StreamAD: A cloud platform metrics-oriented benchmark for unsupervised online anomaly detection by Jiahui Xu, Chengxiang Lin, Fengrui Liu, Yang Wang, Wei Xiong, Zhenyu Li, Hongtao Guan, Gaogang Xie

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Assessing Machine Learning Techniques for Intrusion Detection in Cyber-Physical Systems by Vinícius F. Santos, Célio Albuquerque, Diego Passos, Silvio E. Quincozes, Daniel Mossé

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A study on automatic adjustment of the HCCI engine controller using machine learning by Akihiro TAKESHITA, Yudai YAMASAKI, Mitsuhiro MUTO, Takayuki HIKITA, Takuma FUJII, Saori MIZUNO

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Machine Learning-Driven Approach for Large Scale Decision Making with the Analytic Hierarchy Process by Marcos Antonio Alves, Ivan Reinaldo Meneghini, António Gaspar-Cunha, Frederico Gadelha Guimarães

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Smart data structures: An online learning approach to multicore data structures by Eastep, Jonathan Michael, Wingate, David, Agarwal, Anant

“…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 by Kyriacos Yiannacou, Veikko Sariola

“…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 by Diallo Abdoulaye, Kindy

“…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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On IoT-Friendly Skewness Monitoring for Skewness-Aware Online Edge Learning by Zheng Li, Jhon Galdames-Retamal

“…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 by Herell, Vincentius Dennis

“…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 by Andrés L. Suárez-Cetrulo, Alejandro Cervantes, David Quintana

“…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 by Shouli Zhang, Chen Liu, Xiaohong Li, Yanbo Han

“…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 by Yasmin Adel Hanafy, Maggie Mashaly, Mohamed A. Abd El Ghany

“…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 by Ahmad Al-Khdhairawi, Siti Mariani Mhd-Marzuki, Tan, Zi-Shen, Narin Shan, Danish Sanuri, Akbar, Rahmad, Su, Datt Lam, Fareed Sairi

“…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 by Juan Viera, Jose Aguilar, Maria Rodríguez-Moreno, Carlos Quintero-Gull

“…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 by Evangelos Bagkis, Theodosios Kassandros, Kostas Karatzas

“…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 by Huynh, Ngoc Anh

“…We capitalize on the well-known online machine learning framework of Follow the Regularized Leader (FTRL). …”
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Distribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernization by Sepideh Radhoush, Trevor Vannoy, Kaveen Liyanage, Bradley M. Whitaker, Hashem Nehrir

“…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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