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H-weir is a combination of a rectangular gate and weir. Side weirs are applied to discharge flow and regulate the flow height. The present study presents the modeling of the H-side weirs using experiments and optimization based on SVM in a straight channel under free and subcritical flow conditions. The geometrical and flow parameters affecting the discharge capacity were determined based on dimensional analysis. The findings show that the weir flow is not in contact with the flow below the gate with increased vertical distance between the weir crest and gate top (d) and the upstream Froude number (F1). It was found that the discharge capacity of the H-side weir is 1.3–4.0 times higher than conventional side weirs and 1.6–9.0 times higher than side gates under the same conditions. Moreover, a discharge equation is derived from a/y1, h/b, b/B, F1 and d/a with an absolute percentage error of about 5.84%. The most successful model for predicting the discharge of the H-side weir was VMD-LSSVM. In addition, it was concluded that the established LSSVM, PSO-LSSVM and PSO-VMD-LSSVM models produced very satisfactory predictions. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Inorganic Analytical Chemistry / Zhongguo Wuji Fenxi Huaxue is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Global Navigation Satellite System (GNSS) data preprocessing is crucial for achieving high-precision navigation, positioning, and timing applications to convert the data format, edit the data content, and analyze the data quality. However, existing preprocessing tools are not yet user-friendly enough to handle multi-frequency and multi-system GNSS data due to extra costs, strong professionalism, and complex operations. In this paper, we present an open-source GNSS Data Preprocessing Software (GDPS) written by Python. It compiles and runs on both Windows and Linux operating systems, supports processing Receiver Independent Exchange Format (RINEX) 2.11 to 4.01 format data, and implements four functional modules: format translation, data editing, quality checking, and auxiliary tools. In addition, it also offers a user-friendly graphical user interface (GUI) that enables users to customize and select different processing setting parameters and then analyze the corresponding results through drawing tools. This paper takes the WUH2 station derived from the International GNSS Service (IGS) as an example to evaluate software performance. The results indicate that GDPS software can meet multi-frequency and multi-GNSS data preprocessing requirements and is expected to provide an open-source, convenient, and integrated GNSS data preprocessing solution for relevant researchers. [ABSTRACT FROM AUTHOR]

To enhance the performance and reliability of the face recognition algorithm that is based on deep learning technology, this study utilizes a density-based noise-applied spatial clustering algorithm to cluster a large-scale face image dataset, resulting in a self-constructed dataset. A deep separable center differential convolutional network algorithm is utilized for face recognition. The impact of convolutional parameters on the algorithm's performance is verified through experiments with ablated convolutional parameters. The study found that the density-based noise-applied spatial clustering algorithm resulted in time savings of 43.66% and 51.22% compared to the K-means clustering algorithm and the hierarchical clustering algorithm, respectively, when analyzing 8000 images. Additionally, the depth-separable center difference convolutional network algorithm had a lower average classification error rate compared to the other two algorithms, with reductions of 2.49% and 17.01%, respectively. The depth-separable center difference convolutional network technique is an advanced method for identifying the faces of people of different races, according to the experimental investigation. It can provide efficient and accurate services for the face recognition needs of various races. [ABSTRACT FROM AUTHOR]

This paper presents AutoTemplate, an innovative data preprocessing protocol, addressing the crucial need for high-quality chemical reaction datasets in the realm of machine learning applications in organic chemistry. Recent advances in artificial intelligence have expanded the application of machine learning in chemistry, particularly in yield prediction, retrosynthesis, and reaction condition prediction. However, the effectiveness of these models hinges on the integrity of chemical reaction datasets, which are often plagued by inconsistencies like missing reactants, incorrect atom mappings, and outright erroneous reactions. AutoTemplate introduces a two-stage approach to refine these datasets. The first stage involves extracting meaningful reaction transformation rules and formulating generic reaction templates using a simplified SMARTS representation. This simplification broadens the applicability of templates across various chemical reactions. The second stage is template-guided reaction curation, where these templates are systematically applied to validate and correct the reaction data. This process effectively amends missing reactant information, rectifies atom-mapping errors, and eliminates incorrect data entries. A standout feature of AutoTemplate is its capability to concurrently identify and correct false chemical reactions. It operates on the premise that most reactions in datasets are accurate, using these as templates to guide the correction of flawed entries. The protocol demonstrates its efficacy across a range of chemical reactions, significantly enhancing dataset quality. This advancement provides a more robust foundation for developing reliable machine learning models in chemistry, thereby improving the accuracy of forward and retrosynthetic predictions. AutoTemplate marks a significant progression in the preprocessing of chemical reaction datasets, bridging a vital gap and facilitating more precise and efficient machine learning applications in organic synthesis. Scientific contribution: The proposed automated preprocessing tool for chemical reaction data aims to identify errors within chemical databases. Specifically, if the errors involve atom mapping or the absence of reactant types, corrections can be systematically applied using reaction templates, ultimately elevating the overall quality of the database. [ABSTRACT FROM AUTHOR]

