A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments

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

: Manufacturing process models are developed for a data-driven, reconfiguarable manufacturing facility. A human assembly process model is developed to characterize assembly processes. Machine and tool resources are characterized for manufacturing processes to provide a broad characterization of manufacturing process coverage in a hypothetical final assembly factory for infantry fighting vehicles. A sample population of manufacturing process parameters and resources is collected and incorporated into a manufacturing capability and process model library. Requirements for a virtual manufacturing environment are developed, and an initial, prototype version is implemented to facilitate process model validation, configuration, and experimentation.

Farming, city planning, and energy control are vital in proper forecasting of weather. This research paper proposes a complete machine learning-based prediction model on the maximum temperature with a big historical weather dataset gathered in Bengaluru, India. The data will be in 96432 hourly observations of 25 meteorological variables, such as temperature indices, humidity, wind characteristics, sun exposure, atmospheric pressure, and some astronomical variables. Strict data preprocessing pipeline was applied which included removal of duplicates, categorical encoding, feature scaling by standardization, and elimination of outliers by interquartile range which left a clean dataset of 63,703 records. The relevance of the features was properly analyzed with Sequential Feature Selection and Recursive Feature Elimination with the identification of the following salient predictors: minimum temperature, solar hours, UV index, wind chill, wind gust speed, and ambient temperature. Interdependencies between meteorological variables were also best supported by correlation analysis. Several machine learning models using the multiple regression such as Linear Regression, K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Decision Tree Regressor, and Random Forest Regressor were trained and assessed. Standard evaluation measurements, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and the coefficient of determination (R 2) were used to evaluate the model performance. The results of the experiment show that ensemble and instance-based models are much better than linear models. Random Forest Regressor was found to be the most predictive success with a test R2 of more than 93 and the KNN model right behind it indicates the effectiveness of nonlinear learning in weather prediction. The research establishes that machine learning methods, with an appropriate feature selection and pre-processing, can make up a reliable and accurate short-term weather forecasts.

This paper describes an on going research by the authors who are seeking methods to integrate an earlier developed manufacturing process modeling method in the virtual manufacturing (VM) technology. The primary aim is to attach special manufacturability evaluation to modeling of mechanical products. A more or less generic process model is generated for a cluster of manufacturing tasks then manufacturing process entities are created by evaluation of the manufacturing process model using part, form feature, production environment, production planning and cost information. Petri net manufacturing process model representation is used. It has proved to be a versatile virtual manufacturing tool in manufacturing process planning, evaluation of manufacturability and in manufacturing environment design. A process model evaluation procedure is emphasized in the course of which feasibility and resource requirements of the manufacturing are revealed. Firstly, techniques of evaluation of manufacturability, the applied approach to integration of shape and manufacturing process modeling and the related virtual manufacturing concepts are introduced. The process model is outlined and earlier relevant publications of the authors are cited. Then problems and solutions at evaluating of process model in close connection with design and environmental changes are detailed. Following this, integration of the method into a comprehensive virtual manufacturing concept is explained. Finally, main issues and future research plans are concluded.

