K-fold Cross-validation Method Research Articles

Cost-Effective Multispectral Sensor and Artificial Neural Networks for the Detection of Starch Adulteration in Raw Milk

This study aims to detect starch adulteration in dairy products utilizing an artificial neural network (ANN) model. Globally, milk fraud represents a significant challenge to food safety, posing substantial health risks to consumers. In this context, spectral data derived from milk samples with varying starch concentrations were processed using feature scaling and normalization techniques. The ANN model was rigorously trained and validated employing the stratified k-fold cross-validation method, demonstrating exceptional proficiency in detecting starch-adulterated milk samples and effectively differentiating among various starch concentrations. The principal findings indicate that the model achieved 100% accuracy, coupled with high levels of precision, sensitivity, and F1-scores. Future research should explore the application of this model to different types of adulteration and extend its validation on larger datasets. Furthermore, the potential adaptability of this method for other food products and field applications warrants investigation. This study offers valuable insights for milk producers, food safety professionals, and consumers, particularly highlighting the implications for small-scale rural farms, thereby enriching the discourse on food safety within short food supply chains.

Sleep prediction using data from oximeter, accelerometer and snoring for portable monitor obstructive sleep apnea diagnosis

The aim of this study was to build and validate an artificial neural network (ANN) algorithm to predict sleep using data from a portable monitor (Biologix system) consisting of a high-resolution oximeter with built-in accelerometer plus smartphone application with snoring recording and cloud analysis. A total of 268 patients with suspected obstructive sleep apnea (OSA) were submitted to standard polysomnography (PSG) with simultaneous Biologix (age: 56±11\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$56\\pm 11$$\\end{document} years; body mass index: 30.9±4.6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$30.9\\pm 4.6$$\\end{document}kg/m2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {kg/m}^{2}$$\\end{document}, apnea-hypopnea index [AHI]: 35±30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$35\\pm 30$$\\end{document} events/h). Biologix channels were input features for construction an ANN model to predict sleep. A k-fold cross-validation method (k=10) was applied, ensuring that all sleep studies (N=268; 246,265 epochs) were included in both training and testing across all iterations. The final ANN model, evaluated as the mean performance across all folds, resulted in a sensitivity, specificity and accuracy of 91.5%, 71.0% and 86.1%, respectively, for detecting sleep. As compared to the oxygen desaturation index (ODI) from Biologix without sleep prediction, the bias (mean difference) between PSG-AHI and Biologix-ODI with sleep prediction (Biologix-Sleep-ODI) decreased significantly (3.40 vs. 1.02 events/h, p<0.001). We conclude that sleep prediction by an ANN model using data from oximeter, accelerometer, and snoring is accurate and improves Biologix system OSA diagnostic precision.

Predictive modeling of sustainable recycled materials for stone column construction

Climate change due to carbon dioxide emissions is one of the most significant global challenges. Sustainable recycled materials are recognized as a critical factor in mitigating climate change, representing a fundamental strategy for reducing gas emissions and preserving the environment. Promising sustainable recycled materials in construction, such as crushed asphalt, crushed concrete, crushed ceramic, and treated concrete offer numerous advantages, including reduced carbon dioxide (CO2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{(CO}}_{2} )$$\\end{document} emissions, cost-effectiveness, increased strength, and enhanced mechanical properties. In this study, various machine learning techniques, including the support vector machine (SVM), multiple linear regression (MLR), gaussian process regression (GPR), and artificial neural network (ANN), are used to assess the effectiveness of sustainable recycled materials within stone columns. The MLR model was specifically utilized to predict the ultimate stress equation for these materials within stone columns. Forty-five constructed samples were used in developing the models. The effectiveness of each model was evaluated using various statistical assessment measures, including mean absolute error (MAE), absolute fraction of variation (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document}), and root mean square error (RMSE). The predictive model’s performance was validated using the k-fold cross-validation method. The ANN model with an R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document} value of 0.962 and an RMSE value of 7.268 kPa, performed better compared to GPR, SVM and MLR models. In summary, the results of the study indicate that the MLR model, utilizing the identified input parameters, can accurately predict the ultimate stress for different sustainable recycled materials. Implementing such technologies within the construction sector can expedite and reduce the cost of assessing material characteristics and the influence of input parameters.