Object recognition is a crucial task in computer vision and artificial intelligence, as it plays a vital role in many applications such as autonomous vehicles, surveillance systems, and robotics. In this paper, we present an overview of AI Trained object recognition using Google Teachable Machine. Google Teachable Machine is a web-based platform that allows users to train machine learning models without any coding or programming skills. We explore the steps involved in training an object recognition model using Google Teachable Machine and evaluate the performance of the model on a real-world dataset. Our results show that Google Teachable Machine is a powerful and user-friendly tool for training object recognition models with high accuracy. This research focuses on the development of an AI-ML trained object recognition system using Google Teachable Machine, enhanced by data science methodologies. The objective is to create a robust and efficient system capable of accurately identifying and classifying various objects in real-time. Leveraging the user-friendly interface of Google Teachable Machine, the system is trained with diverse datasets, incorporating advanced machine learning algorithms and data preprocessing techniques. This approach ensures high accuracy and reliability in object recognition tasks. The integration of data science principles allows for thorough analysis and optimization of the training data, improving the system's performance and adaptability. The resulting system demonstrates significant potential for applications in areas such as security, automation, and augmented reality, showcasing the synergy between AI, machine learning, and data sciences in solving complex recognition problems. [ABSTRACT FROM AUTHOR]

Big data present new opportunities for modern society while posing challenges for data scientists. Recent advancements in sensor networks and the widespread adoption of the Internet of Things (IoT) have led to the collection of physical-sensor data on an enormous scale. However, significant challenges arise in conducting high-quality data analytics within the realm of big data. To uncover big data challenges and enhance data quality, it is essential to quantitatively unveil data characteristics. Furthermore, the existing studies lack analysis of the specific time-related characteristics of physical-sensor data. Enhancing the efficiency and precision of data analytics through the big data lifecycle requires a comprehensive understanding of data characteristics to address the hidden big data challenges. To fill in the research gap, this paper proposes a systematic data characteristic framework based on a 6Vs model. The framework aims to unveil the data characteristics in terms of data volume, variety, velocity, veracity, value, and variability through a set of statistical indicators. This model improves the objectivity of data characteristic understanding by relying solely on data-driven indicators. The indicators related to time-related characteristics in physical-sensor data are also included for the analysis of temporal aspects in the physical-sensor data. Furthermore, the big data challenges are linked to each dimension of the 6Vs model to gain a quantitative understanding of the data challenges. Finally, a pipeline is developed to implement the proposed framework, and two case studies are conducted to illustrate the process of understanding the physical-sensor data characteristics and making recommendations for data preprocessing to address the big data challenges. The proposed framework is able to analyze the characteristics of all physical-sensor data, therefore, identifying potential challenges in subsequent analytics, and providing recommendations for data preprocessing. Furthermore, the characteristic indicators can be used to analyze other types of big data. [ABSTRACT FROM AUTHOR]

This paper introduces Weka, which uses data mining algorithms to classify viruses. This paper introduces Weka’s proposed data mining algorithm for classifying viruses. This paper presents a review and evaluation of a data mining algorithm for classifying diseases using Weka. The data mining algorithms ZeroR, Linear Regression, Multilayer Perceptron, Random Forest, Simple K-Means, Hierarchical Clustering, and Farthest First are proposed for disease classification using Weka. Weka acts as the judge in evaluating these algorithms, which possess the capability to accurately classify any ailment. Their performance is scrutinized based on both precision and the time taken to classify diseases. [ABSTRACT FROM AUTHOR]

