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electrical engineering

Newton-Raphson Power Flow

The Newton-Raphson method is a powerful iterative technique for solving the nonlinear power flow equations in electrical power systems. Introduced by Tinney and Hart in 1967, it became the industry standard for computing steady-state voltage and power distributions across transmission networks. The method uses Jacobian

3 sources1967
particle physics

NFW Halo Profile

The Navarro-Frenk-White (NFW) profile is a widely-adopted density profile for dark matter halos emerging from cosmological simulations. It provides a simple parametric description of how dark matter density varies with distance from the halo center, essential for modeling galaxy cluster mass distributions, weak lensing

3 sources1997
deep learning

NMF Topic Model

Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, a

2 sources1999
text mining

NMF Topic Modeling

NMF topic modeling uses Non-negative Matrix Factorization — the parts-based decomposition introduced by Lee and Seung (1999) — to extract document-topic distributions from a corpus. By factoring a document-term matrix into two non-negative matrices, it recovers a small set of topics and tends to produce more interpreta

2 sources1999
machine learning

Non-negative Matrix Factorization

Non-negative Matrix Factorization (NMF) is a family of algorithms, introduced by Lee and Seung in their landmark 1999 Nature paper, that decomposes a non-negative data matrix V into the product of two lower-rank non-negative matrices W (basis components) and H (encoding coefficients). Unlike PCA or SVD, the non-negativ

3 sources1999
deep learning

Non-stationary Transformer

Non-stationary Transformer is a Transformer-based time-series forecasting architecture introduced by Yong Liu, Haixu Wu, Jianmin Wang, and Mingsheng Long at NeurIPS 2022. It addresses a fundamental tension in applying Transformers to real-world time series: over-stationarization during preprocessing strips out non-stat

1 source2022
civil engineering

Nonlinear Time-History Analysis

Nonlinear time-history analysis is a numerical method that solves the equations of motion step-by-step in the time domain, using recorded or synthetic earthquake ground motions as input. Developed by Newmark in 1959, this approach captures the full dynamic response of structures including material nonlinearity, geometr

3 sources1959
model evaluation

Normalized Mutual Information

Normalized Mutual Information (NMI), popularized by Danon et al. in 2005, is an external clustering evaluation metric based on information theory. It measures the amount of information shared between a predicted clustering and ground truth labels, normalized to a scale between 0 and 1. A value of 1 indicates perfect ag

1 source2005
deep learning

Normalizing Flows

Normalizing flows are a class of generative models that learn a complex probability distribution by applying a sequence of invertible, differentiable transformations to a simple base distribution such as a standard Gaussian. Introduced by Rezende and Mohamed (2015) in the context of variational inference, they enable e

1 source2015
nuclear physics

Nuclear Decay Analysis

Nuclear decay analysis is the systematic study of radioactive transformation processes, originating from Rutherford and Soddy's work in the early 1900s. It quantifies the rate and modes of nuclear disintegration using decay constants, half-lives, and branching ratios to predict activity evolution, date samples via radi

2 sources1900
nuclear physics

Nuclear Fuel Cycle Analysis

Nuclear fuel cycle analysis is a comprehensive assessment of uranium and plutonium flows from extraction through enrichment, power generation, and waste management, originating from Fermi's controlled nuclear reaction. It quantifies resource requirements, energy balances, greenhouse gas emissions, and waste streams to

2 sources1942
materials science

Nudged Elastic Band Method

The Nudged Elastic Band (NEB) method is a computational technique for finding minimum-energy transition paths between stable atomic configurations and estimating activation barriers. Developed by Jónsson, Mills, and Jacobsen in 1998, NEB connects initial and final states with a chain of images (configurations) held tog

3 sources1998
deep learning

Object Detection

Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-h

2 sources2014
oceanography

Ocean Color Chlorophyll-a

Ocean color remote sensing is the primary global method for retrieving seawater chlorophyll-a concentrations and phytoplankton productivity from satellite sensors. Based on bio-optical principles established in the 1970s, ocean color algorithms convert satellite spectral reflectance measurements into estimates of chlor

2 sources1978
geophysics

Ocean-Atmosphere Coupled Model

An Ocean-Atmosphere Coupled Model (AOGCM) is a comprehensive climate simulation that couples dynamic general circulation models of the atmosphere and ocean with explicit exchange of heat, momentum, and moisture at the interface. Developed by Manabe, Bryan, and colleagues in the 1970s, coupled models are essential for s