The rapid spread of contagious diseases poses a colossal threat to human existence. Presently, the emergence of coronavirus COVID-19 which has rightly been declared a global pandemic resulting in so many deaths, confusion as well as huge economic losses is a challenge. It has been suggested by the World Health Organization (WHO) in conjunction with different Government authorities of the world and non-governmental organizations, that efforts to curtail the COVID-19 pandemic should rely principally on measures such as social distancing, identification of infected persons, tracing of possible contacts as well as effective isolation of such person(s) for subsequent medical treatment. The aim of this study is to provide a framework for monitoring Movements of Pandemic Disease Patients and predicting their next geographical locations given the recent trend of infected COVID-19 patients absconding from isolation centres as evidenced in the Nigerian case. The methodology for this study, proposes a system architecture incorporating GPS (Global Positioning System) and Assisted-GPS technologies for monitoring the geographical movements of COVID-19 patients and recording of their movement Trajectory Datasets on the assumption that they are assigned with GPS-enabled devices such as smartphones. Accordingly, fifteen (15) participants (patients) were selected for this study based on the criteria of residency and business activity location. The ensuing participants movements generated 157, 218 Trajectory datasets during a period of 3 weeks. With this dataset, mining of the movement trace, Stay Points (hot spots), relationships, and the prediction of the next probable geographical location of a COVID-19 patient was realized by the application of Artificial Intelligence (AI) and Data Mining techniques such as supervised Machine Learning (ML) algorithms (i.e., Multiple Linear Regression (MLR), k-Nearest Neighbor (kNN), Decision Tree Regression (DTR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and eXtreme Gradient Boosting regression(XGBR) as well as density-based clustering methods (i.e., DBSCAN) for the computation of Stay Points (hot spots) of COVID-19 patient. The result of this study showed clearly that it is possible to determine the Stay Points (hot spots) of a COVID-19 patient. In addition, this study demonstrated the possibility of predicting the next probable geographical location of a COVID-19 patient. Correspondingly, Six Machine Learning models (i.e., MLR, kNN, DTR, RFR, GBR, and XGBR) were compared for efficiency, in determining the next probable location of a COVID-19 patient. The result showed that the DTR model performed better compared to other models (i.e., MLR, kNN, RFR, GBR, XGBR) based on four evaluation matrices (i.e., ACCURACY, MAE, MSE, and R2) used. It is recommended that less developed Countries consider adopting this framework as a policy initiative for implementation at this burgeoning phase of COVID-19 infection and beyond. The same applies to the developed Countries. There is indication that GPS Trajectory dataset and Machine Learning algorithms as applied in this paper, appear to possess the potential of performing optimally in a real-life situation of monitoring a COVID-19 patient. This paper is unique given its ability to predict the next probable location of a COVID-19 patient. In the review of extant literature, prediction of the next probable location of a COVID-19 patient was not in evidence using the same Machine Learning algorithms.

Recent time series forecasting developments indicated that simple linear models can be strong competitors to handle a vast majority of real-world datasets. However, the generality of such models can vary significantly between domains, not to mention between tasks. This paper extends the benchmark of Toner and Darlow Ordinary Least Squares (OLS) was investigated as a competitive baseline model for time series forecasting, to include a few more regression-based models. More specifically, we compare the performances of Gaussian Naive Bayes (NB), K-Nearest Neighbors (KNN), Decision Tree Regression (DT), and Random Forest Regression (RF) under the same experimental setup and datasets. Our experiments are performed using the ETTh1 benchmark dataset with fixed context horizon and forecasting horizon at 96-time intervals. Compared to the baseline OLS benchmark MSE of 0.376, and the corresponding Mean Absolute Error (MAE) of 0.399, our modified models clearly fare much better. Random Forest Regression does the best, at an MSE of 0.144, an MAE of 0.245, followed closely by Decision Tree Regression (MSE: 0.155, MAE: 0.262) and the KNN (MSE: 0.271, MAE: 0.369). Surprisingly, the modified version of the usual Gaussian Naive Bayes, which has been conditioned to regression using target binning , does an MSE of 1.053, with the corresponding MAE of 0.670, due to the probabilistic nature of the algorithm where the sample domain is not discrete but rather continuous. Our work highlights that Random Forests, which are tree models, are overwhelmingly powerful baselines, surpassing traditional linear methods. This demands a rethinking of the simplicity of definition of forecasting baselines and the use of more varied ML-based regressors as baselines. Source code and experimental configurations are released to facilitate reproduction and further extendibility of the code.

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.

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.

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

Reproductive-age women worldwide suffer from metabolic problems, hormonal imbalance, and irregular menstruation due to PCOS. Complex symptoms of PCOS often lead to misdiagnosis or underdiagnosis, causing suffering and increasing the risk of obesity, diabetes, and cardiovascular disease. Treating these symptoms requires early, correct diagnosis. The project tests machine learning techniques such as Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regressor, K-Nearest Neighbors, Decision Tree Regressor, and Support Vector Machines employing Mean Squared Error to address diagnostic issues. Since it handled noisy and non-linear data better, the Random Forest Regressor was best. Django-based internet applications and predictive algorithm help clinicians identify PCOS risk. The approach instantly assesses risk using BMI, age, blood pressure, and lifestyle factors. This simple method lets clinicians identify high-risk patients for rapid intervention and personalized treatment. Accuracy, scalability, and usability tests validated the system's clinical value. Finally, our machine learning-based solution will improve early PCOS identification, clinical resource use, and global women's health.

