The Experimental Process Design of Artificial Lightweight Aggregates Using an Orthogonal Array Table and Analysis by Machine Learning

The Moderate Resolution Imaging Spectroradiometer (MODIS) level-3 chlorophyll-a (Chl-a) product is one of the widely used ocean colour products that is often used for water quality monitoring of marine ecosystems. However, this product includes a large amount of missing data due to high surface reflectance and cloudy conditions that inevitably affect its suitability for spatiotemporal analysis of water quality. The objective of this study was to compare four Machine Learning (ML) techniques including K-nearest neighbour (KNN), Support Vector Regression (SVR), Random Forest Regression (RFR), and Artificial Neural Network (ANN) with well-known Data Interpolation Empirical Orthogonal Function (DINEOF) method for spatiotemporal missing imputation of MODIS Chl-a. The Southern Caspian Sea, which has a high Chl-a concentration, was selected as the case study. A cross-validation approach ranging missing data ratio from 0.1 to 0.8 was implemented to investigate the optimal parameters of the models and compare their performance for missing imputation. The results indicated that all ML models, except KNN, outperformed the DINEOF method for missing imputation of MODIS Chl-a. The SVR with the highest accuracy and the lowest variability of errors had the best performance among the five competing models, while the KNN showed the worst performance. The main reason for the better accuracy of the SVR than the other models is its structural risk minimization procedure that leads to the better generalization of the SVR model. The current results showed that the ML techniques used in the current study, the SVR in particular, are able to produce reliable imputations of the MODIS Chl-a missing data and can be a useful tool in water quality monitoring of marine ecosystems.

Purpose This study aims to enhance cryptocurrency price and trend prediction by applying advanced machine learning (ML) techniques. Given the market’s high volatility and complexity, the research identifies effective models for different conditions, providing insights for investors and risk management. Design/methodology/approach This study proposes a six-stage framework for cryptocurrency price prediction, integrating advanced ML techniques. Data from ten cryptocurrencies are processed, extracting 37 key features, including return, the Fear and Greed Index and various technical indicators. The model employs Deep Q-Networks (DQN), Long Short-Term Memory (LSTM) and multiple regression methods such as linear regression, support vector regression, ridge, LASSO, decision tree, Random Forest, multi-layer perceptron, stochastic gradient descent, elastic net and Bayesian regression. Model performance is evaluated using trading strategies and metrics like accuracy, sensitivity, recall, MSE, MAE and F1-score. Findings The results indicate that complex models like DQN and LSTM excel in volatile markets due to their ability to capture intricate price patterns, whereas simpler models such as linear regression and ridge regression perform better in stable conditions. The multi-layered parallel design enhances computational efficiency, enabling independent asset evaluation. These findings highlight the potential of artificial intelligence in improving prediction accuracy and supporting informed investment decisions. Originality/value This research introduces a novel six-stage ML framework incorporating diverse predictive models and key features for cryptocurrency forecasting. The multi-layered parallel approach enhances computational efficiency, setting this study apart from existing research. The comparative analysis of models offers valuable guidance for investors, traders and financial analysts navigating volatile cryptocurrency markets.

As Monkeypox becomes a prevalent public health issue, it is important to develop advanced detection and prediction methods that will inform public health strategies that govern Monkeypox prevention. This study employs machine learning methods to analyze and predict Monkeypox case trends. In particular, features on new cases and deaths were applied to regression and classification models to predict the total number of Monkeypox cases and new case probablity. The regression models that were applied included Linear regression (LR), Decision Tree Regression (DT), Random Forest Regression (RF), Support Vector Regression (SVR), and K-Nearest Neighbor Regression (KNN), with total cases as the outcome. Among regression methods, the Random Forest Regression model performed the best with a Mean Squared Error (MSE) of 92,425,437.81 and R-squared of 0.06, indeicating moderate predictive ability. The methods were also similar to predict new cases, and once again the same algorithms were applied to classification methods, including Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) classification, and each model achieved an accuracy score of one (1.00), indicating no new cases would be missed. These results provide evidence that these are effective machine learning methods, and random forests in particular provides the best predictive capability for Monkeypox case trend analysis. The results illustrate how these models can assist data-driven decisions in public health, and evidence-based preparedness and response for future Monkeypox outbreaks.

