Gated Recurrent Unit Research Articles

The STAP-Net: A new health perception and prediction framework for bearing-rotor systems under special working conditions

The health perception of bearing-rotor systems and their remaining useful life prediction has been a critical and challenging theme in the field of Prognostic and Health Management (PHM). Deep learning has become a prominent area of PHM research. However, current models have difficulty in adequately extracting the deep degradation characteristics of bearings and effectively capturing time-series information during the failure process. Also, most remaining useful life (RUL) prediction methods focus on point estimation, limiting their ability to quantify prediction uncertainty. To address these shortcomings, this study proposes a novel health perception and prediction framework, the Spatiotemporal Self-Attention Mechanism Probabilistic model (STAP-Net). The framework embodies the principles of lightweight design, focusing, and probabilistic approaches, and is tailored for bearing rotor systems operating under unique conditions. The key innovation of STAP-Net is the integration of a modified gate recurrent unit, known as the Weight Diminish Recurrent Unit (WDRU). It greatly reduces the training parameters of the proposed STAP-Net framework and improves the convergence speed of the framework while ensuring the prediction accuracy. Through analyzing the bearing-rotor system degradation data, the efficacy of STAP-Net is validated under special operating conditions such as misalignment and abrasive wear. The superior performance of the proposed framework is evaluated and confirmed based on 3 key metrics: high-precision point prediction, suitable prediction intervals, and reliable probabilistic prediction results.

Multi-Step Ageing Prediction of NMC Lithium-Ion Batteries Based on Temperature Characteristics

The performance of lithium-ion batteries depends strongly on their ageing state; therefore, the monitoring and the prediction of the battery state of health (SoH) is necessary for an optimized and secured functioning of battery systems. This paper evaluates and compares three artificial neural network architectures for multi-step ageing prediction of lithium-ion cells: Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). These models use the features extracted from the cell’s temperature to predict the cell’s capacity. The features are extracted from experimental measurements of the cell’s surface temperature and selected based on Spearman correlation analysis. The prediction results were evaluated and compared considering three different percentages of the training dataset: 60%, 70%, and 80%. Training and testing data were generated experimentally based on accelerated ageing cycling tests. During these experiments, four Nickel Manganese Cobalt/Graphite (NMC) cells were cycled under a controlled temperature environment based on a dynamic current profile extracted from the Worldwide Harmonized Light Vehicles Test Cycles.

Development of Enriched ROA With Dilated Hybrid Network for Automatic Modulation Classification Framework in CRNs

ABSTRACTAutomatic modulation classification (AMC) is explained as accurately identifying a modulation of a received signal. AMC systems are a significant component of cognitive radio network (CRN) systems. It is difficult to perform modulation classification on an unsettled radio signal without any previous knowledge of the signal's properties. In this work, the deep learning‐aided AMC is suggested to solve the difficulties of the existing models. In the proposed approach, the modulation classification is attained by performing two steps: (a) data collection and (b) classification. Initially, the required data related to the cognitive environment is collected from online resources. Later, the garnered data are passed to the classification phase. The AMC is performed by the adaptive and dilated hybrid network (ADHN), which is the combination of a temporal convolution network (TCN) and a gated recurrent unit (GRU). The ADHN accurately classifies the modulation even in a noisy environment. The classification performance of the ADHN is further boosted by tuning the parameters of this network via the enriched remora optimization algorithm (EROA). This proposed modulation classification model is suitable for various channels. The comparative validation is performed to ensure the usefulness of the designed system via several measures. By experimental analysis, the proposed system acquires the high value of accuracy, precision, and f1‐score by 94.2, 80.2, and 86.7, respectively, when compared with classical approaches. In addition to this, other metrics are considered and obtained with more true value and less false value. Thus, it ensures the effectiveness of classifying the modulation types in CRNs.

