Learning Approach Research Articles

Educational Revolution : Digital Project-Based Rotation Learning (DPBRL) Model to Improve Students' Critical Thinking Skills

The research aimed to develop and assess the effectiveness of the Digital Project-Based Rotation Learning (DPBRL) model in enhancing students' critical thinking skills. The study employed a research and development method, using a non-equivalent control group design during the pilot phase, a research method used to describe and measure the characteristics of populations or phenomena systematically using numerical data, involving 83 undergraduate students from Mulawarman University Guidance and Counseling Study Program, divided into two heterogeneous classes. Validation of the DPBRL model was conducted by experts in learning model design, materials, and media, with data collected through validation sheets, interviews, document analysis, questionnaires, observation, and documentation. The data was analyzed using descriptive technique and Independent Samples t-test. The results demonstrated that the DPBRL model effectively improved students' critical thinking skills and digital literacy, particularly through the use of digital modules with Flipbook Maker. The model’s rotation structure and presentation stage provided a structured learning approach, highlighting the importance of feedback. The study concluded that integrating technology and collaboration in project-based learning through the DPBRL model significantly benefited students’ critical thinking, digital literacy, and teamwork, offering a comprehensive and effective learning experience.

Intelligence model on sequence-based prediction of PPI using AISSO deep concept with hyperparameter tuning process

Protein–protein interaction (PPI) prediction is vital for interpreting biological activities. Even though many diverse sorts of data and machine learning approaches have been employed in PPI prediction, performance still has to be enhanced. As a result, we adopted an Aquilla Influenced Shark Smell (AISSO)-based hybrid prediction technique to construct a sequence-dependent PPI prediction model. This model has two stages of operation: feature extraction and prediction. Along with sequence-based and Gene Ontology features, unique features were produced in the feature extraction stage utilizing the improved semantic similarity technique, which may deliver reliable findings. These collected characteristics were then sent to the prediction step, and hybrid neural networks, such as the Improved Recurrent Neural Network and Deep Belief Networks, were used to predict the PPI using modified score level fusion. These neural networks’ weight variables were adjusted utilizing a unique optimal methodology called Aquila Influenced Shark Smell (AISSO), and the outcomes showed that the developed model had attained an accuracy of around 88%, which is much better than the traditional methods; this model AISSO-based PPI prediction can provide precise and effective predictions.

PD-ARnet: a deep learning approach for Parkinson’s disease diagnosis from resting-state fMRI

Objective. The clinical diagnosis of Parkinson's disease (PD) relying on medical history, clinical symptoms, and signs is subjective and lacks sensitivity. Resting-state fMRI (rs-fMRI) has been demonstrated to be an effective biomarker for diagnosing PD.Approach.This study proposes a deep learning approach for the automatic diagnosis of PD using rs-fMRI, named PD-ARnet. Specifically, PD-ARnet utilizes Amplitude of Low Frequency Fluctuations and Regional Homogeneity extracted from rs-fMRI as inputs. The inputs are then processed through a developed dual-branch 3D feature extractor to perform advanced feature extraction. During this process, a Correlation-Driven weighting module is applied to capture complementary information from both features. Subsequently, the Attention-Enhanced fusion module is developed to effectively merge two types of features, and the fused features are input into a fully connected layer for automatic diagnosis classification.Main results.Using 145 samples from the PPMI dataset to evaluate the detection performance of PD-ARnet, the results indicated an average classification accuracy of 91.6% (95% confidence interval [CI]: 90.9%, 92.4%), precision of 94.7% (95% CI: 94.2%, 95.1%), recall of 86.2% (95% CI: 84.9%, 87.4%), F1 score of 90.2% (95% CI: 89.3%, 91.1%), and AUC of 92.8% (95% CI: 91.1%, 95.0%).Significance.The proposed method has the potential to become a clinical auxiliary diagnostic tool for PD, reducing subjectivity in the diagnostic process, and enhancing diagnostic efficiency and consistency.