The prediction of wind energy generation is important to enhance the performance and dependability of renewable energy systems due to the rising demand for wind-generated electricity and advancements in wind energy technology competitiveness. This study leverages advanced machine learning (ML) and some other statistical and deep learning based time series forecasting models to enhance the accuracy of wind energy predictions. This comprehensive analysis includes nine ML models—Linear Regression, Random Forests (RF), Gradient Boosting Machines (GBM), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Support Vector Regression (SVR), and Neural Networks—as well as Four time-series forecasting models—ARIMA, Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks and GRU. Each ML model underwent rigorous cross-validation to ensure optimal performance. The assessment criteria utilized here comprised the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² Score. It was found that among the nine ML models, Random Forests, GBM and KNN consistently provided superior accuracy and robustness, making them the top choices for wind energy prediction whereas the performance of linear regression, SVM and SVR were very poor for the considered dataset. From the experiment, Random Forest, GBM, and KNN showed the best performance with low MSE values of 0.77, 1.95, and 1.51 respectively, while other models had MSEs above 7.5, with AdaBoost reaching 30. Their RMSEs (0.88, 1.40, 1.23) and MAEs (0.093, 0.73, 0.10) also indicate strong predictive accuracy compared to the rest.In this paper, time series forecasting, TCNs, LSTM and GRU networks showed strong capabilities in capturing temporal dependencies and trends within the wind energy data. Visualization techniques were employed to compare model performances comprehensively, providing clear insights into their predictive power. Therefore, this present study offers a robust framework for researchers and practitioners aiming to leverage machine learning and time series forecasting in the realm of renewable energy prediction.

Intensifying requests for energy is preceding towards renewable solar energy integration with nonrenewable energy resources. Unlike other nonrenewable energy resources, solar energy is recurrent. Effectual utilization of spontaneously available energy accurate solar power forecasting is essential. This study aims to predict solar power through deep neural networks (DNNs) and various machine learning (ML) techniques on a solar dataset, namely linear regression, support vector regression, random forest, etc. The dataset that is used contains solar power energy extracted every five minutes. Moreover, a comparative study is carried out between DNNs and ML techniques, which helps in crafting suitable decisions to select appropriate forecasting and prediction techniques.

This study applies machine learning (ML) techniques to model and predict Assam’s agricultural Gross State Domestic Product (GSDP). Three predictive models—multiple linear regression, random forest regression, and gradient boosting—are evaluated. The random forest model achieved the best fit, exhibiting the highest R² and the lowest mean squared error (MSE) and Akaike information criterion (AIC), along with statistically significant coefficients. Ensemble methods (random forest and gradient boosting) markedly improve forecast accuracy of agricultural growth trends compared to traditional regression, yielding more reliable predictions of productivity and GSDP contributions. The findings underscore the vital role of agricultural productivity in driving economic growth, strengthening GSDP, and supporting food security and employment. Integrating advanced ML techniques with statistical analysis provides insights for policymakers to make data-driven decisions that foster sustainable agricultural development and economic prosperity in Assam. Objectives: Predict Assam’s agricultural sector performance using selected machine learning models. Evaluate and compare the effectiveness of these models in assessing the state’s agricultural economy. Methods: Data preprocessing involved handling outliers (using interquartile range and mean-max scaling) and feature selection via correlation heatmaps. Predictive models (multiple linear regression, random forest regression, and gradient boosting) were implemented in Python. Results: The gradient boosting model emerged as the most effective, achieving the highest accuracy and generalization (testing R² = 0.9867). Farm area, labour, maize yield, and autumn rice yield were the most significant positive contributors to GSDP. The random forest model performed similarly well (R² = 0.9867), while the multiple linear regression model was least accurate (R² = 0.9521), likely due to its inability to capture nonlinear relationships. Conclusions: Machine learning models offer transformative potential for Assam’s agricultural sector. Leveraging data-driven insights from these models can empower policymakers to design targeted interventions, promoting inclusive and sustainable economic growth in the region.