Temperature modeling of wave rotor refrigeration process based on variable time-delay compensation and SDAE-GRU

Abstract Aiming at the problem of noise and time-delay in temperature prediction modeling of large wave rotor refrigeration process, this paper proposes a novel modeling algorithm that combines the advantages of stacked denoising autoencoder (SDAE) network and Gate Recurrent Unit (GRU) algorithm and utilizes the Maximal Information Coefficient (MIC) algorithm to solve the targeting problem. The MIC algorithm is utilized to calculate the delay time of each input variable and output variable, and the input matrix is reconstructed to compensate for the time-delay, thus solving the problem afterwards. The SDAE network is utilized for denoising the input data and feature extraction to solve the problem of presence of noise in the data. The simulation results show that the proposed MIC-SDAE-GRU algorithm achieves a Root Mean Squared Error (RMSE) of 0.6432 and an R-squared (R²) value of 0.9638, outperforming traditional machine learning methods and other deep learning approaches. Specifically, compared to the standard GRU algorithm, it improves the accuracy of temperature prediction for the wave rotor refrigeration process by 24.7% and exhibits strong generalization ability across various operating conditions.

A Novel Deep Learning Model for Stock Prediction Integrating Historical Prices, Core Volatility Trends and Financial News

Accurate stock price forecasting is crucial for investors and analysts aiming to make informed decisions and mitigate risks in financial markets. While deep learning models have proven effective in identifying complex patterns in quantitative data, they often struggle to account for non-quantitative factors such as corporate events, market sentiment, and regulatory changes that can cause unpredictable stock price movements. To address this challenge, an ensemble model is introduced that integrates historical price data, financial news, and stock fundamentals to predict next-day stock prices. The model combines one-dimensional Convolutional Neural Networks (1D CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs), applied independently to each data stream and concurrently as a unified ensemble, to capture both short-term and long-term market behaviors. NVIDIA (NVDA) stock was selected for testing due to its high volatility and rapid price fluctuations, presenting a challenging case for predictive modeling. Using data spanning from April 2014 to March 2024, the model achieved a coefficient of determination (R²) of 0.983 and a mean absolute error (MAE) of $12.72. These results suggest that the ensemble approach effectively captures both quantitative and qualitative factors, making it a promising tool for improving stock price prediction in volatile market conditions.

Developing a neural network model to predict the optimal minimum flow rate for effective hole cleaning

The object of the study is effective well cleaning during drilling. The subject of the study is the development of a machine learning model based on a neural network for predicting the optimal minimum drilling fluid flow rate. The challenge is the need to improve well cleaning efficiency to prevent stuck pipes and the associated downtime and costs. During the study, a neural network model was developed and tested to predict the minimum flow rate for cleaning wells. The model was trained and tested on data showing its high accuracy and reliability. The mean square error (MSE) reached 0.019169 for LSTM and 0.0828 for GRU, indicating the accuracy of the predictions. Neural network architectures such as Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) were used to efficiently process time series of data and consider long-term dependencies. The results are explained using advanced neural network architectures and machine learning algorithms, which made it possible to achieve good accuracy of predictions. These architectures enable efficient model training on large amounts of data, allowing complex dependencies and influencing factors to be considered. Distinctive features of the results include good accuracy of predictions and the ability to use the model in real-world conditions. The model demonstrates good performance and reliability in predicting the minimum flow rate. The results of the study can be used to optimize the processes of well drilling. Practical applications include using the model to predict the optimal minimum flow rate in various conditions, which will reduce the risks of stuck pipes and increase the efficiency of drilling operations. The model can be integrated into existing monitoring and control systems for drilling processes to improve their performance

An interval prediction approach for quantifying the thermal error uncertainty of ball screw system

The ball screw system is one of the most core components of CNC machine tool, and its thermal positioning error directly affects the machining accuracy of parts. Establishing a data-based thermal error model is currently a popular method to predict this thermal error. Uncertain disturbances are unavoidable in reality. However, most of the existing thermal error models are deterministic, which cannot consider the uncertainty and generally can only provide the predictive value. To quantify the uncertainty of thermal error from the ball screw system, this paper proposes an interval prediction method, in which gated recurrent unit (GRU) neural network is embedded into the model and prediction interval (PI) evaluation indexes are used to determine the model parameters. Both the temporal and spatial characteristics of thermal error in the ball screw system are taken into account by the proposed method. The experimental data has verified that the proposed method can effectively generate PIs for the thermal error of ball screw system.

