Predictive Modeling and Analysis of Monkeypox Outbreaks Using Machine Learning Techniques

The growth of every nation's economy depends heavily on the stock market. Because of their strong predictive powers, "K-Nearest Neighbor Regression (KNNR), Random Forest Regression (RFR), Decision Tree Regression (DTR), and Support Vector Regression (SVR)" is commonly used. From the previous study, it shows that the chosen state-of-the-art of previous studies is by Shah et al. (2021) using Bi-LSTM. But the state-of-the-art model shows a high Mean Squared Error (MSE) value that is 5% for predict Tesla Stock. Therefore, we propose the SVR, KNNR, DTR, and RFR model hope error value can be reduced. However, those models have difficulties finding suitable parameters for prediction. Due to that, we proposed the best parameters that were commonly used in studies which is SVR with RBF kernel, C = [1; 10; 100], gamma = [0, 1; 0, 2], epsilon = 0,1, KNNR with K = [1, 3, 5, 7, 9], and DTR with a criterion = [‘squared_error’;’friedman_mse’], RFR with 0 < n_estimators < 100 is picked to enhance the predictive capabilities of each proposed model. To prove this, a comparison is made with each proposed model, and choose the best proposed model then compared to the state-of-the-art of the previous study, which is Bi-LSTM. The dataset used was the Tesla Stock historical data from yahoo finance. The result showed RFR with n_estimators = 87 is superior to its comparison with results of 0.005452 RMSE, 0.000029 MSE, and 0.999296 R2 and compared to Bi-LSTM have about 0.11773 RMSE, 0.01386 MSE, and 0.791263 R2. Based on this study, it can be concluded that RFR with n_estimators = 87 has superior predictive capabilities performance compared with other models and the state-of-the-art of previous studies. Doi: 10.28991/HEF-2022-03-04-01 Full Text: PDF

Recently, honey has become a target of falsification using inexpensive artificial sugar syrup. Current methods for detecting honey adulteration are destructive, slow, and expensive. This paper aims to use hyperspectral imaging (HSI) coupled with Machine Learning (ML) techniques to predict and quantify honey adulteration. The honey adulteration prediction approach proposed in this paper comprises two main steps: spatial and spectral dimensionality reduction and adulteration prediction. We used mathematical averaging to reduce spatial features and employed the Principal Component Analysis and Linear Discriminant Analysis algorithms for spectral feature extraction. Five ML regression models, including Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR), and K-Nearest Neighbor Regression (KNNR), were used for predicting the sugar concentration in honey. We used a public honey HSI dataset to assess the proposed system's performance. Results show that KNNR outperformed other models in quantifying honey adulteration, achieving a coefficient of determination R2 of 0.94 and a Root Mean Squared Error (RMSE) of 5.12. Findings indicate that HSI coupled with ML models can provide a fast and nondestructive prediction of honey adulteration.

This study explores the transformative potential of supervised machine learning algorithms in improving rainfall prediction models for Indonesia. Leveraging the NEX-GDDP-CMIP6 dataset's high-resolution, global, and bias-corrected data, we compare various machine learning regression algorithms. Focusing on the EC Earth3 model, our approach involves an in-depth analysis of five weather variables closely tied to daily rainfall. We employed a diverse set of algorithms, including linear regression, K-nearest neighbor regression (KNN), random forest regression, decision tree regression, AdaBoost, extra tree regression, extreme gradient boosting regression (XGBoost), support vector regression (SVR), gradient boosting decision tree regression (GBDT), and multi-layer perceptron. Performance evaluation highlights the superior predictive capabilities of Gradient Boosting Decision Tree and KNN, achieving an impressive RMSE score of 0.04 and an accuracy score of 0.99. In contrast, XGBoost exhibits lower performance metrics, with an RMSE score of 5.1 and an accuracy score of 0.49, indicating poor rainfall prediction. This study contributes in advancing rainfall prediction models, hence emphasizing the improvement of methodological choices in harnessing machine learning for climate research.

