Enhancing Data Processing Efficiency through Machine Learning Algorithms: A Comprehensive Study
--- DOI:
https://doi.org/10.64803/jodsie.v1i1.12Keywords:
machine learning, data processing, railway operations, predictive maintenance, power systemsAbstract
This study explores the transformative potential of machine learning algorithms to optimize data processing efficiency across diverse applications and address the growing challenges posed by big data. Specifically, machine learning can significantly enhance railway operations by optimizing maintenance schedules, reducing service interruptions, and improving overall network velocity. By applying advanced analytical techniques to railway data, it is possible to predict potential failures and proactively schedule maintenance, thereby minimizing costly downtime and enhancing the reliability of rail transportation infrastructure. This approach enables the transition from reactive to predictive maintenance strategies, leading to more efficient resource allocation and improved operational safety. This shift towards predictive maintenance, driven by machine learning, is crucial for mitigating risks and extending the lifespan of critical railway assets. This is particularly evident in power systems, where continuous monitoring and fault detection are paramount for maintaining stability and preventing disruptive outages, highlighting the broad applicability of these methodologies.
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