SMS spam has considerable impacts, affecting users and service providers alike, leading to a substantial trust deficit between both parties. This study aims to add to the expanding knowledge base in SMS spam detection...
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ISBN:
(纸本)9798350389609
SMS spam has considerable impacts, affecting users and service providers alike, leading to a substantial trust deficit between both parties. This study aims to add to the expanding knowledge base in SMS spam detection, emphasizing the potential of various classifier algorithms, both soft and hard voting ensemble methods, and a customized Bi-LSTM (Bidirectional Long Short-Term Memory) model as effective tools in spam SMS classification. The classifier algorithms encompass Random Forest, Support Vector Machine, CatBoost, LightGBM, Decision Tree, XGBoost, Logistic Regression, and K-Nearest Neighbors. These models were employed to evaluate the benchmark dataset SMS Spam Collection sourced from the UCI Machine Learning repository. Additionally, we employed 5-fold Stratified cross-validation, ensuring a more thorough assessment of the model's performance and reducing the risk of overfitting by validating the model on various representative subsets of the data. After cross-validation, the top three classifier algorithms were selected based on their performance metrices. Subsequently, both soft and hard voting ensemble were employed on these algorithms. The customized Bi-LSTM model was trained for 100 epochs as the loss curves showed minimal change after this point. This model was compiled using the adam optimizer (learning rate=0.0001) along with the binary crossentropy loss function. Prior to cross-validation, the Bi-LSTM model attained an accuracy score of 99.30%, the highest among all proposed models. Following cross validation, the soft voting ensemble on top three models achieved the highest accuracy rate of 93.77% among all recommended models, with significant precision, recall, F1-score, MAE, MSE, RMSE, RAE, RRSE, and AUC metrics of 93.50%, 93.77%, 93.51%, 6.23%, 6.23%, 24.96%, 4.30%, 8.28%, and 94.96%, respectively. The hard voting ensemble using the top three models achieved accuracy, precision, recall, F1-score, MAE, MSE, RMSE, RAE, and RRSE rates of 93.49%, 93.3
Microgrids(MGs)are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC ...
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Microgrids(MGs)are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC loads,distributed renewable energy sources,and energy storage systems,as well as a more resilient and economical on/off-grid control,operation,and energy ***,MGs,as newcomers to the utility grid,are also facing challenges due to economic deregulation of energy systems,restructuring of generation,and marketbased *** paper comprehensively summarizes the published research works in the areas of MGs and related energy management modelling and solution ***,MGs and energy storage systems are classified into multiple branches and typical combinations as the backbone of MG energy ***,energy management models under exogenous and endogenous uncertainties are summarized and extended to transactive energy *** programming,adaptive dynamic programming,and deep reinforcement learning-based solution methods are investigated accordingly,together with their implementation ***,problems for future energy management systems with dynamics-captured critical component models,stability constraints,resilience awareness,market operation,and emerging computational techniques are discussed.
Most LiDAR 3D detection tasks are usually performed on 2D BEV generated from 3D voxels using neural networks. This results in significant loss of spatial information and difficulty for extracting multi-scale features....
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This paper proposes a novel adaptive synthetic inertia (SI) control scheme for the battery energy storage system (BESS) in the wind farm. The proposed approach dynamically adjusts the amplitude of the direct current (...
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Federated Learning (FL), a distributed machine learning approach, aims to protect user privacy by performing model training without centralized data collection. However, FL systems are susceptible to malicious partici...
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Adversarial examples have raised public concern about the robustness of deep neural networks (DNNs). One universal approach to enhance the robustness is adversarial training which essentially augments the training dat...
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Agriculture is an important component of every country's economy, supplying the necessary resources to farmers and their families. The livelihoods of farmers are greatly threatened by crop diseases, which highligh...
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In this study, we investigate a traffic monitoring method to detect passing cars, bikes, and humans from roadsides using millimeter-wave radar during road work. Because a road worker is one of the most dangerous occup...
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With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite *** paper notes that four typical application scenarios of integrated terr...
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With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite *** paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks *** the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are *** mobile edge computing(SMEC)with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of ***,a scheme for interference management is presented,involving quality-of-service(QoS)and co-tier/cross-tier interference *** simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks.
As a typical two-dimensional(2D) coating material, graphene has been utilized to effectively reduce secondary electron emission from the surface. Nevertheless, the microscopic mechanism and the dominant factor of seco...
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As a typical two-dimensional(2D) coating material, graphene has been utilized to effectively reduce secondary electron emission from the surface. Nevertheless, the microscopic mechanism and the dominant factor of secondary electron emission suppression remain controversial. Since traditional models rely on the data of experimental bulk properties which are scarcely appropriate to the 2D coating situation, this paper presents the first-principles-based numerical calculations of the electron interaction and emission process for monolayer and multilayer graphene on silicon(111) substrate. By using the anisotropic energy loss for the coating graphene, the electron transport process can be described more realistically. The real physical electron interactions, including the elastic scattering of electron-nucleus, inelastic scattering of the electron-extranuclear electron, and electron-phonon effect, are considered and calculated by using the Monte Carlo method. The energy level transition theory-based first-principles method and the full Penn algorithm are used to calculate the energy loss function during the inelastic scattering. Variations of the energy loss function and interface electron density differences for 1 to 4 layer graphene coating Go Si are calculated, and their inner electron distributions and secondary electron emissions are analyzed. Simulation results demonstrate that the dominant factor of the inhibiting of secondary electron yield(SEY) of Go Si is to induce the deeper electrons in the internal scattering process. In contrast, a low surface potential barrier due to the positive deviation of electron density difference at monolayer Go Si interface in turn weakens the suppression of secondary electron emission of the graphene layer. Only when the graphene layer number is 3, does the contribution of surface work function to the secondary electron emission suppression appear to be slightly positive.
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