Human resource is a key factor which strongly contributes to evolution of developing country. Forecasting human resource demand is conducted in both private and public section in order to make specific-level strategy more adaptable. Furthermore, this frequent work also consolidates macro-economic sustainability and generates motivation for long-term growth. In this work, we propose an approach to forecast human resource demand based on machine learning techniques. We apply regression algorithms such as random forest regression (RFR), linear regression (LR), K-nearest neighbors regression (KNNR) and decision tree regression (DTR) to explore predictably human resource demand from dataset of Binh Duong Career Service Center (BDCSC). Then we benchmark this model based on actual data. The experimental results demonstrate that random forest achieves highest accuracy which is over 90%.KeywordsRegressionHuman resource demandForecasting

The goal of this research is to apply machine learning techniques to forecast a student’s probability of being accepted into a graduate program. Applicants’ GRE and TOEFL grades, university rankings, letters of recommendation, statements of purpose, cumulative grade point averages, and prior research experience are all included in the dataset utilized for this analysis. The goal is to calculate an applicant’s expected acceptance rate. This study uses a combination of Classifiers and regressors. Different prediction models are contrasted in this study: Random Forest Classifier (RFC), Decision Tree Classifier (DTC), K-Neighbors Classifier (KNC), Support Vector Classifier (SVC), Gradient Boosting Classifier (GBC), Logistic regression (LR), Support vector Regressor (SVR), Random Forest Regressor(RFR), Gradient Boosting Regressor(GBR) and Decision Tree Regressor(DTR). Using these characteristics, the models are trained and evaluated. Evaluation criteria such as accuracy, kappa value, AUC-ROC, and confusion matrix are used to find the models’ effectiveness. In order to determine which model performed the best, the assessment results are compared with one another. Based on study findings, the Gradient Boosting Classifier outperforms the other models tested by a significant margin (96 per cent). This model’s AUC-ROC of 0.97 indicates it does a decent job at separating the positive and negative categories.

The effective detection and prevention of CO2 leakage in active injection wells are paramount for safe carbon capture and storage (CCS) initiatives. This study assesses five fundamental machine learning algorithms, namely, Support Vector Regression (SVR), K-Nearest Neighbor Regression (KNNR), Decision Tree Regression (DTR), Random Forest Regression (RFR), and Artificial Neural Network (ANN), for use in developing a robust data-driven model to predict potential CO2 leakage incidents in injection wells. Leveraging wellhead and bottom-hole pressure and temperature data, the models aim to simultaneously predict the location and size of leaks. A representative dataset simulating various leak scenarios in a saline aquifer reservoir was utilized. The findings reveal crucial insights into the relationships between the variables considered and leakage characteristics. With its positive linear correlation with depth of leak, wellhead pressure could be a pivotal indicator of leak location, while the negative linear relationship with well bottom-hole pressure demonstrated the strongest association with leak size. Among the predictive models examined, the highest prediction accuracy was achieved by the KNNR model for both leak localization and sizing. This model displayed exceptional sensitivity to leak size, and was able to identify leak magnitudes representing as little as 0.0158% of the total main flow with relatively high levels of accuracy. Nonetheless, the study underscored that accurate leak sizing posed a greater challenge for the models compared to leak localization. Overall, the findings obtained can provide valuable insights into the development of efficient data-driven well-bore leak detection systems.

Understanding the concentrations of Carbon Dioxide (CO2) and greenhouse gases is very important in solving the problem of climate change. These emissions are the major cause of global warming, which, in turn, has many effects on the environment, economy and society. For this reason, the prediction models for these emissions must be precise to aid policy makers in planning for the effects of the climate in the future. To evaluate the emission data of different continents, this paper seeks to identify related patterns and findings that can help reduce emissions worldwide. The dataset used contains emission data and geographic information from several countries and allows the comparison of several ML models. The models that have been reviewed in this study are linear regression (LR), decision tree regression (DT), random forest regression (RF), support vector regression (SVR), k-nearest neighbor regression (KNN), the XGB regressor, the gradient boosting regressor, Ridge and Lasso. Among the models, the gradient boosting regressor was found to have the best prediction capability, with an R-squared value of 0. The highest value of the mean absolute error (MAE) was 929, and the lowest mean squared error (MSE) was 2535.30. This model outperforms the other models because of its excellent ability to identify the complex interactions between the input variables and emissions. The conclusions stress the possibility of using ensembles, such as gradient boosting, for emission forecasting and present a contribution to studies of this issue for researchers and policymakers. This is a nominal attempt in the ongoing global endeavour to gain insight and curb the determinable levels of CO2 and greenhouse gas emissions for effective decision-maki

