Qingyuan He

Ran Hao, Yanlin Xiang, Junliang Du et al.

This study proposed a hybrid model of a convolutional neural network (CNN) and a Transformer to predict and diagnose heart disease. Based on CNN's strength in detecting local features and the Transformer's high capacity in sensing global relations, the model is able to successfully detect risk factors of heart disease from high-dimensional life history data. Experimental results show that the proposed model outperforms traditional benchmark models like support vector machine (SVM), convolutional neural network (CNN), and long short-term memory network (LSTM) on several measures like accuracy, precision, and recall. This demonstrates its strong ability to deal with multi-dimensional and unstructured data. In order to verify the effectiveness of the model, experiments removing certain parts were carried out, and the results of the experiments showed that it is important to use both CNN and Transformer modules in enhancing the model. This paper also discusses the incorporation of additional features and approaches in future studies to enhance the model's performance and enable it to operate effectively in diverse conditions. This study presents novel insights and methods for predicting heart disease using machine learning, with numerous potential applications especially in personalized medicine and health management.

Qingyuan He, Chang Liu, Juecen Zhan et al.

This paper addresses the challenge of energy efficiency management faced by intelligent IoT devices in complex application environments. A novel optimization method is proposed, combining Deep Q-Network (DQN) with an edge collaboration mechanism. The method builds a state-action-reward interaction model and introduces edge nodes as intermediaries for state aggregation and policy scheduling. This enables dynamic resource coordination and task allocation among multiple devices. During the modeling process, device status, task load, and network resources are jointly incorporated into the state space. The DQN is used to approximate and learn the optimal scheduling strategy. To enhance the model's ability to perceive inter-device relationships, a collaborative graph structure is introduced to model the multi-device environment and assist in decision optimization. Experiments are conducted using real-world IoT data collected from the FastBee platform. Several comparative and validation tests are performed, including energy efficiency comparisons across different scheduling strategies, robustness analysis under varying task loads, and evaluation of state dimension impacts on policy convergence speed. The results show that the proposed method outperforms existing baseline approaches in terms of average energy consumption, processing latency, and resource utilization. This confirms its effectiveness and practicality in intelligent IoT scenarios.