Abstract: This paper presents a Q-Iearning-based adaptive multipath routing algorithm designed specifically for data center networks. By leveraging the discrete nature of Q-Iearning, the proposed ...

Abstract: Heart disease has become very common nowadays. Machine learning-based heart disease prediction has significant potential in clinical applications, enhancing early diagnosis and treatment.

Abstract: Federated learning allows users to collaboratively train models, but studies have shown that local model parameters may leak users’ privacy. Furthermore, the global model in federated ...

Abstract: This study presents deep learning models' design, implementation, and evaluation to generate personalized learning paths in educational environments. Data was collected from diverse sources, ...

Abstract: This paper investigates iterative learning control for linear discrete systems. Under general conditions, an explicit formula for the inverse model-based algorithm is derived. Building on ...

Abstract: With the development of deep learning technology, artificial intelligence has important applications in all aspects of society, but the lack of data has become a vital factor restricting the ...

With the rapid development of 6G space-air–ground integrated network (SAGIN) technologies, satellite networks, particularly LEO satellite networks (LEO-SNs), are poised to become crucial components of ...

Abstract: This paper proposes a deep-learning computer vision algorithm to estimate hand roll angles for metric-based assessment of surgical suturing skills. The number of rolls metric, previously ...

Abstract: This paper investigates the optimization of the FedDyn algorithm in Federated Learning (FL). Federated Learning is a distributed machine learning framework that enables model training on ...

Abstract: Spoken language understanding (SLU) is a structure prediction task in the field of speech. Recently, many works on SLU that treat it as a sequence-to-sequence task have achieved great ...