Comparison of Classification Results of SVM, KNN, Decision Tree, and Ensemble Methods in Diabetes Diagnosis

This study aims to determine which algorithms and test techniques are the most optimal in detecting diabetes mellitus and obtaining the best results based on the value of accuracy, precision, and recall. In this study, approaches were used in early diagnosis of diabetes using KNN, SVM, Decision Tree, and Ensemble Majority Voting methods in Percentage Split and K-Fold Cross Validation methods. Diabetes is a disease characterized by high blood sugar (glucose) levels and can cause a variety of disease complications and damage to the body's organs if not treated immediately. Early diagnosis of diabetes is becoming crucial so that people can take immediate action to the hospital for immediate treatment. The data used is Healthcare-Diabetes from Kaggle. The results of this study have found that the K-Fold Cross Validation method is better because it can provide an average improvement in Ensemble accuracy of 13.42% compared to the Percentage Split method which only gives an average increase in Ensamble accuracy of 9.15%. The best algorithm for classifying diabetes disease is the Ensemble Majority Voting algorithm using the K-Fold Cross Validation method with a 98.81% accuracy rate. These excellent research results may contribute to detecting early symptoms of diabetes before it become too severe.

Vascular Biomarkers for Pulmonary Nodule Malignancy: Arteries vs. Veins.

This study aims to investigate the association between the arteries and veins surrounding a pulmonary nodule and its malignancy. A dataset of 146 subjects from a LDCT lung cancer screening program was used in this study. AI algorithms were used to automatically segment and quantify nodules and their surrounding macro-vasculature. The macro-vasculature was differentiated into arteries and veins. Vessel branch count, volume, and tortuosity were quantified for arteries and veins at different distances from the nodule surface. Univariate and multivariate logistic regression (LR) analyses were performed, with a special emphasis on the nodules with diameters ranging from 8 to 20 mm. ROC-AUC was used to assess the performance based on the k-fold cross-validation method. Average feature importance was evaluated in several machine learning models. The LR models using macro-vasculature features achieved an AUC of 0.78 (95% CI: 0.71-0.86) for all nodules and an AUC of 0.67 (95% CI: 0.54-0.80) for nodules between 8-20 mm. Models including macro-vasculature features, demographics, and CT-derived nodule features yielded an AUC of 0.91 (95% CI: 0.87-0.96) for all nodules and an AUC of 0.82 (95% CI: 0.71-0.92) for nodules between 8-20 mm. In terms of feature importance, arteries within 5.0 mm from the nodule surface were the highest-ranked among macro-vasculature features and retained their significance even with the inclusion of demographics and CT-derived nodule features. Arteries within 5.0 mm from the nodule surface emerged as a potential biomarker for effectively discriminating between malignant and benign nodules.

Prediksi Harga Crypto dengan Algoritma Jaringan Saraf Tiruan

Cryptocurrency has emerged as a crucial element in the global financial market, prompting investor interest in predicting prices for informed investment decision-making. However, the highly volatile nature of cryptocurrency prices has made trading in the crypto market speculative and laden with significant risks. Therefore, this study aims to apply Artificial Neural Networks (ANNs) using the Long Short-Term Memory (LSTM) algorithm to forecast cryptocurrency prices. This method leverages the ANN's ability to recognize complex patterns and trends in historical cryptocurrency price data. The research findings demonstrate significant accuracy levels for three types of crypto: BTC (86.86%), BNB (96.8%), and Doge (97%). Evaluation was conducted using the k-fold cross-validation method with k=5, where data was divided into five equally sized groups. Accuracy was computed by comparing actual prices with those predicted by the LSTM model. This evaluation approach provides robust insights into the LSTM model's effectiveness in predicting cryptocurrency prices, considering significant data variations. The predicted values from this study are BTC at $4306, BNB at $58.7, and Dogecoin at $0.037. These implications underscore the critical role of the number of epochs in influencing the performance and accuracy of cryptocurrency price predictions. Thus, this research is poised to offer deeper insights into cryptocurrency market behavior and provide practical guidance for investors to make more informed and measured investment decisions.