The k nearest neighbor (KNN) classifier is one of the well-known instance-based classifiers. Nevertheless, the low efficiency in both running time and memory usage is a great challenge in the KNN classifier and its improvements due to noise and redundant samples. Although hybrid instance reduction approaches have been postulated as a good solution, they still suffer from the following issues: (a) adopted edition methods in existing hybrid instance reduction approaches are susceptible to harmful samples around the tested sample; (b) existing hybrid instance reduction approaches retain many internal samples, which contributes little to the classification accuracy and (or) leading to the low reduction rate; (c) existing hybrid instance reduction approaches rely on more than one parameter. The chief contributions of this article are that (a) a novel heuristic hybrid instance reduction approach based on adaptive relative distance and k-means clustering (HIRRDKM) is proposed against the above issues; (b) a novel concept, i.e., the adaptive relative distance, is first proposed and calculated for each sample; (c) a novel edition method based on adaptive relative distance in HIRRDKM is second proposed to filter out harmful samples; (d) a novel condensing method based on adaptive relative distance and k-means clustering in HIRRDKM is third proposed to obtain condensed borderline samples from the training set without harmful samples. Experiments have proved that (a) HIRRDKM outperforms 6 state-of-the-art hybrid instance reduction methods on real data sets from various fields in weighing reduction rate and classification accuracy of KNN-based classifiers; (b) the running time of HIRRDKM is competitive. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study on data preprocessing and modeling for predicting corrosion in water pipelines of a steel industrial plant. The use case is a cooling circuit consisting of both direct and indirect cooling. In the direct cooling circuit, water comes into direct contact with the product, whereas in the indirect one, it does not. In this study, advanced machine learning techniques, such as extreme gradient boosting and deep neural networks, have been employed for two distinct applications. Firstly, a virtual sensor was created to estimate the corrosion rate based on influencing process variables, such as pH and temperature. Secondly, a predictive tool was designed to foresee the future evolution of the corrosion rate, considering past values of both influencing variables and the corrosion rate. The results show that the most suitable algorithm for the virtual sensor approach is the dense neural network, with MAPE values of (25 ± 4)% and (11 ± 4)% for the direct and indirect circuits, respectively. In contrast, different results are obtained for the two circuits when following the predictive tool approach. For the primary circuit, the convolutional neural network yields the best results, with MAPE = 4% on the testing set, whereas for the secondary circuit, the LSTM recurrent network shows the highest prediction accuracy, with MAPE = 9%. In general, models employing temporal windows have emerged as more suitable for corrosion prediction, with model performance significantly improving with a larger dataset. [ABSTRACT FROM AUTHOR]

Air quality prediction technology can provide effective technical means for environmental governance. In recent years, due to the strong nonlinearity of data, there has been extensive research on data analysis and preprocessing techniques. This paper aims to comprehensively summarize and analyze the methods used in air quality forecasting, specifically focusing on four categories: data decomposition, dimensionality reduction, data correction, and spatial interpolation. Each method's purpose, characteristics, improvements, and implementation details are described in detail. The evaluation of data preprocessing methods is based on popularity, accuracy improvements, time consumption, maturity, and implementation difficulty. Among the existing methods, data decomposition and feature selection are commonly used and well-developed. However, outlier detection and spatial interpolation have limited applications and require further research. Furthermore, this paper discusses current challenges in applying these methods and future development trends, providing a valuable reference for future research. [ABSTRACT FROM AUTHOR]