2 sources1975
telecommunications

OFDM

OFDM is a multicarrier modulation technique that divides a wideband channel into many narrowband orthogonal subcarriers. Introduced by Weinstein and Ebert in 1971, it exploits the duality between time and frequency domains to efficiently use spectrum while mitigating intersymbol interference in frequency-selective chan

2 sources1971
telecommunications

Okumura-Hata Model

The Okumura-Hata model is an empirical propagation model for predicting path loss in mobile radio systems. Developed by Okumura (1968) and mathematically formalized by Hata (1980), it is one of the most widely used models for cellular network planning. The model predicts median path loss as a function of frequency, dis

2 sources1968
machine learning

One-class SVM

One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the sin

2 sources1999
machine learning

Online Active learning

Online active learning combines two complementary paradigms: it processes data as a stream (online learning) and selectively requests labels only for the most informative instances (active learning). The result is a model that adapts continuously to new data while keeping labeling costs low — useful whenever labeled da

2 sources2000
machine learning

Online Association Rules

Online association rule mining discovers if-then patterns (e.g., buying bread implies buying butter) from transactional data that arrives incrementally or as a stream, updating existing rules and item counts without re-scanning the entire historical database each time new records arrive.

2 sources1996
machine learning

Online Autoencoder Anomaly Detection

Online Autoencoder Anomaly Detection trains an autoencoder incrementally on a continuous data stream, flagging observations whose reconstruction error exceeds an adaptive threshold as anomalies. This approach combines the representational power of deep autoencoders with the incremental update capability of online learn

2 sources2010
machine learning

Online Boosting

Online Boosting adapts the classical boosting framework to data streams, updating an ensemble of weak learners one example at a time without storing the full dataset. The Oza-Russell formulation approximates AdaBoost's reweighting using Poisson-sampled instance counts, enabling accurate, adaptive classification in real

2 sources2001
machine learning

Online DBSCAN

Online DBSCAN extends the classic density-based clustering algorithm to handle continuously arriving data points without re-clustering the entire dataset from scratch. Each new observation is integrated into the existing cluster structure by local neighborhood queries, making it practical for streaming and data-warehou

2 sources1998
machine learning

Online Decision Tree

An Online Decision Tree is a decision tree that grows incrementally from a continuous stream of data without revisiting past examples. The dominant algorithm, the Hoeffding Tree (VFDT), uses the Hoeffding bound to decide when enough examples have been seen at a node to split it confidently, enabling scalable, real-time

2 sources2000
machine learning

Online Federated Learning

Online Federated Learning (OFL) combines the privacy-preserving, decentralised structure of federated learning with the sequential, sample-by-sample update regime of online learning. Clients — such as mobile devices or edge sensors — receive a global model, update it on newly arriving local data without sharing raw obs

2 sources2019
machine learning

Online Few-shot Learning

Online Few-shot Learning combines the streaming update principle of online learning with the data-efficiency goal of few-shot learning, enabling a model to continuously adapt to new tasks or classes from only a handful of labeled examples as data arrives sequentially — without access to the full historical dataset.

2 sources2019
machine learning

Online FP-growth

Online FP-growth is an incremental extension of the FP-growth algorithm that mines frequent itemsets from continuously arriving transaction streams without rebuilding the full FP-tree from scratch. It updates an existing compact tree structure as new transactions arrive, making it suitable for real-time and high-veloci

2 sources2004
machine learning

Online Gaussian Mixture Model

Online Gaussian Mixture Model adapts the classic GMM to streaming or large-scale data by replacing full-batch EM with incremental updates — processing one observation or mini-batch at a time and continuously refining component means, covariances, and mixing weights without revisiting the entire dataset.

2 sources2000
machine learning

Online Gaussian Process

Online Gaussian Process (OGP) extends the Bayesian nonparametric GP framework to streaming or sequentially arriving data. Instead of recomputing the full GP posterior from scratch as each observation arrives, OGP maintains a compact summary — a sparse set of inducing points — and updates it incrementally, making probab

2 sources2002
machine learning

Online Gradient Boosting

Online Gradient Boosting adapts the gradient boosting framework for streaming settings where data arrives one sample at a time rather than as a fixed batch. At each step the model computes a pseudo-residual for the incoming observation and updates a weak learner in place, growing an additive ensemble without storing or

2 sources2011
machine learning

Online HDBSCAN

Online HDBSCAN extends the HDBSCAN hierarchical density-based clustering algorithm to incrementally process streaming or sequentially arriving data. Rather than rebuilding the full hierarchy from scratch with each new observation, it maintains and locally updates the mutual reachability graph, minimum spanning tree, co

2 sources2015
machine learning

Online Isolation Forest

Online Isolation Forest extends the Isolation Forest anomaly-detection algorithm to streaming or continuously arriving data. Instead of rebuilding isolation trees from scratch when new observations arrive, the forest is updated incrementally so that anomaly scores remain current without reprocessing the entire history.