Comparison of machine learning algorithms and multiple linear regression for live weight estimation of Akkaraman lambs

This study was designed to predict the post-weaning weights of Akkaraman lambs reared on different farms using multiple linear regression and machine learning algorithms. The effect of factors the age of the dam, gender, type of lambing, enterprise, type of flock, birth weight, and weaning weight was analyzed. The data was collected from a total of 25,316 Akkaraman lambs raised at multiple farms in the Çiftlik District of Niğde province. Comparative analysis was conducted by using multiple linear regression, Random Forest, Support Vector Machines (and Support Vector Regression), Extreme Gradient Boosting (XGBoost) (and Gradient Boosting), Bayesian Regularized Neural Network, Radial Basis Function Neural Network, Classification and Regression Trees, Exhaustive Chi-squared Automatic Interaction Detection (and Chi-squared Automatic Interaction Detection), and Multivariate Adaptive Regression Splines algorithms. In this study, the test dataset was divided into five layers using the K-fold cross-validation method. The performance of models was compared using performance criteria such as Adjusted R-squared (Adj-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document}), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE) by utilizing test populations in the predicted models. Additionally, the presence of low standard deviations for these criteria indicates the absence of an overfitting problem. \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document}The comparison results showed the Random Forest algorithm had the best predictive performance compared to other algorithms with Adj-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document}, RMSE, MAD, and MAPE values of 0.75, 3.683, 2.876, and 10.112, respectively. In conclusion, the results obtained through Multiple Linear Regression for the live weights of Akkaraman lambs were less accurate than the results obtained through artificial neural network analysis.

Machine Learning-Based Models for Accurate Car Prices Prediction

The used car market is becoming more and more popular in various countries, but some car trading platforms have inaccuracies in predicting prices. So, it is necessary to select machine learning models with high accuracy to predict used car prices for both buyers and sellers. This study selected three factors that have the greatest impact on used car prices, which are: car name, car use year, and car use mileage. In the selection of dataset, this study chooses more than 2000 data from four brands of BMW, Volkswagen, Acura, and Tesla which are very popular in the market to predict. In the part of comparing the accuracy of machine learning models, this paper uses three machine learning models: linear regression, decision tree regressor and random forest regressor. Based on criteria such as root-mean-square error and R-variance, with K-Fold cross validation method to compare the advantages and disadvantages of every model. Through the analysis, all the test criteria consistently show that random forest regressor has the highest performance and can achieve an R-square value of 0.8562. After getting the suitable model, accurate price prediction will be realized in more car trading platforms to help buyers and sellers to understand the real price of used cars.

A Novel Classification Model Based on Hybrid K-Means and Neural Network for Classification Problems

We propose a new classification model—a new classification model for clustering overlapping problems based on K-Means and neural networks. K-means clustering algorithm belongs to unsupervised learning. It is a classic algorithm for solving clustering problems. Since this algorithm calculates its categories based on distance, the results tend to converge to the local optimal solution and have poor boundary clustering properties. The K-Means classification algorithm defines clusters by the distance between the cluster center value and the target object, and the optimal result is obtained through continuous iteration. Therefore, clustering results are overlapped, and there are often outliers that do not belong to the current cluster, resulting in unsatisfactory clustering results. Our model offers a new method to segment non-ideal data in overlapping regions. Since clustering algorithms cannot effectively identify and classify this part of the data, we split this part of the data and train it using a neural network. The results are then integrated into the clustered data. In the experiment, the k-fold cross-validation method ensures the model stability of the results. We used the accuracy to evaluate the quality of the model, and we used standard deviation and mean deviation to detect clustering results. Five sets of experimental data from the cross-experiment show that compared with the K-Means classification model, the accuracy of our model is effectively improved. Doi: 10.28991/HIJ-2024-05-03-012 Full Text: PDF

A comparative study on breast cancer classification with stratified shuffle split and K-fold cross validation via ensembled machine learning

In breast cancer, early diagnosis and treatment method hold paramount significance for the augmented survival rates. Through a comprehensive dataset including clinical and genomic information, this study assesses the diverse analytical techniques used in breast cancer classification by the employment of four different machine learning algorithms. There were notable differences in classification findings, emphasizing the necessity of using adept analytical tools to improve the accuracy of breast cancer classification. Among individual algorithms, LGBM has the highest F1 score of 99.2% and a remarkable accuracy of 98.9%. Ensembles comprising AdaBoost, GBM, and RGF outperformed individual techniques with an astonishing 99.5% accuracy. The best ensemble algorithms prioritize features like worst texture, worst concave points, mean concave points, and mean texture, crucial for the classification. The examination of the advantages of ensemble learning methods, which combine predictions from many classifiers to improve classification performance, is at the heart of this the study. In particular, it is revealed how the k-fold and stratified shuffle split cross-validation methods differ in the classification results, providing clinicians a thorough understanding of the clinical ramifications to decipher the complex facets of breast cancer classification and identify crucial tumor traits that can distinguish malignant from benign cases.

