A hybrid analytical and machine learning framework for groundwater resources management

Failure in brittle materials led by the evolution of micro- to macro-cracks under repetitive or increasing loads is often catastrophic with no significant plasticity to advert the onset of fracture. Early failure detection with respective location are utterly important features in any practical application, both of which can be effectively addressed using artificial intelligence. In this paper, we develop a supervised machine learning (ML) framework to predict failure in an isothermal, linear elastic and isotropic phase-field model for damage and fatigue of brittle materials. Time-series data of the phase-field model is extracted from virtual sensing nodes at different locations of the geometry. A pattern recognition scheme is introduced to represent time-series data/sensor nodes responses as a pattern with a corresponding label, integrated with ML algorithms, used for damage classification with identified patterns. We perform an uncertainty analysis by superposing random noise to the time-series data to assess the robustness of the framework with noise-polluted data. Results indicate that the proposed framework is capable of predicting failure with acceptable accuracy even in the presence of high noise levels. The findings demonstrate satisfactory performance of the supervised ML framework, and the applicability of artificial intelligence and ML to a practical engineering problem, i.,e, data-driven failure prediction in brittle materials.

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 (ML) algorithms, as the core technology in Artificial Intelligence (AI) applications, such as self-driving vehicles, make important decisions by performing a variety of data classification or prediction tasks. Attacks on data or algorithms in AI applications can lead to misclassification or misprediction, which can fail the applications. For each dataset separately, the parameters of ML algorithms should be tuned to reach a desirable classification or prediction accuracy. Typically, ML experts tune the parameters empirically, which can be time consuming and does not guarantee the optimal result. To this end, some research suggests an analytical approach to tune the ML parameters for maximum accuracy. However, none of the works consider the ML performance under attack in their tuning process. This paper proposes an analytical framework for tuning the ML parameters to be secure against attacks, while keeping its accuracy high. The framework finds the optimal set of parameters by defining a novel objective function, which takes into account the test results of both ML accuracy and its security against attacks. For validating the framework, an AI application is implemented to recognize whether a subject's eyes are open or closed, by applying k-Nearest Neighbors (kNN) algorithm on her Electroencephalogram (EEG) signals. In this application, the number of neighbors (k) and the distance metric type, as the two main parameters of kNN, are chosen for tuning. The input data perturbation attack, as one of the most common attacks on ML algorithms, is used for testing the security of the application. Exhaustive search approach is used to solve the optimization problem. The experiment results show k = 43 and cosine distance metric is the optimal configuration of kNN for the EEG dataset, which leads to 83.75% classification accuracy and reduces the attack success rate to 5.21%.

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.

Over the past decades, Egypt has made significant progress toward improving the health outcomes of its population through the implementation of the Universal Health Insurance (UHI) Law enacted in 2018. The implementation of the new UHI Law of 2018 requires a major transformation to the health financing system. In addition to, it is essential to move into the digitalization age of healthcare in Egypt.Artificial Intelligence (AI) and Machine Learning (ML) tools have the potential to improve the efficiency of healthcare system, decision-making processes, and optimizing healthcare financing systems. Additionally, AI applications can also set priorities for the resource allocation of governments that are involved in achieving Sustainable Development Goals (SDGs).This study provides an overview of the integration the Machine Learning (ML) approaches into healthcare financing functions to overcome the issues related to the health financing system that accelerate achieving UHC in the Egyptian health system.To achieve this objective, the researchers used a systematic Literature Review by reviewing full papers, charting extracted data, and screening and summarizing findings. This Literature Review was done through the database of PubMed, Google Scholar, Scopus, and Web of Science from January 2000 to December 2023.This research revealed that ML and AI applications have a significant impact on many aspects of the health financing domain in the Egyptian health system that supports health Digital Transformation and the role of E-Health Company, the first health digitalization company in Egypt for progress toward UHC in 2030.

Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams

Background and purposeThis study aims to develop a robust and user-friendly prediction model for radiation-induced hypothyroidism (RIHT) in nasopharyngeal carcinoma (NPC) patients.Materials and methodsNPC patients treated with IMRT between Jan. 2019 and Dec. 2021 were randomly assigned to a training cohort (n = 328) and a validation cohort (n = 141) at a ratio of 7:3. A total of 33 clinical and dose-volume variables were collected. Significant variables (p < 0.05) were identified through univariate Cox analysis and further refined using a 101-combination machine learning (ML) framework to develop a robust predictive model. The model was subsequently simplified through multivariate Cox analysis and a nomogram. Finally, the performance of the model was evaluated using the C-index, calibration plots, and decision curve analysis.ResultsUsing a 101-combination ML framework, we developed a predictive model for RIHT in NPC. The Coxboost + RSF method with 11 predictors achieved the best performance (C-index: 0.91 [training], 0.71 [validation]). A simplified five-variable model (pre-treatment TSH, TSH-to-thyroid-volume ratio, age, V45, V20) was created via multi-cox regression, with a C-index of 0.80 [training] and 0.71 [validation]. High-risk patients had significantly higher three-year RIHT incidences (72.3% vs. 18.6%, p < 0.0001) in the training cohort, and 67.9% versus 24.4% (p < 0.0001) in the validation cohort. The model showed strong calibration and confirmed clinical utility through decision curve analysis, supporting its use in personalized treatment planning.ConclusionWe developed a ML framework to identify key predictive factors for RIHT, which was simplified into a five-variable model for clinical use, offering a robust tool for predicting RIHT risk in decision-making.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13014-025-02725-5.

Hospital-acquired complications (HACs) have an adverse impact on patient recovery by impeding their path to full recovery and increasing healthcare costs. The aim of this study was to create a HAC risk prediction machine learning (ML) framework using hospital administrative data collections within North Metropolitan Health Service (NMHS), Western Australia. A retrospective cohort study was performed among 64,315 patients between July 2020 to June 2022 to develop an automated ML framework by inputting HAC and the healthcare site to obtain site-specific predictive algorithms for patients admitted to the hospital in NMHS. Univariate analysis was used for initial feature screening for 270 variables. Of these, 77 variables had significant relationship with any HAC. After excluding non-contemporaneous data, 37 variables were included in developing the ML framework based on logistic regression (LR), decision tree (DT) and random forest (RF) models to predict occurrence of four specific HACs: delirium, aspiration pneumonia, pneumonia and urinary tract infection. All models exhibited similar performance with area under the curve scores around 0.90 for both training and testing datasets. For sensitivity, DT and RF exceeded LR performance while on average, false positives were lowest for LR-based models. Patient's length of stay, Charlson Index, operation length and intensive care unit stay were common predictors. Integrating ML-based risk detection systems into clinical workflows can potentially enhance patient safety and optimise resource allocation. LR-based models exhibited best performance. We have successfully developed a "real-time" risk prediction model, where patient risk scores are calculated and reviewed daily.

Eutectic alloys have garnered significant attention due to their promising mechanical and physical properties, as well as their technological relevance. However, the discovery of eutectic compositionally complex alloys (ECCAs) (e.g. high entropy eutectic alloys) remains a formidable challenge in the vast and intricate compositional space, primarily due to the absence of readily available phase diagrams. To address this issue, we have developed an explainable machine learning (ML) framework that integrates conditional variational autoencoder (CVAE) and artificial neutral network (ANN) models, enabling direct generation of ECCAs. To overcome the prevalent problem of data imbalance encountered in data-driven ECCA design, we have incorporated thermodynamics-derived data descriptors and employed K-means clustering methods for effective data pre-processing. Leveraging our ML framework, we have successfully discovered dual- or even tri-phased ECCAs, spanning from quaternary to senary alloy systems, which have not been previously reported in the literature. These findings hold great promise and indicate that our ML framework can play a pivotal role in accelerating the discovery of technologically significant ECCAs.

In hydraulic engineering, accurately predicting energy dissipation at vertical drop structures is crucial for ensuring structural integrity and optimizing design efficiency. This study presents a comparative analysis of energy dissipation calculations using two methodologies: numerical modeling through Computational Fluid Dynamics (CFD) and advanced Machine Learning (ML) techniques. The study is performed at vertical drop structure at Lower Gogera Branch Canal (LGBC) in Pakistan, experimental data has been utilized alongside CFD simulations to establish a reliable benchmark for energy dissipation at a vertical drop of 3.69 meters, with a discharge rate of 63.73 m³/s. The CFD method employs the Volume of Fluid (VOF) technique to simulate flow dynamics, providing precise insights into energy dissipation. In parallel, we implement various ML algorithms, including decision trees, random forests, and neural networks, to model energy dissipation based on key input parameters such as flow rate, height of fall, and downstream conditions. The performance of the ML models is evaluated against the numerical results, allowing for a comprehensive comparison of their predictive capabilities. Initial findings indicate that while the CFD model shows a high level of accuracy with discrepancies within 5% of empirical measurements, the ML techniques also demonstrate significant potential in capturing complex, nonlinear relationships inherent in energy dissipation phenomena. The use of ML not only provides a faster alternative for predictions but also offers insights into optimizing design parameters in hydraulic structures. This research highlights the transformative impact of AI methodologies on hydraulic engineering, illustrating how integrating ML can enhance traditional modeling techniques. The results contribute to the ongoing discourse on the application of artificial intelligence in water resources management, paving the way for more efficient and sustainable engineering practices.