2 sources2008
machine learning

Online K-means

Online K-means is a streaming variant of the classical K-means algorithm that updates cluster centroids one observation at a time — or in small mini-batches — without storing the entire dataset in memory. It is particularly suited to large-scale, real-time, or continuously arriving data where batch recomputation would

2 sources1967
machine learning

Online K-nearest neighbors

Online K-Nearest Neighbors (Online KNN) adapts the classic KNN algorithm to a data-stream setting where observations arrive sequentially and the model must update incrementally without full retraining. Instead of storing all historical instances, it maintains a bounded sliding window or adaptive memory, using the most

2 sources2010
machine learning

Online Learning

Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is meas

2 sources1958
machine learning

Online LightGBM

Online LightGBM applies the Light Gradient-Boosting Machine framework incrementally: instead of requiring all training data at once, the model is updated in mini-batches or data chunks as they arrive. This allows LightGBM's efficient histogram-based boosting to be deployed in streaming, continual-learning, and data-exp

2 sources2017
machine learning

Online Linear Regression

Online Linear Regression fits a linear model one observation at a time, updating weights incrementally as each new data point arrives. Unlike batch least-squares, it never needs to store or re-process the full dataset, making it the natural choice for streaming data, very large datasets, and environments where the data

2 sources1960
machine learning

Online Metric Learning

Online Metric Learning adapts a Mahalanobis distance metric incrementally as new labeled examples or pairwise constraints arrive one at a time, without storing the full dataset. It merges the efficiency of online learning with the representational power of metric learning, making it suitable for streaming, large-scale,

2 sources2004
machine learning

Online Naive Bayes

Online Naive Bayes is an incremental adaptation of the classical Naive Bayes classifier that updates its class-conditional statistics one observation (or one mini-batch) at a time, making it well suited to data streams, very large datasets that cannot be held in memory, and settings where the model must adapt continuou

2 sources2000
machine learning

Online One-class SVM

Online One-Class SVM is an incremental extension of the classical One-Class Support Vector Machine that updates its decision boundary as new data arrive one sample at a time, making it suitable for streaming environments and real-time anomaly or novelty detection without retraining from scratch.

2 sources2006
machine learning

Online Random Forest

Online Random Forest (ORF) extends the classic Random Forest to streaming settings, updating each tree incrementally as new observations arrive without storing or replaying the full training set. Algorithms such as Adaptive Random Forests (ARF) add drift detection so the ensemble adapts when the data distribution chang

2 sources2009
machine learning

Online Self-supervised Learning

Online Self-supervised Learning (online SSL) trains neural networks on unlabeled data that arrives sequentially or in streams, using automatically generated supervisory signals (pretext tasks) instead of human labels. By updating the model continuously as new data flows in, it enables perpetually evolving representatio

2 sources2020
machine learning

Online Semi-supervised learning

Online semi-supervised learning combines the incremental, one-pass nature of online learning with the ability to exploit unlabeled data alongside sparse labeled observations. It is designed for settings where data arrives as a stream and obtaining labels for every instance is expensive or impractical — such as real-tim

2 sources2000
machine learning

Online Support Vector Machine

Online SVM adapts the classical support vector machine to streaming or sequentially arriving data by updating the decision boundary one example at a time rather than solving a global quadratic program. Algorithms such as Pegasos and LASVM make this tractable at large scale, preserving the margin-maximising spirit of SV

2 sources2005
machine learning

Online Transfer learning

Online Transfer Learning (OTL) extends transfer learning to sequential, streaming settings: instead of training on a fixed dataset, the model processes examples one at a time and simultaneously leverages knowledge from a related source domain to improve predictions on the target domain without requiring large labeled t

2 sources2010
machine learning

Online Voting Ensemble

Online Voting Ensemble is an incremental ensemble method that maintains a pool of base classifiers — each updated continuously on arriving data — and combines their predictions through a weighted or unweighted majority vote. Designed for data streams, it adapts to non-stationary distributions without retraining from sc

