Accuracy Of Machine Learning Models Research Articles

PREDICTION OF CONCRETE MIXTURE DESIGN AND COMPRESSIVE STRENGTH THROUGH DATA ANALYSIS AND MACHINE LEARNING

Concrete is the most used building material in civil engineering. The mechanical properties of concrete depend on the percentage of materials used in the mix design. There are different types of mixture methods, and the purpose of this study is to investigate the mechanical properties of concrete using the mixture method through data analysis. In this case, more than 45 mixture designs are collected to find the estimated mixture design. The estimated mixture design was found by correlation matrix and the correlation between materials of concrete. Moreover, to find the reliability of the compressive strength of concrete through data mining, two models have been established. In this term, Linear Regression (LR), Ridge Regression (RR), Support Vector Machine Regression (SVR), and Polynomial Regression (PR) have been applied to predict compressive strength. In this study, the stress-strain curve of the compressive strength of concrete was also investigated. To find the accuracy of machine learning models, Correlation Coefficient (R2), Mean Absolute Errors (MAE), and Root Mean Squared Errors (RMSE) are established. However, the machine learning prediction model of RR and PR shows the best results of prediction with R2 0.93, MAE 3.7, and RMSE 5.3 for RR. The PR R2 was more than 0.91, moreover, the stress-strain of compressive strengths has been predicted with high accuracy through Logistic Algorithm Function. The experimental results were acceptable. In the compressive strength experimental results R2 was 0.91 MAE was 1.07, and RMSE was 2.71 from prediction mixture designs. Finally, the prediction and experimental results have indicated that the current study was reliable.

Empowering prediction of miRNA–mRNA interactions in species with limited training data through transfer learning

MicroRNAs (miRNAs) play a crucial role in mRNA regulation. Identifying functionally important mRNA targets of a specific miRNA is essential for uncovering its biological function and assisting miRNA–based drug development. Datasets of high-throughput direct bona fide miRNA–target interactions (MTIs) exist only for a few model organisms, prompting the need for computational prediction. However, the scarcity of data poses a challenge in training accurate machine learning models for MTI prediction. In this study, we explored the potential of transfer learning technique (with ANN and XGB) to address the limited data challenge by leveraging the similarities in interaction rules between species. Furthermore, we introduced a novel approach called TransferSHAP for estimating the feature importance of transfer learning in tabular dataset tasks. We demonstrated that transfer learning improves MTI prediction accuracy for species with limited datasets and identified the specific interaction features the models employed to transfer information across different species.

Assessing the performance of state-of-the-art machine learning algorithms for predicting electro-erosion wear in cryogenic treated electrodes of mold steels

In manufacturing, predicting and reducing electro-erosion wear during the electric discharge machining (EDM) process is critical to minimize delays, financial losses and product defects. Achieving this requires developing and evaluating accurate machine learning models. In our study, we focus on cryogenically treated mold steel electrodes to investigate the potential of different machine learning algorithms to predict EDM wear. We considered five machine learning algorithms—artificial neural networks, ensemble learning, boosting algorithms, tree-based algorithms, and k-nearest neighbors—to evaluate their ability to predict wear patterns accurately. Each algorithm was trained and tested using actual experimental data from EDM processes. Our results show that the machine learning models demonstrated exceptional accuracy, accurately predicting EDM wear in training and testing datasets with almost 99% accuracy. In addition, we identified the most influential characteristics that affect wear patterns, including operating current, cryogenic process parameters, and electrode composition. Based on these findings, manufacturers can gain valuable insight into the factors that cause EDM wear and optimize their EDM processes accordingly to improve productivity, reduce wear-related costs, and increase production quality across multiple manufacturing industries. Furthermore, this research provides insights into the possibilities of implementing these models in real manufacturing contexts and motivates future research on this topic. Ultimately, integrating advanced computing techniques and prudent decision-making strategies will shape the future of manufacturing operations management and promote sustainable and profitable business growth.

Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials

High energy and low sensitivity have been the focus of developing new energetic materials (EMs). However, there has been a lack of a quick and accurate method for evaluating the stability of diverse EMs. Here, we develop a machine learning prediction model with high accuracy for bond dissociation energy (BDE) of EMs. A reliable and representative BDE dataset of EMs is constructed by collecting 778 experimental energetic compounds and quantum mechanics calculation. To sufficiently characterize the BDE of EMs, a hybrid feature representation is proposed by coupling the local target bond into the global structure characteristics. To alleviate the limitation of the low dataset, pairwise difference regression is utilized as a data augmentation with the advantage of reducing systematic errors and improving diversity. Benefiting from these improvements, the XGBoost model achieves the best prediction accuracy with R2 of 0.98 and MAE of 8.8kJ mol-1, significantly outperforming competitive models.

Implementation of Polynomial Functions to Improve the Accuracy of Machine Learning Models in Predicting the Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds as Corrosion Inhibitors

Historically, the exploration of corrosion inhibitor technology has relied extensively on experimental methodologies, which are inherently associated with substantial costs, prolonged durations, and significant resource utilization. However, the emergence of ML approaches has recently garnered attention as a promising avenue for investigating potential materials with corrosion inhibition properties. This study endeavors to enhance the predictive capacity of ML models by leveraging polynomial functions. Specifically, the investigation focuses on assessing the effectiveness of pyridinequinoline compounds in mitigating corrosion. Diverse ML models were systematically evaluated, integrating polynomial functions to augment their predictive capabilities. The integration of polynomial functions notably amplifies the predictive accuracy across all tested models. Notably, the SVR model emerges as the most adept, exhibiting R² of 0.936 and RMSE of 0.093. The outcomes of this inquiry underscore a significant enhancement in predictive accuracy facilitated by the incorporation of polynomial functions within ML models. The proposed SVR model stands out as a robust tool for prognosticating the corrosion inhibition potential of pyridine-quinoline compounds. This pioneering approach contributes invaluable insights into advancing machine learning methodologies geared toward designing and engineering materials with promising corrosion inhibition properties.
 Keywords: machine learning, polynomial, corrosion inhibition, pyridine-quinoline

Assessing the knot in a noose position by thyrohyoid and cervical spine fracture patterns in suicidal hangings using machine learning algorithms: A new insight into old dilemmas

Hanging is one of the most common suicide methods worldwide. Neck injuries that occur upon such neck compression – fractures of the thyrohyoid complex and cervical spine, occupy forensic pathologists for a long time. However, research failed to identify particular patterns of these injuries corresponding to the force distribution a ligature applies to the neck: the issue of reconstructing the knot in a noose position persists. So far, machine learning (ML) models were not utilized to classify knot positions and reconstruct this event. We conducted a single-institutional, retrospective study on 1235 autopsy cases of suicidal hanging, developed several ML models, and assessed their classification performance in a stepwise manner to discriminate between: 1. typical (‘posterior) and atypical (‘anterior’ and ‘lateral’) hangings, 2. anterior and lateral hangings, and 3. left and right lateral hangings. The variable coding was based on the presence/absence of fractures of greater hyoid bone horns (GHH), superior thyroid cartilage horns (STH), and cervical spine. Subject age was considered. The models’ parameters were optimized by the Genetic Algorithm. The accuracy of ML models in the first step was very modest (c. 60%) but increased subsequently: Multilayer Perceptron – Artificial Neural Network and k-Nearest Neighbors performed excellently discriminating between left and right lateral hangings (accuracy 91.8% and 90.6%, respectively). The latter is of great importance for clarifying probable hanging fracture biomechanics. Alongside the conventional inferential statistical analysis we performed, our results further indicate the association of the knot position with ipsilateral GHH and contralateral STH fractures in lateral hangings. Moreover, odds for unilateral GHH fracture, simultaneous GHH and STH fractures, and cervical spine fracture were significantly higher in atypical (‘anterior’ and ‘lateral’) hangings, compared to typical (‘posterior’) hangings.

Energy or Accuracy? Near-Optimal User Selection and Aggregator Placement for Federated Learning in MEC

