Ensemble Learning For Television Program Rating Prediction

Globally, wind energy if properly harnessed, could serve as a source of energy generation in Africa. This study compared the performance of two Machine Learning (ML) algorithms (Linear regression and Random Forest) in predicting wind speed in five major cities in Africa (Yaoundé, Pretoria, Nairobi, Cairo and Abuja). Wind data were collected between January 1, 2000, and December 31, 2022, using the Solar Radiation Data Archive. The data preprocessing was carried out with 80% of the data used for training and 20% for validation. The performance of these ML algorithms was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The result shows that Nairobi (3.814795 m/s) closely followed by Cairo (3.606453 m/s) has the highest mean wind speed while Yaoundé (1.090512 m/s) has the lowest. Based on the performance metrics used, the two Machine Learning algorithms were competitive. Still, the Linear Regression (LR) algorithm outperformed the Random Forest Algorithm in predicting wind speed in all the selected major African cities. In Yaoundé (RMSE = 0.3892, MAE= 0.3001, MAPE =0.5030), Pretoria (RMSE=1.2339, MAE=0.9480, MAPE=0.7450) Nairobi (RMSE= 0.4223, MAE =0.6499, MAPE =0.1872), Nairobi (RMSE=0.6499, MAE=0.5171, MAPE =0.1872), Cairo (RMSE =1.0909, MAE =0.8544, MAPE =0.3541) and Abuja (RMSE = 0.70245, MAE =0.5441, MAPE= 0.4515) the Linear regression algorithms was found to outperformed Random Forest Regression. Therefore, the Linear regression algorithm is more reliable in predicting wind speed compared with the Random Forest regression.

Nighttime high-power aircraft engine run-ups represent a critical operational activity with potential implications for airport management and environmental assessment; yet, their quantitative prediction remains limited in existing literature. The objective of this study is to develop and evaluate a data-driven machine learning framework capable of accurately predicting nighttime high-power engine run-up characteristics using historical operational data. A supervised learning methodology is adopted using an open-access dataset comprising approximately 860 nighttime run-up records. The proposed framework includes data preprocessing, temporal feature engineering (month, season, and quarter), exploratory data analysis, correlation assessment, and comparative model development using Linear Regression, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and an Artificial Neural Network (multilayer perceptron). Model performance is evaluated using coefficient of determination (R²), root mean square error (RMSE), mean absolute error, and mean absolute percentage error, supported by predicted-versus-actual comparisons and learning-curve analysis. The results demonstrate that ensemble-based models outperform linear and neural network approaches, with XGBoost achieving the highest predictive accuracy on the testing dataset (R² ≈ 0.90, RMSE ≈ 2.15). Learning curves further indicate stable validation performance with increasing training data size. The proposed machine learning framework provides a structured and data-driven approach for predicting nighttime high-power aircraft engine run-up activity within the analyzed operational dataset, offering a valuable analytical tool for data-driven assessment of airport ground operations.

Optimized machine learning models for predicting crown convergence of plateau mountain tunnels

This study examines the wear characteristics of A356-Al 2 O 3 -B 4 C composites produced via stir casting, incorporating three reinforcement compositions (5, 10, and 15 wt.% Al 2 O 3 with a constant 5 wt.% B 4 C). We used seven different machine learning algorithms to predict wear rates based on experimental tribological data: Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Gradient Boosting (GB), Light GBM, and AdaBoost. The Random Forest model was better at making predictions, with a R 2 of 0.83, a mean absolute error (MAE) of 0.0012 m 3 /Nm, a mean absolute percentage error (MAPE) of 1.3%, and a root mean squared error (RMSE) of 0.0094 m 3 /Nm, which means it was 83% accurate. Experimental wear rates ranged from 4.3 × 10 −14 to 3.43 × 10 −13 m 3 /Nm across varying sliding distances (1500–6000 m), loads (10–20 N), and velocities (1.45–2.66 m/s). Systematic k-fold cross-validation and grid search hyperparameter Optimisation to make sure the model worked well. The results show that machine learning, especially ensemble tree-based methods, can accurately predict how well composite materials will wear, which speeds up the process of optimizing material design and cuts down on the cost of experiments. This study shows that combining artificial intelligence with traditional tribological characterization can improve the ability to predict how composite materials will behave in the future. It also sheds light on the relative importance of tribological operating parameters.