Implications of crop yield distributions for multiperil crop insurance rating in Ghana: a lasso model application

PurposeThe objective of this paper is to examine crop yield predictions and their implications on MPCI in Ghana. Farmers in developing countries struggle with their ability to deal with agricultural risks. Providing aid for farmers and their households remains instrumental in combatting poverty in Africa. Several studies have shown that correctly understanding and implementing risk management strategies will help in the poverty alleviation agenda.Design/methodology/approachThis study examines the importance of crop yield distributions in Ghana and its implication on multiperil crop insurance (MPCI) rating using the Lasso regression model. A Bonferroni test was employed to test the independence of crop yields across the regions while the Kruskal-Wallis H test was conducted to examine statistical differences in mean yields of crops across the ten regions. The Bayesian information criteria and k-fold cross-validation methods are used to select an appropriate Lasso regression model for the prediction of crop yields. The study focuses on the variability of the threshold yields across regions based on the chosen model.FindingsIt is revealed that threshold yields differ significantly across the regions in the country. This implies that the payment of claims will not be evenly distributed across the regions, and hence regional disparities need to be considered when pricing MPCI products. In other words, policymakers may choose to assign respective weights across regions based on their threshold yields.Research limitations/implicationsThe primary limitation is the unavailability of regional climate data which could have helped in a better explanation of the variation across the regions.Originality/valueThis is the first study to examine the implications of regional crop yield variations on multiperil crop insurance rating in Ghana.

Recognizing salat activity using deep learning models via smartwatch sensors

In this study, we focus on human activity recognition, particularly aiming to distinguish the activity of praying (salat) from other daily activities. To achieve this goal, we have created a new dataset named HAR-P (Human activity recognition for Praying), which includes eight different activities: walking, running, sitting, standing, walking upstairs, walking downstairs, typing with a keyboard, and praying (salat). The HAR-P dataset was collected from 50 male individuals, who wore smartwatches on their dominant wrists. We compare the activity classification performance using three state-of-the-art algorithms from the literature: Long Short-Term Memory, Convolutional Long Short-Term Memory, and Convolutional Neural Network—Long Short-Term Memory. To assess the influence of sensors, data from accelerometer, gyroscope, linear acceleration sensor, and magnetic field sensor were utilized. The impact of individual sensor data as well as combinations thereof was investigated. The highest classification accuracy within single sensor groups, reaching 95.7%, was achieved using the accelerometer data with the Convolutional Long Short-Term Memory method. Combining two sensor groups resulted in an increase in accuracy of up to 9%. The highest accuracy of 96.4% was obtained by utilizing three sensor groups together with the Convolutional Neural Network—Long Short-Term Memory method. Furthermore, the evaluation of sensor and model performance was conducted using the stratified k-fold cross-validation method with 5-folds. These findings contribute significantly to evaluating the performance of sensor combinations and different algorithms in activity classification. This study may provide an effective foundation for the automatic recognition and tracking of human activities and offer an applicable model, particularly for the recognition of religious practices such as praying.

Evaluating and Enhancing Artificial Intelligence Models for Predicting Student Learning Outcomes