PurposeGiven the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance of microfinance institutions (MFIs).Design/methodology/approachThis study gathered 9,059 firm-year observations spanning from 2003 to 2018 from the World Bank's Mix Market database. To predict the financial performance of MFIs, the authors applied a range of machine learning regression approaches to both training and testing data sets. These included linear regression, partial least squares, linear regression with stepwise selection, elastic net, random forest, quantile random forest, Bayesian ridge regression, K-Nearest Neighbors and support vector regression. All models were implemented using Python.FindingsThe findings revealed the random forest model as the most suitable choice, outperforming the other models considered. The effectiveness of the random forest model varied depending on specific scenarios, particularly the balance between training and testing data set proportions. More importantly, the results identified operational self-sufficiency as the most critical factor influencing the financial performance of MFIs.Research limitations/implicationsThis study leveraged machine learning on a well-defined data set to identify the factors predicting the financial performance of MFIs. These insights offer valuable guidance for MFIs aiming to predict their long-term financial sustainability. Investors and donors can also use these findings to make informed decisions when selecting their potential recipients. Furthermore, practitioners and policymakers can use these findings to identify potential financial performance vulnerabilities.Originality/valueThis study stands out by using a global data set to investigate the best model for predicting the financial performance of MFIs, a relatively scarce subject in the existing microfinance literature. Moreover, it uses advanced machine learning techniques to gain a deeper understanding of the factors affecting the financial performance of MFIs.

In this study, machine learning (ML) techniques are employed to predict used car prices. Several features are used to calculate the price of used cars, but in this paper, we find efficient ways to find the most precise car prices. Despite the fact that there are websites offering this service, they could not employ the most precise prediction system. It is also possible to predict a used car's true market value using a variety of models and techniques. It's important to understand their genuine market value before buying or selling. Both buyers and sellers will be benefitted from these accurate predictions. Support Vector regression, Random Forest regression, and CatBoost regression techniques are used in the proposed system. In the existing method [13], mean absolute error for decision tree regression was 0.6711, which was the least among other algorithms like Linear regression, Lasso regression, Ridge regression, Bayesian Ridge regression, and etc., they used. In the proposed system, mean absolute error (MAE) for Support Vector regression, CatBoost regression and Random Forest regression techniques are 0.1459, 0.1371 and 0.1284 respectively. The prices of second hand/used cars are predicted using the CatBoost regression, Support Vector regression, and Random Forest regression techniques. The accuracy of these algorithms are 86.28%, 85.40% and 87.16%. Among these three algorithms, Random Forest regression gives the least MAE of 0.1317 and the highest accuracy of 87.16%.

Personal identification of an individual has always been a major concern in forensic science. Reconstruction of the facial profile is considered as one of the final stages in the process of identification. Nevertheless, recent advancements in artificial intelligence (AI) and machine learning (ML) have demonstrated remarkable potential in predictive modelling and forensic applications. The current study uses customised machine learning models to predict facial dimensions based on dental and jaw parameters. A sample of 422 participants (201 males and 221 females) from a North Indian population was collected and analysed. Dental casts, anthropometric facial measurements and photographs of the participants were collected with informed consent. ML models such as Support Vector Regression (SVR), Random Forest Regression (RFR), Decision Tree Regression (DTR), and Linear Regression (LR) were trained using dental and jaw measurements as input features for the models. The results show that the ML models predicted the facial dimensions with an accuracy of 90–94% and a very low prediction error of 0.1–0.9 across all facial measurements. Among the models, SVR and LR models perform well, followed by RFR, whereas DFR yielded comparatively lower accuracy. The findings demonstrate that machine learning models (SVR, RFR, DTR, and LR) can be used as novel approach to predict facial dimensions from jaw and teeth parameters. These techniques can be combined with other facial reconstruction techniques to produce more precise and accurate outcomes. The reliability and accuracy in predicting the facial dimensions indicate that the results can be applied in the practical and real situations such as personal identification, forensic investigations, disaster victim identification cases, and archaeological remains where only jaw and teeth are available for examination. Integrating ML-based predictions with traditional facial reconstruction techniques could enhance the accuracy and reliability of forensic identification methodologies.