Harmonic Migration Algorithm for Virtual Machine Migration and Switching Strategy in Cloud Computing

ABSTRACTNowadays, cloud computing (CC) has been utilized broadly owing to the services it provides which can be received from any location at any time on the basis of the customer's requirements. A huge amount of data transmission is made from both user to host as well as host to the customer in the cloud environment, but here placing the virtual machine (VM) on a suitable host and data transferring is a challenging task. In this research, a harmonic migration algorithm (HMA) is developed by combining both the migration algorithm (MA) and the harmonic analysis (HA) for migrating a VM from an overloaded to an under‐loaded physical machine (PM) and enabling or disabling the VM through switching strategies in CC. The tasks are allocated to the corresponding VM in a round‐robin (RR) manner and subsequently, the load of the VM is predicted through the gated recurrent unit (GRU). The HMA technique migrates the VM when the predicted load is higher than the value of threshold and also, it enables or disables the VM when necessary. Thus, the performance of the developed HMA is improved over the other previous schemes at tasks 100, 200, 300, and 400 by varying the iterations. Therefore, the predicted load, makespan, and resource utilization of the developed HMA are 0.148, 0.327 s, and 0.482% for task 100 at iteration 100.

Comparison of time-series models for predicting physiological metrics under sedation.

This study presents a comprehensive comparison of multiple time-series models applied to physiological metric predictions. It aims to explore the effectiveness of both statistical prediction models and pharmacokinetic-pharmacodynamic prediction model and modern deep learning approaches. Specifically, the study focuses on predicting the bispectral index (BIS), a vital metric in anesthesia used to assess the depth of sedation during surgery, using datasets collected from real-life surgeries. The goal is to evaluate and compare model performance considering both univariate and multivariate schemes. Accurate BIS prediction is essential for avoiding under- or over-sedation, which can lead to adverse outcomes. The study investigates a range of models: The traditional mathematical models include the pharmacokinetic-pharmacodynamic model and statistical models such as autoregressive integrated moving average (ARIMA) and vector autoregression (VAR). The deep learning models encompass recurrent neural networks (RNNs), specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), as well as Temporal Convolutional Networks (TCNs) and Transformer models. The analysis focuses on evaluating model performance in predicting the BIS using two distinct datasets of physiological metrics collected from actual surgical procedures. It explores both univariate and multivariate prediction schemes and investigates how different combinations of features and input sequence lengths impact model accuracy. The experimental findings reveal significant performance differences among the models: In univariate prediction scenarios for predicting BIS, the LSTM model demonstrates a 2.88% improvement over the second-best performing model. For multivariate predictions, the LSTM model outperforms others by 6.67% compared to the next best model. Furthermore, the addition of Electromyography (EMG) and Mean Arterial Pressure (MAP) brings significant accuracy improvement when predicting BIS. The study emphasizes the importance of selecting and building appropriate time-series models to achieve accurate predictions in biomedical applications. This research provides insights to guide future efforts in improving vital sign prediction methodologies for clinical and research purposes. Clinically, with improvements in the prediction of physiological parameters, clinicians can be informed of interventions if an anomaly is detected or predicted.

Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning?

In this study, several machine learning (ML) models consisting of shallow learning (SL) models (e.g., random forest (RF), K-nearest neighbor (KNN), weighted K-nearest neighbor (WKNN), support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) models (e.g., long short-term memory (LSTM), gated recurrent unit (GRU), recurrent neural network (RNN), and convolutional neural network (CNN)) have been employed for predicting air pollution and its classification. The models were selected based on factors such as prediction accuracy, model generalization, model complexity, and training time. Our study focuses on analyzing and predicting the air quality index (AQI) using daily PM10 concentration as natural pollutants and nine meteorological parameters from March 2013 to February 2022 in Zabol. We also utilized the information gain (IG) method for feature selection. Several measures including accuracy, F1 score, precision, recall, and the area under the curve (AUC), are computed to assess model performance. This study demonstrates the efficacy of DL models, particularly CNN, in predicting the AQI with remarkable accuracy. Our findings reveal that all models effectively classify air quality levels, with an AUC of 0.95 for the good class in both DL and ANN models, significantly outperforming SL models. The AUC values for the hazardous and moderate classes of DL models were also impressive, at 0.90 and 0.83, respectively, underscoring their effectiveness in critical classifications. In terms of performance, CNN achieved an accuracy of 0.60, leading the models, while RF followed closely at 0.58. RNN, GRU, ANN, and SVM each reached an accuracy of 0.57, demonstrating a competitive edge. LSTM and WKNN recorded an accuracy of 0.55, and KNN was slightly lower at 0.53. These results highlight the superior capabilities of DL models in addressing complex air quality classifications, providing invaluable insights for policymakers. By leveraging these advanced techniques, stakeholders can implement more effective strategies to combat air pollution and safeguard public health. It is worth noting that irregular monitoring of air quality data may affect the robustness of our predictions, highlighting the need for more consistent data collection to ensure an accurate representation of pollution levels.