Predicting student outcomes is an essential task and a central challenge among artificial intelligence-based personalised learning applications. Despite several studies exploring student performance prediction, there is a notable lack of comprehensive and comparative research that methodically evaluates and compares multiple machine learning models alongside deep learning architectures. In response, our research provides a comprehensive comparison to evaluate and improve ten different machine learning and deep learning models, either well-established or cutting-edge techniques, namely, random forest, decision tree, support vector machine, K-nearest neighbours classifier, logistic regression, linear regression, and state-of-the-art extreme gradient boosting (XGBoost), as well as a fully connected feed-forward neural network, a convolutional neural network, and a gradient-boosted neural network. We implemented and fine-tuned these models using Python 3.9.5. With a keen emphasis on prediction accuracy and model performance optimisation, we evaluate these methodologies across two benchmark public student datasets. We employ a dual evaluation approach, utilising both k-fold cross-validation and holdout methods, to comprehensively assess the models’ performance. Our research focuses primarily on predicting student outcomes in final examinations by determining their success or failure. Moreover, we explore the importance of feature selection using the ubiquitous Lasso for dimensionality reduction to improve model efficiency, prevent overfitting, and examine its impact on prediction accuracy for each model, both with and without Lasso. This study provides valuable guidance for selecting and deploying predictive models for tabular data classification like student outcome prediction, which seeks to utilise data-driven insights for personalised education.

Predicting the potential toxicity of the metal oxide nanoparticles using machine learning algorithms

Over the years, machine learning (ML) algorithms have proven their ability to make reliable predictions of the toxicity of metal oxide nanoparticles. This paper proposed a predictive ML model of the potential toxicity of metal oxide nanoparticles. A dataset consisting of 79 descriptors including 24 metal oxide nanoparticles (MexOy NPs) and their physicochemical and structural characteristics is adopted. The proposed model comprises of three main phases. The first phase is used to analyze the characteristics of nanoparticles along with their toxicity behavior. In the second phase, the problems associated with the metal oxide nanoparticles dataset are tackled. The first problem namely the class imbalance problem is handled through utilizing synthetic minority over-sampling technique (SMOTE). The second problem namely the outliers is handled through applying a novel feature selection algorithm based on the enhanced binary version of the sine tree-seed algorithm (EBSTSA). The proposed EBSTSA is used to find the relevant features affecting toxicity. The density-based spatial clustering of applications with noise (DBSCAN) is utilized as a tool for identifying outliers in the dataset and for visualizing the impact of the feature selection on the performance of the subsequent classification. Finally, in the third phase, the support vector machine (SVM) supervised machine learning algorithm and k-fold cross-validation method are applied to classify the mode of action of each instance of nanoparticle as toxic or nontoxic. The simulation results showed that the EBSTSA-based feature selection algorithm is reliable and robust across 23 benchmark datasets from the UCI machine learning repository. The results also showed that proposed EBSTSA can effectively find the relevant descriptors for nano-particles. Furthermore, the results demonstrated the efficacy of the proposed ML toxicity prediction model. It is obtained on average 1.02% of error rate, 100% of specificity, 98.87% of sensitivity, and 99.47% of f1-score.

Mitigating adversarial threats in deep CT image diagnosis models via a dual-stage inference-time defense

Artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML) have revolutionized disease diagnosis using complex medical images such as X-rays and CT scans, significantly improving accuracy in identifying various medical conditions. However, concerns arise regarding the trustworthiness of these models due to their vulnerability to adversarial attacks, potentially leading to inaccurate predictions. In response, we propose an innovative pipeline defensive approach that integrates denoising techniques like Total Variation Minimization (TVM) and Non-local (NL) means, followed by a fuzzy logic-based image transformer called Fuzzy Image Transformations (FIT). Deployed during the inferencing phase as a preprocessing module, this strategy aims to fortify DL medical diagnosis models, addressing adversarial vulnerabilities and ensuring their reliability in critical healthcare applications. Our focus is narrowed down to COVID-19 diagnosis, where we initially developed a model for accurately classifying lung CT images, covering both normal and COVID-19 pneumonia cases. Our experimental procedures, employing the Stratified K-Fold cross-validation method with K=5, systematically evaluate the model’s susceptibility to benchmark adversarial attacks such as the Fast Gradient Sign Method (FGSM), revealing a significant drop in aggregate average metrics across all folds under adversarial conditions. Specifically, precision (P), recall (R), accuracy (A), and F1-score (F) metrics decrease from 95.86 %, 96.16 %, 95.85 %, and 96.00–12.54 %, 13.13 %, 12.63 %, and 12.83 %, respectively. Notably, our proposed pipeline defense approach demonstrates substantial efficacy in enhancing diagnostic models, with average P, R, A, and F metrics improving impressively to 92.65 %, 92.39 %, 92.65 %, and 92.51 %, respectively, when applied to the same adversarial images. These promising results underscore the potential of our proposed pipeline defense techniques to enhance the resilience and reliability of AI-based diagnostic systems.

