Optimized feature selection based on machine learning models for robust stock market prediction

Rainfall plays a crucial role in agricultural practices, influencing the growth of essential crops and impacting economies reliant on agriculture. In Rajasthan, India, where agriculture is predominantly rainfed and dependent on unpredictable monsoon rains, accurate rainfall forecasting is vital for effective crop management, irrigation, optimizing agricultural productivity, managing water resources, and addressing drought conditions and. This study evaluates various machine learning models for predicting rainfall in East and West Rajasthan. The models assessed include Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Networks (ANN), and K-Nearest Neighbours (KNN). These models were compared based on their accuracy measures, including Root Mean Squared Error (RMSE), Relative Root Squared Error (RRSE), and Relative Absolute Error (RAE). The results indicate that ANN and KNN perform poorly compared to SVR and RF suggesting their inadequacy for rainfall forecasting in water-scarce regions. The performance of SVR and RF are comparable as the difference is less. However SVR is identified as the most effective model for rainfall prediction in both subdivisions, as highlighted in the study. This study suggest that SVR and RF model should be includes in prediction of amount of rainfall.

This study presents a comparative analysis of machine learning models for predicting carbon monoxide (CO) emissions in automotive engines. Four models—Linear Regression, Decision Tree, Random Forest, and Support Vector Regression—were evaluated using a dataset of engine performance parameters and emission measurements. Among these, the Random Forest model demonstrated the highest predictive accuracy, achieving an R² score of 0.8965. Feature importance analysis identified nitrogen oxides (NOX), engine speed (RPM), and hydrocarbons (HC) as the most significant predictors of carbon monoxide emissions. Learning curve analysis provided insights into model generalization and highlighted potential limitations. The study underscores the value of data-driven approaches in optimizing engine design and controlling emissions. The findings contribute to the development of cleaner, more efficient vehicles, supporting sustainability efforts in the automotive industry. This research bridges data science and automotive engineering, offering a framework for advanced emission prediction and control that can be applied to other pollutants and engine types.

Electrodialysis (ED) based desalination is widely recognized as an effective water purification technique that conserves water by selective removal of dissolved salts and impurities. Herein, machine learning (ML) models such as Multiple Linear Regression (MLR), Support Vector Regression (SVR) Multi-Layer perceptron (MLP), and Random Forest (RF) have been developed to accurately forecast salt removal ratio, energy consumption, and current efficiency of electrodialysis toward the desalination. An electrodialysis system having 65 membrane cell pairs of 30 × 60 cm2 is used for the present study. The 144 datasets of selected independent variables such as time, feed concertation, treated flow rate, concentrated flow rate, and applied potential were used for the ML study. The models underwent validation through the assessment of the determination coefficient (R2) and Mean Squared Error (MSE). The MLP and RF algorithm prediction accuracy was higher than MLR and SVR. The MLP model prediction was higher due to R2 of 0.9730, and 0.8292 than the R2 of 0.9496, and 0.8053 for salt removal and current efficiency, respectively. However, RF predicts energy consumption more effectively than MLP (R2: 0.8875 vs. 0.8157). Additionally, RF achieved the fastest computational time among all models. Additionally, physicochemical and electrochemical properties of membranes ensured the stability of membranes.

This paper discusses different supervised machine learning models in order to come up with a predictive model of crop yield, which utilizes the soil and environmental parameters. The data is a creation of the Kaggle in the project of Samrudha hackathon and is intended to support AI-based applications to smart and sustainable farming. We have done strict data preprocessing, which includes the elimination of outliers, the processes of duplicate and missing data. Various regression algorithms, such as K-Nearest Neighbors (KNN), Linear Regression, Ridge and Lasso Regression, Support Vector Regression (SVR), Decision Trees, and Random Forests were used and tested. R 2 and Mean Squared Error (MSE) were used as performance measurements. The Random Forest Regressor was the best performing model out of all the models tested with a test R 2= 0.9394 and a test MSE= 4.0840. This indicates the strength and capability of generalizing of ensemble approaches of agricultural yield forecasting activities. The originality of this study lies in its systematic and rigorous comparison of multiple supervised machine learning models for crop yield prediction using carefully preprocessed soil and environmental data. It further contributes by demonstrating the superior generalization capability of ensemble methods, particularly Random Forests, in supporting accurate and sustainable smart farming applications.

