Cloud-based XGBoost Framework: Enhancing Scalability and Performance in Machine Learning

To understand students’ learning behaviors, this study uses machine learning technologies to analyze the data of interactive learning environments, and then predicts students’ learning outcomes. This study adopted a variety of machine learning classification methods, quizzes, and programming system logs, found that students’ learning characteristics were correlated with their learning performance when they encountered similar programming practice. In this study, we used random forest (RF), support vector machine (SVM), logistic regression (LR), and neural network (NN) algorithms to predict whether students would submit on time for the course. Among them, the NN algorithm showed the best prediction results. Education-related data can be predicted by machine learning techniques, and different machine learning models with different hyperparameters can be used to obtain better results.

Parkinson’s disease is a neurodegenerative disease that is associated with genetic and environmental factors. However, the genes causing this degeneration have not been determined, and no reported cure exists for this disease. Recently, studies have been conducted to classify diseases with RNA-seq data using machine learning, and accurate diagnosis of diseases using machine learning is becoming an important task. In this study, we focus on how various feature selection methods can improve the performance of machine learning for accurate diagnosis of Parkinson’s disease. In addition, we analyzed the performance metrics and computational costs of running the model with and without various feature selection methods. Experiments were conducted using RNA sequencing—a technique that analyzes the transcription profiling of organisms using next-generation sequencing. Genetic algorithms (GA), information gain (IG), and wolf search algorithm (WSA) were employed as feature selection methods. Machine learning algorithms—extreme gradient boosting (XGBoost), deep neural network (DNN), support vector machine (SVM), and decision tree (DT)—were used as classifiers. Further, the model was evaluated using performance indicators, such as accuracy, precision, recall, F1 score, and receiver operating characteristic (ROC) curve. For XGBoost and DNN, feature selection methods based on GA, IG, and WSA improved the performance of machine learning by 10.00% and 38.18%, respectively. For SVM and DT, performance was improved by 0.91% and 7.27%, respectively, with feature selection methods based on IG and WSA. The results demonstrate that various feature selection methods improve the performance of machine learning when classifying Parkinson’s disease using RNA-seq data.

These days, online learning for kids has become essential due to the development of technology and the need for fast learning, however, online learning for kids can bring some risks to children, for example, some students find difficulties in their learning which affect on their performance, so machine learning (ML) approaches play essential roles in monitoring kids’ learning performance through online. Therefore, the aim of this chapter is to review some aspects of machine learning approaches in monitoring online learning for children and discuss the roles of machine learning (ML) methods in exam preparation and management by reviewing papers over the last five years. The steps in predicting at-risk students are explained to help kids by providing early intervention before exams. And finally, the issues and threats related to using ML in the examination system are discussed briefly.KeywordsMachine learning (ML)Electronic learningMonitoring

We aimed to evaluate the diagnostic performance of neuroimaging-based machine learning (ML) models for non-invasive prediction of H3K27M mutations in diffuse midline gliomas (DMG). Following PRISMA-DTA guidelines, we searched four databases up to May 2025 to identify eligible studies evaluating neuroimaging-based ML for predicting H3K27M mutations in DMG. Study quality was assessed using PROBAST+AI and GRADE. Bivariate random-effects models were used to pool sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Sixteen studies were included, comprising 2,357 patients in internal validation cohorts and 1,792 patients in external validation cohorts. MRI-based ML models showed strong diagnostic performance in internal validation, with pooled sensitivity of 0.86 (95% CI: 0.79-0.91), specificity of 0.82 (95% CI: 0.75-0.87), and AUC of 0.91 (95% CI: 0.88-0.93). For PET/CT-based ML models, pooled sensitivity was 0.58 (95% CI: 0.44-0.71) and pooled specificity was 0.65 (95% CI: 0.46-0.81), with an AUC of 0.61 (95% CI: 0.57-0.66). MRI-based ML showed significantly higher sensitivity (Z = 3.71; P < 0.01) and AUC (Z = 11.42; P < 0.01) than PET/CT-based ML. Subgroup analysis indicated that MRI-based deep learning (DL) models outperformed conventional machine learning (cML) algorithms (P = 0.01). Models using DNA sequencing as the reference standard showed higher specificity than those using immunohistochemistry (P < 0.001). MRI-based ML demonstrates high accuracy and generalizability for non-invasive H3K27M prediction in DMG, seemingly outperforming current PET/CT-based ML. The adoption of DL architectures and DNA sequencing as the reference standard may further improve performance, supporting the clinical utility of MRI-based ML for molecular stratification. https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=1078673, identifier CRD420251078673.