This study leverages sophisticated machine learning methodologies, particularly XGBoost, to analyze cardiovascular diseases through cardiac datasets. The methodology encompasses meticulous data pre-processing, training of the XGBoost algorithm, and its performance evaluation using metrics such as accuracy, precision, and ROC curves. This technique represents a notable progression in the realm of medical research, potentially leading to enhanced diagnostic precision and a deeper comprehension of cardiovascular ailments, thereby improving patient care and treatment modalities in cardiology. Furthermore, the research delves into the utilization of deep learning methodologies for the automated delineation of cardiac structures in MRI and mammography images, aiming to boost diagnostic precision and patient management. [24][3][5][6] In assessing machine learning algorithms' efficacy in diagnosing cardiovascular diseases, this analysis underscores the pivotal role of such algorithms and their possible data inputs. Additionally, it investigates promising directions for future exploration, such as the application of reinforcement learning. A significant aspect of our investigation is the development and deployment of sophisticated deep learning models for segmenting right ventricular images from cardiac MRI scans, aiming at heightened accuracy and dependability in diagnostics. Through the utilization of advanced techniques like Fourier Convolutional Neural Network (FCNN) and improved versions of Vanilla Convolutional Neural Networks (Vanilla-CNN) and Residual Networks (ResNet), we achieved a substantial improvement in accuracy and reliability. This enhancement allows for more precise and quicker identification and diagnosis of cardiovascular diseases, which is of utmost importance in clinical practice. Evaluation of Machine Learning Algorithms: We conducted a comprehensive evaluation of machine learning algorithms in the context of cardiovascular disease diagnosis. This assessment emphasized the fundamental role of machine learning algorithms and their potential data sources. We also explored promising avenues, such as reinforcement learning, for future research. Factors Affecting Predictive Models: We highlighted the critical factors affecting the effectiveness of machine learning-based predictive models. These factors include data heterogeneity, depth, and breadth, as well as the nature of the modeling task, and the choice of algorithms and feature selection methods. Recognizing and addressing these factors are essential for building reliable models. [ABSTRACT FROM AUTHOR]

Copyright of Journal of University of Anbar for Pure Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The theme of this study is to find a method that can more accurately classify the spatiotemporal distribution characteristics of power system voltage dynamics. As more and more new energy power generation devices are added to the power network, the accuracy of traditional methods for classifying the spatiotemporal distribution characteristics of power system voltage dynamics has gradually decreased. It is significant to reduce the power loss caused by error in identifying voltage dynamic spatiotemporal distribution characteristics. Hence it is necessary to design methods that can more accurately classify power system voltage dynamic spatiotemporal distribution characteristics data. This study proposes a method for studying the spatiotemporal distribution characteristics of power system voltage dynamics based on an improved random forest algorithm. The information entropy calculation method of decision tree in random forest algorithm is improved. According to the experimental results, the classification accuracy of the method for voltage dynamic spatiotemporal distribution characteristics is 99.55%. It can effectively achieve the dynamic spatiotemporal distribution of power system voltage. [ABSTRACT FROM AUTHOR]

The development of ship engine fault diagnosis has led to an increased interest in predicting marine engine performance under various environmental and operating conditions. However, predicting engine performance using a single model is limited due to the various characteristics of ship engines depending on speed and environmental conditions. To address this issue, we propose a grey box model (GBM)-based ship engine performance prediction framework that combines the white box model and black box model (BBM) appropriately for both low- and high-speed operating conditions. The application of data preprocessing techniques, such as clustering and cleaning, under specific engine and environmental conditions, combined with dimensionality reduction techniques (partial least square and principal component) and the use of WBM/BBM models under classified speed conditions, makes the proposed framework accessible to real-world operators through data-driven approaches and domain knowledge. We demonstrate that the proposed framework improves the robustness, accuracy, and efficiency of engine performance predictions by considering the characteristics of each speed in real-world navigation data, compared to using single models. [ABSTRACT FROM AUTHOR]

Diabetes is caused by insulin deficiency or impaired biological action, and long-term hyperglycemia leads to a variety of tissue damage and dysfunction. Therefore, the early prediction of diabetes and timely intervention and treatment are crucial. This paper proposes a robust framework for the prediction and diagnosis of type 2 diabetes (T2DM) to aid in diabetes applications in clinical diagnosis. The data-preprocessing stage includes steps such as outlier removal, missing value filling, data standardization, and assigning class weights to ensure the quality and consistency of the data, thereby improving the performance and stability of the model. This experiment used the National Health and Nutrition Examination Survey (NHANES) dataset and the publicly available PIMA Indian dataset (PID). For T2DM classification, we designed a convolutional neural network (CNN) and proposed a novel attention-oriented convolutional neural network (SECNN) through the channel attention mechanism. To optimize the hyperparameters of the model, we used grid search and K-fold cross-validation methods. In addition, we also comparatively analyzed various machine learning (ML) models such as support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), and artificial neural network (ANN). Finally, we evaluated the performance of the model using performance evaluation metrics such as precision, recall, F1-Score, accuracy, and AUC. Experimental results show that the SECNN model has an accuracy of 94.12% on the NHANES dataset and an accuracy of 89.47% on the PIMA Indian dataset. SECNN models and CNN models show significant improvements in diabetes prediction performance compared to traditional ML models. The comparative analysis of the SECNN model and the CNN model has significantly improved performance, further verifying the advantages of introducing the channel attention mechanism. The robust diabetes prediction framework proposed in this article establishes an effective foundation for diabetes diagnosis and prediction, and has a positive impact on the development of health management and medical industries. [ABSTRACT FROM AUTHOR]

