Results 11 to 20 of about 88,867 (255)

An In-Depth Study and Improvement of Isolation Forest [PDF]

IEEE Access, 2022
Historically, anomalies detection was an important issue for industrial applications such as the detection of a manufacturing failure or defect. It is still a current topic that tries to meet the ever increasing demand in different fields such as intrusion detection, fraud detection, ecosystem change detection or event detection in sensor networks ...
Yousra Chabchoub, Maurras Ulbricht Togbe, Aliou Boly, Raja Chiky   +3 more
openaire   +3 more sources

Functional Isolation Forest

CoRR, 2019
For the purpose of monitoring the behavior of complex infrastructures (e.g. aircrafts, transport or energy networks), high-rate sensors are deployed to capture multivariate data, generally unlabeled, in quasi continuous-time to detect quickly the occurrence of anomalies that may jeopardize the smooth operation of the system of interest. The statistical
Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc   +3 more
openaire   +4 more sources

Accurate and noninvasive prostate cancer detection using plasma-derived extracellular vesicle RNA. [PDF]

FEBS Open Bio
Plasma extracellular vesicles were captured with WGA‐conjugated magnetic beads and profiled for RNA biomarkers. A three‐RNA panel (NM_024955, NR_047469, and NR_002564) distinguished prostate cancer from healthy controls and benign prostatic hyperplasia, supporting a simple, noninvasive approach to improve prostate cancer detection.
Wei H, Wu H, Feng W.
europepmc   +2 more sources

Signature Isolation Forest

CoRR
Functional Isolation Forest (FIF) is a recent state-of-the-art Anomaly Detection (AD) algorithm designed for functional data. It relies on a tree partition procedure where an abnormality score is computed by projecting each curve observation on a drawn dictionary through a linear inner product.
Campi, Marta, Staerman, Guillaume, Peters, Gareth W., Matsui, Tomoko   +3 more
openaire   +4 more sources

Magnetic Anomaly Detection Method Based on Feature Fusion and Isolation Forest Algorithm

IEEE Access, 2022
In order to improve the weak magnetic detection ability under the background of Gaussian colored magnetic environment noise, a magnetic anomaly detection method based on feature fusion and isolation forest (IForest) algorithm is proposed in this paper ...
Ning Zhang, Yifei Liu, Lei Xu, Pengfei Lin, Heda Zhao, Ming Chang   +5 more
doaj   +1 more source

Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection

Computers, 2022
Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data.
Diogo Ribeiro, Luís Miguel Matos, Guilherme Moreira, André Pilastri, Paulo Cortez   +4 more
doaj   +1 more source

Improved Anomaly Detection by Using the Attention-Based Isolation Forest

Algorithms, 2022
A new modification of the isolation forest called the attention-based isolation forest (ABIForest) is proposed for solving the anomaly detection problem.
Lev Utkin, Andrey Ageev, Andrei Konstantinov, Vladimir Muliukha   +3 more
doaj   +1 more source

SI2FM: SID Isolation Double Forest Model for Hyperspectral Anomaly Detection

Remote Sensing, 2023
Hyperspectral image (HSI) anomaly detection (HSI-AD) has become a hot issue in hyperspectral information processing as a method for detecting undesired targets without a priori information against unknown background and target information, which can be ...
Zhenhua Mu, Ming Wang, Yihan Wang, Ruoxi Song, Xianghai Wang   +4 more
doaj   +1 more source

Cloud-Empowered Data-Centric Paradigm for Smart Manufacturing

Machines, 2023
In the manufacturing industry, there are claims about a novel system or paradigm to overcome current data interpretation challenges. Anecdotally, these studies have not been completely practical in real-world applications (e.g., data analytics).
Sourabh Dani, Akhlaqur Rahman, Jiong Jin, Ambarish Kulkarni   +3 more
doaj   +1 more source

Online Isolation Forest

CoRR
Accepted at International Conference on Machine Learning (ICML 2024)
Filippo Leveni, Guilherme Weigert Cassales, Bernhard Pfahringer, Albert Bifet, Giacomo Boracchi   +4 more
openaire   +3 more sources