To unveil the hidden value in the datasets of user equipments (UEs) while preserving user privacy, federated learning (FL) is emerging as a promising technique to train a machine learning model using the datasets of UEs locally without uploading the datasets to a central location. Customers require to train machine learning models based on different datasets of UEs, through issuing FL requests that are implemented by FL services in a mobile edge computing (MEC) network. A key challenge of enabling FL in MEC networks is how to minimize the energy consumption of implementing FL requests while guaranteeing the accuracy of machine learning models, given that the availabilities of UEs usually are uncertain. In this paper, we investigate the problem of energy minimization for FL in an MEC network with uncertain availabilities of UEs. We first consider the energy minimization problem for a single FL request in an MEC network. We then propose a novel optimization framework for the problem with a single FL request, which consists of (1) an online learning algorithm with a bounded regret for the UE selection, by considering various contexts (side information) that influence energy consumption; and (2) an approximation algorithm with an approximation ratio for the aggregator placement for a single FL request. We thirdly deal with the problem with multiple FL requests, for which we devise an online learning algorithm with a bounded regret. We finally evaluate the performance of the proposed algorithms by extensive experiments. Experimental results show that the proposed algorithms outperform their counterparts by reducing at least 13% of the total energy consumption while achieving the same accuracy.

Fair and green hyperparameter optimization via multi-objective and multiple information source Bayesian optimization

AbstractIt has been recently remarked that focusing only on accuracy in searching for optimal Machine Learning models amplifies biases contained in the data, leading to unfair predictions and decision supports. Recently, multi-objective hyperparameter optimization has been proposed to search for Machine Learning models which offer equally Pareto-efficient trade-offs between accuracy and fairness. Although these approaches proved to be more versatile than fairness-aware Machine Learning algorithms—which instead optimize accuracy constrained to some threshold on fairness—their carbon footprint could be dramatic, due to the large amount of energy required in the case of large datasets. We propose an approach named FanG-HPO: fair and green hyperparameter optimization (HPO), based on both multi-objective and multiple information source Bayesian optimization. FanG-HPO uses subsets of the large dataset to obtain cheap approximations (aka information sources) of both accuracy and fairness, and multi-objective Bayesian optimization to efficiently identify Pareto-efficient (accurate and fair) Machine Learning models. Experiments consider four benchmark (fairness) datasets and four Machine Learning algorithms, and provide an assessment of FanG-HPO against both fairness-aware Machine Learning approaches and two state-of-the-art Bayesian optimization tools addressing multi-objective and energy-aware optimization.

Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model: A cohort study.

Liver transplant (LT) patients have become older and sicker. The rate of post-LT major adverse cardiovascular events (MACE) has increased, and this in turn raises 30-d post-LT mortality. Noninvasive cardiac stress testing loses accuracy when applied to pre-LT cirrhotic patients. To assess the feasibility and accuracy of a machine learning model used to predict post-LT MACE in a regional cohort. This retrospective cohort study involved 575 LT patients from a Southern Brazilian academic center. We developed a predictive model for post-LT MACE (defined as a composite outcome of stroke, new-onset heart failure, severe arrhythmia, and myocardial infarction) using the extreme gradient boosting (XGBoost) machine learning model. We addressed missing data (below 20%) for relevant variables using the k-nearest neighbor imputation method, calculating the mean from the ten nearest neighbors for each case. The modeling dataset included 83 features, encompassing patient and laboratory data, cirrhosis complications, and pre-LT cardiac assessments. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC). We also employed Shapley additive explanations (SHAP) to interpret feature impacts. The dataset was split into training (75%) and testing (25%) sets. Calibration was evaluated using the Brier score. We followed Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for reporting. Scikit-learn and SHAP in Python 3 were used for all analyses. The supplementary material includes code for model development and a user-friendly online MACE prediction calculator. Of the 537 included patients, 23 (4.46%) developed in-hospital MACE, with a mean age at transplantation of 52.9 years. The majority, 66.1%, were male. The XGBoost model achieved an impressive AUROC of 0.89 during the training stage. This model exhibited accuracy, precision, recall, and F1-score values of 0.84, 0.85, 0.80, and 0.79, respectively. Calibration, as assessed by the Brier score, indicated excellent model calibration with a score of 0.07. Furthermore, SHAP values highlighted the significance of certain variables in predicting postoperative MACE, with negative noninvasive cardiac stress testing, use of nonselective beta-blockers, direct bilirubin levels, blood type O, and dynamic alterations on myocardial perfusion scintigraphy being the most influential factors at the cohort-wide level. These results highlight the predictive capability of our XGBoost model in assessing the risk of post-LT MACE, making it a valuable tool for clinical practice. Our study successfully assessed the feasibility and accuracy of the XGBoost machine learning model in predicting post-LT MACE, using both cardiovascular and hepatic variables. The model demonstrated impressive performance, aligning with literature findings, and exhibited excellent calibration. Notably, our cautious approach to prevent overfitting and data leakage suggests the stability of results when applied to prospective data, reinforcing the model's value as a reliable tool for predicting post-LT MACE in clinical practice.