Forecasting streamflow in stream basin systems plays a crucial role in facilitating effective urban planning to mitigate floods. In addition to employing intricate hydrological modeling systems, machine learning and statistical techniques offer an alternative means for streamflow forecasts. Nonetheless, the precision and dependability of these methods are subjects of paramount importance. This study rigorously investigates the effectiveness of three distinct machine learning techniques and two statistical approaches when applied to streamflow data from the Göksu Stream in the Marmara Region of Turkey, spanning from 1984 to 2022. Through a comparative analysis of these methodologies, this examination aims to contribute innovative advancements to the existing methodologies used in the prediction of streamflow data. The methodologies employed include machine learning methods such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) and statistical methods such as Simple Exponential Smoothing (SES) and Autoregressive Integrated Moving Average (ARIMA) model. In the study, 444 data points between 1984 and 2020 were used as training data, and the remaining data points for the period 2021–2022 were used for streamflow forecasting in the test validation period. The results were evaluated using various metrics, such as the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). Upon analyzing the results, it was found that the model generated using the XGBoost algorithm outperformed other machine learning and statistical techniques. Consequently, the models implemented in this study demonstrate a high level of accuracy in predicting potential streamflow in the river basin system.

This study trained six machine learning models to predict meteorological variables at a tropical location. The models used are: Multiple linear regression, Decision tree, Random forest, Support vector machine, Extreme gradient boosting and Multilayer perceptron. This was with the aim of determining the best machine learning model for weather forecasting in a tropical location. The meteorological variables that were predicted are: Temperature, Solar radiation, Relative humidity and Wind speed. To identify the efficiency and to quantify the predictive capacity of each models, evaluation metrics such as coefficient of determination (R2), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square error (RMSE) were employed. The best performed model for temperature is the Random Forest which has R2 of 0.93, MAE of 0.78 0C, MAPE of 2.84 % and RMSE of 1.13 0C. Also, the best performed model for solar radiation is the Random Forest having an R2 value of 0.72, MAE of 85.34 W/m2 and RMSE of 19008.45 W/m2. For relative humidity, Random Forest also has the best performance. From the evaluation metrics, it has R2 of 0.92, MAE of 3.41 %, MAPE of 0.75 % and RMSE of 24.71 %. The best performed technique for predicting the wind speed was also the Random Forest having an R2 value of 0.79, MAE of 0.16 m/s and RMSE of 0.044 m/s. The study concluded that the best machine learning model for predicting meteorological variables in a tropical location is the Random Forest.

Performance evaluation of machine learning techniques in predicting cumulative absolute velocity

Cone-beam CT (CBCT) is widely used in dental clinics but exhibits limitations in assessing soft tissue pathology because of its lack of contrast resolution and low Hounsfield Units (HU) quantification accuracy. We aimed to increase the image quality and HU accuracy of CBCTs while preserving anatomical structures. We generated CT-like images from CBCT images using a patchwise contrastive learning-based GAN model. Our model was trained on unpaired CT and CBCT datasets with the novel combination of losses and the feature extractor pretrained on our training dataset. We evaluated the quality of the images generated by our model in terms of Fréchet inception distance (FID), peak signal-to-noise ratio (PSNR), mean absolute error (MAE), and root mean square error (RMSE). Additionally, the structure preservation performance was assessed by the structure score. As a result, the generated CT-like images by our model were significantly superior to those generated by various baseline models in terms of FID, PSNR, MAE, RMSE, and structure score. Therefore, we demonstrated that our model provided the complementary benefits of preserving the anatomical structures of the input CBCT images and improving the image quality to be similar to those of CT images.

The objective of this study is to systematically compare the predictive performance of Long Short- Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), and a Hybrid LSTM–XGBoost model for next-day Bitcoin (BTC–USD) closing-price forecasting. The research method employs a quantitative time-series modeling approach using a decade-long daily Bitcoin price dataset. A strictly chronological train–test split and a one-step-ahead forecasting scheme are applied to prevent lookahead bias and ensure experimental validity. Model performance is evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), symmetric Mean Absolute Percentage Error (sMAPE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination R2 on the original price scale. The results demonstrate that the Hybrid LSTM–XGBoost model consistently outperforms the standalone LSTM and XGBoost models across all evaluation metrics, indicating superior predictive accuracy and robustness under high market volatility. The contribution of this study lies in providing a controlled, uniform, and methodologically rigorous head-to-head comparison of deep learning, machine learning, and hybrid architectures for Bitcoin price forecasting, thereby enriching the empirical literature and offering a reliable foundation for the development of adaptive decision-support systemsin volatile cryptocurrency investment environments.

