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Random walk with restart on hypergraphs: fast computation and an application to anomaly detection
Random walk with restart (RWR) is a widely-used measure of node similarity in graphs, and it has proved useful for ranking, community detection, link...
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Improving hyper-parameter self-tuning for data streams by adapting an evolutionary approach
Hyper-parameter tuning of machine learning models has become a crucial task in achieving optimal results in terms of performance. Several researchers...
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AIoT-CitySense: AI and IoT-Driven City-Scale Sensing for Roadside Infrastructure Maintenance
The transformation of cities into smarter and more efficient environments relies on proactive and timely detection and maintenance of city-wide...
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OEC: an online ensemble classifier for mining data streams with noisy labels
Distilling actionable patterns from large-scale streaming data in the presence of concept drift is a challenging problem, especially when data is...
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Structure-aware decoupled imputation network for multivariate time series
Handling incomplete multivariate time series is an important and fundamental concern for a variety of domains. Existing time-series imputation...
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Anomaly Detection with Sub-Extreme Values: Health Provider Billing
Anomaly detection within the context of healthcare billing requires a method or algorithm which is flexible to the practicalities and requirements of...
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Graph Neural Network-Based Short‑Term Load Forecasting with Temporal Convolution
An accurate short-term load forecasting plays an important role in modern power system’s operation and economic development. However, short-term load...
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Navigating the metric maze: a taxonomy of evaluation metrics for anomaly detection in time series
The field of time series anomaly detection is constantly advancing, with several methods available, making it a challenge to determine the most...
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Anomaly detection in sleep: detecting mouth breathing in children
Identifying mouth breathing during sleep in a reliable, non-invasive way is challenging and currently not included in sleep studies. However, it has...
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Multiple-input neural networks for time series forecasting incorporating historical and prospective context
Individual and societal systems are open systems continuously affected by their situational context. In recent years, context sources have been...
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Federated singular value decomposition for high-dimensional data
Federated learning (FL) is emerging as a privacy-aware alternative to classical cloud-based machine learning. In FL, the sensitive data remains in...
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Somtimes: self organizing maps for time series clustering and its application to serious illness conversations
There is demand for scalable algorithms capable of clustering and analyzing large time series data. The Kohonen self-organizing map (SOM) is an...
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Optimal selection of benchmarking datasets for unbiased machine learning algorithm evaluation
Whenever a new supervised machine learning (ML) algorithm or solution is developed, it is imperative to evaluate the predictive performance it...
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Mondrian forest for data stream classification under memory constraints
Supervised learning algorithms generally assume the availability of enough memory to store data models during the training and test phases. However,...
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Joint Representation Learning with Generative Adversarial Imputation Network for Improved Classification of Longitudinal Data
Generative adversarial networks (GANs) have demonstrated their effectiveness in generating temporal data to fill in missing values, enhancing the...
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Fast, accurate and explainable time series classification through randomization
Time series classification (TSC) aims to predict the class label of a given time series, which is critical to a rich set of application areas such as...
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Design and evaluation of highly accurate smart contract code vulnerability detection framework
Smart contracts are self-executing programs stored and executed on a blockchain platform. However, previous studies demonstrated that develo**...
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MODE-Bi-GRU: orthogonal independent Bi-GRU model with multiscale feature extraction
The core of sentence classification is to extract sentence semantic features. The existing hybrid methods have huge parameters and complex models....
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The art of centering without centering for robust principal component analysis
Many robust variants of Principal Component Analysis remove outliers from the data and compute the principal components of the remaining data. The...