Particle characterization by analyzing light scattering signals with a machine learning approach

Particle characterization by analyzing light scattering signals with a machine learning approach

Machine learning regression-based prediction model for the autonomous control of coagulant dosing in smart water purification plants

ABSTRACT The global issue of water scarcity is escalating due to urbanization and increased demand. This paper proposes a machine learning (ML) regression-based model for automatic coagulant dosing control in smart water purification plants (SWPPs).1 The model uses random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), and k-nearest neighbors (KNN) algorithms. Performance metrics include MAE, MSE, RMSE, MAPE, and R2. The RF algorithm showed superior performance, with MAE of 0.005, MSE of 0.002, RMSE of 0.05, and MAPE of 0.000 for anion-poly aluminum chloride dosing, and MAE of 0.007, MSE of 0.00, RMSE of 0.02, and MAPE of 0.000 for Polymax dosing. The RF model's performance is due to its robust handling of large datasets and ensemble learning approach. Limitations include testing only two coagulants and reliance on historical data. These findings suggest integrating advanced water management, energy systems, and facility management to make SWPPs feasible and efficient, establishing a foundation for future applications of ML in chemical processes.

An Automated Machine Learning Approach to the Retrieval of Daily Soil Moisture in South Korea Using Satellite Images, Meteorological Data, and Digital Elevation Model

Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence (AI) approaches have shown promising potential for soil moisture retrieval at the local scale but have rarely demonstrated substantial products for spatially continuous grids. This paper presents the retrieval of daily soil moisture (SM) over a 500 m grid for croplands in South Korea using random forest (RF) and automated machine learning (AutoML) models, leveraging satellite images and meteorological data. In a blind test conducted for the years 2013–2019, the AutoML-based SM model demonstrated optimal performance, achieving a root mean square error of 2.713% and a correlation coefficient of 0.940. Furthermore, the performance of the AutoML model remained consistent across all the years and months, as well as under extreme weather conditions, indicating its reliability and stability. Comparing the soil moisture data derived from our AutoML model with the reanalysis data from sources such as the European Space Agency Climate Change Initiative (ESA CCI), GLDAS, the Local Data Assimilation and Prediction System (LDAPS), and ERA5 for the South Korea region reveals that our AutoML model provides a much better representation. These experiments confirm the feasibility of AutoML-based SM retrieval, particularly for local agrometeorological applications in regions with heterogeneous land surfaces like South Korea.

Comprehensive analysis of stress factors affecting students: a machine learning approach

BackgroundThe increasing levels of stress among students worldwide pose a significant challenge to educational institutions. This study aims to systematically identify and analyse the factors contributing to student stress using advanced machine learning techniques.ObjectiveTo explore the primary stressors affecting students and to evaluate the interrelations among psychological, physiological, environmental, academic, and social factors in influencing student stress levels.MethodsThe study utilized a comprehensive dataset, StressLevelDataset.csv, collected from a diverse group of 1100 students across various educational institutions. We employed machine learning tools, including correlation analysis and feature importance analysis using Random Forest models, to identify and rank the most significant stressors.ResultsKey findings suggest that psychological factors like self-esteem and physiological factors like sleep quality are crucial predictors of stress levels in students. A significant negative correlation was found between students’ anxiety levels and their academic performance, highlighting the adverse impacts of psychological stress on educational outcomes.ConclusionThe results underscore the importance of targeted interventions focusing on mental health and well-being within educational settings. By addressing the identified stressors, particularly in the psychological and physiological domains, educational institutions can enhance student well-being and improve academic performance.

Social Media Sentiment and Gender on Peer-To-Peer Lending Performance: Machine Learning Approaches

One innovation brought about by fintech is peer-to-peer lending. However, in recent years with rapid growth, unfortunately, there are still many negative sentiments about the peer-to-peer lending industry. Especially with the advancement of technology and the internet, all information about peer-to-peer lending is easily accessible through social media. The amount of negative information about peer-to-peer lending can influence opinions or sentiments towards the performance of peer-to-peer lending platforms and invite the public to comment on social media. Additionally, the gender factor of the information provider also affects the performance of the peer-to-peer market. Based on the explanation above, there has never been any previous research conducted in Indonesia, so this study will examine how the influence of social media information sentiment and the gender factor of the information provider affects peer-to-peer lending performance. The analysis method used is logistic regression, and the data used is obtained through machine learning using the Python programming language on Google Collab from social media opinions and the gender that provides opinions on social media. Meanwhile, the financial performance of peer-to-peer lending is taken from the annual performance report of the Financial Services Authority (OJK) by default. From the results of this study, it was found that the gender of the information provider affects the financial performance of peer-to-peer lending. However, simultaneously, social media sentiment and the gender of the information provider affect the financial performance of peer-to-peer lending.

