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StreamAD: A cloud platform metrics-oriented benchmark for unsupervised online anomaly detection
Published 2023-06-01Subjects: Get full text
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Assessing Machine Learning Techniques for Intrusion Detection in Cyber-Physical Systems
Published 2023-08-01Subjects: Get full text
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A study on automatic adjustment of the HCCI engine controller using machine learning
Published 2022-06-01Subjects: Get full text
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Machine Learning-Driven Approach for Large Scale Decision Making with the Analytic Hierarchy Process
Published 2023-01-01Subjects: Get full text
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Smart data structures: An online learning approach to multicore data structures
Published 2014“…To help mitigate these complexities, this work develops a new class of parallel data structures called Smart Data Structures that leverage online machine learning to adapt automatically. We prototype and evaluate an open source library of Smart Data Structures for common parallel programming needs and demonstrate significant improvements over the best existing algorithms under a variety of conditions. …”
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Acoustic Manipulation of Particles in Microfluidic Chips with an Adaptive Controller that Models Acoustic Fields
Published 2023-09-01“…The method is based on online machine learning of the acoustic fields. Starting with no knowledge of the fields, the controller can manipulate particles even on the first attempt, and its performance improves in subsequent attempts, yet can still readapt if the models are invalidated by a sudden change in system parameters. …”
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Using rough set through for classification of image segmentation data
Published 2014“…In this work, we are using the Rough Set Classifier (RSC) for mining image segmentation data set obtained from an online machine learning data repository. The RSC is a rule based data mining technique which generates rules from large databases and has great capabilities to deal with noise and uncertainty in data set. …”
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28
On IoT-Friendly Skewness Monitoring for Skewness-Aware Online Edge Learning
Published 2021-08-01“…Nevertheless, this pre-processing strategy cannot be employed for online machine learning, especially in the context of edge computing, because it is barely possible to foresee and store the continuous data flow on IoT devices on the edge. …”
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Industry 4.0 enabled electrochemical process lines and data analysis for quality control
Published 2022“…Online machine learning or stream learning is a machine learning algorithm that updates its model with each new data instead of the standard batch learning that need the whole dataset at the time of training. …”
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Incremental Market Behavior Classification in Presence of Recurring Concepts
Published 2019-01-01“…RCARF is benchmarked against other popular methods from the incremental online machine learning literature and is able to achieve competitive results.…”
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Runtime reconfiguration of data services for dealing with out-of-range stream fluctuation in cloud-edge environments
Published 2022-12-01“…To effectively predict upcoming workloads, we combine the online machine learning methods with an online optimization algorithm for service deployment. …”
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An Efficient Hardware Design for a Low-Latency Traffic Flow Prediction System Using an Online Neural Network
Published 2021-08-01“…Consequently, the proposed hardware model is faster than the software model and more suitable for time-critical online machine learning models.…”
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Collagen-derived cryptides : machine-learning prediction and molecular dynamic interaction against Klebsiella pneumoniae biofilm synthesis precursor
Published 2022“…Therefore, cryptides derived from collagen amino acid sequences of various types and species were subjected to online machine-learning platforms (i.e., CAMPr3, DBAASP, dPABBs, Hemopred, and ToxinPred). …”
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Analysis of the Behavior Pattern of Energy Consumption through Online Clustering Techniques
Published 2023-02-01“…Specifically, two unsupervised online machine learning techniques ideal for the stated objective will be analyzed, X-Means and LAMDA, since they are capable of varying and adapting the number of clusters at runtime. …”
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Learning Calibration Functions on the Fly: Hybrid Batch Online Stacking Ensembles for the Calibration of Low-Cost Air Quality Sensor Networks in the Presence of Concept Drift
Published 2022-03-01“…GAHS employs a combination of batch machine learning algorithms and regularly updated online machine learning calibration function(s) for the whole network when a small number of reference instruments are present. …”
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Frequency analysis and online learning in malware detection
Published 2019“…We capitalize on the well-known online machine learning framework of Follow the Regularized Leader (FTRL). …”
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Thesis -
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Distribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernization
Published 2023-06-01“…In this paper, a new method is proposed to hybridize traditional and advanced measurements using an online machine learning model. In this work, we assume that an ADN has been monitored using traditional measurements and the Weighted Least Square (WLS) method to obtain DSSE results, and the voltage magnitude and phase angle at each bus are considered as state vectors. …”
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