Machine learning-based prediction of outdoor thermal comfort: Combining Bayesian optimization and the SHAP model

Rising global temperatures have resulted in urban heat waves in recent years, endangering residents' health and even their lives. As a result, accurate outdoor thermal comfort prediction is critical. The goal of this research is to develop highly accurate and interpretable machine learning models of outdoor thermal comfort. We created a summer outdoor thermal comfort dataset for cold regions using microclimate parameter measurements and questionnaires, and divided it into two datasets: without and with shading. The prediction performance of nine machine learning models was compared, as well as their prediction performance following Bayesian optimization. Finally, the SHAP model was used to explain the important features of the best machine learning models and their impact on thermal comfort. The results show that after partitioning the dataset by shading, the accuracy of the best machine learning prediction models for Thermal sensation vote (TSV), Thermal acceptable vote (TA), and Thermal comfort vote (TCV) in the unshaded space improves by 9.2%, 9.31%, and 6.16%, respectively, while the prediction accuracy in the shaded space remains basically unchanged. Extreme gradient boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and categorical boosting (CatBoost) outperform other methods. After Bayesian optimization (BO), the machine learning models' TSV, TA, and TCV prediction accuracies improved by 6.83%, 4.05%, and 2.55%, respectively. CatBoost model with Bayesian optimization (CatBoost + BO) was the best model in most cases. TSV, TA, and TCV predictions were heavily influenced by microclimate parameters, with mean radiant temperature being the most important in unshaded spaces and air temperature being critical in shaded spaces. Furthermore, age, body mass index, and emotional state all had a significant impact on TA and TCV. This research will contribute to improving the quality of life of urban residents by providing a scientific foundation for the design and planning of urban open space.

The accuracy of machine learning models relies on hyperparameter tuning: student result classification using random forest, randomized search, grid search, bayesian, genetic, and optuna algorithms

The accuracy of machine learning models relies on hyperparameter tuning: student result classification using random forest, randomized search, grid search, bayesian, genetic, and optuna algorithms

Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction.

Post mastectomy radiotherapy (PMRT) is an independent predictor of reconstructive complications. PMRT may alter the timing and type of reconstruction recommended. This study aimed to create a machine learning model to predict the probability of requiring PMRT after immediate breast reconstruction (IBR). In this retrospective study, breast cancer patients who underwent IBR from January 2017 to December 2020 were reviewed and data were collected on 81 preoperative characteristics. Primary outcome was recommendation for PMRT. Four algorithms were compared to maximize performance and clinical utility: logistic regression, elastic net (EN), logistic lasso, and random forest (RF). The cohort was split into a development dataset (75% of cohort for training-validation) and 25% used for the test set. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), precision-recall curves, and calibration plots. In a total of 800 patients, 325 (40.6%) patients were recommended to undergo PMRT. With the training-validation dataset (n = 600), model performance was logistic regression 0.73 AUC [95% confidence interval (CI) 0.65-0.80]; RF 0.77 AUC (95% CI, 0.74-0.81); EN 0.77 AUC (95% CI, 0.73-0.81); logistic lasso 0.76 AUC (95% CI, 0.72-0.80). Without significantly sacrificing performance, 81 predictive factors were reduced to 12 for prediction with the EN method. With the test dataset (n = 200), performance of the EN prediction model was confirmed [0.794 AUC (95% CI, 0.730-0.858)]. A parsimonious accurate machine learning model for predicting PMRT after IBR was developed, tested, and translated into a clinically applicable online calculator for providers and patients.

A HYBRID FEATURE SELECTION APPROACH FOR ROMAN URDU TEXT CLASSIFICATION

Text classification is the task of assigning labels to unlabeled text data. Text classification has several applications like sentiment analysis, document classification, and fake news detection such as Machine learning (ML) methods have been used commonly in text classification in the last several years. The fundamental problem in ML is that these approaches heavily depend on feature selection methods. The models and feature selection methods used in this research. Several past types of research conclude that there is no uniform feature selection method that works well for all types of classifier tasks as well as Urdu is a resource-poor language. In this study, a proposed hybrid feature selection approach for Roman Urdu text not only reduces the dimension of the feature map but also increases the accuracy of ML models. Using 11000 and 20000 records have been used for Support Vector Classifier, Naive Base and Decision Tree which have given 80.81%, 72.94% and 76.78% respectively, among other tested methods. The best accuracy values achieved by each classifier and the hybrid features ChiSAE, CorrelationAE, and GainRAE. In future, text classification for better understanding of human being self-analysis as well as deep learning methods will be utilized for better authenticity.

