PREDIKSI KONSUMSI HARIAN PROGRAM MAKAN BERGIZI

YouTube, as the largest video platform in Indonesia, faces significant challenges due to spam in the comment sections, which can reduce the quality of discussions. This study aims to develop a spam detection model for YouTube comments using the Extreme Gradient Boosting (XGBoost) algorithm, optimized through Randomized Search CV. This model is compared with several other algorithms, such as Random Forest, Naïve Bayes, and Support Vector Machine (SVM). The methodology used in this study involves developing a machine learning-based model with a primary focus on the XGBoost algorithm. The dataset used is the YouTube Spam Collection, which underwent preprocessing using Natural Language Processing (NLP) techniques such as tokenization and lemmatization to extract important features from the comments. The model was tested using various test data ratios (20%, 25%, 30%, and 35%) and optimized using Randomized Search CV to determine the best parameter combinations.The results indicate that Random Forest achieved the highest accuracy of 92.34% at a 30% test data ratio, while XGBoost demonstrated stable accuracy across different test data ratios, reaching 91.70%. Compared to previous studies, the accuracy of 92.34% is still lower than the previous results of 96.94%. Nevertheless, this study provides important insights by demonstrating that hyperparameter optimization can significantly improve the performance of machine learning algorithms. Based on these findings, it can be concluded that Random Forest and XGBoost are effective models for detecting spam in YouTube comments, with Random Forest excelling in accuracy and XGBoost offering consistent performance stability.

Background/Objectives: Dental age estimation is a vital component of forensic science, helping to determine the identity and actual age of an individual. However, its effectiveness is challenged by methodological variability and biological differences between individuals. Therefore, to overcome the drawbacks such as the dependence on manual measurements, requiring a lot of time and effort, and the difficulty of routine clinical application due to large sample sizes, we aimed to automatically estimate tooth age from panoramic radiographs (OPGs) using artificial intelligence (AI) algorithms. Methods: Two-Dimensional Deep Convolutional Neural Network (2D-DCNN) and One-Dimensional Deep Convolutional Neural Network (1D-DCNN) techniques were used to extract features from panoramic radiographs and patient records. To perform age estimation using feature information, Genetic algorithm (GA) and Random Forest algorithm (RF) were modified, combined, and defined as Modified Genetic-Random Forest Algorithm (MG-RF). The performance of the system used in our study was analyzed based on the MSE, MAE, RMSE, and R2 values calculated during the implementation of the code. Results: As a result of the applied algorithms, the MSE value was 0.00027, MAE value was 0.0079, RMSE was 0.0888, and R2 score was 0.999. Conclusions: The findings of our study indicate that the AI-based system employed herein is an effective tool for age detection. Consequently, we propose that this technology could be utilized in forensic sciences in the future.

To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency. A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled. Demography information, etiology, past history, and clinical indicators and imaging data within 48 hours of admission were collected according to the medical record system and image system, and the modified CT severity index (MCTSI), Ranson score, bedside index for severity in acute pancreatitis (BISAP) and acute pancreatitis risk score (SABP) were calculated. The data sets of the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University were randomly divided into training set and validation set according to 8 : 2. Based on XGBoost algorithm, the SAP prediction model was constructed on the basis of hyperparameter adjustment by 5-fold cross validation and loss function. The data set of the Second Affiliated Hospital of Soochow University was served as independent test set. The predictive efficacy of the XGBoost model was evaluated by drawing the receiver operator characteristic curve (ROC curve), and compared it with the traditional AP related severity score; variable importance ranking diagram and Shapley additive explanation (SHAP) diagram were drawn to visually explain the model. A total of 1 183 AP patients were enrolled finally, of which 129 (10.9%) developed SAP. Among the patients from the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University, there were 786 patients in the training set and 197 in the validation set; 200 patients from the Second Affiliated Hospital of Soochow University were used as the test set. Analysis of all three datasets showed that patients who advanced to SAP exhibited pathological manifestation such as abnormal respiratory function, coagulation function, liver and kidney function, and lipid metabolism. Based on the XGBoost algorithm, an SAP prediction model was constructed, and ROC curve analysis showed that the accuracy for prediction of SAP reached 0.830, the area under the ROC curve (AUC) was 0.927, which was significantly improved compared with the traditional scoring systems including MCTSI, Ranson, BISAP and SABP, the accuracy was 0.610, 0.690, 0.763, 0.625, and the AUC was 0.689, 0.631, 0.875, and 0.770, respectively. The feature importance analysis based on the XGBoost model showed that the top ten items ranked by the importance of model features were admission pleural effusion (0.119), albumin (Alb, 0.049), triglycerides (TG, 0.036), Ca2+ (0.034), prothrombin time (PT, 0.031), systemic inflammatory response syndrome (SIRS, 0.031), C-reactive protein (CRP, 0.031), platelet count (PLT, 0.030), lactate dehydrogenase (LDH, 0.029), and alkaline phosphatase (ALP, 0.028). The above indicators were of great significance for the XGBoost model to predict SAP. The SHAP contribution analysis based on the XGBoost model showed that the risk of SAP increased significantly when patients had pleural effusion and decreased Alb. A SAP prediction scoring system was established based on the machine automatic learning XGBoost algorithm, which can predict the SAP risk of patients within 48 hours of admission with good accuracy.

