High Prediction Accuracy Research Articles

Development and Validation of a Nomogram for Predicting Systemic Inflammatory Response Syndrome Following Percutaneous Nephrolithotomy.

Objective: To develop and validate a nomogram for predicting the occurrence of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL), aiming to enhance clinical decision-making and treatment planning. Methods: Clinical data of 1,047 patients undergoing PCNL at a single-center hospital between 2017 and 2023 were retrospectively analyzed. Independent risk factors influencing SIRS occurrence were identified through multi-variable logistic regression analysis, and a predictive model was constructed. The model's accuracy and reliability were evaluated through internal training and validation set. Results: Multi-variable regression analysis identified six key predictive factors: gender, diabetes, urine culture results, stone surface, staghorn stones, and operative time, leading to the establishment of a nomogram predictive model. Internal validation and validation set data demonstrated the model's high predictive accuracy and reliability, with areas under the receiver operating characteristic curve of 0.718 and 0.723, respectively. Conclusions: A nomogram predictive model for assessing SIRS following PCNL was successfully developed and validated. This model provides clinicians with a valuable tool for personalized treatment planning and implementing preventive measures.

Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids.

The accurate and rapid diagnosis of bacterial vaginosis (BV) is crucial due to its high prevalence and association with serious health complications, including increased risk of sexually transmitted infections and adverse pregnancy outcomes. Although widely used, traditional diagnostic methods have significant limitations in subjectivity, complexity, and cost. The development of a novel diagnostic approach that integrates SERS with ML offers a promising solution. The CNN model's high prediction accuracy, cost-effectiveness, and extraordinary rapidity underscore its significant potential to enhance the diagnosis of BV in clinical settings. This method not only addresses the limitations of current diagnostic tools but also provides a more accessible and reliable option for healthcare providers, ultimately enhancing patient care and health outcomes.

Risk prediction of computer investment database information management system based on machine learning algorithms

In recent years, with the continuous development of the financial market, the risk prediction of computer investment database information management systems (IMS) has high practical value. At present, there are risk issues in the information management system, which may cause drawbacks to investment data processing. To address these issues, this article used Machine Learning (ML) algorithms to analyze the risk prediction of computer investment database IMS. This article introduced and utilized typical Self-Organizing Map (SOM) and Artificial Neural Network (ANN) combination algorithms, regression algorithms, and Gradient Boosting Decision Tree (GBDT) algorithms to compare and analyze the prediction accuracy of these three algorithms. This article found that the GBDT algorithm has the highest prediction accuracy. Through a large amount of experimental data, it has been proven that the average testing accuracy using regression algorithms was 3.5% higher than that using neural network algorithms. It was found that the average test accuracy using the GBDT algorithm was 7.2% higher than the average test accuracy using the regression algorithm. The study also explores the combination of physiological and behavioral data collected by wearable devices to provide more comprehensive risk assessment and decision support, which provides an important reference for the optimization of enterprise risk management. Through this innovative data source integration, this paper provides a new perspective for the application and development of machine learning algorithms in computer investment database IMS.

Establishment of multiple machine learning prognostic model for gene differences between primary tumors and lymph nodes in luminal breast cancer.

This study aimed to explore the correlation between primary tumors (PT) and paired metastatic lymph nodes (LN) and to develop a predictive model to provide evidence for forecasting patient prognoses. We obtained single-cell and bulk transcriptome data from the Gene Expression Omnibus database. Furthermore, mRNA transcriptomic data, encompassing 112 normal tissues and 1066 breast cancer samples, along with survival, clinical, and mutation information for breast cancer patients, were acquired from The Cancer Genome Atlas (TCGA). Employing a machine learning integration framework incorporating ten distinct algorithms, we developed and validated a prognostic model. We constructed a prognostic model named Lymph Node Metastasis-Related Scores (LMRS) using 26 differentially expressed genes trained on eight TCGA datasets. Across validation sets, the model demonstrated a high C-index, signifying its stability and effectiveness, outperforming 64 models from other studies. Notably, cytolytic activity and T cell co-stimulation were downregulated in the high LMRS group, alongside a downregulation of immune cells, including B cells, CD8 + T cells, iDCs, and TILs. Similarly, most immune checkpoints exhibited a decreasing trend with high LMRS expression. Finally, we selected the hub biomarkers PGK1 and HSP90 for pathological verification. Results indicated higher expression levels in PT and LN compared to normal and benign tumors, with higher expression levels in LN than in PT. This comprehensive analysis sheds light on gene expression differences between PT and LN in breast cancer, culminating in the development of a multiple-gene prognostic model with high clinical accuracy for prognosis prediction.