Enhancing the reliability and accuracy of machine learning models for predicting carbonation progress in fly ash‐concrete: A multifaceted approach

AbstractExisting machine learning models for carbonation in fly ash blended concrete, developed and validated using accelerated or combined datasets, lack validation against natural carbonation processes in concrete. Furthermore, the reliability and accuracy of these models are directly influenced by the sets of input variables used in model training. This research specifically aims to investigate the reliability and accuracy of machine learning models, trained with accelerated datasets, in predicting the natural carbonation process. Additionally, this study introduces Gaussian process regression (GPR) as an alternative modeling tool, due to its potential for enhanced prediction accuracy. The results of this study indicate that GPR can satisfactorily predict natural carbonation progress in fly ash concrete. Moreover, the study carried out a model interpretation to provide intuitive feature contributions in the black‐box model. The results indicate that the accuracy of the natural carbonation process evaluation of models trained with accelerated carbonation dataset is less than 0.2 R2 value, indicating the inappropriateness of using accelerated carbonation data to train reliable models. Additionally, the model interpretation shows a significant dependency of the natural carbonation process on the binder's chemical composition, highlighting key chemical factors influencing carbonation.

Machine Learning Models for Predicting Zirconocene Properties and Barriers.

Zr metallocenes have significant potential to be highly tunable polyethylene catalysts through modification of the aromatic ligand framework. Here we report the development of multiple machine learning models using a large library (>700 systems) of DFT-calculated zirconocene properties and barriers for ethylene polymerization. We show that very accurate machine learning models are possible for HOMO-LUMO gaps of precatalysts but the performance significantly depends on the machine learning algorithm and type of featurization, such as fingerprints, Coulomb matrices, smooth overlap of atomic positions, or persistence images. Surprisingly, the description of the bonding hapticity, the number of direct connections between Zr and the ligand aromatic carbons, only has a moderate influence on the performance of most models. Despite robust models for HOMO-LUMO gaps, these types of machine learning models based on structure connectivity type features perform poorly in predicting ethylene migratory insertion barrier heights. Therefore, we developed several relatively robust and accurate machine learning models for barrier heights that are based on quantum-chemical descriptors (QCDs). The quantitative accuracy of these models depends on which potential energy surface structure QCDs were harvested from. This revealed a Hammett-type principle to naturally emerge showing that QCDs from the π-coordination complexes provide much better descriptions of the transition states than other potential-energy structures. Feature importance analysis of the QCDs provides several fundamental principles that influence zirconocene catalyst reactivity.

Soil Classification Mapping Using a Combination of Semi-Supervised Classification and Stacking Learning (SSC-SL)

In digital soil mapping, machine learning models have been widely applied. However, the accuracy of machine learning models can be limited by the use of a single model and a small number of soil samples. This study introduces a novel method, semi-supervised classification combined with stacking learning (SSC-SL), to enhance soil classification mapping in hilly and low-mountain areas of Northern Jurong City, Jiangsu Province, China. This study incorporated Gaofen-2 (GF-2) remote sensing imagery along with its associated remote sensing indices, the ALOS Digital Elevation Model (DEM) and their derived topographic factors, and soil parent material data in its modelling process. We first used three base learners, Ranger, Rpart, and XGBoost, to construct the SL model. In addition, we employed the fuzzy c-means clustering algorithm (FCM) to construct a clustering map. To fully leverage the information from a multitude of environmental variables, understand the distribution of data, and enhance the effectiveness of the classification, we selected unlabelled samples near the boundaries of the patches on the clustering map. The SSC-SL model demonstrated superior stability and performance, with optimal accuracy at a 0.9 confidence level, achieving an overall accuracy of 0.77 and a kappa coefficient of 0.73. These metrics exceeded those of the highest performing base learner (Ranger model) by 10.4% and 12.3%, respectively, and they outperformed the least effective base learner (Rpart model) by 27.3% and 32.9%. It notably improves the spatial distribution accuracy of soil types. Key environmental variables influencing soil type distribution include soil parent material (SPM), land use (LU), the multi-resolution valley bottom flatness index (MRVBF), and Elevation (Ele). In conclusion, the SSC-SL model offers a novel and effective approach for enhancing the predictive accuracy of soil classification mapping.