2 sources2001
text mining

Open Information Extraction

Open Information Extraction (Open IE) is a text-mining task that automatically extracts subject-relation-object triples from text without requiring a predefined relation schema. Introduced by Banko and colleagues (2007) for extraction over the open web, it converts free-running text into structured assertions used to b

2 sources2007
text mining

Opinion Mining

Opinion mining is a natural-language-processing task that systematically extracts and analyses user opinions about a product, service, or topic — identifying the specific features (aspects) being discussed, the sentiment expressed toward each, and the opinion holders. Consolidated by Bing Liu (2012), it goes beyond a s

2 sources2012
machine learning

OPTICS

OPTICS (Ordering Points To Identify the Clustering Structure) is a density-based clustering algorithm introduced by Ankerst, Breunig, Kriegel, and Sander in 1999. It generalizes DBSCAN by processing points in an ordering that encodes the full density-based cluster structure of a dataset, enabling the detection of clust

3 sources1999
electrical engineering

Optimal Power Flow

Optimal Power Flow (OPF) is a fundamental optimization framework for computing the most economical and secure operating point of an electrical power system. Introduced by Jean Carpentier in 1962, OPF minimizes operational costs (fuel, losses, or other expenses) while satisfying physical and operational constraints. Mod

3 sources1962
computer vision

ORB Feature Descriptor

ORB (Oriented FAST and Rotated BRIEF) combines the FAST corner detector with the BRIEF binary descriptor to create a fast, rotation-invariant feature detector and descriptor. Introduced by Rublee et al. in 2011, ORB is designed as a free, efficient alternative to patented methods like SIFT and SURF, making it ideal for

2 sources2011
applied physics

Orbit Determination (Lambert's Problem)

Lambert's problem is a classical astrodynamics boundary-value problem that determines an orbit connecting two points in space given a transfer time. Formulated by Johann Heinrich Lambert in the 18th century, it is fundamental to trajectory design for interplanetary missions and spacecraft maneuvers. The solution provid

3 sources1761
telecommunications

OSPF

OSPF is a link-state interior gateway protocol (IGP) for routing within an autonomous system. Introduced by John Moy in 1998, OSPF converges faster than distance-vector protocols and supports equal-cost multipath (ECMP). It remains widely deployed in enterprise and ISP networks for intra-domain routing, though IS-IS is

2 sources1998
machine learning

Out-of-Distribution Detection

Out-of-Distribution (OOD) detection is a set of techniques that identify when a deployed machine learning model receives inputs that differ significantly from its training data distribution. Introduced as a formal problem by Hendrycks and Gimpel in 2017, these methods enable models to flag unfamiliar inputs rather than

1 source2017
network analysis

PageRank

PageRank is a link-based centrality algorithm that assigns an importance score to each node in a directed graph by measuring how many high-quality nodes point to it. Introduced by Larry Page, Sergey Brin, Rajeev Motwani, and Terry Winograd at Stanford University in 1999, it became the mathematical foundation of the Goo

1 source1999
geophysics

Paleomagnetic Analysis

Paleomagnetic analysis is the study of remnant magnetization in rocks and sediments to determine the direction and magnitude of the Earth's ancient magnetic field and to establish the ages and tectonic histories of crustal rocks. Formalized by Fisher (1953) and Butler (1992), paleomagnetism underpins plate tectonics pl

2 sources1953
geoscience

Paleomagnetism Analysis

Paleomagnetism analysis is the study of ancient magnetic properties of rocks, measuring fossil magnetization to determine paleomagnetic field history and assign geological ages. Pioneered by Brunhes (1906) and systematized by Tauxe (2010), this method reveals geomagnetic reversals, polar wander paths, and paleomagnetic

3 sources1906
biomechanics

Pan-Tompkins QRS Detection

The Pan-Tompkins algorithm is a real-time QRS detection method for electrocardiograms (ECGs) that identifies the R-peaks (ventricular depolarization) and QRS complexes from continuous cardiac waveforms. Published by Jiapu Pan and Willis Tompkins in 1985, it remains a standard reference for ECG processing and is widely

2 sources1985
text mining

Paraphrase Detection

Paraphrase detection is a natural-language-processing task that decides whether two sentences expressed in different wordings carry the same meaning. The task and its benchmark resources were established by Dolan and Brockett (2005), and it underpins plagiarism detection, question matching, and data deduplication.

2 sources
machine learning

Partial Least Squares

Partial least squares regression predicts a response from many, often highly collinear predictors by projecting them onto a small set of latent components — but, unlike principal components regression, it chooses those components to maximize their covariance with the response, not just the variance of the predictors. T

2 sources1975
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