Diagnosis of high‐speed railway ballastless track arching based on unsupervised learning framework

AbstractVehicle‐mounted detection methods have been widely applied in the maintenance of high‐speed railways (HSRs), providing feasibility for diagnosing ballastless track arching. However, applying detection data faces several key limitations: (1) The threshold mostly requires manual setting, making recognition accuracy highly subjective; (2) the extensive workload of manual inspections makes it challenging to label detection data, hindering the application of supervised learning approaches. To address these problems, this paper utilizes the longitudinal level irregularity data obtained from vehicle‐mounted detection, employing the concept of unsupervised learning for dimensionality reduction, combined with clustering algorithms and minimal label fine‐tuning, to design two frameworks: the fully unsupervised framework (FUF) and the few‐shot fine‐tuned framework (FFF). Experiments on dynamic detection data from a Chinese HSR line were conducted, comparing the performance of data dimensionality reduction, clustering, and classification under different strategy combinations. The results show that the improved variational autoencoder significantly enhances the performance of the encoder in dimensionality reduction, facilitating better feature extraction; the FUF achieves effective clustering outcomes without any labeled samples and its adjusted rand index score exceeded 0.8, showcasing its robustness and applicability in scenarios with no prior annotations; the FFF requires only a small number of labeled samples (labeling ratio of 5%) and achieves excellent performance, with metrics such as accuracy exceeding 0.85, thus greatly reducing the reliance on labeled data. This study offers a novel method for solving engineering issues with limited labeled data, providing an efficient solution for identifying track arching defects and advancing railway infrastructure monitoring.

YOLOSAR-Lite: a lightweight framework for real-time ship detection in SAR imagery

ABSTRACT Real-time ship detection using SAR images is crucial for maritime operations but remains challenging due to issues such as sidelobes, complex background interferences, and target defocusing. Studies have shown that deep learning can address these problems, current deep learning algorithms often suffer from achieving the necessary accuracy due to complex noise and background characteristics. Additionally, advanced algorithms excelling in feature extraction usually require significant computational resources. This paper proposes a novel deep learning approach based on YOLOv8 to improve accuracy while reducing model complexity and computational burden. The approach innovatively integrates a knowledge distillation module into the YOLOv8. We further enhance the model by replacing the original YOLOv8 backbone, neck, and head with lightweight alternatives: HGNetv2, SlimNeck, and a newly designed decoupled head. The enhanced model, YOLOSAR-Lite, demonstrated exceptional performance on both accuracy and model complexity metrics. Rigorous evaluation on the Official-SSDD dataset and the SAR-Ship-Dataset revealed recognition accuracies of 95.32% and 96.06%, respectively. Furthermore, YOLOSAR-Lite achieved significant reduction in complexity compared to the YOLOv8: 4.48G FLOPs (45.30% reduction), 2.05M parameters (31.89% reduction), and 3.91MB model size (31.76% reduction). These improvements demonstrate YOLOSAR-Lite achieves higher accuracy with minimal complexity. Consequently, YOLOSAR-Lite is a promising solution for real-time ship detection.

Intelligence computational analysis of letrozole solubility in supercritical solvent via machine learning models

Supercritical fluids (SCFs) can be used to prepare drugs nanoparticles with improved solubility. SCFs have shown superior advantages in pharmaceutical industry as an environmentally friendly alternative to toxic/harmful organic solvents. They possess gas-like transport characteristics and liquid-like solvation power for solutes. Evaluation of chemotherapeutic drugs’ solubility in supercritical carbon dioxide (SCCO2) has been recently an attractive subject for developing this method in pharmaceutical sector. To reach this purpose, the utilization of accurate models is of great necessity to estimate experimental-based solubility data. In this paper, the authors tried to employ machine learning (ML) approaches to estimate the solubility of Letrozole (LET) drug as chemotherapeutic agent and correlate its values in wide ranges of temperature and pressure. To do this, PAR (Passive Aggressive Regression), RF (Random Forest), and RBF-SVM are the models used (Support Vector Machine with RBF kernel). These models optimized in terms of their hyper-parameters using GA algorithm. The optimized PAR, RF, RBF-SVM models obtained coefficients of determination (R-squared) of 0.8277, 0.9534, and 0.9947. Also, the MSE error rate of the models are 0.1342, 0.0305, and 0.0045, in the same order. The final result of the evaluations shows the optimized RBF-SVM model as the most appropriate model in this research. The model exhibits a maximum prediction error of 0.1289.