In the telecom sector, predicting customer churn has increased in importance in recent years. Developing a robust and accurate churn prediction model takes time, but it is crucial. Early churn prediction avoids revenue loss and improves customer retention. Telecom companies must identify these customers before they leave to solve this issue. Researchers have used a variety of applied machine-learning approaches to reveal the hidden relationships between different features. A key aspect of churn prediction is the accuracy level that affects the learning model's performance. This study aims to clarify several aspects of customer churn prediction accuracy and investigate state-of-the-art techniques' performance. However, no previous research has investigated performance using a hybrid framework combining the advantages of selecting suitable data preprocessing, ensemble learning, and resampling techniques. The study introduces a proposed hybrid framework that improves the accuracy of customer churn prediction in the telecom industry. The framework is built by integrating the XGBOOST classifier with the hybrid resampling method SMOTE-ENN, which concerns applying effective techniques for data preprocessing. The proposed framework is used for two experiments with three datasets in the telecom industry. This study determines which features are most crucial and influence customer churn, introduces the impact of data balancing, compares the classifiers' pre- and post-data balancing performances, and examines a speed-accuracy trade-off in hybrid classifiers. Many metrics, including accuracy, precision, recall, F1-score, and ROC curve, are used to analyze the results. All evaluation criteria are used to identify the most effective experiment. The results of the accuracy of the hybrid framework that respects balanced data outperformed applying the classifier only to imbalanced data. In addition, the results of the proposed hybrid framework are compared to previous studies on the same datasets, and the result of this comparison is offered. Compared with the review of the latest works, our proposed hybrid framework with the three datasets outperformed these works. [ABSTRACT FROM AUTHOR]

Categorical attributes are prevalent in many datasets used for training Machine Learning models. However, most ML models are designed to handle only numerical inputs. Therefore, converting these categorical attributes into numerical values is necessary to utilize them effectively. During this conversion process, it is essential to preserve the underlying patterns. A loss of such information could adversely affect the performance of ML algorithms. Several encoding techniques have been developed to map categorical instances to numbers. This study evaluates commonly used encoders alongside CESAMO, a novel encoder designed to capture relationships between categorical attributes and other variables using what is referred to as Pattern Preserving Codes. We conducted a statistically supported assessment of these categorical encoders using synthetic data and compared the encoders' performance. The results show that CESAMO outperforms all other evaluated encoding techniques, confirming its ability to identify patterns in categorical data effectively. [ABSTRACT FROM AUTHOR]

Plant trait data are used to quantify how plants respond to environmental factors and can act as indicators of ecosystem function. Measured trait values are influenced by genetics, trade‐offs, competition, environmental conditions, and phenology. These interacting effects on traits are poorly characterized across taxa, and for many traits, measurement protocols are not standardized. As a result, ancillary information about growth and measurement conditions can be highly variable, requiring a flexible data structure. In 2007, the TRY initiative was founded as an integrated database of plant trait data, including ancillary attributes relevant to understanding and interpreting the trait values. The TRY database now integrates around 700 original and collective datasets and has become a central resource of plant trait data. These data are provided in a generic long‐table format, where a unique identifier links different trait records and ancillary data measured on the same entity. Due to the high number of trait records, plant taxa, and types of traits and ancillary data released from the TRY database, data preprocessing is necessary but not straightforward. Here, we present the 'rtry' R package, specifically designed to support plant trait data exploration and filtering. By integrating a subset of existing R functions essential for preprocessing, 'rtry' avoids the need for users to navigate the extensive R ecosystem and provides the functions under a consistent syntax. 'rtry' is therefore easy to use even for beginners in R. Notably, 'rtry' does not support data retrieval or analysis; rather, it focuses on the preprocessing tasks to optimize data quality. While 'rtry' primarily targets TRY data, its utility extends to data from other sources, such as the National Ecological Observatory Network (NEON). The 'rtry' package is available on the Comprehensive R Archive Network (CRAN; https://cran.r‐project.org/package=rtry) and the GitHub Wiki (https://github.com/MPI‐BGC‐Functional‐Biogeography/rtry/wiki) along with comprehensive documentation and vignettes describing detailed data preprocessing workflows. [ABSTRACT FROM AUTHOR]