A Study of Recommendation Methods Based on Graph Hybrid Neural Networks and Deep Crossing

In the face of complex user behavior patterns and massive data, improving the performance of recommender system models is an urgent challenge. Traditional methods often struggle to effectively handle feature interactions and complex user-item relationships. Combining the advantages of graph neural networks and the Deep Crossing network, this paper proposes a recommendation method based on hybrid neural networks with Deep Crossing (Deep Crossing with Graph Convolution and GRU, DCGCN-GRU). First, by constructing the graph structure of users and items, higher-order feature representations are extracted, and node features are updated using a multilayer graph convolution operation. Then, the higher-order features learned by the graph convolution network are spliced and weighted with the original features to form new feature inputs. Next, a Gated Recurrent Unit (GRU) is introduced to capture the inter-feature temporal dynamic relationships and sequence information. Finally, the Deep Crossing model is utilized to learn the interactions between the fused features at multiple levels and enhance the interactions between the features. Comparative experiments on three public datasets, MovieLens-ml-25m, Book-Crossings, and Amazon Reviews’23, show that the model achieves significant improvements in accuracy, mean square error (MSE), and mean absolute error (MAE).

A robust multi-model framework for groundwater level prediction: The BFSA-MVMD-GRU-RVM model

Groundwater level prediction is critical for environmental protection and agricultural planning. Accurate predictions help manage risks associated with excessive groundwater extraction and land subsidence. This study introduces a novel model combining multivariate variational mode decomposition (MVMD), gated recurrent unit (GRU), and relevance vector machine (RVM), along with the Boruta feature selection algorithm (BFSA), for precise groundwater level forecasting. The model operates in several stages. First, BFSA selects the most informative features as input data. Next, the MVMD method decomposes the time series of these features. The GRU model then extracts crucial information from these decompositions. Finally, the extracted data is fed into the RVM model to predict groundwater levels one month ahead in Iran's Bastam Plain. To assess the model's accuracy, multiple error indices were employed. The MVMD-GRU-RVM model outperformed other models, improving the testing Nash–Sutcliffe Efficiency and mean absolute error by 24–31 % and 6–61 %, respectively. Additionally, it enhanced R2 values of the MVMD-CNN, MVMD-RNN, MVMD-MLP, and MVMD-RBFNN models by 1.0–3.33 %. These results demonstrate that the MVMD-GRU-RVM model significantly improves groundwater level prediction accuracy. The key points of the paper are the successful effectiveness of MVMD in processing non-stationary time series and improving the predictive performance of the models, as well as the high ability of the GRU model in data feature extraction. The study also shows that the novel model can provide outputs with lower uncertainty.

Feasibility Analysis for Predicting Indian Ocean Bigeye Tuna (Thunnus obesus) Fishing Grounds Based on Temporal Characteristics of FY-3 Microwave Radiation Imager Data

Efficient and accurate fishery forecasting is of great significance in ensuring the efficiency of fishery operations. This paper proposes a fishery forecasting method using a brightness temperature (TB) time series spatial feature extraction and fusion model. Using Indian Ocean bigeye tuna fishery data from 2009 to 2021 as a reference, this paper discusses the feasibility of fishery forecasting using FY-3 Microwave Radiation Imager (MWRI) Level 1 TB data. For this paper, we designed a deep learning network model for radiometer TB time series feature extraction (TimeTB-FishNet) based on the Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Attention mechanism. After expanding the dimensions of TB features, the model uses them together with spatiotemporal feature factors (year, month, longitude, and latitude) as features. By adding the GRU and Attention to the CNN, the CNN-GRU-Attention model architecture is established and can extract deep time series spatial features from the data to achieve the best results. In the model validation experiments, the TimeTB-FishNet model performed optimally, with a coefficient of determination (R2) of 0.6643. In the generalization experiments, the R2 also reached 0.6261, with a root mean square error (RMSE) of 46.6031 kg/1000 hook. When the sea surface height (SSH) was introduced, the R2 further reached 0.6463, with a lower RMSE of 45.1318 kg/1000 hook. The experimental results show that the proposed method and model are feasible and effective. The proposed model can directly use enhanced radiometer TB data without relying on lagging ocean environmental product data, performing deep temporal and spatial feature extraction for fishery forecasting. This method can provide a reference for the fishing of bigeye tuna in the Indian Ocean.