This research aims to estimate the delivery time and energy cost of e-scooter vehicles for distributing mail or packages and to show the usage efficiency of e-scooter sharing services in postal service delivery in Turkey. The machine learning (ML) methods used to implement the prediction of delivery time and energy cost as output variables include random forest (RF), gradient boosting (GB), k-nearest neighbour (kNN), and neural network (NN) algorithms. Fifteen input variables under demographic, environmental, geographical, time, and meta-features are utilised in the ML algorithms. The correlation coefficient (R2) values of RF, GB, NN, and kNN algorithms were computed for delivery time as 0.816, 0.845, 0.821, and 0.786, respectively. The GB algorithm, which has a high R2 and the slightest margin of error, exhibited the best prediction performance for delivery time and energy cost. Regarding delivery time, the GB algorithm’s MSE, RMSE, and MAE values were calculated as 149.32, 12.22, and 6.08, respectively. The R2 values of RF, GB, NN, and kNN algorithms were computed for energy cost as 0.917, 0.953, 0.400, and 0.365, respectively. The MSE, RMSE, and MAE values of the GB algorithm were calculated as 0.001, 0.019, and 0.009, respectively. The average energy cost to complete a package or mail delivery process with e-scooter vehicles is calculated as 0.125 TL, and the required time is approximately computed as 11.21 min. The scientific innovation of the study shows that e-scooter delivery vehicles are better for the environment, cost, and energy than traditional delivery vehicles. At the same time, using e-scooters as the preferred way to deliver packages or mail has shown how well the delivery service works. Because of this, the results of this study will help in the development of ways to make the use of e-scooters in delivery service even more efficient.

In this study, the performances of Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms in agricultural crop classification were compared using multi-temporal Sentinel-2 images. The study area is located within an area of approximately 45* 45 km2 within the borders of Çukurova Plain. Within the scope of the study, agricultural crop pattern classification including corn, cotton, wheat, sunflower, sunflower, watermelon, peanut, and citrus trees, as well as double crop corn, soya, and cotton crops planted after wheat using RF, ANN, SVM, XGBoost algorithms with multi-time Sentinel-2 images of 2021. The study used parcels registered in the Farmer Registration System (FRS) as reference data. Before defining the parcels that are called Farmer Declaration Parcels (FDP) as ground truth data, pre-editing and rule-based deletion operations were performed, afterwards false and incorrect declarations were eliminated.10 bands (B2, B3, B4, B5, B6, B7, B8, B8A, B11, B12) of Sentinel-2 imagery from 11 different dates and Enhanced Vegetation Index (EVI) were used. All classification results showed a high overall accuracy (OA) ranging from 85% to 92%. As a result of the classification using a total of 121 different features, the highest overall accuracy OA (92.14%) belongs to the XGBoost algorithm. XGBoost algorithm was followed by RF (89.15%), SVM (86.14%) and ANN (85.48%). In all algorithms, although cotton, maize and wheat gave the highest accuracy in the classification results, double crops (wheat-maize, wheat-soybean, wheat-cotton) especially those with very close phenological periods, could not reach very high accuracy values. As a result of the study, it was observed that crops at different phenological stages achieved high classification accuracy using all algorithms, while crops at very similar phenological stages lower classification results. With the methodology applied in this study, a large number of ground truth data were generated from FDP plots and used for classification.

