effectiveness of our innovative approach, emphasizing its potential to fortify the reliability of Cyber‐Physical Systems.

N − 1 Security Criteria Based Integrated Deterministic and Probabilistic Framework for Composite Power System Reliability

Unpredictable variations in load demand and unanticipated component failures are progressively impacting the operation of modern power systems, making system evaluation more stochastic in nature. Although deterministic approaches were formerly the norm for determining system status, probabilistic approaches have greatly improved the capacity to capture the stochastic behavior characteristic of power system operations. The presented work in the paper recommends the use of probabilistic modelling approaches with deterministic approaches, highlighting their crucial function in augmenting the reliability and security of contemporary power systems to unanticipated failures. In this paper, N − 1 security criteria based reliability of the composite power system (CPS) is proposed using an integrated deterministic and probabilistic framework (D-P) considering outage of the transmission line. For the deterministic approach (DA), line overloading on available lines is determined using the static security index (SSI). For the probabilistic approach (PA), reliability indices such as expected loss of power (ELOP), expected frequency of contingency (EFOC), expected loss of load (ELOL), probability of load curtailment (PLC), and expected duration of load curtailments (EDLC) are calculated. Further, for each contingency, a performance index is determined using both approaches to assess the severity of the contingency that occurred on the power system. Based on the N − 1 security criteria based reliability analysis using an integrated D-P framework, a credible critical set of transmission lines is obtained, which can serve as important information to system operators. The proposed techniques have been tested on IEEE 24 bus reliability test system (RTS).

Multimorbidity impacts cardiovascular disease risk following percutaneous coronary intervention: latent class analysis of the Melbourne Interventional Group (MIG) registry

BackgroundMultimorbidity is strongly associated with disability or functional decline, poor quality of life and high consumption of health care services. This study aimed (1) To identify patterns of multimorbidity among patients undergoing first recorded percutaneous coronary intervention (PCI); (2) To explore the association between the identified patterns of multimorbidity on length of hospital stay, 30-day and 12- month risk of major adverse cardiac and cerebrovascular events (MACCE) after PCI.MethodsA retrospective cohort study of the Melbourne Interventional Group (MIG) registry. This study included 14,025 participants who underwent their first PCI from 2005 to 2015 in Victoria, Australia. Based on a probabilistic modelling approach, Latent class analysis was adopted to classify clusters of people who shared similar combinations and magnitude of the comorbidity of interest. Logistic regression models were used to estimate odd ratios and 95% confidence interval (CI) for the 30-day and 12-month MACCE.ResultsMore than two-thirds of patients had multimorbidity, with the most prevalent conditions being hypertension (59%) and dyslipidaemia (60%). Four distinctive multimorbidity clusters were identified each with significant associations for higher risk of 30-day and 12-month MACCE. The cluster B had the highest risk of 30-day MACCE event that was characterised by a high prevalence of reduced estimated glomerular filtration rate (92%), hypertension (73%) and reduced ejection fraction (EF) (57%). The cluster C, characterised by a high prevalence of hypertension (94%), dyslipidaemia (88%), reduced eGFR (87%), diabetes (73%) and reduced EF (65%) had the highest risk of 12-month MACCE and highest length of hospital stay.ConclusionHypertension and dyslipidaemia are prevalent in at least four in ten patients undergoing coronary angioplasty. This study showed that clusters of patients with multimorbidity had significantly different risk of 30-day and 12-month MACCE after PCI. This suggests the necessity for treatment approaches that are more personalised and customised to enhance patient outcomes and the quality of care delivered to patients in various comorbidity clusters. These results should be validated in a prospective cohort and to evaluate the potential impacts of these clusters on the prevention of MACCE after PCI.