For forecasting the spread of dengue, monitoring climate change and its effects specific to the disease is necessary. Dengue is one of the most rapidly spreading vector-borne infectious diseases. This paper proposes a forecasting model for predicting dengue incidences considering climatic variability across nine cities of Maharashtra state of India over 10 years. The work involves the collection of five climatic factors such as mean minimum temperature, mean maximum temperature, relative humidity, rainfall, and mean wind speed for 10 years. Monthly incidences of dengue for the same locations are also collected. Different regression models such as random forest regression, decision trees regression, support vector regress, multiple linear regression, elastic net regression, and polynomial regression are used. Time-series forecasting models such as holt's forecasting, autoregressive, Moving average, ARIMA, SARIMA, and Facebook prophet are implemented and compared to forecast the dengue outbreak accurately. The research shows that humidity and mean maximum temperature are the major climate factors and exhibit strong positive and negative correlation, respectively, with dengue incidences for all locations of Maharashtra state. Mean minimum temperature and rainfall are moderately positively correlated with dengue incidences. Mean wind speed is a less significant factor and is weakly negatively correlated with dengue incidences. Root mean square error (RMSE), mean absolute error (MAE), and R square error (R2) evaluation metrics are used to compare the performance of the prediction model. Random Forest Regression is the best-fit regression model for five out of nine cities, while Support Vector Regression is for two cities. Facebook Prophet Model is the best fit time series forecasting model for six out of nine cities. Based on the prediction, Mumbai, Thane, Nashik, and Pune are the high-risk regions, especially in August, September, and October. The findings exhibit an effective early warning system that would predict the outbreak of other infectious diseases. It will help the relevant authorities to take accurate preventive measures.

Eğitim, her birey ve bir bütün olarak toplum için ekonomik, sosyal ve kültürel gelişimin temelidir. Ülkemizde orta öğretim kurumlarına Milli Eğitim Bakanlığı tarafından yapılan lise giriş sınavı ile öğrenci kabul edilmektedir. Bu çalışmada, 2019 yılında Türkiye'nin 81 ilinde lise giriş sınavına giren öğrencilerin başarı oranları makine öğrenimi regresyon ve beta regresyon modeli ile ele alınmıştır. Bu makale, boşanma oranı, gayri safi yurtiçi hasıla, okuma yazma bilmeyenlerin sayısı ve yüksek öğretim nüfusu sayısı gibi değişkenleri kullanarak öğrencilerin başarı oranlarını modellemeyi, tahmin etmeyi ve açıklamayı amaçlamaktadır. Başarı oranlarını tahmin etmek için destek vektörü regresyon, rastgele orman, karar ağacı ve beta regresyon modeli uygulanmıştır. En yüksek R2 değerine sahip iki modelin beta regresyon ve rastgele orman modelleri olduğu bulunmuştur. Beta regresyon ve rastgele orman modelinin tahmin hataları incelendiğinde, başarı oranlarını tahmin etmede rastgele orman modelinin beta regresyon modeline göre nispeten üstün olduğu görülmektedir. Beta regresyon modeli Çanakkale ilinin başarı oranlarının en iyi yordayıcısı iken, rastgele orman modeli Ankara'nın başarı oranlarını iyi tahmin etmiştir. Ayrıca beta regresyon modelinde başarı oranları için anlamlı bulunan değişkenlerin rastgele orman modelinde de önemli olduğu görülmüştür. Öğrencilerin başarı oranlarını tahmin etmek için hem beta hem de rastgele orman modellerinin kullanılması önerilir.

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.

Clinical prediction rules could help identify children at risk of slowed growth after an episode of diarrheal illness, and these rules may be generalizable to all children, regardless of diarrhea status.

