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Tree-HP2PL: A Hierarchical Priority-Based Two-Phase Locking Protocol for Nested Transactions in Distributed Real-Tim...

Modern applications require advanced concurrency control protocols to efficiently manage nested transactions within Distributed Real-Time Database Systems (DRTDBS). This paper introduces Tree-HP2PL, a Hierarchical Priority-Based Two-Phase Locking protocol designed to optimize conflict resolution within nested transactions. The proposed method employs a binary search tree stru...


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A Genetic Algorithm-Based Scheduling Framework for Hospital Resource Allocation in Edge-Terminal Collaborative Netwo...

Under the background of increasing medical demand and digital transformation, hospital resource scheduling in edge-terminal collaborative environments faces challenges such as information delay and insufficient decision-making accuracy. This study proposes a hospital resource allocation and scheduling framework based on genetic algorithm, which integrates edge terminal collab...


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Data Collection and Analysis of Psychological Health Signs of College Students Based on Artificial Intelligence Tech...

The problem of mental health concerns among university students is growing more and more noticeable, and early identification of mental health signs is crucial for intervention. Current assessment methods rely on questionnaire data, which suffer from inefficiencies in data statistics. Taking Rednote as a case study, this research employs web crawling technology to gather dive...


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Recommendation Algorithm and Simulation Experiment Based on Graph Representation Learning

Based on the GAN construction, the recommendation model in this paper preprocesses the interaction score between users and items, mines the implicit relationship embedding vector representation between users and items according to the user-item interaction bipartitic graph, extracts the explicit relationship between users and items from the graph, fuses the implicit relat...


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Application of Machine Learning Algorithms for Anomaly Detection in Cybersecurity Threat Mitigation

The integration of Artificial Intelligence (AI) into cybersecurity has transformed the landscape of threat detection, analysis, and mitigation. As cyber-attacks become increasingly sophisticated and evasive, traditional rule-based defences are no longer sufficient to identify zero-day exploits and advanced persistent threats. AI-driven approaches, leveraging machine learning ...


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