The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic flow prediction is essential for mitigating congestion, improving road safety, optimizing traffic signal control, and enhancing overall transportation efficiency. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of traffic management, offering sophisticated algorithms capable of modeling, analyzing, and predicting complex traffic patterns with high accuracy. The application of AI in traffic flow prediction leverages vast amounts of real-time and historical data to generate precise forecasts, supporting data-driven decision-making by urban planners and traffic control authorities. The prediction of traffic flow involves analyzing time-series data that exhibit nonlinear, dynamic, and often stochastic behavior. Traditional statistical models, such as autoregressive integrated moving average (ARIMA), have proven to be limited in handling the high dimensionality and variability inherent in traffic systems. In contrast, AI algorithms possess the capacity to learn and adapt from complex data inputs without the need for explicit programming, making them particularly suitable for traffic-related applications. AI algorithms used in traffic flow prediction can be broadly categorized into machine learning (ML) and deep learning (DL) approaches. Machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees, and random forests have demonstrated effectiveness in short-term traffic prediction tasks. These algorithms are capable of identifying hidden patterns in traffic data and adjusting to changes in traffic behavior over time. Ensemble methods, which combine the strengths of multiple learning models, further enhance prediction accuracy and robustness. Deep learning algorithms, a subfield of AI inspired by the human brain’s neural architecture, have shown exceptional performance in capturing spatial-temporal dependencies in traffic data. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks and gated recurrent units (GRUs), are widely used for their ability to process sequential data and retain information over extended time intervals. Convolutional neural networks (CNNs) are employed to extract spatial features from traffic sensor data or road network imagery. Hybrid models that integrate CNNs with RNNs have achieved high levels of predictive precision by simultaneously learning spatial and temporal correlations. In addition to supervised learning methods, unsupervised and reinforcement learning techniques are also applied in traffic flow prediction. Clustering algorithms, such as k-means and DBSCAN, assist in identifying traffic patterns, while reinforcement learning models optimize adaptive traffic signal control systems by learning optimal actions through environmental interaction. This study explores the different types of AI algorithms used in traffic flow prediction, examining their theoretical foundations, structural differences, and practical applications. It aims to evaluate the comparative advantages of various algorithms in addressing the challenges of real-time traffic prediction in increasingly complex transportation networks.

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

Groundwater depletion in the Bist-Doab region of the Punjab State of India is a significant threat to sustainable agricultural practices, underscoring the need for effective management strategies. Modelling groundwater heads is essential for understanding groundwater flow dynamics, trends, and their interaction with surface water. It helps assess the aquifer's health, prevent over-extraction and contamination, and predict ambient groundwater responses to extreme events such as droughts or floods.&amp;#160;Inaccurate groundwater models, which overestimate or underestimate groundwater levels and fail to capture temporal fluctuations, hinder proper water management. These errors lead to suboptimal decisions regarding water allocation and resource sustainability and ultimately impact crop yields and water availability.This study aims to integrate physically-based models, such as those developed by MODFLOW, with machine-learning algorithms to improve prediction accuracy and support more informed decision-making. MODFLOW was used to simulate groundwater flow under both steady-state and transient conditions, utilizing field hydrogeological data from existing literature. Machine learning (ML) models, including Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANN), were trained and tested on historical groundwater levels and meteorological data to enhance prediction accuracy.The methodology employs data-driven models (DDMs) as error-correcting tools for the physically-based models. Historical residuals, calculated as the difference between observed and simulated groundwater heads, were used as inputs alongside features such as well location coordinates, simulated groundwater heads, and time of measurements. ML techniques such as SVR, RF and ANN were used to train the DDMs, which learn systematic errors in the physically-based model by analysing these historical residuals. Outputs include predicted systematic errors and updated groundwater heads, where corrections are applied to the initial simulated values. The effectiveness of the DDMs relies on the structure and patterns of the residuals in the physically-based model, with strong correlations between the groundwater heads, leading to better error correction and improved predictive accuracy.Results show that integrating MODFLOW with ML, significantly reduces model error compared to traditional simulation approaches. The combined model effectively captures both seasonal fluctuations and long-term trends in groundwater levels, leading to more accurate predictions. The developed framework provides a reliable tool for improving groundwater resource management and optimizing water allocation strategies, ultimately supporting the sustainable management of groundwater in agriculturally stressed regions like Bist-Doab.