Leveraging graph-based learning for credit card fraud detection: a comparative study of classical, deep learning and graph-based approaches

Leveraging graph-based learning for credit card fraud detection: a comparative study of classical, deep learning and graph-based approaches

An Extended Learning Analytics Framework Integrating Machine Learning and Pedagogical Approaches for Student Performance Prediction and Intervention

AbstractThe use of educational data mining and machine learning to analyse large data sets collected by educational institutions has the potential to discover valuable insights for decision-making. One such area that has gained attention is to predict student performance by analysing large educational data sets. In the relevant literature, many studies have focused on developing prediction models on student performance but comparatively less work exists on actions taken based on predicted at-risk students and evaluating their impact. Learning Analytics Intervention (LAI) studies have emerged as an approach that aims to address this gap. In LAI studies, student risk levels are predicted and disseminated to relevant stakeholders (academics, administrators and students) using learning analytics (LA) tools for targeted interventions. The interventions themselves are mainly left under the discretion of the academics and/or administrators, who are aware of the learning context and have the authority to make decisions, with LA tools facilitating this process. LAI studies have shown success in improving outcomes (e.g. improve pass rates, retention, grades), but their uptake has been slow. The main impediment to piloting LAIs by academics has been the lack of access to LAI infrastructure, which requires institutional investments to develop predictive models collecting data from diverse IT systems. Another challenge in LAIs is the development of effective interventions. This paper presents an extended LAI framework, termed Student Performance Prediction and Action (SPPA), which provides access to LAI infrastructure for academics to pilot LAIs in their courses without the need for institution-wide efforts. SPPA and its features are seamlessly accessed via a web browser and academics can develop course-specific predictive models based on historical course assessment data. Furthermore, SPPA integrates sound pedagogical approaches and provides relevant information (such as students’ knowledge gaps, personalised study plans) to assist academics in providing effective interventions. SPPA was evaluated by a number of academics piloting LAIs in their courses. Quantitative and qualitative data was collected and analysed. Academics were able to provide effective interventions using SPPA and also had a positive outlook on using SPPA and its features. SPPA is also provided as an open-source project for further development and can be a catalyst for widescale uptake in LAIs. Furthermore, a model for continuous improvement in LAIs is outlined along with a number of areas for future research and development.

A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions.

The complex, multidimensional energy landscape of biomolecules makes the extraction of suitable, nonintuitive collective variables (CVs) that describe their conformational transitions challenging. At present, dimensionality reduction approaches and machine learning (ML) schemes are employed to obtain CVs from molecular dynamics (MD)/Monte Carlo (MC) trajectories or structural databanks for biomolecules. However, minimum sampling conditions to generate reliable CVs that accurately describe the underlying energy landscape remain unclear. Here, we address this issue by developing a Mode evolution Metric (MeM) to extract CVs that can pinpoint new states and describe local transitions in the vicinity of a reference minimum from nonequilibrated MD/MC trajectories. We present a general mathematical formulation of MeM for both statistical dimensionality reduction and machine learning approaches. Application of MeM to MC trajectories of model potential energy landscapes and MD trajectories of solvated alanine dipeptide reveals that the principal components which locate new states in the vicinity of a reference minimum emerge well before the trajectories locally equilibrate between the associated states. Finally, we demonstrate a possible application of MeM in designing efficient biased sampling schemes to construct accurate energy landscape slices that link transitions between states. MeM can help speed up the search for new minima around a biomolecular conformational state and enable the accurate estimation of thermodynamics for states lying on the energy landscape and the description of associated transitions.