Automatic lithology identification in meteorite impact craters using machine learning algorithms

Identifying lithologies in meteorite impact craters is an important task to unlock processes that have shaped the evolution of planetary bodies. Traditional methods for lithology identification rely on time-consuming manual analysis, which is costly and limits the efficiency of rapid decision-making. This paper utilizes different machine learning algorithms namely Random Forest, Decision Tree, K Nearest Neighbors, and Logistic Regression with Grid Search to classify rock lithologies using data from the Bosumtwi impact crater in Ghana. A repeated stratified k-fold cross-validation method is applied to Grid Search to select the best combination of hyperparameters. The findings demonstrate that the Random Forest algorithm achieves the most promising results in classifying lithologies in the meteorite impact crater with an accuracy score of 86.89%, a recall score of 84.88%, a precision score of 87.21%, and an F1 score of 85.48%. The findings also suggest that more high-quality data has the potential to further increase the accuracy scores of the machine learning algorithm. In conclusion, this study demonstrates the significant potential of machine learning techniques to revolutionize lithology identification in meteorite impact craters, thus paving the way for their influential role in future space exploration endeavors.

Study on the relationship between body mass index and blood pressure indices in children aged 7-17 during COVID-19.

To explore the relationship between body mass index (BMI), age, sex, and blood pressure (systolic blood pressure, SBP; diastolic blood pressure, DBP) in children during COVID-19, providing reference for the prevention and screening of hypertension in children. This study adopted a large-scale cross-sectional design to investigate the association between BMI and blood pressure in 7-17-year-old students in City N, China, during COVID-19. Thirty-six primary and secondary schools in City N were sampled using a stratified cluster sampling method. A total of 11,433 students aged 7-17 years in City N, China, were selected for blood pressure (Diastolic blood pressure, DBP, Systolic blood pressure, SBP), height, and weight, Resting heart rate (RHR), chest circumference, measurements, and the study was written using the STROBE checklist. Data analysis was conducted using SPSS 26.0, calculating the mean and standard deviation of BMI and blood pressure for male and female students in different age groups. Regression analysis was employed to explore the impact of BMI, age, and sex on SBP and DBP, and predictive models were established. The model fit was evaluated using the model R2. The study included 11,287 primary and secondary school students, comprising 5,649 boys and 5,638 girls. It was found that with increasing age, BMI and blood pressure of boys and girls generally increased. There were significant differences in blood pressure levels between boys and girls in different age groups. In regression models, LC, Age, BMI, and chest circumference show significant positive linear relationships with SBP and DBP in adolescents, while RHR exhibits a negative linear relationship with SBP. These factors were individually incorporated into a stratified regression model, significantly enhancing the model's explanatory power. After including factors such as Age, Gender, and BMI, the adjusted R2 value showed a significant improvement, with Age and BMI identified as key predictive factors for SBP and DBP. The robustness and predictive accuracy of the model were further examined through K-fold cross-validation and independent sample validation methods. The validation results indicate that the model has a high accuracy and explanatory power in predicting blood pressure in children of different weight levels, especially among obese children, where the prediction accuracy is highest. During COVID-19, age, sex, and BMI significantly influence blood pressure in children aged 7-17 years, and predictive models for SBP and DBP were established. This model helps predict blood pressure in children and reduce the risk of cardiovascular diseases. Confirmation of factors such as sex, age, and BMI provide a basis for personalized health plans for children, especially during large-scale infectious diseases, providing guidance for addressing health challenges and promoting the health and well-being of children.