Introduction: Heart failure (HF) in critical care settings exhibits complex interactions with glycemic dysregulation. Traditional metrics fail to capture dynamic glucose fluctuations linked to poor outcomes. This study defines ICU-specific glycemic targets and establishes a machine learning (ML) framework integrating novel glucose indicators to predict 28-day mortality. Methods: Cohort: 18,211 ICU HF patients (MIMIC-IV, 2008-2019). Analysis: 1. Glycemic Metrics: Calculated time-weighted average glucose (TWABG), time-in-range (TIR), time-below-range (TBR), time-above-range (TAR) 2. Association Studies: Restricted cubic splines (RCS) to identify optimal glycemic ranges. Multivariable Cox regression with adjustment and subgroup analysis with interaction testing. 3. ML Modeling: Dual-stage feature selection: Lasso regression and Boruta algorithm. Model development: Developed models using 14 algorithms; SHAP interpretation applied to best-performing model (highest ROC-AUC). Validation: SHAP analysis of an exemplar case demonstrated individualized mortality risk interpretation without formal model validation. Results: 1. Optimal Glycemic Ranges: Patients within the optimal TWABG range (85–133 mg/dL) demonstrated the lowest mortality (15.09%,P< 0.001), with improved survival associated with both higher time-in-range (TIR) proportion and absence of glycemic excursions (time-below-range [TBR] or time-above-range [TAR] exposure) 2. ML Prediction: Feature selection via Lasso regression identified 30 predictors. Boruta algorithm confirmed 29 significant features. The optimized XGBoost model predicted 28-day mortality with AUC 0.779 (0.762-0.796) Conclusions: Maintaining blood glucose within TWABG 85–133 mg/dL and maximizing TIR significantly reduces 28-day mortality in ICU HF patients, while avoiding excursions independently improves survival. Our ML framework, integrating dynamic glucose metrics with clinically selected features, accurately predicted mortality risk (AUC 0.779). Findings advocate ICU-specific glucose management and demonstrate ML’s potential for risk stratification and personalized interventions.

Indian summer monsoon governs agricultural productivity, water resources, and economic activity affecting 1.4 billion people, yet accurate prediction remains challenging due to complex non-linear dynamics involving atmosphere-ocean interactions, land surface processes, and stochastic forcing. Traditional numerical weather prediction models solve deterministic partial differential equations but struggle capturing uncertainty quantification and sub-grid scale processes. Machine learning approaches demonstrate skill learning patterns from historical data but lack physical constraints and interpretability. This research develops hybrid framework integrating stochastic differential equations representing atmospheric dynamics with deep learning capturing non-linear relationships, enabling probabilistic monsoon forecasting with improved accuracy and uncertainty quantification. The research formulated Indian monsoon system as coupled stochastic differential equation system: dX_t = μ(X_t, θ)dt + σ(X_t, θ)dW_t, where X_t represents state variables (rainfall, temperature, humidity, pressure), μ denotes drift coefficient encoding deterministic dynamics, σ represents diffusion coefficient capturing stochastic fluctuations, W_t denotes Wiener process, and θ parameters learned from data. Long Short-Term Memory neural networks parameterized drift and diffusion coefficients enabling flexible representation of complex non-linear relationships while preserving stochastic process structure. Training utilized 40 years of daily rainfall data (1980-2019) from 64 India Meteorological Department stations across eight monsoon regions, supplemented by atmospheric reanalysis including temperature, humidity, wind, and sea surface temperature. Numerical integration employed Euler-Maruyama scheme with adaptive time stepping ensuring stability and accuracy. Model evaluation compared hybrid stochastic-machine learning approach against traditional numerical prediction, pure stochastic models, and pure machine learning on 2017-2019 validation period. Performance metrics included root mean square error, mean absolute error, R-squared, continuous ranked probability score, and skill scores assessing probabilistic forecast quality. Results demonstrated hybrid model achieved RMSE of 2.1 mm daily rainfall compared to 3.2 mm for pure stochastic models and 5.4 mm for deterministic numerical predictions, representing 34% accuracy improvement. R-squared reached 0.967 indicating excellent predictive skill. Probabilistic forecasts provided well-calibrated uncertainty estimates with continuous ranked probability score of 1.8 mm outperforming deterministic predictions. Spatial analysis revealed consistent performance across regions with coastal areas (R²=0.967) and northeast India (R²=0.956) achieving highest accuracy, while northwest plains showed moderate performance (R²=0.912) due to continental influences. Temporal validation demonstrated robust monsoon onset prediction with 89.3% accuracy detecting rainfall commencement within ±3 days. Computational efficiency analysis revealed hybrid model required 2.3 hours training on GPU hardware compared to 48 hours for traditional numerical models, enabling operational forecasting. The research establishes mathematical framework for physics-informed machine learning combining stochastic calculus with deep learning, providing interpretable probabilistic forecasts supporting agricultural planning, water resource management, and disaster preparedness across India's monsoon-dependent economy.