An Approach to Human Resource Demand Forecasting Based on Machine Learning Techniques

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 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

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.

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.

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.

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

Maintenance and repair of ship systems and prediction of fault probability have become an important issue for dynamic ship systems recently. Increasing the usability of systems by detecting the fault analysis is one of the current work areas. In recent years, the rapid development of information technologies and the machine learning approaches developing accordingly have made it possible to integrate machine learning techniques into ship systems. The use of machine learning in the studies has enabled this method to be tested in the areas of maintenance, repair, and fault analysis. To be able to predict the fault that may occur in the ship machinery systems and prevent the fault accordingly, can increase the lifespan of ship machinery's. In this study, the data obtained from an LM-2500 type ship engine were analyzed by regression and Artificial Neural Networks (ANN) algorithms to predict the fault of ship machinery. The results were compared for linear regression, decision tree regression, k nearest neighbours’ regression, random forest regression, bayesian ridge regression, extra tree regression, linear SVR regression and ANN algorithms. As a result of the analysis, it was revealed that the ANN method gave better results in machine fault prediction compared to the regression methods.

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.

Predicting the critical superconducting temperature using the random forest, MLP neural network, M5 model tree and multivariate linear regression

Forecasting future citations based on trends in A.I. publications in libraries is essential for understanding the long-term impact of academic research. This study evaluates how well historical citation data from 2003 to 2023 can predict future citation patterns and compares the effectiveness of various forecasting models. The analysis includes traditional time series models like ARIMA and Exponential Smoothing, as well as machine learning techniques such as Linear Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression. ARIMA and Exponential Smoothing are chosen for their robustness in handling historical trends, while machine learning models are applied to capture complex, nonlinear relationships. The study assesses model performance using metrics such as Mean Squared Error (MSE) and R-squared values, identifying the most accurate models for forecasting. The findings reveal that Exponential Smoothing provides reliable forecasts of future citations, while Random Forest Regression demonstrates superior performance among machine learning techniques. Additionally, linear regression models predict citation and publication trends effectively, though they may benefit from more sophisticated approaches for greater accuracy. This research offers valuable insights for researchers, institutions, and policymakers, helping them anticipate the future impact of academic publications and guiding strategic decisions in research and funding.

In the present paper are compared six machine learning techniques (decision tree regression, linear regression, KN eighbors regression, support vector regression, gradient boosting regression, and random forest regression) using Python 3.9 software, in order to identify the most appropriate machine learning technique for wastewater pH process modeling. The purpose is to find a data-driven type model for such a process (with strong nonlinearity in dynamic behavior), a model that can be used by the plant human operator as support in the decision-making process regarding the reagent (acid or alkaline) dosing flow.

Workability is one of the key property of concrete which is governed by water cement ratio. In order to improve the workability of concrete without any variations in water cement ratio Superplasticizers(SPs) are added. Cement paste helps us to analyze the property of fresh concrete where the dispersion of cement particle is taken into account. SP’s Cement dispersive properties are governed by dosage and the family. Various dosages and families of SP are considered for estimating workability feature of cement paste which is picked for investigating on rheological properties through Mini slump spread diameter. The prime motive of this analysis includes measuring the workability of different superplasticizers by conducting a minislump test and hence modelling the flow rate of the superplasticized Portland Pozzolona Cement (PPC)paste using the application of random forest(RF), decision tree(DT) and multiple regression algorithms. Testing and training data for a model were 287 unique mixture compositions at a water by cement ratio was 0.37. This mixture was tested experimentally in a laboratory using four types of locally available PPC’s and of SP which can be broadly categorised in to four families. Amount of seven types of SP brands, water content, cement weight were the input parameters for the model and flow rate was the output parameter. The model’s predicted and experimentally measured values of flow speed were compared and the amount of deviation was recorded.

This paper investigates various Machine learning techniques such as Linear Regression, Decision Tree Regressor, Random Forest Regressor, and a neural network Multilayer Perceptron (MLP Regressor) to predict the opening price of the Nifty 50 index (on Indian National Stock Exchange (NSE)) based on the previous day’s closing price of the Singapore Exchange (SGX) Nifty index (which is traded on the Singapore Stock Exchange). The models use various parameters such as closing price of SGX Nifty, India volatility index (India VIX) and exchange value (of Singapore Dollar (SGD) to Indian Rupee (INR)) along with sentiment analysis based off thousands of tweets that included the hashtag "#Nifty50". The accuracy and errors for each model are calculated and compared. The impact of COVID-19 on the accuracy of model prediction is also analyzed. It is observed that the artificial neural network and linear regression offer the highest accuracy of 0.999, and the random forest regressor offers the lowest accuracy. COVID-19’s effect on the market is seen to impact the accuracy of model prediction, and the accuracy of prediction during the second wave is the lowest due to high market volatility averaged over a short period of time.