Based on machine learning model for prediction of CO2 adsorption of synthetic zeolite in two-step solid waste treatment

Accurate risk assessment of high-risk patients is essential in clinical practice. However, there is no practical method to predict or monitor the prognosis of patients with ST-segment elevation myocardial infarction (STEMI) complicated by hyperuricemia. We aimed to evaluate the performance of different machine learning models for the prediction of 1-year mortality in STEMI patients with hyperuricemia. We compared five machine learning models (logistic regression, k-nearest neighbor, CatBoost, random forest, and XGBoost) with the traditional global (GRACE) risk score for acute coronary event registrations. We registered patients aged >18 years diagnosed with STEMI and hyperuricemia at the Affiliated Hospital of Zunyi Medical University between January 2016 and January 2020. Overall, 656 patients were enrolled (average age, 62.5 ± 13.6 years; 83.6%, male). All patients underwent emergency percutaneous coronary intervention. We evaluated the performance of five machine learning classifiers and the GRACE risk model in predicting 1-year mortality. The area under the curve (AUC) of the six models, including the GRACE risk model, ranged from 0.75 to 0.88. Among all the models, CatBoost had the highest predictive accuracy (0.89), AUC (0.87), precision (0.84), and F1 value (0.44). After hybrid sampling technique optimization, CatBoost had the highest accuracy (0.96), AUC (0.99), precision (0.95), and F1 value (0.97). Machine learning algorithms, especially the CatBoost model, can accurately predict the mortality associated with STEMI complicated by hyperuricemia after a 1-year follow-up.

Recently, great progress has been achieved in diabetes management through minimally invasive continuous glucose monitoring, which involves piercing the skin to identify changes in glucose levels in the interstitial fluid. Nevertheless, the development of accurate fully noninvasive (NI) glucose monitors remains a topic of great interest. This work examines the processing of data collected from a multisensor system, which combines millimeter-wave (mm-wave), near-infrared (NIR), and other auxiliary sensors’ technology, coupled with a random forest machine learning (ML) model for prediction. We investigated possible correlations between mm-wave, NIR, skin temperature, pressure data, and blood glucose values and deployed an ML model for prediction. The model is based on the random forest algorithm that was trained and tested on data collected from a study involving five healthy human subjects undergoing an intravenous glucose tolerance test. Our model achieves a root mean square error of 21.06 mg/dl and a mean absolute relative difference of 7.31% for glucose prediction in the collected clinical subjects dataset. In addition, 96.1% of the model’s predictions fall within the clinically acceptable zones according to the Clarke error grid analysis. The preliminary results suggest that combining multiple sensor data with ML techniques is promising for the development of NI glucose monitoring.

Forecasting China's stock market variance

Financial markets and products have increasingly become complicated as a result of the influx of financial products hence consumers are now required to make complex financial decisions about their day to day investment decisions, portfolio choice and future financial planning. To make an informed decision, a considerable level of financial literacy and trust is required. Globally, households are encouraged to take a more active approach to personal finance. However, the success depends largely on the level of financial literacy of the individual. Again, global evidence of stock market non-participation puzzle has warranted several studies to ascertain what explains the phenomenon. For this reason, this paper examines the impact of financial literacy and trust on stock market participation and also ascertains if there exists any gender gap in financial literacy and share ownership. Using survey data on a sample of 398 respondents, there was an evidence of low financial literacy levels. The probit regression results show that less financially literate investors are significantly less likely to invest in stocks if socio-demographic characteristics and risk attitudes are accounted for. However, the individual’s subjective level of trust in the stock market has no explanatory effect on the individual’s decision to invest in stocks. Hence, financial literacy partly explains the non-participation puzzle in financial literacy and stock market participation. It is also found that financial literacy is affected by one’s level of education and access to financial information, hence financial literacy programs should be given much attention especially through the use of electronic media.