Non-Destructive Detection and Visualization of Chlorophyll Content in Cherry Tomatoes Based on Hyperspectral Technology and Machine Learning

The cherry tomato has an important economic value and an increasingly broad market, and the chlorophyll content of cherry tomato leaves can directly reflect the plant’s photosynthetic ability, thus indirectly reflecting its growth status. Therefore, this paper proposes a regression detection method for chlorophyll in cherry tomato leaves by combining machine learning and hyperspectral technology to realize non-destructive, fast, and more accurate detection. Firstly, Moving-Average (MA) preprocessing was chosen as the pretreatment method for this paper, and three regression models of principal component regression (PCR), random forest (RF), and partial least squares regression (PLSR) were established with leaf chlorophyll under different nitrogen concentrations. The CARS_PLSR algorithm has the highest prediction accuracy with accuracy, precision, RMSEC, and RMSEP of 0.8790, 0.9187, 2.9581, and 2.5578, respectively. The study examined the impact of various nitrogen concentrations on the chlorophyll content of cherry tomato leaves, and it was found that the high concentration of nitrogen inhibited the SPAD value of cherry tomato leaves more than that of the low concentration, and the optimal concentration of nitrogen fertilization for tomatoes was 300 mg·L−1. Finally, a regression model was established by using CARS-PLSR combined with the pseudo-color map technology, and a distribution map of chlorophyll content in different SPAD value gradients of cherry tomato leaves was obtained, which could visualize the distribution of chlorophyll and its distribution sites in the leaves and understand the growth status of cherry tomatoes. The distribution of chlorophyll content in different SPAD values of cherry tomato leaves was obtained by using the CARS-PLSR regression model combined with pseudo-color map technology, which can visualize the distribution of chlorophyll in the leaves and the parts of distribution and understand the growth condition of cherry tomatoes. Finally, the optimal model is applied in conjunction with a sprayer to automate fertilizer application.

Checkpoint data-driven GCN-GRU vehicle trajectory and traffic flow prediction

With the development of information technology, massive traffic data-driven short-term traffic situation analysis of urban road networks has become a research hotspot in urban traffic management. Accurate vehicle trajectory and traffic flow prediction can provide technical support for vehicle path planning and road congestion warning. Unlike most studies that use GPS data to predict vehicle trajectories, this paper combines the broad coverage, high reliability, and lighter weight of traffic checkpoint data to propose a method that uses trajectory prediction technology to forecast the traffic flow in urban road networks accurately. The method adopts a checkpoint data-driven approach for data collection, combines graph convolutional neural network (GCN) and gated recurrent unit (GRU) models to more effectively learn and extract spatiotemporal correlation features of vehicle trajectories, which significantly improves the accuracy of vehicle trajectory prediction, and uses the output of the trajectory prediction model to forecast traffic flow more accurately. Firstly, transforming the checkpoint data into daily vehicle trajectories with time series characteristics, realizing the vehicle trajectory travel chain division. Secondly, the adjacency matrix is established by using the spatial relationship of each checkpoint, and the feature matrix of the vehicle’s driving trajectory over time is established, which is used as the input of GCN to learn the spatial characteristics of the vehicle while driving on the road network, and then GRU is added to further process the data after GCN training, constructing a GCN-GRU vehicle trajectory prediction model for vehicle trajectory prediction. Finally, the traffic flow of each checkpoint is calculated based on the prediction result of vehicle trajectory and compared with the real checkpoint flow. This paper conducts many experiments on the Qingdao City Shinan district checkpoint dataset. The results show that compared with the single models GCN, GRU, BiGRU, and BiLSTM, the GCN-GRU model has reduced the MAE by 0.75, 0.46, 0.52, and 0.57, and the RMSE by 0.76, 0.52, 0.58, and 0.68, respectively, demonstrating stronger spatial and temporal correlation characteristics and higher prediction accuracy. The MAPE between the forecasted flow and the real flow is 0.18, which verifies the reliability of the proposed method.

