Probabilistic Machine Learning and Artificial Intelligence

Efficient seismic fragility analysis method utilizing ground motion clustering and probabilistic machine learning

Although the overall incidence of pediatric oncological diseases tends to increase over the years, it is among the rare diseases of the pediatric population. The diagnosis, treatment, and healthcare management of this group of diseases are important. Prevention of treatment-related complications is vital for patients, particularly in the pediatric population. Nowadays, the use of artificial intelligence and machine learning technologies in the management of oncological diseases is becoming increasingly important. With the advancement of software technologies, improvements have been made in the early diagnosis of risk groups in oncological diseases, in radiology, pathology, and imaging technologies, in cancer staging and management. In addition, these technologies can be used to predict the outcome in chemotherapy treatment of oncological diseases. In this context, this study identifies artificial intelligence and machine learning methods used in the prediction of complications due to chemotherapeutic agents used in childhood cancer treatment. For this purpose, the concepts of artificial intelligence and machine learning are explained in this review. A general framework for the use of machine learning in healthcare and pediatric oncology has been drawn and examples of studies conducted on this topic in pediatric oncology have been given. IMPACT: Artificial intelligence and machine learning are advanced tools that can be used to predict chemotherapy-related complications. Algorithms can assist clinicians' decision-making processes in the management of complications. Although studies are using these methods, there is a need to increase the number of studies on artificial intelligence applications in pediatric clinics.

The utilization of learning analytics to identify at-risk students for early intervention has exhibited promising results. However, most predictive models utilized to address this issue have been based on non-probabilistic machine learning models. In response, this study incorporated probabilistic machine learning for two reasons: (1) to facilitate the inclusion of domain knowledge, and (2) to enable the quantification of uncertainty in model parameters and predictions. The study developed a five-stage, probabilistic logistic regression model to identify at-risk students at different stages throughout the academic calendar. Rather than predicting a student’s final or exam mark, the model was focused on predicting the at-risk probabilities for subsequent assessments—specifically, the probability of a student failing an upcoming assessment. The model incorporated student engagement data from Moodle, as well as demographic and student performance data. The study’s findings indicate that the significance and certainty of student engagement and demographic variables decreased after incorporating student-performance variables, such as assignments and tests. The most effective week for identifying at-risk students was found to be week 6, when the accuracy was 92.81%. Furthermore, the average level of uncertainty exhibited by the models decreased by 60% from stage 3 to 5, indicating more reliable predictions at later than earlier stages. The study highlights the potential of a probabilistic machine learning model to aid instructors and practitioners in identifying at-risk students, and thereby to enhance academic outcomes.

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.

Machine learning is a powerful data-driven technology for regression and classification tasks in numerous branches of science and technology. With probabilistic machine learning, we can approximate the probability density function of a given digital data set, unlocking the sampling of new and realistic data. We implement probabilistic machine learning as a statistical-based data augmentation tool to effectively improve the performance of a classifier on scarce data sets. Limited data are the norm in most practical situations, as is the case with core samples retrieved from unconventional oil and gas reservoirs. Obtaining and properly labeling rock sample images is a manual and time-consuming task. This leads to scarce data sets that make the lithologic classification of rock samples challenging. We illustrate the technology on images taken from well cores from the Argentinean Neuquén and Austral Basins, where by introducing generated samples in the training stage of a machine-learning classifier, we achieve a 5% higher accuracy with respect to the baseline obtained using the same number of samples from the original data set. We reach these results by proposing a lightweight workflow that can be run on very limited hardware. Our workflow is an alternative to classical data augmentation strategies for dealing with incomplete information, such as geometric transformations.

Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm

Deep underground rock formations are widely used for geological carbon dioxide (CO2) storage due to their large-scale, long-term capacity. However, geophysical and petrophysical complexities can lead to challenges such as gas migration and potential leaks, posing risks to groundwater and subsurface systems. Recent advancements increasingly integrate Artificial Intelligence (AI) and Machine Learning (ML) to mitigate these risks and enhance CO2 storage efficiency. This review explores ML applications in geological CO2 storage, highlighting recent advancements and their implications. ML has demonstrated effectiveness in enhancing CO2 storage efficiency. However, the complexities of geological storage necessitate further improvements in ML model applicability, particularly in real-world projects. Since ML models depend on the availability of data, ensuring high data integrity and quality is crucial. Moreover, CO2 storage projects involve significant risks and uncertainties, making advanced probabilistic ML models essential for quantifying uncertainties and mitigating associated risks. Lastly, integrating real-time monitoring systems with sensor data and ML algorithms can enhance anomaly detection, provide early warnings, and enable timely interventions. Addressing these challenges will strengthen the adoption of advanced ML techniques in geological CO2 storage, improving efficiency, safety, and reliability.