Construction and Analysis of Machine Learning Based Transportation Carbon Emission Prediction Model

Addressing the issue of carbon emissions in the transportation sector, this research constructed various predictive models using multiple machine learning algorithms based on panel data from 30 provinces in China from 2005 to 2019. The study aimed to identify the optimal machine learning algorithm and key factors influencing the carbon emissions of transportation, providing potent references for policymakers and decision-makers to reduce carbon emissions and promote the sustainable development of the transportation sector. Initially, drawing from the concept of the fixed effects model, we included the heterogeneity differences among provinces as an important factor. We further employed a combined method of Pearson's correlation coefficient and Spearman's rank correlation coefficient to screen 18 factors influencing transportation carbon emissions. We then made a preliminary selection of seven common machine learning algorithms and used the screened factors as explanatory variables for model training. The three algorithms with the best performance were further optimized and trained. Subsequently, we utilized the K-fold cross-validation method; plotted learning curves to test the performance of each predictive model; and used MSE, MAE, R2, and MAPE as evaluation indicators to determine the best predictive model. SHAP values were chosen to calculate the importance of each explanatory variable in the optimal predictive model. The results indicated that the multicollinearity among the seven factors of provincial differences, total consumption of social goods, urban green space area, freight turnover, number of private cars, transportation industry output, and permanent population was weak, and all passed the significance test. They could be used as explanatory variables in the prediction model of transportation carbon emissions. The prediction results of the Random Forest and XGBoost algorithms were both outstanding, with R2 values above 0.97 and errors below 10 %, showing no signs of overfitting or underfitting. Among them, the XGBoost algorithm performed the best, whereas the KNN algorithm performed poorly. The importance ranking of the explanatory variables was as follows:provincial differences &gt; total consumption of social goods &gt; number of private cars &gt; permanent population &gt; freight turnover &gt; urban green space area &gt; transportation industry output. A comprehensive analysis of relevance and importance showed that provincial differences were an indispensable variable in the prediction of transportation carbon emissions. In conclusion, this study provides a new approach to the governance of carbon emissions in the transportation industry, and the results can serve as a reference for policymakers and decision-makers. In future policy design and decision-making, the distinctive factors of each province should not be overlooked. Measures targeted at specific regions need to be formulated to promote the sustainable development of the transportation industry.

Optimized long short-term memory with rough set for sustainable forecasting renewable energy generation

Research and development in the field of renewable energy is receiving more attention as a result of the growing demand for clean, sustainable energy. This paper proposes a model for forecasting renewable energy generation. The proposed model consists of three main phases: data preparation, feature selection-based rough set and nutcracker optimization algorithm (NOA), and data classification and cross-validation. First, the missing values are tackled using the mean method. Then, data normalization and data shuffling are applied in the data preparation phase. In the second phase, a new feature selection algorithm is proposed based on rough set theory and NOA, namely RSNOA. The proposed RSNOA is based on adopting the rough set method as the fitness function during the searching mechanism to find the optimal feature subset. Finally, a custom long-short-term memory architecture with the k-fold cross-validation method is utilized in the last phase. The experimental results revealed that the proposed model is very competitive. It is achieved with 4.2113 root mean square error, 0.96 R², 2.835 mean absolute error, and 4.6349 mean absolute percentage error. The findings also show that the proposed model has great promise as a useful tool for accurately forecasting renewable energy generation across various sources.

Prediction of Deleterious Single Amino Acid Polymorphisms with a Consensus Holdout Sampler.

Single Amino Acid Polymorphisms (SAPs) or nonsynonymous Single Nucleotide Variants (nsSNVs) are the most common genetic variations. They result from missense mutations where a single base pair substitution changes the genetic code in such a way that the triplet of bases (codon) at a given position is coding a different amino acid. Since genetic mutations sometimes cause genetic diseases, it is important to comprehend and foresee which variations are harmful and which ones are neutral (not causing changes in the phenotype). This can be posed as a classification problem. Computational methods using machine intelligence are gradually replacing repetitive and exceedingly overpriced mutagenic tests. By and large, uneven quality, deficiencies, and irregularities of nsSNVs datasets debase the convenience of artificial intelligence-based methods. Subsequently, strong and more exact approaches are needed to address these problems. In the present work paper, we show a consensus classifier built on the holdout sampler, which appears strong and precise and outflanks all other popular methods. We produced 100 holdouts to test the structures and diverse classification variables of diverse classifiers during the training phase. The finest performing holdouts were chosen to develop a consensus classifier and tested using a k-fold (1 ≤ k ≤5) cross-validation method. We also examined which protein properties have the biggest impact on the precise prediction of the effects of nsSNVs. Our Consensus Holdout Sampler outflanks other popular algorithms, and gives excellent results, highly accurate with low standard deviation. The advantage of our method emerges from using a tree of holdouts, where diverse LM/AI-based programs are sampled in diverse ways.