Industrial waste materials are increasingly used in geotechnical engineering as partial replacements for cement, offering cost-effective and environmentally sustainable alternatives. This study investigates the California Bearing Ratio (CBR) and unconfined compressive strength (UCS) of lateritic soil stabilized with red mud (RM), copper slag (CS), and iron ore tailings (IOT) in proportions of 5–45%. A systematic laboratory program generated 155 experimental datasets, which were further used to develop predictive models with machine learning algorithms including K-Nearest Neighbours (KNN), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), and Multi-Layer Perceptron (MLP). Statistical indices the coefficient of determination (R 2 ), root mean square error (RMSE), and mean absolute error (MAE) along with Taylor diagrams and Regression Error Characteristic (REC) curves were applied for model evaluation. RFR and MLP achieved R 2 values above 0.90, showing superior performance. SHAP (SHapley Additive exPlanations) analysis highlighted curing period, maximum dry density (MDD), and CS dosage as the most influential features. Results confirmed that 30% CS significantly enhances both UCS and CBR, demonstrating its potential as a supplementary stabilizer. The study contributes a robust experimental machine learning framework that not only predicts UCS and CBR with high accuracy but also provides mechanistic insights, supporting circular economy practices and low-carbon pavement design.

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.

Air pollution has not received much attention until recent years when people started to understand its dreadful impacts on human health. According to air pollution and the meteorological monitoring data from 1 January 2016 to 31 December 2017 in Ningxia, we analyzed the impact of ground surface temperature, air temperature, relative humidity and the power of wind on air pollutant concentrations. Meanwhile, we analyze the relationships between air pollutant concentrations and meteorological variables by using the mathematical model of decision tree regressor (DTR), feedforward artificial neural network with back-propagation algorithm (FFANN-BP) and random forest regressor (RFR) according to air-monitoring station data. For all pollutants, the RFR increases R2 of FFANN-BP and DTR by up to 0.53 and 0.42 respectively, reduces root mean square error (RMSE) by up to 68.7 and 41.2, and MAE by up to 25.2 and 17. The empirical results show that the proposed RFR displays the best forecasting performance and could provide local authorities with reliable and precise predictions of air pollutant concentrations. The RFR effectively establishes the relationships between the influential factors and air pollutant concentrations, and well suppresses the overfitting problem and improves the accuracy of prediction. Besides, the limitation of machine learning for single site prediction is also overcame.

Engaging in investment activities plays a crucial and strategic role in fostering the growth of businesses and ensuring their resilience in the market. This involvement entails expenditures on acquiring assets, embracing technological advancements, expanding production capacities, conducting research and development, among various other domains. Collectively, these aspects form the foundation for the sustained success of an organization over the long term. This thesis will delve into an exploration of leveraging machine learning techniques to forecast key parameters in business, including investments and their impact on the financial health of the company. In this research, explored a variety of time series models and identified that both the Random Forest Regressor and Decision Tree Regressor models deliver superior accuracy, showcasing identical RMSE values of 88.36 on the validation dataset. Furthermore, the Cat Boost and Light GBM models exhibited praiseworthy performance, registering RMSE values of 92.47 and 104.69, respectively. These findings highlight the robust performance of Random Forest Regressor and Decision Tree Regressor, emphasizing their capability to provide accurate predictions. It is noted that Random Forest Regressor and Decision Tree Regressor are distinguished by high accuracy in time series forecasting, and the choice between them should take into account the trade-offs between computational efficiency and interpretability of the model. These results allow us to propose practical strategies for managing investment resources to ensure the sustainable development and prosperity of the enterprise in the long term.

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.