To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI ...
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Task offloading management in 6G vehicular net-works is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduce...
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ISBN:
(数字)9798350368369
ISBN:
(纸本)9798350368376
Task offloading management in 6G vehicular net-works is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50 % due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1].
To enable precision medicine and remote patient monitoring,internet of healthcare things(IoHT)has gained significant interest as a promising *** the widespread use of IoHT,nonetheless,privacy infringements such as IoH...
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To enable precision medicine and remote patient monitoring,internet of healthcare things(IoHT)has gained significant interest as a promising *** the widespread use of IoHT,nonetheless,privacy infringements such as IoHT data leakage have raised serious public *** the other side,blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT *** this survey,a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy *** addition,various types of privacy challenges in IoHT are identified by examining general data protection regulation(GDPR).More importantly,an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is ***,several challenges in four promising research areas for blockchain-based IoHT systems are pointed out,with the intent of motivating researchers working in these fields to develop possible solutions.
Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, m...
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In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study ai...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN— a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model’s effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets.
In recent years, gene expression data analysis has gained growing significance in the fields of machine learning and computational biology. Typically, microarray gene datasets exhibit a scenario where the number of fe...
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While unmanned aerial vehicles (UAVs) with flexible mobility are envisioned to enhance physical layer security in wireless communications, the efficient security design that adapts to such high network dynamics is rat...
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The detection of spam reviews in multilingual environments remains a challenging task due to linguistic diversity, data imbalance, and semantic complexity. This paper proposes a novel hybrid model that integrates Twin...
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With the rise of knowledge graph based retrieval-augmented generation (RAG) techniques such as GraphRAG and Pike-RAG, the role of knowledge graphs in enhancing the reasoning capabilities of large language models (LLMs...
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The prediction of breast cancer prognosis has been a significant challenge in medical research due to the complex nature of cancer progression and the variability in patient-specific factors. Machine learning techniqu...
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ISBN:
(数字)9798331522193
ISBN:
(纸本)9798331522209
The prediction of breast cancer prognosis has been a significant challenge in medical research due to the complex nature of cancer progression and the variability in patient-specific factors. Machine learning techniques have emerged as powerful tools for enhancing prediction accuracy in cancer diagnosis and prognosis. This study evaluates the performance of three widely used machine learning classifiers—Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM)—in predicting breast cancer outcomes using the Wisconsin Breast Cancer database. A robust framework was designed to preprocess the dataset, scale features, and assess the classifiers using key evaluation metrics, including accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). The results reveal that all classifiers demonstrated high predictive accuracy, with SVM achieving the highest accuracy of 97.08%, followed by RF and ANN with 96.67%. While RF recorded superior precision (95.40%), SVM outperformed others in recall (97.70%) and F1-score (96.05%), showcasing its ability to balance prediction precision and sensitivity. These findings underscore the potential of machine learning to augment traditional cancer prognosis methods, providing healthcare professionals with data-driven insights for personalized treatment strategies. This research highlights the critical role of machine learning in transforming cancer care and lays the groundwork for future studies focused on hybrid models and real-world implementation.
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