Toward the Best Generalizable Performance of Machine Learning in Modeling Omic and Clinical Data.

Understanding the Performance of Machine Learning Models to Predict Credit Default: A Novel Approach for Supervisory Evaluation

This study aims to assess the performance of machine learning (ML) techniques in optimising nurse staffing and evaluating the appropriateness of nursing care delivery models in hospital wards. The primary outcome measures include the adequacy of nurse staffing and the appropriateness of the nursing care delivery system. Historical and current healthcare challenges, such as nurse shortages and increasing patient acuity, necessitate innovative approaches to nursing care delivery. For instance, the COVID-19 pandemic highlighted the need for flexible and scalable staffing models to manage surges in patient volume and acuity. A descriptive study was conducted in 39 inpatient wards across a university hospital and three state hospitals, involving 117 ward-level observations. Data were collected using the Rush Medicus Patient Classification Scale and analysed using k-Nearest Neighbour, Support Vector Machine, Random Forest, and Logistic Regression algorithms. Effectiveness was measured by the accuracy of machine learning predictions regarding nurse staffing adequacy, while suitability was determined by the congruence between observed nursing care models and patient needs. STROBE checklist. The Random Forest algorithm demonstrated the highest accuracy in predicting both nurse staffing adequacy and the appropriateness of nursing care delivery systems. The study found that 68.4% of wards had sufficient nurse staffing and 26.5% of wards used appropriate care delivery models, with functional nursing and total patient care models being the most commonly used. The study highlights functional nursing and total patient care models, emphasising the need to consider nurse qualifications and patient needs in selecting care systems. Machine learning, particularly the Random Forest algorithm, proved effective in aligning staffing with patient requirements. Machine learning, particularly the Random Forest algorithm, proves effective in optimising nursing care delivery models, suggesting significant potential for enhancing patient care and nurse satisfaction. The research underscores machine learning's role in improving nursing care delivery, aligning nurse staffing with patient needs, and advancing healthcare outcomes. The findings advocate for integrating machine learning in the planning of nursing care delivery models. This study sets a precedent for using data-driven approaches to improve nurse staffing and care delivery, potentially enhancing global clinical outcomes and operational efficiencies. The global clinical community can learn from this study the value of employing machine learning techniques to make informed, evidence-based decisions in healthcare management. While the study lacked direct patient involvement, its goal was to enhance patient care and healthcare efficiency. Future research will aim to incorporate patient and public insights more directly.

We present a path-integral methods field solution that merges machine learning with microscopic physics models for mesoscopic phenomena. This interpretable multiscale algorithm treats physical and machine learning field solutions as equivalent, enabling seamless integration of microscopic physics intomachine learning algorithms for mesoscopic pattern learning and generation. Our approach incorporates microscopic physics mechanisms as hidden fields and represents their interactions with mesoscopic fields through auxiliary fields. Rather than imposing statistical assumptions on hidden nodes and learning weight statistics from data, our method derives a hidden fields formalism based on physics interaction mechanisms and determines connecting weights through action functional minimization and neural operators machine learning.Combining the strengths of both physicsmodeling and machine learning techniques,our method achieves strong performance in learning and generating mesoscopic patterns from limited data. It can capture physics interactions occurring at different scales,allowing forextrapolation when dealing with patterns with different interacting parameters and pattern evolution dynamics. We demonstrate our solution through a concrete case of two interacting species with microscopic chain structures, widely used for polymer material and biomolecular simulation. Our mesoscopicfield approach unifying machine learning and physics modelscan be readily usedin various areas of material science, biology, and social dynamics.