This study investigates prediction of HbA1c level which is a principal biomarker of diabetes control based on patient biographical and health data from a publicly accessible dataset [1].I tried regression models like Linear Regression [2], Decision Tree Regressor [3], and Random Forest Regressor [4] to predict accurate HbA1c levels.Upon facing poorly performing models, most likely because of data bias and feature insufficiency, I restructured the task as a classification problem by approximating the ranges of HbA1c levels into significant categories.I implemented models including Random Forest Classifier [5], Decision Tree Classifier [6], K-Nearest Neighbors [7], and an ensemble Voting Classifier [8].The Voting Classifier increased the best accuracy to 72.5%, improving over Random Forest's standalone accuracy of 68.1% [5].Model tuning focused on parameters such as the number of trees and maximum depth.Variance Inflation Factor analysis was executed to evaluate feature multicollinearity and it confirmed that multicollinearity was not a major issue.Results show that classification models are more suitable for this dataset and confirm the importance of feature engineering and hyperparameter adjustment.This finding demonstrates that classification models better suit this dataset, showing how predictive instruments can assist medical personnel in approximating HbA1c values without resorting to decisions purely based on costly or time-consuming laboratory testing.

In this work, a study was conducted to investigate the effects of different biodiesel blends with hydrogen peroxide additive on the performance and emissions of an internal combustion engine under various operating parameters. A CI engine was operated with diesel, four dissimilar biodiesels, and H2O2 at various proportions. The biodiesel blends used were Jatropha (D60JB30A10, D60JB34A6, D60JB38A2, D60JB40), Honge (D60HB30A10, D60HB34A6, D60HB38A2, D60HB40), Simarouba (D60SB30A10, D60SB34A6, D60SB38A2, D60SB40), and Neem (D60NB30A10, D60NB34A6, D60NB38A2, D60NB40). The engine was tested at different injection operating pressures (200, 205, and 210 bar), a speed of 1500 rpm, and a CR of 17.5:1. From the experiments conducted, it was highlighted that, under specific conditions, i.e., with an injection pressure of 205 bar, 80% load, a compression ratio of 17.5, an injection timing set at 230 before top dead center, and an engine speed of 1500 rpm, the biodiesel blends D60JB30A10, D60HB30A10, D60SB30A10, and D60NB30A10 achieved the highest brake thermal efficiencies of 24%, 23.9675%, 23.935%, and 23.9025%, respectively. Notably, the blend D60JB30A10 stood out with the highest brake thermal efficiency of 24% among these tested blends. Similarly, when evaluating emissions under the same operational conditions, the D60JB30A10 blend exhibited the lowest emissions levels: CO (0.16% Vol), CO2 (7.8% Vol), HC (59 PPM), and Smoke (60 HSU), while NOx (720 PPM) emissions showed a relative increase with higher concentrations of the hydrogen-based additive. The D60HB30A10, D60SB30A10, and D60NB30A10 blends showed higher emissions in comparison. Additionally, the study suggests that machine learning techniques can be employed to predict engine performance and emission characteristics, thereby cutting down on time and costs associated with traditional engine trials. Specifically, machine learning methods, like XG Boost, random forest regressor, decision tree regressor, and linear regression, were utilized for prediction purposes. Among these techniques, the XG Boost model demonstrated highly accurate predictions, followed by the random forest regressor, decision tree regressor, and linear regression models. The accuracy of the predictions for XG Boost model was assessed through evaluation metrics such as R2-Score (0.999), Root Mean Squared Error (0.540), Mean Squared Error (0.248), and Mean Absolute Error (0.292), which allowed for a thorough analysis of the algorithm’s performance compared to actual values.

In today's digital environment, it's reassuring to know that analysis and modelling have gone into improving security in information systems via better trust management of personnel. Security mechanisms like trust are used to deal with varying degrees of authorization within an organization. Building and testing different machine learning models of trust based on the behaviour of an organization's employee data set is the first stage in our trust study. In this work, we show trust modelling on security systems using various machine learning (ML) techniques such as Random Forest (RF), Decision Tree (RF), XG Boost (XGB) and Logistic Regression (LR). We perform the training and the testing of our ML models based on stochastic pattern recognition to classify the Trust of an employee into four classes namely, Trusted, Moderate, U ntrusted and Unexpected. Later a rigorous comparison of all these models is done based on a Model Error Rate (MER) of a recommended trust board.

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.