Results 71 to 80 of about 234,412 (208)
Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation
CAAI Transactions on Intelligence Technology, EarlyView.Abstract Evolutionary algorithms face significant challenges when dealing with dynamic multi‐objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroid‐based prediction (denoted by KAEP), for solving ...
Zhanglu Hou +4 more
wiley +1 more source
mappl-pldi24-ae/mappl-pldi24-ae: v0.2-alpha
<p>[PLDI'24 Artifact] Variable Elimination for an Expressive Probabilistic Programming Language</p> <h2>What's Changed</h2> <ul> <li>Newexamples by @mappl-pldi24-ae in https://github.com/mappl-pldi24-ae/mappl-pldi24-ae/
mappl-pldi24-ae
core +1 more source
Journal of Electrical and Computer EngineeringIn recent machine learning applications, promising outcomes have emerged through the integration of Deep Learning (DL) and Extreme Learning Machine (ELM) techniques with wavelet networks (WN), leading to high classification accuracy.
Salwa Said +4 more
doaj +1 more source
mappl-pldi24-ae/mappl-pldi24-ae: v0.1-gamma
<p>[PLDI'24 Artifact] Variable Elimination for an Expressive Probabilistic Programming Language</p> <h2>What's Changed</h2> <ul> <li>Newexamples by @mappl-pldi24-ae in https://github.com/mappl-pldi24-ae/mappl-pldi24-ae/
mappl-pldi24-ae
core +1 more source
Magnetic Resonance in Medicine, Volume 96, Issue 5, Page 2359-2369, November 2026.ABSTRACT Purpose To present a novel, nonlinear subspace modeling and joint k–q‐space reconstruction technique for high‐resolution, multi‐band, multi‐shell diffusion‐weighted imaging (DWI). Methods High b‐value (> 1000 s/mm2), high resolution DWI has the drawback of generally low signal‐to‐noise ratios (SNRs).
Julius Glaser +4 more
wiley +1 more source
Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.Abstract We propose a variational autoencoder framework to directly assess uncertainties in subsurface models produced by single‐ and multiparameter full waveform inversion (FWI). The new method does not require pretraining on labeled data, thus it significantly reduces computational cost and storage requirements.
Abdelrahman Elmeliegy +4 more
wiley +1 more source
Explaining anomalies through semi-supervised Autoencoders
ArrayThis work tackles the problem of designing explainable by design anomaly detectors, which provide intelligible explanations to abnormal behaviors in input data observations.
Fabrizio Angiulli +3 more
doaj +1 more source
Cochrane Evidence Synthesis and Methods, Volume 4, Issue 5, September 2026.ABSTRACT Introduction Artificial intelligence (AI) is a branch of technology enabling machines to emulate complex human skills; it can also entail problem‐solving using bioinspired methods. It is used for automating systematic literature reviews (SLR), that is, defining a clinical question, locating relevant literature, preliminary screening, study ...
Ana M. Barragán +5 more
wiley +1 more source
Column-Wise Autoencoder Representation Learning for Intrusion Detection in Multi-MEC Edge Networks
Applied SciencesMobile Edge Computing (MEC) is a key enabler of 5G/6G services, but multi-base-station deployment enlarges the attack surface and motivates edge-native intrusion detection systems (IDSs).
Min-Gyu Kim, Jonghyun Kim
doaj +1 more source
Concurrency and Computation: Practice and ExperienceSummaryUnknown cyber‐attack detection in network traffic streams is challenging but crucial to ensure network security. It is observed that new security threats occur on a daily basis and make cyberspace vulnerable. In the literature, machine learning and deep learning‐based network intrusion detection systems have gained a lot of success but still ...
Khushnaseeb Roshan, Aasim Zafar
openaire +2 more sources