Wind waves are natural phenomena primarily generated by the wind. Information about wave height and period is highly crucial in various marine fields such as coastal engineering, fisheries, and maritime transportation. However, accurately predicting wave height remains a challenge due to the stochastic nature of ocean waves themselves. Several approaches to predicting wave height have been developed, including numerical models and machine learning methods, such as the Long-Short Term Memory (LSTM) algorithm, which has currently garnered significant attention from researchers. The objective of this research is to develop a forecast model for wind wave height using the LSTM algorithm in Seibu Island Waters, DKI Jakarta. The ERA5 dataset comprises zonal and meridional wind components and significant wave height, along with wind measurement data using the Automatic Weather System (AWS) instrument, are used to train and test to train and test the LSTM model. The research results show that the LSTM model can predict significant wave height effectively. Predictions using the ERA5 significant height dataset are observed to be closer to field data, with RMSE, MAE, and MAPE values of 0.1535 m, 0.1181 m, and 37.11% respectively. Thus, the model evaluation results indicate good performance, with relatively low RMSE and MAE values, and a good MAPE value. The highest accuracy in significant wave height prediction is found for forecasts one week (7 days) ahead

This study provides a multi-faceted and detailed perspective on the relationships between logistics performance, environmental degradation, and economic growth in 38 OECD countries, using each as an individual target variable. In the Analysis section, the relationship between logistics and environment is examined within a broader context, taking economic indicators into account. This examination utilizes the machine learning algorithms Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). For each algorithm, the dataset is split into training and testing sets using three different ratios: 90:10, 80:20, and 70:30. A comprehensive performance evaluation is conducted on each of these splits by applying 5-fold and 10-fold cross-validation (CV). Considering economic indicators, the analysis section examines how the logistics-environment relationship is shaped in a broader context using the machine learning algorithms RF, XGBoost, and LightGBM. MSE, MAE, RMSE, MAPE, and R2 metrics are utilized to evaluate model performance, while MDA and SHAP are employed to assess feature importance. Furthermore, a bee swarm plot is leveraged for visualizing the results. The XGBoost algorithm can successfully predict carbon dioxide (CO2) emissions from transport and economic growth with high accuracy. However, the logistics performance model achieves high performance only with the LightGBM algorithm using a 90% train, 10% test split, and 5-fold CV setup. Based on the variable importance levels of the best-performing algorithm for each of the three target variables separately, the prediction of logistics performance is largely dependent on the economic growth predictor, and secondly, on the trade openness predictor. In predicting CO2 emissions from transport, economic growth is identified as the most effective predictor, while logistics performance and trade openness contribute the least to the prediction. The findings also reveal that transport-related emissions and environmental indicators are prominent in the prediction of economic growth, whereas logistics performance and trade openness play a supportive, yet secondary role.

Predicting Prostate Radiotherapy Toxicity in a Phase III Trial Using Clinical/Dosimetric Variables in Logistic Regression and Random Forest Models

Duplex stainless steel (DSS) welding is critical for producing structures and components in a variety of industries. Because traditional optimization approaches cannot manage the complexity of welding, additional solutions must be investigated. This study addresses the need for a systematic exploration of the welding parameters affecting the quality and performance of DSS butt joints, specifically employing fiber laser welding. This study focuses on butt joints in UNS S32304 and 304L using a fiber laser. Welding process parameters were systematically varied, including laser power, scanning speed, beam diameter, and focal position. To efficiently examine the parameters in the design space, the response surface methodology (RSM) with Box–Behnken design (BBD) was used. The combination of the specific parameter values used in the 7th run (Laser Power: 1600 W, Scanning Speed: 2000 mm/min, Beam Diameter: 1.5 [Formula: see text]m, Focal Position: 30 mm) led to better yield point stress (YPS), maximum tensile stress (MTS), break stress (BS) and reduction in area (RA). ANOVA and lack of fit analysis confirmed the significance of the experiment. In particular, the selected welding parameters significantly influenced all responses. A hybrid machine learning method of deep neural network (DNN) and human eye vision algorithm (HEVA) was used for predicting weld quality during laser beam welding (LBW). The hybrid DNN-HEVA consistently outperformed other optimization methods (RSM-BBD, FNN, CNN, and RNN) across key welding quality parameters. High [Formula: see text]2 values (0.9922, 0.9962, 0.9983, and 0.9907) indicated a strong correlation between predicted and actual values for all calculated responses. Lower RMSE, MSE, and MAE values were also highlighted in the precision of predicted values to the experimental data.