Long-term price guidance mechanism for integrated energy systems based on gated recurrent unit - vision transformer prediction and fractional-order stochastic dynamic calculus control

Against the backdrop of global climate change, renewable energy (RE) performs significant effects in reducing carbon emissions. Nevertheless, volatile RE sources disrupt the energy supply-demand balance (ESDB) in the energy market. Flexible energy (FE) can effectively mitigate the problem with high flexibility and controllability. FE sources are widely distributed in integrated energy systems (IESs). However, existing methods guide scattered FE in IESs with insufficient accuracy. To accurately guide FE, this study proposes an improved long-term price guidance mechanism (ILPGM) based on gated recurrent unit (GRU)- vision Transformer (ViT) prediction and fractional-order stochastic dynamic calculus (FSDC) control. The ILPGM combines GRU and ViT for baseline load forecasting and applies the FSDC method. The ILPGM can optimize the energy consumption of IESs and promote the ESDB while decreasing energy costs. The ILPGM is applied to the IES at Arizona State University-Tempe. The experimental results indicate that: the average coefficient of determination of electrical and heat energy under ILPGM is 97.69 %, realizing flexible regulation of energy consumption; the ILPGM can save 26.36 % actual cost and 94.62 % potential cost savings for the IES; compared to the previous long-term price guidance mechanism, the ILPGM provides an additional 5.87 % total cost savings and 28.65 % potential savings.

Comparisons of data-driven models for detecting slip occurrence and direction based on simulations of tactile sensing

Slip detection based on tactile sensing plays a crucial role for a robot to achieving stable grasps by promptly adjusting its grasping state. Detecting the occurrence of slippage has been a focus of previous research, but identifying the direction of slippage can provide richer reference information for correcting grasp issues. Due to the variations of experiment setup, the performances of these data-driven models have not been sufficiently compared. In this study, we compared the performance of three traditional machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN)—with four deep learning models—Gated Recurrent Unit (GRU), Convolutional Gated Recurrent Unit (CNN-GRU), Convolutional Long Short-Term Memory (ConvLSTM), and 3D Convolution(Conv3D)—in detecting slippage states and directions. We conducted experiments using a simulated dataset collected in Gazebo. Additionally, we compared the noise resistance capability of each model and their generalization performance when facing new objects. The results show that when facing known grasped objects, all models can effectively detect slippage occurrences and their directions. However, as noise increases, Conv3D exhibits the strongest robustness. When generalized to unknown grasped objects, the deep learning models outperforms the three traditional learning algorithms, with CNN-GRU showing the best performance.

Federated Deep Learning Model for False Data Injection Attack Detection in Cyber Physical Power Systems

Cyber-physical power systems (CPPS) integrate information and communication technology into conventional electric power systems to facilitate bidirectional communication of information and electric power between users and power grids. Despite its benefits, the open communication environment of CPPS is vulnerable to various security attacks. This paper proposes a federated deep learning-based architecture to detect false data injection attacks (FDIAs) in CPPS. The proposed work offers a strong, decentralized alternative with the ability to boost detection accuracy while maintaining data privacy, presenting a significant opportunity for real-world applications in the smart grid. This framework combines state-of-the-art machine learning and deep learning models, which are used in both centralized and federated learning configurations, to boost the detection of false data injection attacks in cyber-physical power systems. In particular, the research uses a multi-stage detection framework that combines several models, including classic machine learning classifiers like Random Forest and ExtraTrees Classifiers, and deep learning architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results demonstrate that Bidirectional GRU and LSTM models with attention layers in a federated learning setup achieve superior performance, with accuracy approaching 99.8%. This approach enhances both detection accuracy and data privacy, offering a robust solution for FDIA detection in real-world smart grid applications.