The early and accurate detection of Distributed Denial of Service (DDoS) attacks is a fundamental area of research to safeguard the integrity and functionality of organizations' digital ecosystems. Despite the growing importance of neural networks in recent years, the use of classical techniques remains relevant due to their interpretability, speed, resource efficiency, and satisfactory performance. This article presents the results of a comparative analysis of six machine learning techniques, namely, Random Forest (RF), Decision Tree (DT), AdaBoost (ADA), Extreme Gradient Boosting (XGB), Multilayer Perceptron (MLP), and Dense Neural Network (DNN), for classifying DDoS attacks. The CICDDoS2019 dataset was used, which underwent data preprocessing to remove outliers, and 22 features were selected using the Pearson correlation coefficient. The RF classifier achieved the best accuracy rate (99.97%), outperforming other classifiers and even previously published neural network-based techniques. These findings underscore the feasibility and effectiveness of machine learning algorithms in the field of DDoS attack detection, reaffirming their relevance as a valuable tool in advanced cyber defense. [ABSTRACT FROM AUTHOR]

Copyright of Pamukkale University Journal of Education is the property of Pamukkale University Journal of Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Applied statistics has been instrumental in predicting behaviours and future market trends. In the field of financial time series analysis, the incorporation of deep learning (DL) methods and applied statistics has made a significant contribution to the prediction model. Practitioners and researchers can extract complex features and dependencies from past financial data by leveraging neural network structures like long short-term memory (LSTM) and recurrent neural networks (RNNs). These DL approaches advance the development of predictive models prone to forecasting different financial metrics, such as asset returns, stock prices, and market volatility, with outstanding accuracy. With the combination of statistical approaches with DL techniques, researchers can leverage the power of both worlds to make more informed investment decisions and improve forecasting capabilities in volatile and dynamic financial markets. This study develops a new Applied Statistics with Single Valued Neutrosophic Fuzzy Soft Expert Sets (AS-SVNFSES) technique for Financial Time Series Forecasting. The presented AS-SVNFSES technique aims to forecast the input financial time series data. The AS-SVNFSES technique primarily applies data preprocessing using a Z-score normalization approach. For the forecasting of financial data, the AS-SVNFSES technique makes use of the SVNFSES technique. Finally, the parameter tuning of the SVNFSES technique is performed using the chimp optimization algorithm's (ChOA) design. A series of experimentations have illustrated the amended performance of the AS-SVNFSES model. The experimental value inferred that the AS-SVNFSES technique gains improved performance over other models. [ABSTRACT FROM AUTHOR]

The excitation current has a significant impact on the operational efficiency of synchronous motors, which hold a pivotal position in several industrial applications. The main contribution of this study has been to conduct a comprehensive analysis of various machine learning models to make accurate predictions regarding the excitation current. Normalization and feature selection have been applied to the dataset before using various regression models, including Linear, Lasso, Ridge, and Elastic Net Regression, as well as Decision Tree and Random Forest Regression. The Random Forest Regression model demonstrates a test accuracy of 97.01%, indicating its superior performance compared to other models. The good performance of the linear models indicates a linear relationship between the predictors and the excitation current. Regression model has achieved 98% accuracy during training. The findings from this work have extensive implications, encompassing enhanced energy efficiency, extended motor lifespans, and reduced instances of operational interruptions. While the models show promise, it is crucial to recognize their limitations and the need for more research, particularly in the realms of nonlinear models and empirical verification in real-world scenarios. [ABSTRACT FROM AUTHOR]