Federated learning (FL) has emerged as a key technique for distributed machine learning (ML). Most literature on FL has focused on ML model training for (i) a single task/model, with (ii) a synchronous scheme for updating model parameters, and (iii) a static data distribution setting across devices, which is often not realistic in practical wireless environments. To address this, we develop DMA-FL considering dynamic FL with multiple downstream tasks/models over an asynchronous model update architecture. We first characterize convergence via introducing scheduling tensors and rectangular functions to capture the impact of system parameters on learning performance. Our analysis sheds light on the joint impact of device training variables (e.g., number of local gradient descent steps), asynchronous scheduling decisions (i.e., when a device trains a task), and dynamic data drifts on the performance of ML training for different tasks. Leveraging these results, we formulate an optimization for jointly configuring resource allocation and device scheduling to strike an efficient trade-off between energy consumption and ML performance. Our solver for the resulting non-convex mixed integer program employs constraint relaxations and successive convex approximations with convergence guarantees. Through numerical experiments, we reveal that DMA-FL substantially improves the performance-efficiency tradeoff.

Machine learning integration of circulating and imaging biomarkers for explainable patient-specific prediction of cardiac events: A prospective study

In a recent development, attention has shifted to the application of artificial intelligence for the optimization of wastewater treatment processes. This research compared the performances of the machine learning (ML) model: random forest, decision tree, support vector machine, artificial neural network, convolutional neural network, long-short term memory, and multiple linear regressors for optimization in effluent treatment. The training, testing, and validation datasets were obtained via the design of an experiment conducted on the removal of total dissolved solids (TDS) from pharmaceutical effluent. The breadfruit-activated carbon (BFAC) adsorbent was characterized using scanning electron microscopy and X-ray diffraction techniques. The predictive capacity of an ML algorithm, and neural network architecture implemented to optimize the treatment process using statistical metrics. The results showed that MSE ≤ 1.68, MAE ≤ 0.95, and predicted-R2 ≥ 0.9035 were recorded across all ML. The ML output with minimum error functions that satisfied the criterion for clean discharge was adopted. The predicted optimum conditions correspond to BFAC dosage, contact time, particle size, and pH of 2.5 mg/L, 10 min, 0.60 mm, and 6, respectively. The optimum transcends to a reduction in TDS concentration from 450 mg/L to a residual ≤ 40 mg/L and corresponds to 90% removal efficiency, indicating ± 1.01 standard deviation from the actual observation practicable. The findings established the ML model outperformed the neural network architecture and affirmed validation for the optimization of the adsorption treatment in the pharmaceutical effluent domain. Results demonstrated the reliability of the selected ML algorithm and the feasibility of BFAC for use in broad-scale effluent treatment.

Learning the underlying dynamics of periodic and aperiodic systems evolving over space and time provides substantial challenges across scientific fields. Despite recent advances in machine learning (ML), our understanding of its limits to learning, identifying, and emulating real physical systems remains incomplete. With the increasing complexity of dynamical systems and higher power consumption, ML systems take inspiration from the complex learning mechanisms of the mammalian brain, for biologically plausible learning. As we delve into the designing of brain- inspired ML systems, we deal with the challenges of the learning procedures, network sizes, energy efficiency and training complexities involved with it. It's crucial to characterize their performance in learning and controlling unknown dynamical systems to address them. We address these issues by designing sparsity aware learning mechanisms in feedback driven recurrent neural networks (RNNs), incorporate biologically plausible neuron models, and integrate computational neuroscience to build data driven AI systems. We explore brain-inspired neuronal dynamics to build AI systems that can mimic and classify dynamical systems. We propose full-FORCE (FF) SNN, Reservoir-in-reservoir (R-i-R) architecture, and SNN based applications learning, We pro- pose sparsity aware learning in RNNs, full-FORCE based sparsity regularization in recurrent spiking neural networks (RSNN) and an RSNN based BLE application for dynamical systems learning. Our proposed learning methods and architectures exhibit strong performance, noise robustness and energy efficiency in simulated and real-world adaptive system emulation. We evaluate bio signals, motion trajectories, Lorenz systems, real-world dynamic variable datasets, and other dynamical systems in this dissertation.