For a multiple-fluid system, CG models capable of accurately predicting the interfacial properties as a function of curvature are still lacking. In this work, we propose a new probabilistic machine learning (ML) model for learning CG potentials for binary fluids. The water-hexane mixture is selected as a typical immiscible binary liquid-liquid system. We develop a new CG force field (FF) using the Shinoda-DeVane-Klein (SDK) FF framework and compute parameters in this CG FF using the proposed probabilistic ML method. It is shown that a standard response-surface approach does not provide a unique set of parameters, as it results in a loss function with multiple shallow minima. To address this challenge, we develop a probabilistic ML approach where we compute the probability density function (PDF) of parameters that minimize the loss function. The PDF has a well-defined peak corresponding to a unique set of parameters in the CG FF that reproduces the desired properties of a liquid-liquid interface. We compare the performance of the new CG FF with several existing FFs for the water-hexane mixture, including two atomistic and three CG FFs with respect to modeling the interface structure and thermodynamic properties. It is demonstrated that the new FF significantly improves the CG model prediction of both the interfacial tension and structure for the water-hexane mixture.

Artificial Intelligence in Nephrology: Clinical Applications and Challenges

Artificial intelligence for patient scheduling in the real-world health care setting: A metanarrative review

Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives

<p>As the number of underground activities increase, the need for better understanding of the geospatial properties become more and more essential for correct engineering designs and optimal decision making. However, gathering subsurface data is still an extremely costly and imprecise endeavour. Geological modelling has played a crucial role for years helping to understand and correlate the complex geometries encountered underground but single deterministic models fail to capture all possible configurations given the limited data. Probabilistic machine learning allows to integrate domain knowledge and observations of the physical world on a rigorous and consistent manner. Inferences to the probabilistic model implements an automatic learning-from-observations process.</p><p> </p><p>In this work, we show how by embedding state-of-the-art implicit interpolants into probabilistic frameworks, we can integrate the information of distinct data sets in one single common earth model. We will present results from a minimal working example to introduce Bayesian statistics, to full 3-D probabilistic inversions. All the models used for this demonstration are implemented in the open-source library GemPy ( www.gempy.org) allowing full reproducibility of the results.</p><p> </p>

Probabilistic graphical models are a fundamental tool in probabilistic modeling, machine learning and artificial intelligence. They allow us to integrate in a natural way expert knowledge, physical modeling, heterogeneous and correlated data and quantities of interest. For exactly this reason, multiple sources of model uncertainty are inherent within the modular structure of the graphical model. In this paper we develop information-theoretic, robust uncertainty quantification methods and non-parametric stress tests for directed graphical models to assess the effect and the propagation through the graph of multi-sourced model uncertainties to quantities of interest. These methods allow us to rank the different sources of uncertainty and correct the graphical model by targeting its most impactful components with respect to the quantities of interest. Thus, from a machine learning perspective, we provide a mathematically rigorous approach to correctability that guarantees a systematic selection for improvement of components of a graphical model while controlling potential new errors created in the process in other parts of the model. We demonstrate our methods in two physico-chemical examples, namely quantum scale-informed chemical kinetics and materials screening to improve the efficiency of fuel cells.

Predicting undergraduate student evaluations of teaching using probabilistic machine learning: The importance of motivational climate

This research examines what impact the Big Data Processing Framework (BDPF) has on Artificial Intelligence (AI) applications within Corporate Marketing Communication (CMC), and thereby the research question stated is: What is the potential impact of the BDPF on AI applications within the CMC tactical and managerial functions? To fulfill the purpose of this research, a qualitative research strategy was applied, including semi-structured interviews with experts within the different fields of examination: management, AI technology and CMC. The findings were analyzed through performing a thematic analysis, where coding was conducted in two steps. AI has many useful applications within CMC, which currently mainly are of the basic form of AI, so-called rule-based systems. However, the more complicated communication systems are used in some areas. Based on these findings, the impact of the BDPF on AI applications is assessed by examining different characteristics of the processing frameworks. The BDPF initially imposes both an administrative and compliance burden on organizations within this industry, and is particularly severe when machine learning is used. These burdens foremost stem from the general restriction of processing personal data and the data erasure requirement. However, in the long term, these burdens instead contribute to a positive impact on machine learning. The timeframe until enforcement contributes to a somewhat negative impact in the short term, which is also true for the uncertainty around interpretations of the BDPF requirements. Yet, the BDPF provides flexibility in how to become compliant, which is favorable for AI applications. Finally, BDPF compliance can increase company value, and thereby incentivize investments into AI models of higher transparency. The impact of the BDPF is quite insignificant for the basic forms of AI applications, which are currently most common within CMC. However, for the more complicated applications that are used, the BDPF is found to have a more severe negative impact in the short term, while it instead has a positive impact in the long term.