Gradient Boosted Tree Approaches for Mapping European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 Onto 5-Level Version of EQ-5D Index for Patients With Cancer

Machine learning predictions for bending capacity of ECC-concrete composite beams hybrid reinforced with steel and FRP bars

This study aimed to improve age prediction in a Danish cohort of 150 individuals by in-depth analyses of DNA methylation sequencing data. In previous work, 11 well-known age-associated CpGs located on 10 PCR amplicons associated with 10 distinct genes were selected, and a support vector machine model with a polynomial function (SVMp) was developed for age estimation in blood. The 10 amplicons were PCR-amplified after bisulphite conversion and the methylation levels were assessed by massively parallel sequencing. The 10 amplicons contained a total of 73 CpG sites, of which 63 CpG sites were shown to be medium to highly associated with age in this work (Pearson correlation coefficient, |r| > 0.3). More than 70 000 SVMp models with various combinations of the 63 CpGs were tested to identify the model with the lowest mean absolute error (MAE) and root mean square error (RMSE) in training and test datasets from the Danish cohort. A final model with 34 CpGs (SVMp-34CpG) was selected. The SVMp-34CpG model showed considerable improvement over many other forensic prediction models and achieved mean absolute errors of 1.64 years and 1.76 years for the training and test datasets, respectively. The SVMp-34CpG model indicated robust generalization and minimal prediction bias across the 18–50 years age range (MAE: 1.47 years), although slightly higher errors were observed for individuals aged 50–70 years (MAE: 2.62 years). This was most likely due to epigenetic drift, together with the small number of individuals analysed in this age group. Compared to the previous 11-CpG model, the SVMp-34CpG model showed superior accuracy in the Danish cohort by predicting the age of 91% of the individuals within 4 years of their actual age, versus 68% for the previous model.

ObjectivesHuman brucellosis is a public health problem endangering health and property in China. Predicting the trend and the seasonality of human brucellosis is of great significance for its prevention. In...

Hospital outpatient volume is influenced by a variety of factors, including environmental conditions and healthcare resource availability. Accurate prediction of outpatient demand can significantly enhance operational efficiency and optimize the allocation of medical resources. This study aims to develop a predictive model for daily hospital outpatient volume using the XGBoost algorithm. Meanwhile, the forecasting performance was compared with that of the Seasonal AutoRegressive Integrated Moving Average with exogenous regressors (SARIMAX) and Random Forest (RF) models. The dataset comprises daily climate data (e.g., temperature, precipitation, PM2.5 levels), historical outpatient volume records, and the number of outpatient specialists available each day. The data range involved spans from January 1, 2014, to October 31, 2024. Data preprocessing involved addressing missing values and encoding categorical variables. Model performance was assessed using three metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) , Mean Absolute Percentage Error (MAPE), and R-squared (R2) metrics. The XGBoost model exhibited superior predictive accuracy compared to both the SARIMAX and RF models, with the lowest MAE, RMSE, MAPE, and the highest R2, successfully capturing key relationships between climate factors, resource availability, and outpatient volume. The number of outpatient specialists, temporal variables (such as year, quarter, month, and weekday), meteorological conditions (average temperature), and air quality (PM2.5) had the most significant impact on the prediction model. This study underscores the potential of machine learning algorithms like XGBoost in effectively predicting hospital outpatient demand. The findings offer valuable insights for hospitals to make proactive adjustments to their resource allocation, thereby improving their service capacity.

Solar energy produced by photovoltaic panels is a vital energy source that offers numerous benefits to both the environment and society. However, meteorological variables such as solar irradiation, weather patterns, precipitation and climate conditions present significant challenges to seamless energy integration into the power grid. Accurate forecasting is essential to maintain supply-demand balance, optimize energy storage and ensure grid stability. This study leverages machine learning (ML) techniques to predict solar power generation and address renewable energy integration challenges. Nine ML models were employed, including linear regression, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), support vector machines (SVM), random forest (RF), decision tree, gradient boosting machine (GBM), light gradient boosting (LGBM) and extreme gradient boosting (XGBM). Inputs such as irradiance, humidity, minimum temperature, maximum temperature and surface pressure were used to train these models. The model performances were evaluated using metrics like root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The results highlighted ANN as the most effective model, achieving an RMSE of 274.84 kWh, MAE of 245.93 kWh and a MAPE of 5.26 %. This research contributes to the existing literature by addressing the relatively unexplored application of multiple machine learning models for predicting energy output from photovoltaic systems. A key novelty of this study is its ability to achieve accurate solar power forecasts using a limited dataset from a newly installed solar power plant, unlike many existing studies that rely on large volumes of data. Additionally, it explores the solar power production potential of Namakkal district in Tamil Nadu, India-a region with limited prior research.