All‐Sky Microwave Radiance Observation Operator Based on Deep Learning With Physical Constraints

AbstractSatellite data assimilation relies on the radiative transfer models (RTMs) to establish the relationships between model state variables and satellite radiances. However, atmospheric radiative transfer calculations are computationally expensive, especially when involving multiple‐scattering calculations in cloudy areas. In recent years, deep learning (DL) models have been increasingly applied to emulate and accelerate physical models. This study, for the first time, explores DL techniques to emulate all‐sky radiative transfer in microwave bands. The FengYun‐3E (FY‐3E) Microwave Humidity Sounder‐2 (MWHS‐2) was selected as the target instrument due to its comprehensive spectral coverage, with the radiative transfer for TOVS scattering module (RTTOV‐SCATT) serving as the reference model. Three DL architectures were trained and compared, including multilayer perceptron (MLP), Bidirectional Long Short‐Term Memory with Attention (BiLSTM‐Attention), and Transformer. The BiLSTM‐Attention architecture demonstrated superior performance in both clear‐sky and cloudy radiance simulations. This may be attributed to its bidirectional recurrent structure resembling physical radiative transfer processes and the attention mechanism's ability to link MWHS‐2 channels with corresponding vertical layers. Although DL models achieve high accuracy in forward prediction, they often struggle with instability in Jacobian calculations. To address this issue, the trained BiLSTM‐Attention model was fine‐tuned using the reference model Jacobians as physical constraints. The fine‐tuned BiLSTM‐Attention model accurately characterized radiance sensitivities to temperature, water vapor, and hydrometeors under different cloud conditions, indicating its potential to serve as a radiance observation operator in data assimilation and physical retrieval applications.

Explainable AI: Enhancing Interpretability of Machine Learning Models

Explainable Artificial Intelligence (XAI) is emerging as a critical field to address the “black box” nature of many machine learning (ML) models. While these models achieve high predictive accuracy, their opacity undermines trust, adoption, and ethical compliance in critical domains such as healthcare, finance, and autonomous systems. This research explores methodologies and frameworks to enhance the interpretability of ML models, focusing on techniques like feature attribution, surrogate models, and counterfactual explanations. By balancing model complexity and transparency, this study highlights strategies to bridge the gap between performance and explainability. The integration of XAI into ML workflows not only fosters trust but also aligns with regulatory requirements, enabling actionable insights for stakeholders. The findings reveal a roadmap to design inherently interpretable models and tools for post-hoc analysis, offering a sustainable approach to democratize AI.

Integrative bioinformatics analysis reveals novel insights into osteoarthritis pathogenesis and diagnostic biomarkers

BackgroundOsteoarthritis (OA) is a prevalent joint disorder characterized by degeneration and inflammation. Understanding its molecular mechanisms is crucial for diagnosis and treatment.MethodsWe employed bioinformatics analyses to study OA using gene expression data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis were conducted. Enrichment analyses were performed to elucidate the biological significance of identified genes. Additionally, signature genes were identified using LASSO regression analysis, and a diagnostic nomogram was developed. qRT-PCR was conducted to confirm the expression levels of signature genes.ResultsWe identified 200 differentially expressed genes (DEGs) and a lightgreen module strongly correlated with OA. Within this module, 97 core genes were identified. Fifteen core lipopolysaccharide-related genes (LRGs) were found, enriched in immune and inflammatory pathways. Three hub genes (CCL3, ZFP36, and CCN1) emerged as potential biomarkers for OA diagnosis, with a nomogram showing high predictive accuracy, and validated by using clinical samples. Gene set enrichment analysis (GSEA) revealed distinct signaling pathways associated with the signature genes. Immunological analysis indicated altered immune profiles in OA, with the signature genes influencing immune cell infiltration and immune response pathways.ConclusionOur study provides insights into OA pathogenesis and identifies potential diagnostic biomarkers. The developed nomogram shows promise for accurate OA diagnosis. Furthermore, the signature genes play crucial roles in modulating the immune microenvironment in OA, suggesting their therapeutic potential.Clinical trial numberNot applicable.

Optimizing Urban Traffic Flow through Advanced Tensor Analysis and Multilinear Algebra: A Computational Approach to Enhancing Smart City Dynamics

Growing traffic congestion is a worldwide problem that collides against the aims of environmental sustainability, economic productivity, and the quality of life in cities. This research proposes a new computational framework for traffic management that integrates advanced tensor analysis and methods from multilinear algebra. We have developed and validated a new predictive model that greatly improves the optimization of traffic flows by synthesizing the naturally complex multi-dimensional traffic data analysis. Our results demonstrate that, compared with existing systems, the proposed approach results in higher accuracy of prediction, much improved computational efficiency, and provides scalable and adaptable solutions for application in a wide range of urban habitats. Such research may push the boundaries further on the smart city infrastructures to provide a very well-founded mathematical framework for the dynamics of improved urban mobility through high-level data-oriented information.

Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability

Cancer is one of the diseases that kill the most women in the world, with breast cancer being responsible for the highest number of cancer cases and consequently deaths. However, it can be prevented by early detection and, consequently, early treatment. Any development for detection or perdition this kind of cancer is important for a better healthy life. Many studies focus on a model with high accuracy in cancer prediction, but sometimes accuracy alone may not always be a reliable metric. This study implies an investigative approach to studying the performance of different machine learning algorithms based on boosting to predict breast cancer focusing on the recall metric. Boosting machine learning algorithms has been proven to be an effective tool for detecting medical diseases. The dataset of the University of California, Irvine (UCI) repository has been utilized to train and test the model classifier that contains their attributes. The main objective of this study is to use state-of-the-art boosting algorithms such as AdaBoost, XGBoost, CatBoost and LightGBM to predict and diagnose breast cancer and to find the most effective metric regarding recall, ROC-AUC, and confusion matrix. Furthermore, previous studies have applied Optuna to individual algorithms like XGBoost or LightGBM, but no prior research has collectively examined all four boosting algorithms within a unified Optuna framework, a library for hyperparameter optimization, and the SHAP method to improve the interpretability of our model, which can be used as a support to identify and predict breast cancer. We were able to improve AUC or recall for all themodels and reduce the False Negative for AdaBoost and LigthGBM the final AUC were more than 99.41% for all models.

Application of deep learning models with spectral data augmentation and Denoising for predicting total phosphorus concentration in water pollution

BackgroundWith the increasing severity of global water pollution, accurate prediction models of water pollution content are critical for effective environmental management. However, traditional methods often exhibit low prediction accuracy for pollutant concentrations when data samples are limited and do not adequately address data noise. This study focuses on predicting total phosphorus (TP) concentrations in the Yangtze River Basin by integrating data augmentation and denoising methods with spectral technology and deep learning, using water samples collected from Wuhan to Anhui, China. MethodThe study utilized an improved Conditional Generative Adversarial Networks (CGAN) for data augmentation, increasing dataset diversity and training effectiveness. Adaptive threshold wavelet denoising is applied to reduce noise and improve data quality. A Convolutional Neural Network (CNN) with a coordinate attention (CA) mechanism is used to extract key spectral features linked to TP concentration prediction. Significant FindingsThis study introduces an innovative approach that combines advanced CGAN-based data augmentation, adaptive threshold wavelet denoising, and a CNN model incorporating a CA mechanism, achieving high accuracy in TP concentration prediction. The proposed model outperforms traditional methods, achieving R² = 0.9805, RMSE = 0.0019, and MAE = 0.0009. This novel method significantly enhances prediction performance, providing an effective solution particularly in scenarios with limited data samples.

Machine learning-based variable selection for clustered credit risk modeling

AbstractSeveral studies have demonstrated the high prediction accuracy of clustered credit risk modeling. In clustered modeling, borrowers are segmented based on their similarities through cluster analysis, and a separate predictive model is developed for each cluster, resulting in increased predictive accuracy. Unambiguously, its effectiveness depends on the quality of the segmentation, which in turn depends primarily on the choice of variables used in the cluster analysis. However, appropriate variable selection for clustering is a major challenge, particularly for high-dimensional data. In the present study, we propose a machine learning-based variable selection method based on theoretical and regulatory considerations. Formally, the most influential risk drivers from a best-in-class machine learning model are identified using Shapley values and employed as clustering variables. Thus, the information of the explanatory variables crucial for the prediction of the dependent variable is already processed during data segmentation, making each individual predictive model more effective. Through a comparative analysis using two real-world credit default datasets, we show that our proposed approach to clustered modeling leads to the highest prediction accuracy among various clustering models.