Software defect prediction is a vital area in software engineering that helps developers detect potential faults before software is deployed. Cross-Project Defect Prediction (CPDP) is particularly valuable, as it enables the use of defect data from one project to predict errors in another, making it beneficial in cases where project-specific defect data is insufficient. However, the effectiveness of CPDP largely depends on how well the machine learning models are trained, and a key factor influencing their performance is the size of the feature set used. This study focuses on evaluating the impact of feature set size on the performance of two widely used machine learning models, Random Forest (RF) and Support Vector Machine (SVM), in the context of CPDP. We used defect datasets from the AEEEM repository, which consists of multiple real-world software projects. An outlier detection technique was applied to select the number of features in the training and testing data, ensuring a systematic analysis of their impact on model performance. The F1-score was used as the primary evaluation metric, as it provides a balance between precision and recall, making it a reliable measure of defect prediction accuracy. Our findings suggest that the size of the feature set plays a crucial role in determining the effectiveness of both RF and SVM models. Too many features introduce noise, reducing predictive accuracy, while too few cause underfitting, leading to the missed detection of defect patterns. Identifying an optimal feature set size improves model performance, providing practical insights for enhancing CPDP. Optimizing feature selection can lead to more accurate predictions, thereby aiding software maintenance and enhancing overall software quality.

Accurate forecast of carbon dioxide (CO2) emissions plays a significant role in China's carbon peaking and carbon neutrality policies. A novel two-stage forecast procedure based on support vector regression (SVR), random forest (RF), ridge regression (Ridge), and artificial neural network (ANN) is proposed and evaluated by comparing it with the single-stage forecast procedure. Nine independent variables’ data (study period: 1985–2020) are used to forecast the CO2 emissions in China. Our results reveal that, when the time gap, h increases from 1 to 8, the average root mean squared error (RMSE) and mean absolute error (MAE) of SVR–SVR, SVR–RF, SVR–Ridge, and SVR–ANN are almost uniformly lower than errors arising from their single-stage version, respectively. Among these two-stage models, SVR–ANN exhibits the lowest forecast errors, whereas SVR–RF admits the highest. The mean percentage decrease in forecast errors of SVR–SVR vs. SVR, SVR–RF vs. RF, SVR–Ridge vs. Ridge, and SVR–ANN vs. ANN are 36.06, 5.98, 43.05, and 14.81 for RMSE, and 36.06, 6.91, 43.27, and 15.35 for MAE. Our two-stage procedure is also suitable to forecast other variables, such as fossil fuel and renewable energy consumption.

Comparative analysis of machine learning models for rainfall prediction

This paper presents a data-driven approach to determine the load and flexural capacities of reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix (FRCM) composites in flexure. A total of seven machine learning (ML) models such as kernel ridge regression, K-nearest neighbors, support vector regression, classification and regression trees, random forest, gradient boosted trees, and extreme gradient boosting (xgBoost) are evaluated to propose the best predictive model for FRCM-strengthened beams. Beam geometry, internal steel reinforcement area, FRCM reinforcement area, and mechanical characteristics of concrete, steel, and FRCM are the main input parameters included in the database. Among the studied ML models, the xgBoost model is the most accurate model with the highest coefficient of determination (R2=99.3%) and least root mean square (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). A comparative study of the performance of the proposed and existing analytical models revealed the superior predictive capability and robustness of the proposed model. The predicted flexural and load capacities of the beams based on the existing analytical models are highly scattered and either over-conservative or unsafe. A unified SHapley Additive exPlanations approach is employed to explain the output of the best ML model and identify the most significant input features and interactions that influence the capacity of FRCM-strengthened RC beams in flexure. Furthermore, a reliability analysis is performed to calibrate the resistance reduction factor (ϕ) to achieve a specified target reliability index (βT=3.5).

Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants

Support vector regression with chaos-based firefly algorithm for stock market price forecasting