An efficient and productive IoT application may ease some real-time tasks however, it is at risk of cyber-attack. Intrusion Detection Systems (IDS) are of significant importance for the security measures of IoT applic...
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The progress of information technology is directly related to different elements of daily life, providing people with services that make living more comfortable. As the network infrastructure grows to support new serv...
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Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational ***,accurately classifying diverse and complex weather conditions remains a significant **...
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Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational ***,accurately classifying diverse and complex weather conditions remains a significant *** advanced techniques such as Vision Transformers have been developed,they face key limitations,including high computational costs and limited generalization across varying weather *** challenges present a critical research gap,particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’intricate and dynamic nature in *** address this gap,we propose a Multi-level Knowledge Distillation(MLKD)framework,which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational ***,we employ ResNet50V2 and EfficientNetV2B3 as teacher models,known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network(CNN)student *** framework balances the trade-off between high classification accuracy and efficient resource consumption,ensuring real-time applicability in autonomous *** Response-based Multi-level Knowledge Distillation(R-MLKD)approach effectively transfers rich,high-level feature representations from the teacher models to the student model,allowing the student to perform robustly with significantly fewer parameters and lower computational *** proposed method was evaluated on three public datasets(DAWN,BDD100K,and CITS traffic alerts),each containing seven weather classes with 2000 samples per *** results demonstrate the effectiveness of MLKD,achieving a 97.3%accuracy,which surpasses conventional deep learning *** work improves classification accuracy and tackles the practical challenges of model complexity,resource consumption,and real-time deployment,of
When dealing with complex statements containing multiple aspects in the existing model, most of them ignore the dependencies between context and aspect words, this makes it difficult for the model to learn local infor...
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While the world is booming with Electric Vehicles (EVs), countries where bikes are a common mode of transport still require adequate charging infrastructure to cope up with the slow EV growth. Efficient power conversi...
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In the rapidly evolving telecommunications sector, maintaining profitability and growth depends on customer retention. With the goal of identifying the critical elements influencing customer attrition and creating a u...
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ISBN:
(纸本)9798350367904
In the rapidly evolving telecommunications sector, maintaining profitability and growth depends on customer retention. With the goal of identifying the critical elements influencing customer attrition and creating a useful predictive model, this study offers a thorough investigation of customer churn prediction using a telecom dataset. This study uses a dataset that contains a variety of client features, such as account details, demographic data, and service consumption trends. Here, the data preparation techniques are used to manage anomalies, missing values, and data normalisation. The study uses a range of machine learning methods to forecast churn, such as support vector machines, random forests, decision trees, logistic regression, and gradient boosting. Metrics including accuracy, then precision, also the recall, then F1 score, and also the area under the curve of receiver operating characteristic are used to assess each model's performance (AUC-ROC). By use of cross-validation and hyperparameter adjustment, we guarantee the models' resilience and generalizability. Significant churn predictors, including contract type, duration, monthly costs, and customer support interactions, are identified by our investigation. According to the research, month-to-month contract holders who have higher monthly fees and frequent contact with customer service are more likely to experience customer attrition. The model with the highest degree of prediction accuracy is the random forest, which has an AUC-ROC of 0.85, making it the best-performing model. This paper offers a useful foundation for putting churn prediction models into practice in addition to highlighting the important variables causing customer churn in the telecom industry. Telecom firms may lower churn rates by creating focused retention tactics, such personalised offers and better customer care, by proactively identifying at-risk clients. The findings highlight how crucial it is to use machine learning and data a
Network security game was usually considered as attack-defense confrontation;however, we need to think out of the box because any network entity may get a chance to launch an attack. The problem of network security wh...
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Transfer learning has become a key technique in deep learning, widely adopted in the industry and academia for developing customized models, especially for specific and downstream tasks solving. Despite the prevalence...
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This research proposes a new technique that uses a reconstruction strategy for categorical datasets to preserve the privacy of the association rules. In order to identify all frequent item association sets and determi...
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Vehicular networks with service robots and vehicles have been widely studied in the past few years, and a large number of these vehicles and robots may appear in people's lives, such as in science and technology p...
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