Temperature changes have widespread impacts on the environment, economy, and municipal planning. Generating accurate climate prediction at finer spatial resolution through downscaling could help better assess the future effects of climate change on a local scale. Ensembles of multiple climate models have been proven to improve the accuracy of temperature prediction. Meanwhile, machine learning techniques have shown high performance in solving various predictive modeling problems, which make them a promising tool for temperature downscaling. This study investigated the performance of machine learning (long short-term memory (LSTM) networks and support vector machine (SVM)) and statistical (arithmetic ensemble mean (EM) and multiple linear regression (MLR)) methods in developing multi-model ensembles for downscaling long-term daily temperature. A case study of twelve meteorological stations across Ontario, Canada, was conducted to evaluate the performance of the proposed ensembles. The results showed that both machine learning and statistical techniques performed well at downscaling daily temperature with multi-model ensembles and had similar performance with relatively high accuracy. The R2 of 12 stations ranged between 0.756 and 0.820 and RMSE ranged between 4.318 and 7.063 °C. Both machine learning and statistical ensembles for downscaling had difficulty in predicting extreme values for temperature below − 10 °C and above 20 °C. The results provided technical support for using statistical and machine learning methods to generate high-resolution daily temperature prediction.

ObjectivesTo develop and validate an algorithm to predict occult nodal metastasis in clinically node negative oral cavity squamous cell carcinoma (OCSCC) using machine learning. To compare algorithm performance to a model based on tumor depth of invasion (DOI). Materials and methodsPatients who underwent primary tumor extirpation and elective neck dissection from 2007 to 2013 for clinical T1-2N0 OCSCC were identified from the National Cancer Database (NCDB). Multiple machine learning algorithms were developed to predict pathologic nodal metastasis using clinicopathologic data from 782 patients.The algorithm was internally validated using test data from 654 patients in NCDB and was then externally validated using data from 71 patients treated at a single academic institution. Performance was measured using area under the receiver operating characteristic (ROC) curve (AUC). Machine learning and DOI model performance were compared using Delong’s test for two correlated ROC curves. ResultsThe best classification performance was achieved with a decision forest algorithm (AUC = 0.840). When applied to the single-institution data, the predictive performance of machine learning exceeded that of the DOI model (AUC = 0.657, p = 0.007). Compared to the DOI model, machine learning reduced the number of neck dissections recommended while simultaneously improving sensitivity and specificity. ConclusionMachine learning improves prediction of pathologic nodal metastasis in patients with clinical T1-2N0 OCSCC compared to methods based on DOI. Improved predictive algorithms are needed to ensure that patients with occult nodal disease are adequately treated while avoiding the cost and morbidity of neck dissection in patients without pathologic nodal disease.

Training and testing process for the classification of biomedical datasets in machine learning is very important. The researcher should choose carefully the methods that should be used at every step. However, there are very few studies on method choices. The studies in the literature are generally theoretical. Besides, there is no useful model for how to select samples in the training and testing process. Therefore, there is a need for resources in machine learning that discuss the training and testing process in detail and offer new recommendations. This article provides a detailed analysis of the training and testing process in machine learning. The article has the following sections. The third section describes how to prepare the datasets. Four balanced datasets were used for the application. The fourth section describes the rate and how to select samples at the training and testing stage. The fundamental sampling theorem is the subject of statistics. It shows how to select samples. In this article, it has been proposed to use sampling methods in machine learning training and testing process. The fourth section covers the theoretic expression of four different sampling theorems. Besides, the results section has the results of the performance of sampling theorems. The fifth section describes the methods by which training and pretest features can be selected. In the study, three different classifiers control the performance. The results section describes how the results should be analyzed. Additionally, this article proposes performance evaluation methods to evaluate its results. This article examines the effect of the training and testing process on performance in machine learning in detail and proposes the use of sampling theorems for the training and testing process. According to the results, datasets, feature selection algorithms, classifiers, training, and test ratio are the criteria that directly affect performance. However, the methods of selecting samples at the training and testing stages are vital for the system to work correctly. In order to design a stable system, it is recommended that samples should be selected with a stratified systematic sampling theorem.