AI-Powered Weather System with Disaster Prediction

This project develops an AI-powered weather system that aims to improve disaster prediction and preparedness through advanced machine learning algorithms and real-time meteorological data. By collecting data from the OpenWeatherMap API, the system analyzes key environmental indicators, including rainfall, temperature, and wind speed, to detect risks of floods, droughts, cyclones, hailstorms, and wildfires. Through machine learning models like linear regression and threshold-based heuristics, the system achieves high predictive accuracy for natural disasters. This study demonstrates the effectiveness of AI in early warning systems, thus enhancing community preparedness and supporting timely interventions by authorities. The threshold-based heuristics method involved setting predetermined weather condition limits based on historical disaster data, where surpassing these thresholds indicates high disaster risk. Linear regression was used to forecast variables like rainfall and temperature, essential for early warning in disaster management. Both techniques were implemented in tandem, with linear regression providing probabilistic outputs that feed into the heuristic system to improve prediction accuracy

An Efficient Model for Lung Cancer Detection through the Integration of Genetic Algorithm and Machine Learning

Prompt lung cancer detection is essential for patient health. Deep Learning (DL) models have been intensively used for lung cancer screening, as they provide high accuracy in diagnoses. However, DL models require significant computational power, which may not be accessible in all settings. Conventional Machine Learning (ML) models may not produce high prediction accuracy, especially with large data. This study uses a Genetic Algorithm (GA) approach to select optimal features from lung cancer images and reduce their dimensionality. This allows conventional ML models to achieve a high prediction accuracy when classifying medical images while using lower computational power compared with DL models. The proposed model integrates GA along with ML for lung cancer detection. The experimental results show that using GA with a feed-forward neural network classifier achieved high performance, reaching 99.70% classification accuracy.

Identification of diagnostic biomarkers and molecular subtype analysis associated with m6A in Tuberculosis immunopathology using machine learning

Tuberculosis (TB), ranking just below COVID-19 in global mortality, is a highly complex infectious disease involving intricate immunological molecules, diverse signaling pathways, and multifaceted immune processes. N6-methyladenosine (m6A), a critical epigenetic modification, regulates various immune-metabolic and pathological pathways, though its precise role in TB pathogenesis remains largely unexplored. This study aims to identify m6A-associated genes implicated in TB, elucidate their mechanistic contributions, and evaluate their potential as diagnostic biomarkers and tools for molecular subtyping. Using TB-related datasets from the GEO database, this study identified differentially expressed genes associated with m6A modification. We applied four machine learning algorithms—Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Generalized Linear Model—to construct diagnostic models focusing on m6A regulatory genes. The Random Forest algorithm was selected as the optimal model based on performance metrics (area under the curve [AUC] = 1.0, p < 0.01), and a clinical predictive model was developed based on these critical genes. Patients were stratified into distinct subtypes according to m6A gene expression profiles, followed by immune infiltration analysis across subtypes. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses elucidated the biological functions and pathways associated with the identified genes. Quantitative real-time PCR (RT-qPCR) was used to validate the expression of key m6A regulatory genes. Analysis of the GSE83456 dataset revealed four differentially expressed m6A-related genes—YTHDF1, HNRNPC, LRPPRC, and ELAVL1—identified as critical m6A regulators in TB through the Random Forest model. The diagnostic significance of these genes was further supported by a nomogram, achieving a high predictive accuracy (95% confidence interval [CI]: 0.87–0.94). Consensus clustering classified patients into two m6A subtypes with distinct immune profiles, as principal component analysis (PCA) showed significantly higher m6A scores in Group A than in Group B (p < 0.05). Immune infiltration analysis highlighted significant correlations between key m6A genes and specific immune cell infiltration patterns across subtypes. This study highlights the potential of key m6A regulatory genes as diagnostic biomarkers and immunotherapy targets for TB, supporting their role in TB pathogenesis. Future research should aim to further validate these findings across diverse cohorts to enhance their clinical applicability.

Risk classification assessment and early warning of large deformation of soft rock in tunnels based on CNN-LSTM model

Tunnels in the Western Plateau region of China often face numerous problems of high ground stress, as well as highly fractured and weak rock masses in deep sections of the tunnel, which results in an increased risk of large soft rock deformation in tunnels, posing substantial challenges to safe and efficient construction. Hence, risk assessment and establishment of an early safety warning system are essential. By examining factors affecting the large soft rock deformation in tunnels, a set of index system consisting of three primary indicators (i.e., hydrogeological conditions, design factors, and construction factors) and eight secondary indicators (i.e., strength-to-stress ratio, rock mass index, groundwater influence, groundwater influence, etc.) for assessing these risks is constructed, while the risk classification and identification criteria are also established. As an example, a tunnel on the Xicheng Railway is adopted for regression prediction of rock mass deformation and risk warning classification based on CNN-LSTM model. The results indicate that the model is effective in predicting rock mass deformation with minimal fluctuations in settlement data, a MAPE of 0.29525, and a RMSE of 6.1311. The model demonstrates a high accuracy rate of 91.67% for early risk warning prediction of soft rock deformation in the tunnel, moreover, when applied to 10 cross-sections of the Xicheng Railway Tunnel for deformation risk early warning, the model demonstrated a high prediction accuracy of 90%. which serves as a reference for the assessment and prediction of large deformation risks in similarly complex and challenging tunnels in the western region of China.

