In distributed file systems (DFS), deadlocks present significant challenges that adversely affect both performance and reliability. Conventional methods for deadlock detection and resolution are typically resource-intensive, time-consuming, and often fail to provide timely solutions. This paper introduces an innovative approach that leverages the synergy of daemon processes and artificial intelligence (AI) to enhance deadlock detection and resolution mechanisms. Our proposed method deploys autonomous daemon processes to continuously monitor the system states, providing real-time insights into the operating environment. Additionally, we employ advanced AI algorithms to analyze these states, predict potential deadlocks, and proactively implement resolution strategies before system performance is compromised. Experimental results from our comprehensive testing reveal notable improvements in overall system performance, a significant reduction in deadlock occurrences, and enhanced reliability. These findings affirm the efficacy of the proposed approach and its potential to revolutionize deadlock management in distributed file systems, paving the way for more robust and efficient computing environments.

The global COVID-19 pandemic has strained health-care systems and highlighted the need for accessible and efficient diagnostic methods. Traditional diagnostic tools, such as nasal swabs and biosensors, while accurate, pose significant logistical challenges and high costs, limiting their scalability. This paper explores an alternative, non-invasive approach to COVID-19 detection using machine learning algorithms to analyze vocal patterns, particularly cough and breathing sounds. Leveraging a publicly available dataset, we developed machine learning models capable of classifying audio samples as COVID-19 positive or negative. Our models achieve an AUC of up to 85% and an F1-score of 81%, demonstrating the potential of machine learning in enabling rapid, cost-effective COVID-19 diagnosis. These findings suggest that audio-based diagnostics could be a practical and scalable solution, particularly in resource-limited settings where traditional methods are less feasible.