Grammatical Error Correction Detection of English Conversational Pronunciation Under a Deep Learning Algorithm

Grammar correction in spoken English can enhance proficiency. This paper briefly introduces the gate recurrent unit (GRU) algorithm and its appli- cation in English speech recognition and grammatical error correction of speech recognition results. The GRU algorithm was firstly used to recognize English speech, then transform it into a text, and finally correct the English grammar of the text. Additionally, the attention mechanism was incorporated to enhance the performance of grammatical error correction. Subsequently, simulation experiments were conducted. Firstly, speech recognition and grammatical error correction were independently verified. The performance of the proposed algorithm in correcting grammatical errors in spoken English was evaluated using a self-built speech database. The results demonstrated that the proposed GRU-based algorithm yielded the best performance in independent speech recognition, independent grammatical error correction, and the overall spoken grammatical error correction. The contribution of this study lies in using the GRU algorithm to convert speech into text and perform grammar correction on the text, providing an effective reference for grammar correction in English communication.

Surrogate model of turbulent transport in fusion plasmas using machine learning

Abstract The advent of machine learning (ML) has revolutionized the research of plasma confinement, offering new avenues for exploration. It enables the construction of models that effectively streamline the simulation process. While previous first-principles simulations have provided physics-based transport information, they have been inadequate fast for real-time applications or plasma control. In order to address this challenge, we introduce SExFC, a surrogate model based on the Gyro-Landau Extended Fluid Code (ExFC). An approach of physics-based database construction is detailed, as well the validity is illustrated. Through harnessing the power of ML, SExFC offers the capability to deliver rapid and precise predictions, facilitating real-time applications and enhancing plasma control. The proposed model integrates the recurrent neural network (RNN) algorithm, specifically leveraging the Gated Recurrent Unit (GRU) for iterative prediction of flux evolutions based on radial profiles. Therefore, the SExFC model has the potential to enable rapid and physics-based predictions that can be seamlessly integrated into future real-time plasma control systems.

A Novel WTG Method for Predicting Ship Trajectories in the Fujian Inshore Area Based on AIS Data

The increasing congestion in major global maritime routes poses significant threats to international maritime safety, exacerbated by the proliferation of large, high-speed vessels. To improve the detection of abnormal ship behavior, this research employed automatic identification system (AIS) data for ship trajectory forecasting. Traditional methods primarily focus on spatial and temporal correlations but often lack accuracy and reliability. In this study, ship path predictions were enhanced using the WTG model, which combines wavelet transform, temporal convolutional networks (TCN), and gated recurrent units (GRU). Initially, wavelet decomposition was applied to deconstruct the input trajectory time series. The TCN and GRU modules then extracted features from both the time series and the decomposed data. The predicted elements were reassembled using a multi-head attention mechanism and a pooling layer to produce the final predictions. Comparative experiments demonstrated that the WTG model surpasses other models in the accuracy of ship trajectory prediction. The model proposed in this study proves to be reliable for forecasting ship paths, which is crucial for marine traffic management and ensuring safe navigation.

EGMA: Ensemble Learning-Based Hybrid Model Approach for Spam Detection

Spam messages have emerged as a significant issue in digital communication, adversely affecting users’ mental health, personal safety, and network resources. Traditional spam detection methods often suffer from low detection rates and high false positives, underscoring the need for more effective solutions. This paper proposes the EGMA model, an ensemble learning-based hybrid approach for spam detection in SMS messages, which integrates gated recurrent unit (GRU), multilayer perceptron (MLP), and hybrid autoencoder models utilizing a majority voting algorithm. The EGMA model enhances performance by incorporating additional statistical features extracted from message content and employing text vectorization techniques, such as Term Frequency–Inverse Document Frequency (TF-IDF) and CountVectorizer. The proposed model achieved impressive classification accuracies of 99.28% on the SMS Spam Collection dataset, 99.24% on the Email Spam dataset, 99.00% on the Enron-Spam dataset, 98.71% on the Super SMS dataset, and 95.09% on UtkMl’s Twitter Spam dataset. These results demonstrate that the EGMA model outperforms individual models and existing methods in the literature, providing a robust solution for enhancing spam detection performance and effectively mitigating the threats that spam messages pose in digital communication.