Machine Learning Algorithms for Water Quality Management Using Total Dissolved Solids (TDS) Data Analysis

Our research project specifically focuses on evaluating groundwater quality in six West Texas counties. We aim to determine whether environmental changes have any impact on the levels of Total Dissolved Solids (TDS) in the water supplied to the public. To achieve this goal, we will be utilizing advanced machine learning algorithms to analyze TDS levels and create geospatial maps for each year between the 1990s and 2010s. To ensure the accuracy of our data, we have gathered information from two trusted sources: the Texas Water Development Board (TWDB) and the Groundwater Database (GWDB). We have analyzed the TDS and other elemental analyses from TWDB–GWDB lab reports and compared them with the quality cutoff set by the World Health Organization (WHO). Our approach involves a thorough examination of the data to identify any emerging patterns. The machine learning algorithm has been successfully trained and tested, producing highly accurate results that effectively predict water quality. Our results have been validated through extensive testing, highlighting the potential of machine learning approaches in the fields of environmental research. Overall, our findings will contribute to the development of more effective policies and regulations in predicting groundwater quality and improving water resource management in Texas. Therefore, this research provides important information for groundwater protection and the development of plans for water resource use in the future.

An interpretable deep learning framework identifies proteomic drivers of Alzheimer’s disease

Alzheimer’s disease (AD) is the leading neurodegenerative pathology in aged individuals, but many questions remain on its pathogenesis, and a cure is still not available. Recent research efforts have generated measurements of multiple omics in individuals that were healthy or diagnosed with AD. Although machine learning approaches are well-suited to handle the complexity of omics data, the models typically lack interpretability. Additionally, while the genetic landscape of AD is somewhat more established, the proteomic landscape of the diseased brain is less well-understood. Here, we establish a deep learning method that takes advantage of an ensemble of autoencoders (AEs) — EnsembleOmicsAE–to reduce the complexity of proteomics data into a reduced space containing a small number of latent features. We combine brain proteomic data from 559 individuals across three AD cohorts and demonstrate that the ensemble autoencoder models generate stable latent features which are well-suited for downstream biological interpretation. We present an algorithm to calculate feature importance scores based on the iterative scrambling of individual input features (i.e., proteins) and show that the algorithm identifies signaling modules (AE signaling modules) that are significantly enriched in protein–protein interactions. The molecular drivers of AD identified within the AE signaling modules derived with EnsembleOmicsAE were missed by linear methods, including integrin signaling and cell adhesion. Finally, we characterize the relationship between the AE signaling modules and the age of death of the patients and identify a differential regulation of vimentin and MAPK signaling in younger compared with older AD patients.

Pedagogical translation for reading comprehension in mixed HL‐L2 secondary classrooms

AbstractThis exploratory, mixed methods study proposes that pedagogical translation be implemented for biliteracy acquisition in mixed heritage language (HL)‐second language (L2) secondary Spanish classrooms. It investigates the mediation of pedagogical translation for reading comprehension in two mixed HL‐L2 secondary Spanish classrooms at a suburban high school in the southwestern United States. The research question, How does pedagogical translation influence reading comprehension in a mixed HL‐L2 secondary Spanish classroom is addressed in this work. Findings indicated that through a pedagogical translation activity, heritage language learners (HLLs) and second language learners (L2Ls) demonstrated metalinguistic awareness and vocabulary learning, which are indicators of reading comprehension. Differing HLL and L2L learning approaches during the activities are discussed, followed by pedagogical and research implications.

Predicting newborn birth outcomes with prenatal maternal health features and correlates in the United States: a machine learning approach using archival data

BackgroundNewborns are shaped by prenatal maternal experiences. These include a pregnant person’s physical health, prior pregnancy experiences, emotion regulation, and socially determined health markers. We used a series of machine learning models to predict markers of fetal growth and development—specifically, newborn birthweight and head circumference (HC).MethodsWe used a pre-registered archival data analytic approach. These data consisted of maternal and newborn characteristics of 594 maternal-infant dyads in the western U.S. Participants also completed a measure of emotion dysregulation. In total, there were 22 predictors of newborn HC and birthweight. We used regularized regression for predictor selection and linear prediction, followed by nonlinear models if linear models were overfit.ResultsHC was predicted best with a linear model (ridge regression). Newborn sex (male), number of living children, and maternal BMI predicted a larger HC, whereas maternal preeclampsia, number of prior preterm births, and race/ethnicity (Latina) predicted a smaller HC. Birthweight was predicted best with a nonlinear model (support vector machine). Occupational prestige (a marker similar to socioeconomic status) predicted higher birthweight, maternal race/ethnicity (non-White and non-Latina) predicted lower birthweight, and the number of living children, prior preterm births, and difficulty with emotional clarity had nonlinear effects.ConclusionsHC and birthweight were predicted by a variety of variables associated with prenatal stressful experiences, spanning medical, psychological, and social markers of health and stress. These findings may highlight the importance of viewing prenatal maternal health across multiple dimensions. Findings also suggest that assessing difficulties with emotional clarity during standard obstetric care (in the U.S.) may help identify risk for adverse newborn outcomes.

