Relevant bibliographies by topics / Outlier Detection, Random Forest, Pattern Recognition, Anomaly Detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Outlier Detection, Random Forest, Pattern Recognition, Anomaly Detection'

Author: Grafiati
Published: 11 March 2023
Last updated: 19 July 2025

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Journal articles on the topic "Outlier Detection, Random Forest, Pattern Recognition, Anomaly Detection"

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Kim, Taegong, and Cheong Hee Park. "Anomaly Pattern Detection in Streaming Data Based on the Transformation to Multiple Binary-Valued Data Streams." Journal of Artificial Intelligence and Soft Computing Research 12, no. 1 (2021): 19–27. http://dx.doi.org/10.2478/jaiscr-2022-0002.

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Abstract Anomaly pattern detection in a data stream aims to detect a time point where outliers begin to occur abnormally. Recently, a method for anomaly pattern detection has been proposed based on binary classification for outliers and statistical tests in the data stream of binary labels of normal or an outlier. It showed that an anomaly pattern can be detected accurately even when outlier detection performance is relatively low. However, since the anomaly pattern detection method is based on the binary classification for outliers, most well-known outlier detection methods, with the output o
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Tan, Xu, Jiawei Yang, and Susanto Rahardja. "Sparse random projection isolation forest for outlier detection." Pattern Recognition Letters 163 (November 2022): 65–73. http://dx.doi.org/10.1016/j.patrec.2022.09.015.

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Ajeigbe, Olusayo Adekunle, Olabisi Yinka Ogunkeyede, and Ayomikun Olalekan Popoola. "Optimization of Power Flow Outage Detection using Machine Learning Algorithm." European Journal of Artificial Intelligence and Machine Learning 4, no. 3 (2025): 29–36. https://doi.org/10.24018/ejai.2025.4.3.61.

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This study investigates the application of Machine Learning (ML) methods to detect problems in distribution networks. The main goal is to swiftly and reliably identify and categorize disturbances, hence improving the network's reliability and accelerating restoration efforts. The proposed methodology leverages the functionalities of Supervised Machine Learning algorithms such as Random Forest, Logistic Regression, and K-Nearest Neighbours, which offer user-friendliness and adaptability in addressing both classification and regression tasks for pattern recognition and anomaly detection. Through
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Cheung, Catherine, Julio J. Valdés, Richard Salas Chavez, and Srishti Sehgal. "Failure Modeling of a Propulsion Subsystem: Unsupervised and Semi-Supervised Approaches to Anomaly Detection." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 11 (2019): 1940019. http://dx.doi.org/10.1142/s0218001419400196.

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In this work, the sensor data related to a diesel engine system and specifically its turbocharger subsystem were analyzed. An incident where the turbocharger seized was recorded by dozens of standard turbocharger-related sensors. By training models to distinguish between normal healthy operating conditions and deteriorated conditions, there is an opportunity to develop prognostic and predictive tools to ideally help prevent a similar occurrence in the future. Analysis of this event provides an opportunity to identify changes in equipment indicators with a known outcome. A number of data analys
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Hao, Yinhui, and Fuqiang Qiu. "Research on the Application of DM Technology with RF in Enterprise Financial Audit." Mobile Information Systems 2022 (May 26, 2022): 1–9. http://dx.doi.org/10.1155/2022/4051469.

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Data mining (DM), as a new technology in the information age, is applied to modern audit work, which is more effective than traditional audit methods. In view of the problems existing in traditional tax audit methods, such as the huge amount of audit data, limited knowledge and experience of auditors, and difficult tracking of audit data, this paper uses computer-aided audit technology to collect, clean up, convert, and analyze data, comprehensively uses data warehouse technology, pattern recognition method, data analysis method, and anomaly detection theory as research methods, and makes a co
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Idhaya, T., A. Suruliandi, and S. P. Raja. "Drug-Protein Interactions Prediction Models Using Feature Selection and Classification Techniques." Current Drug Metabolism 24, no. 12 (2023): 817–34. http://dx.doi.org/10.2174/0113892002268739231211063718.

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Background:: Drug-Protein Interaction (DPI) identification is crucial in drug discovery. The high dimensionality of drug and protein features poses challenges for accurate interaction prediction, necessitating the use of computational techniques. Docking-based methods rely on 3D structures, while ligand-based methods have limitations such as reliance on known ligands and neglecting protein structure. Therefore, the preferred approach is the chemogenomics-based approach using machine learning, which considers both drug and protein characteristics for DPI prediction. Methods:: In machine learnin
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Thi Ngoc Anh, Nguyen, Pham Ngoc Quang Anh, Vu Hoai Thu, Doan Van Thai, Vijender Kumar Solanki, and Dang Minh Tuan. "A novel approach for anomaly detection in automatic meter intelligence system using machine learning and pattern recognition." Journal of Intelligent & Fuzzy Systems, March 12, 2022, 1–10. http://dx.doi.org/10.3233/jifs-219285.

