The term 'cloud computing' describes a way of using the Internet to access resources, software, and databases without being constrained by local hardware. Businesses adopting this technology can grow their ope...
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Anomaly detection is different in different areas or context and the definition of anomaly also changes from problem to problem. Classical techniques like classification and clustering are not effective when it comes ...
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The use of extended time series to gather and store huge datasets has been made simpler by information science and data capture technologies. In many fields, such as astronomy, the environment, economics, business, me...
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Within the rapidly evolving realms of artificial intelligence and machinelearning, quantum computing has emerged as a pivotal frontier. However, its progress is significantly hindered by the prevailing limitations of...
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ISBN:
(纸本)9798350330656;9798350330649
Within the rapidly evolving realms of artificial intelligence and machinelearning, quantum computing has emerged as a pivotal frontier. However, its progress is significantly hindered by the prevailing limitations of quantum hardware. This paper introduces an innovative quantum-classical hybrid algorithm, intricately designed for multi-class classification. The algorithm prioritizes memory efficiency, capitalizing on advanced amplitude encoding techniques and a specialized error-code output correction mechanism to reduce the quantum computational resources needed for data processing while maintaining exceptional speed and classification accuracy. Rigorous testing across a range of datasets, varying in complexities like feature count, class number, and data points, yields notable achievements, particularly in simpler datasets. Nevertheless, the presence of errors and occasional failures with more complex datasets highlights the ongoing need for refinement and optimization, positioning the algorithm toward practical applicability in the field.
This paper explores the potential of data augmentation techniques to improve the detection of bee activities using machinelearning models applied to bioacoustic data. Traditional machinelearning methods used in bee ...
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Cloud data centers strive to provide efficient, scalable, and reliable computing resources while addressing challenges related to security, privacy, cost, and more. Their objectives include delivering value to users t...
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Malicious URLs can be detected by leveraging lexical analysis and machinelearning, where the parsed components of the URL are parsed, and its features include domain reputation, URL length, and the presence of suspic...
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Stroke, a potentially deadly medical disorder, requires excellent prediction and prevention measures to minimize its impact on individuals and healthcare systems. In this study, ensemble learning techniques are employ...
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Phasor measurement unit (PMU) networks deliver accurate and timely measurements, which is essential for managing today's electric power systems. To ensure data quality and enhance the cyber-resilience of PMU netwo...
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ISBN:
(纸本)9798350318562;9798350318555
Phasor measurement unit (PMU) networks deliver accurate and timely measurements, which is essential for managing today's electric power systems. To ensure data quality and enhance the cyber-resilience of PMU networks against malicious attacks and data errors, this study presents an online PMU missing data recovery scheme by leveraging P4 programmable switches. The data plane incorporates a customized PMU protocol parser that abstracts the necessary payload data for recovery. Recovery processes are executed in the control plane using a pre-trained machinelearning model. Both traditional and advanced ML models, such as transformer and TimeGPT, are explicitly employed for data prediction. This approach ensures rapid and precise data recovery. Performance evaluations focus on recovery speed and accuracy, using a real dataset from a campus microgrid. With 20% missing PMU data, the mean absolute percentage error for voltage magnitude is 0.0384%, and the phase angle error discrepancy is approximately 0.4064%.
Accurate weather forecasting is imperative across various sectors, impacting decisions in agriculture, transportation, and emergency planning. Conventional methods often struggle with the intricate patterns in weather...
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