UI/UX Design for Online Learning Approach by Predictive Student Experience

In recent years it has been shown that the secure exchange of medical information significantly benefits people’s life quality, improving their care and treatment. The interoperability of the entire healthcare ecosystem is a constant challenge, and even more, with all the risks posed to the security of healthcare information. Blockchain technology is emerging as one of the main alternatives when it comes to finding a balance in the healthcare ecosystem. However, the constant development of new Blockchain technologies and the evolution of healthcare systems make it difficult to find established proposals. From an architectural point of view, the design of blockchain-based solutions requires trade-offs e.g., security and interoperability. This paper focuses on two main objectives, in the first one, it was carried out a Systematic Literature Review for exploring architectural mechanisms used to support the interoperability and security of Blockchain-based Health Management Systems. Taking into account of results, a series of scenarios were generated where these mechanisms can be used along with their context, issues, and various architectural concerns (interoperability and security). In the second objective, a high-level architecture and its validation were proposed through an experiment for the whole process of developing a Domain Specific Language, using the Model Driven Engineering methodology for specific Smart Contracts.

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Machine Learning Techniques to Optimize CPU Scheduling in Real-Time Systems: A Comprehensive Review and Analysis

C. Nagesh,

G. Sudha Gowd,

Naidu Kiran Kumar

et al. 2024

IJARSCT

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Real-time systems demand stringent adherence to timing constraints, making CPU scheduling a critical factor for ensuring timely and reliable task execution. Traditional CPU scheduling algorithms, while effective in many scenarios, often fall short in handling the dynamic and complex nature of modern real-time applications. This paper provides a comprehensive review and analysis of machine learning (ML) techniques employed to optimize CPU scheduling in real-time systems. We explore various ML methodologies including supervised learning, reinforcement learning, and deep learning, examining their applications, advantages, and limitations in the context of real-time CPU scheduling. By leveraging ML, these systems can dynamically adapt to changing workloads, predict task execution times, and optimize scheduling policies, thereby improving overall system performance and predictability. Key contributions of this review include a detailed comparison of ML-based approaches against traditional scheduling techniques, insights into their real-time applicability, and identification of future research directions. The analysis underscores the potential of ML to transform CPU scheduling by providing adaptive, intelligent solutions that cater to the evolving demands of real-time systems

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UI/UX Design for Online Learning Approach by Predictive Student Experience

Cited by 5 publications

References 12 publications

Machine Learning for Web Security: Strategies to Detect and Prevent Malicious Activities

Machine Learning for Web Security: Strategies to Detect and Prevent Malicious Activities

Blockchain for Healthcare Management Systems : A Survey on Interoperability and Security

Machine Learning Techniques to Optimize CPU Scheduling in Real-Time Systems: A Comprehensive Review and Analysis

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