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Anomaly detection for sensor systems is one of the most researched topics for the Internet of Thing systems. Researchers have been attracted to machine learning classification problems that are considered the most effective techniques. The novel model is proposed by combining anomaly pattern Symbolic Aggregate Approximation (SAX), processing imbalance data and machine learning techniques for sensor anomaly detection. The advantage of anomaly patterns and machine learning leads to the the proposed model to have better performance. The proposed model consists of three phases: finding anomaly pat
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Qu, Haicheng, Jianzhong Zhou, Jitao Qin, and Xiaorong Tian. "Anomaly Detection for Industrial Control Networks Based on Improved One-Class Support Vector Machine." International Journal of Pattern Recognition and Artificial Intelligence, December 16, 2020, 2150012. http://dx.doi.org/10.1142/s0218001421500129.

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In traditional network anomaly detection algorithms, the anomaly threshold needs to be defined manually. Keeping this as background, this study proposes an anomaly detection algorithm (VAEOCSVM), which combines the variable auto-encoder (VAE) and one-class support vector machine (OCSVM) to realize anomaly detection in industrial control networks. First, the VAE model is used to obtain the distribution of the original normal sample data represented by the low-dimensional code; the reconstruction error of the VAE model is merged into the new input. Then, using OCSVM’s hinge-loss objective functi
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Shaikh, Jamshed Ali, Chengliang Wang, Wajeeh Us Sima Muhammad, et al. "RCLNet: an effective anomaly-based intrusion detection for securing the IoMT system." Frontiers in Digital Health 6 (October 3, 2024). http://dx.doi.org/10.3389/fdgth.2024.1467241.

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The Internet of Medical Things (IoMT) has revolutionized healthcare with remote patient monitoring and real-time diagnosis, but securing patient data remains a critical challenge due to sophisticated cyber threats and the sensitivity of medical information. Traditional machine learning methods struggle to capture the complex patterns in IoMT data, and conventional intrusion detection systems often fail to identify unknown attacks, leading to high false positive rates and compromised patient data security. To address these issues, we propose RCLNet, an effective Anomaly-based Intrusion Detectio
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Dissertations / Theses on the topic "Outlier Detection, Random Forest, Pattern Recognition, Anomaly Detection"

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Antonella, Mensi. "Advanced random forest approaches for outlier detection." Doctoral thesis, 2022. http://hdl.handle.net/11562/1067504.

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Outlier Detection (OD) is a Pattern Recognition task which consists of finding those patterns in a set of data which are likely to have been generated by a different mechanism than the one underlying the rest of the data. The importance of OD is visible in everyday life. Indeed, fast, and accurate detection of outliers is crucial: for example, in the electrocardiogram of a patient, an abnormality in the heart rhythm can cause severe health problems. Due to the high number of fields in which OD is needed, several approaches have been designed. Among them, Random Forest-based techniques have rai
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Conference papers on the topic "Outlier Detection, Random Forest, Pattern Recognition, Anomaly Detection"

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Devshali, Sagun, Shailesh Kumar Tripathi, Dhiraj Dodda, et al. "Predicting ESP failures Using Artificial Intelligence for Improved Production Performance in One of the Offshore Fields in India." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211031-ms.

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Abstract Field X is situated at a water depth of 90 meters in the western continental shelf at a distance of 200 Kilometers from Mumbai. It is one of the few fields in the world operating entirely on Electric Submersible Pumps with 36 wells in 5 wellhead platforms producing 62907 barrels of liquid per day with an average water cut of 68%. The performance of ESPs is being continuously monitored in the field. With continuous improvement, the run life of ESPs has increased from a few months to an average of 3 years. Despite the improvement in the run life, unexpected failures still occur from tim
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Figueirêdo, Ilan Sousa, Tássio Farias Carvalho, Wenisten José Dantas Silva, Lílian Lefol Nani Guarieiro, and Erick Giovani Sperandio Nascimento. "Detecting Interesting and Anomalous Patterns In Multivariate Time-Series Data in an Offshore Platform Using Unsupervised Learning." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/31297-ms.

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Abstract Detection of anomalous events in practical operation of oil and gas (O&G) wells and lines can help to avoid production losses, environmental disasters, and human fatalities, besides decreasing maintenance costs. Supervised machine learning algorithms have been successful to detect, diagnose, and forecast anomalous events in O&G industry. Nevertheless, these algorithms need a large quantity of annotated dataset and labelling data in real world scenarios is typically unfeasible because of exhaustive work of experts. Therefore, as unsupervised machine learning does not require an
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