The current study aimed to investigate the possibility of predicting the compressive strength of geopolymer mortar by mix design parameters, ultrasonic pulse velocity (UPV) and machine learning techniques. Here the geopolymer mortar is produced from eggshell ash and rice husk ash as precursors, NaOH solution as activator and quarry waste as fine aggregate. Twenty-seven combinations of geopolymer mix and a total of 189 mortar cubes were cast and tested for UPV and compressive strength. Seven different machine learning techniques were used to predict the compressive strength assessment tools: linear regression, artificial neural networks, boosted tree regression, random forest regression, K-Nearest Neighbor, support vector regression and XGboost. Among the diverse machine learning models evaluated in this study, XGboost exhibited remarkable efficacy in forecasting the compressive strength of geopolymer mortar. The investigation conducted using SHAP indicates that the concentration of UPV shows the most substantial influence on the prediction of compressive strength.

Air pollution is a leading cause of mortality in the developing world, with industrial regions experiencing particularly high levels of fine particulate matter (PM2.5). The Assam Oil Field Region, a significant industrial hub, is characterized by elevated PM2.5 concentrations, yet predictive modeling studies in such environments remain scarce. This study presents a preliminary exploration of statistical and machine learning techniques, aiming to identify potential tools for future environmental monitoring, in oil field localities. Using a limited dataset (n = 109), we modelled PM2.5 levels based on multiple predictor variables, including meteorological parameters (temperature, humidity, wind speed, and wind direction). We employed multiple linear regression alongside six machine learning models—Linear Regression, Decision Tree Regression, Support Vector Regression, KNN Regression, XGBoost Regression (XGBR), and Random Forest Regression, and one Feed Forward Deep Learning model. Additionally, Principal Component Analysis was employed to investigate the impact of dimensionality reduction on model performance. Among the models, XGBR consistently demonstrated superior predictive performance, achieving high R2 scores (0.78 in non-PCA, 0.59 in PCA) and minimal error metrics (MAE: 6.75, MSE: 62.72, RMSE: 7.92). Its robustness across scenarios and ability to handle complex relationships efficiently make it particularly effective compared to other ensemble methods like Random Forest Regression. While initial outcomes suggest the potential of machine learning models, especially XGBoost, for PM2.5 predictions in industrial contexts, further validation using larger and more diverse datasets is necessary. These early findings may serve as a basis for future work on data-driven air quality forecasting in oil field regions.

PurposeThis study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.Design/methodology/approachA data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.FindingsAmong five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.Originality/valueAddressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

Demand forecasting is a critical function in Enterprise Resource Planning (ERP) systems, directly influencing inventory management, production planning, and overall operational efficiency. Traditional statistical models often fall short in handling the complexity and variability of modern supply chains. This study investigates the application of Artificial Intelligence (AI), specifically Machine Learning (ML) algorithms, to enhance demand forecasting accuracy within ERP environments. I conduct a comparative analysis of four widely used ML models: Linear Regression, Random Forest, Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) networks. Using real-world ERP datasets, each model is evaluated based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and computational performance. The results reveal that while Random Forest and LSTM models outperform others in terms of accuracy, their complexity and training time vary significantly. My findings highlight the trade-offs between model accuracy and computational efficiency, offering practical insights for ERP stakeholders. This study contributes to the growing field of AI-driven enterprise analytics and provides guidance on selecting appropriate ML techniques tailored to specific forecasting needs within ERP systems.

Accurate leaf area index estimation in sorghum using high-resolution UAV data and machine learning models

This research compares four machine learning techniques: linear regression, support vector regression, random forests, and artificial neural networks, with regard to the determination of mechanical stress in power transformer winding conductors due to three-phase electrical faults. The accuracy compared with finite element results was evaluated for each model. The input data were the transient electrical fault currents of power system equivalents with impedances from low to high values. The output data were the mechanical stress in the conductors located in the middle of the winding. To simplify the design, only one hyperparameter was varied on each machine learning technique. The random forests technique had the most accurate results. The highest errors were found for low-stress values, mainly due to the high difference between maximum and minimum stresses, which made the training of the machine learning models difficult. In the end, an accurate model that could be used in the continuous monitoring of mechanical stress was obtained.