Experiences Using Media Health Claims to Teach Evidence-Based Practice to Healthcare Students: A Mixed Methods Study

Background A multifaceted and interactive teaching approach is recommended for achieving proficiency in evidence-based practice, with critical thinking considered vital for connecting theory and practice. In this context, we advocate the strategic use of health claims in the media to promote critical thinking, complemented by a blended learning approach and a group exam. Method We conducted a convergent mixed methods study, including a cross-sectional survey with structured and open-ended questions as well as focus group interviews, at Oslo Metropolitan University, during the 2020-2021 academic year. Participants were bachelor’s students in healthcare. We employed a blended learning approach, combining digital learning resources, teaching in critical assessment of health claims and evidence-based practice, and a concurrent group exam assignment centered around media health claims. The outcome measures included students’ experiences integrating health claims into evidence-based practice teaching and their experiences with teaching approaches and the group exam. Results Out of 465 participants, 136 (29.2%) responded to the structured questions in the survey. In response to the open-ended questions within the survey, 109 (80.1%) of the respondents shared positive experiences about the course, while 98 (72%) suggested improvements. Additionally, 25 students participated in focus group interviews. Synthesizing the results, we found that students viewed the inclusion of health claim assessment as a useful entry point for learning evidence-based practice. In addition, the students identified both the blended learning design and the group exam as contributors to a positive perception of learning outcomes from the course. Conclusions Based on student feedback, integrating critical reflection on media health claims into evidence-based practice education, alongside a blended learning approach and a group exam, may be beneficial in teaching evidence-based practice to bachelor’s healthcare students. However, further rigorous study designs are needed to objectively assess the effect of the course on learning outcomes. Registration DOI 10.5281/zenodo.6985449

An explainable machine learning approach to predict the compressive strength of graphene oxide-based concrete

Graphene oxide (GO) has shown promise in improving concrete strength. Despite its frequent use in cement composites, its effect on concrete properties is less explored. The influence of GO on concrete remains unclear due to various interactions among constituents such as GO type and percentage, Superplasticiser (SP) type and percentage, dispersion technique, and curing age. Therefore, making prediction of compressive strength using simple mathematical formation is challenging. This study represents the first-ever attempt at developing a comprehensively validated machine learning model to predict GO-concrete's compressive strength characteristics by considering all key variable parameters. A comprehensive laboratory experiment program was conducted to collect the data required for training the machine learning (ML) models. Once the ML models were trained, they were used to predict the relationship of each of those input variables towards the compressive strength properties. Four machine learning models—(a) multiple linear regression (MLR), (b) k-nearest neighbour (KNN), (c) random forest (RF) and (d) extreme gradient boost (XGB)—were utilised to model the relationship between input parameters and compressive strength. Also, explainable machine learning (XML) methods were employed to elucidate the impact of mixed constituents on the compressive strength of GO concrete. The results from test predictions showcase that the XGB has attained a coefficient of determination (R2) of 0.981, coefficient of correlation (R) of 0.99, mean square error (MSE) of 0.9, mean normalised bias (MNB) of 0.004, scatter index (SI) of 0.2 with mean absolute error (MAE) of 0.8 MPa for the predictions, outperforming the remaining models. XML highlighted the true impact of GO and the remaining variables, emphasising that the presence of GO significantly improves compressive strength. The optimum GO content and optimum superplasticiser content observed from the experimental work agreed with results obtained from the XML analysis, showing the consistency of the explanation with the experimental results. This implies the superiority of using XML methods in concrete mix design applications in industry.