Applying graph neural network models to molecular property prediction using high-quality experimental data

Graph neural networks have been successfully applied to machine learning models related to molecules and crystals, due to the similarity between a molecule/crystal and a graph. In this paper, we present three models that are trained with high-quality experimental data to predict three molecular properties (Kováts retention index, normal boiling point, and mass spectrum), using the same GNN architecture. We show that graph representations of molecules, combined with deep learning methodologies and high-quality data sets, lead to accurate machine learning models to predict molecular properties.

Combining Deep Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification

The quantification of emotional states is an important step to understanding wellbeing. Time series data from multiple modalities such as physiological and motion sensor data have proven to be integral for measuring and quantifying emotions. Monitoring emotional trajectories over long periods of time inherits some critical limitations in relation to the size of the training data. This shortcoming may hinder the development of reliable and accurate machine learning models. To address this problem, this article proposes a framework to tackle the limitation in performing emotional state recognition: (1) encoding time series data into coloured images; (2) leveraging pre-trained object recognition models to apply a Transfer Learning (TL) approach using the images from step 1; (3) utilising a 1D Convolutional Neural Network (CNN) to perform emotion classification from physiological data; (4) concatenating the pre-trained TL model with the 1D CNN. We demonstrate that model performance when inferring real-world wellbeing rated on a 5-point Likert scale can be enhanced using our framework, resulting in up to 98.5% accuracy, outperforming a conventional CNN by 4.5%. Subject-independent models using the same approach resulted in an average of 72.3% accuracy (SD 0.038). The proposed methodology helps improve performance and overcome problems with small training datasets.

Energy Decomposition Scheme for Rectangular Graphene Flakes.

We show-to our own surprise-that total electronic energies for a family of m × n rectangular graphene flakes can be very accurately represented by a simple function of the structural parameters m and n with errors not exceeding 1 kcal/mol. The energies of these flakes, usually referred to as multiple zigzag chains Z(m,n), are computed for m, n < 21 at their optimized geometries using the DFTB3 methodology. We have discovered that the structural parameters m and n (and their simple algebraic functions) provide a much better basis for the energy decomposition scheme than the various topological invariants usually used in this context. Most terms appearing in our energy decomposition scheme seem to have simple chemical interpretations. Our observation goes against the well-established knowledge stating that many-body energies are complicated functions of molecular parameters. Our observations might have far-reaching consequences for building accurate machine learning models.

The cellulose hydrolysis into glucose with carbon-based solid acid catalyst via machine learning, life cycle assessment and bibliometric analysis

The cellulose hydrolysis into glucose with carbon-based solid acid catalyst contributes to sustainable and low-carbon development. Determining the quantitative impact of key variables on glucose yield and environmental sustainability is a notoriously time-consuming and labor-intensive process. In this study, a bibliometric analysis was carried out to examine the current research on cellulose hydrolysis into glucose using solid acid catalyst. The application of machine learning (ML) has demonstrated that the random forest (RF) model possesses optimal predictive performance concerning the glucose yield (Training R2 = 0.89, Testing R2 = 0.80, MAE = 6.52, RMSE = 8.29). The SO3H acid densities was emerged as a most crucial influential parameter. The interactions relationship of influenced factors for glucose production was clarified by the partial dependence plot analysis (PDPs). Furthermore, the life cycle assessment (LCA) further indicated that the carbon solid acid catalysts used in cellulose hydrolysis to produce glucose have lower emissions of CO2, NOx, and SO2 compared to traditional acid catalysts, which is 85.5 kg ep of CO2, 0.17 kg ep of NOx, 0.23 kg ep of SO2 for carbon solid acid catalysts, while 91.84 kg ep of CO2, 0.24 kg ep of NOx, 0.61 kg ep of SO2 emission for acid hydrolysis. Thus, carbon solid acid catalysts have a comparably lower negative environmental impact than traditional acid hydrolysis. This study provides crucial insights into improving the prediction accuracy of ML models and reducing the carbon footprint involved in glucose production.