Rain is an important component of the overall cycle since it is capable of accumulating the majority of the freshwater on the planet. Hence, all variations in the weather have an impact on the agriculture as well as on the economy of the country. India being a farmer’s-dependent economy indirectly depends on good rainfall. India gets a yearly precipitation of roughly 4000 billion cubic meter (BCM) which include snowfall, with an average rainfall of about 1170 mm. In the current times, climate change is a matter of major concern. Autoregressive integrated moving average (ARIMA) and point spread function (PSF) were used. But traditional methods have some limitations, are expensive and require large data set. Predictions are created by mathematical model defined by a set of equations that predict future rainfall with the help of a dynamical strategy. The paper performs a comparison between multiple linear regression (MLR), K-nearest neighbour (KNN), backpropagation with ANN and long short-term memory (LSTM) on the prediction of rainfall. Statistical methods are performed such as MAE, RMSE, MSE to compare the different prediction model and to choose the best suitable one. The test cases provided a lower MAE, RMSE and MSE value in case of LSTM model as it can memorize how elements in a series are ordered in a time series data. Instead of having the information which was before and unchangeable, LSTMs have the capacity to identify the context necessary to make forecasts in time series forecasting scenario.KeywordsMultiple linear regressionArtificial neural networkK-nearest neighbourLong short-term memoryPrecipitation

This study investigates the impact of desert dust on precipitation patterns using multi-model simulations. Dust-based processes of formation/removal of ice nuclei (IN) and cloud condensation nuclei (CCN) are investigated by using both the online access model WRF-CHIMERE and the online integrated model WRF-Chem. Comparisons of model predictions with rainfall measurements (GRISO: Spatial Interpolation Generator from Rainfall Observations) over the Italian peninsula show the models’ ability to reproduce heavy orographic precipitation in alpine regions. To quantify the impact of the mineral dust transport concomitant to the atmospheric river (AR) on cloud formation, a sensitivity study is performed by using the WRF-CHIMERE model (i) by setting dust concentrations to zero and (ii) by modifying the settings of the Thompson Aerosol-Aware microphysics scheme. Statistical comparisons revealed that WRF-CHIMERE outperformed WRF-Chem. It achieved a correlation coefficient of up to 0.77, mean bias (MB) between +3.56 and +5.01 mm/day, and lower RMSE and MAE values (~32 mm and ~22 mm, respectively). Conversely, WRF-Chem displayed a substantial underestimation, with an MB of −25.22 mm/day and higher RMSE and MAE values. Our findings show that, despite general agreement in spatial precipitation patterns, both models significantly underestimated the peak daily rainfall in pre-alpine regions (e.g., 216 mm observed at Malga Valine vs. 130–140 mm simulated, corresponding to a 35–40% underestimation). Although important instantaneous changes in precipitation and temperature were modeled at a local scale, no significant total changes in precipitation or air temperature averaged over the entire domain were observed. These results underline the complexity of aerosol–cloud interactions and the need for improved parameterizations in coupled meteorological models.

BackgroundLumbar drainage is widely used in the clinic; however, forecasting lumbar drainage-related meningitis (LDRM) is limited. We aimed to establish prediction models using supervised machine learning (ML) algorithms.MethodsWe utilized a cohort of 273 eligible lumbar drainage cases. Data were preprocessed and split into training and testing sets. Optimal hyper-parameters were archived by 10-fold cross-validation and grid search. The support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were adopted for model training. The area under the operating characteristic curve (AUROC) and precision-recall curve (AUPRC), true positive ratio (TPR), true negative ratio (TNR), specificity, sensitivity, accuracy, and kappa coefficient were used for model evaluation. All trained models were internally validated. The importance of features was also analyzed.ResultsIn the training set, all the models had AUROC exceeding 0.8. SVM and the RF models had an AUPRC of more than 0.6, but the ANN model had an unexpectedly low AUPRC (0.380). The RF and ANN models revealed similar TPR, whereas the ANN model had a higher TNR and demonstrated better specificity, sensitivity, accuracy, and kappa efficiency. In the testing set, most performance indicators of established models decreased. However, the RF and AVM models maintained adequate AUROC (0.828 vs. 0.719) and AUPRC (0.413 vs. 0.520), and the RF model also had better TPR, specificity, sensitivity, accuracy, and kappa efficiency. Site leakage showed the most considerable mean decrease in accuracy.ConclusionsThe RF and SVM models could predict LDRM, in which the RF model owned the best performance, and site leakage was the most meaningful predictor.