Deep-learning surrogate models for the stability of a wide rectangular tunnel

The stability of a wide rectangular tunnel in spatially variable soils is investigated using deep-learning surrogate models combined with a mixed formulation of limit analysis. The mixed formulation is employed to perform stochastic simulations of tunnels in spatially random soils, generating the data to train the surrogate models. Two types of deep-learning models are tested: Fully Connected Neural Networks (FCNs) and Convolutional Neural Networks (CNNs). To enhance model performance, the study introduces two novel encoding techniques to enhance the identification of tunnel regions in the input data. The first, Mask-FCN, directly incorporates spatial information into the model by adding a binary mask layer to distinguish tunnel regions from the surrounding soil. The second, Grad-FCN, modifies training gradients to indirectly highlight these differences. Additionally, a Mask-CNN model is developed, leveraging spatial filtering to better capture local patterns and correlations. Our results show that the surrogate models, trained on data from stochastic reliability analyses, demonstrate robustness and accuracy across various evaluation metrics. The encoding neural networks outperform the baseline model in accuracy and overall regression performance on training datasets. Importantly, both encoding networks achieve acceptable accuracy on unseen datasets with new tunnel geometric features, a task the baseline model fails to accomplish. The FCN-based encoding neural network achieves prediction capabilities comparable to ordinary CNNs, while the CNN-based encoding neural network delivers high prediction accuracy for unknown datasets with new tunnel geometric characteristics. The study concludes that the generated surrogate models offer a computationally efficient alternative for predicting tunnel stability in spatially variable soils.

Multi-Label Feature Aware XGBoost Model For Student Performance Assessment Using Behavior Data in Online Learning Environment

In light of recent outbreaks like COVID19, the use of online-based learning streams (i.e., e-Learning systems) has increased significantly. Institutional efforts to boost student achievement have made precise predictions of academic success a priority. To analyze student sessions-streams and anticipate academic success, e-Learning platforms are starting to combine Datamining (DM) with Machine-Learning (ML) techniques. Recent research highlights the difficulties that ML-based methods have while dealing with unbalanced data. In tackling ensemble-learning, we combine several ML algorithms to select the most appropriate approach for the given data. Current ensemble-based approaches for predicting student achievement, nevertheless, don't do exceptionally well, particularly when it comes to multi-label classification, because they don't factor the relevance of features into their approaches. This study presents multi-label feature aware XGBoost (MLFA-XGB) method that improves upon the previously used ensemble-learning technique. The MLFA-XGB makes use of a robust cross-validation approach for gaining a deeper understanding of feature relationships. The experimental results demonstrate that in comparison with the state-of-the-art ensemble-based student achievement predictive approach, this suggested MLFA-XGB based approach provides much higher accuracy for prediction.

Deep learning assisting construction of heat transfer constitutive relationships for porous media

Predicting heat transfer in porous materials is challenging due to their complex microstructure, and traditional experimental and theoretical methods often fall short. To address this, we present a machine learning-based approach using a single Descriptor-to-Property Network (D2P-Net) to identify the relationships between the effective thermal conductivity (ETC) of porous media with the structural descriptors. The D2P-Net, an ensemble of decision trees, uses eleven structural descriptors, such as porosity, pore size, and connectivity, derived from four distinct types of three-dimensional porous structures. These structures are computationally generated, and their ETC is computed using the Lattice Boltzmann Method (LBM) to create a robust dataset for training. The model is trained using mean squared error (MSE) employed as the loss function, and achieves an R2 value of 0.994 and a root mean square error (RMSE) of 0.229, indicating high predictive accuracy. Additionally, SHapley Additive exPlanations (SHAP) values are employed to analyze the importance of each structural descriptor, offering valuable insights into their contributions to ETC predictions. This approach provides a reliable and interpretable method for understanding and optimizing the thermal properties of porous materials, with potential applications in areas requiring efficient thermal management, such as hypersonic vehicles.