A predictive model was performed to describe Pseudomonas stutzeri strain DN2 growth behaviour in tributyltin chloride, using primary Modelling and a polynomial model as a secondary predictive model. In this investigation, data predicted using the modified Logistic (ML) was the most accurate. The Bias Factor (Bf) and Accuracy Factor (Af) values for the (ML) model were 1.39 and 1.51, indicating that the predictions were within a reliable range. The low RMSE value of 0.14, R2 and adj R2 (0.99) value closer to 1, showing that modified logistics is better than the other models at describing the growth behaviour of Pseudomonas stutzeri strain DN2 in toxic tributyltin chloride. Both the Aiba and Haldane models on the other hand, among the secondary model best fit the behaviours having low RMSE and MSE values and adjR2 value closer to 1. In this study, the primary and secondary kinetics of Pseudomonas stutzeri strain DN2 growth behaviour in tributyltin chloride was explored and it was shown in this study that the modified logistic and the Haldane models better suit the growth behavior of Pseudomonas stutzeri strain DN2 in tributyltin chloride. The parameters obtained from the modelling exercise will be very valuable in transferring the laboratory results to the field.

Context USEPA 3051a is a standard analytical methodology for the extraction of inorganic substances in soils. However, these analyses are expensive, time-consuming and produce chemical residues. Conversely, proximal sensors such as portable X-ray fluorescence (pXRF) spectrometry reduce analysis time, costs and consequently offer a valuable alternative to laboratory analyses. Aim We aimed to investigate the feasibility to predict the results of the USEPA 3051a method for 28 chemical elements from pXRF data. Methods Samples (n = 179) representing a large area from Brazil were analysed for elemental composition using the USEPA 3051a method and pXRF scanning (Al, Ca, Cr, Cu, Fe, K, Mn, Ni, P, Pb, Sr, Ti, V, Zn and Zr). Linear regressions (simple linear regression – SLR and stepwise multiple linear regressions – SMLR) and machine learning algorithms (support vector machine – SVM and random forest – RF) were tested and compared. Modelling was developed with 70% of the data, while the remaining 30% were used for validation. Key results Results demonstrated that SVM and RF performed better than SLR and SMLR for the prediction of Al, Ba, Bi, Ca, Cd, Ce, Co, Cr, Cu, Fe, Mg, Mn, Mo, P, Pb, Sn, Sr, Ti, Tl, V, Zn and Zr; R2 and RPD values ranged from 0.52 to 0.94 and 1.43 to 3.62, respectively, as well as the lowest values of RMSE and NRMSE values (0.28 to 0.70 mg kg-1). Conclusions and implications Most USEPA 3051a results can be accurately predicted from pXRF data saving cost, time, and ensuring large-scale routine geochemical characterisation of tropical soils in an environmentally friendly way.

Early disease detection in commercial poultry farms is critical for preventing outbreaks and minimizing economic losses. Conventional inspection is labor-intensive and frequently results in delayed diagnosis. This paper proposes an ensemble machine learning system for automated binary broiler health classification and evaluates the feasibility of non-invasive vocalization-based monitoring using acoustic analysis. Audio recordings were collected from 17–30-day-old broiler chickens in two closed-house commercial facilities: an academic research farm at the Faculty of Animal Science, Universitas Gadjah Mada (Yogyakarta), and a commercial farm in Blitar, Indonesia. Individual birds were recorded in isolated pens to eliminate background noise and ensure signal quality. A total of 22 acoustic features were extracted, comprising Mel-Frequency Cepstral Coefficients (13 features), time-domain features (5 features), and frequency-domain features (4 features). Three machine learning algorithms (SVM, Random Forest, and Logistic Regression) were evaluated across seven feature combinations using 5-fold cross-validation. Random Forest with MFCC features achieved the best individual performance (96.49% F1-score). An ensemble classifier with weighted soft voting was developed, integrating SVM (Time+MFCC), Logistic Regression (Time+Frequency+MFCC), and Random Forest (MFCC), with optimal weights determined through grid search, achieving 98.29% F1-score and 98.25% accuracy, outperforming the individual models. The high classification F1-score and accuracy demonstrate the feasibility of acoustic-based health monitoring for broiler chickens under controlled recording conditions to support early disease detection.