Suchergebnisse - Graph conventional and variational autoencoder*

Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data.

Autoren: Choi, Seung-Hwan An, Dawn Lee, Inho et al.

Quelle: Mathematics (2227-7390). Dec2024, Vol. 12 Issue 23, p3750. 16p.

Schlagwörter: *INDUSTRIAL robots, *ANOMALY detection (Computer security), *AUTOENCODERS, *SYSTEM failures, *FEATURE extraction, *DEEP learning

Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays

Autoren: Murshedul Arifeen Andrei Petrovski Md Junayed Hasan et al.

Quelle: Machines, Vol 12, Iss 12, p 894 (2024)

Schlagwörter: solar array, photovoltaic array, fault detection, fault diagnosis, graph convolutional network, variational autoencoder, Mechanical engineering and machinery, TJ1-1570

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2075-1702/12/12/894; https://doaj.org/toc/2075-1702

Zugangs-URL: https://doaj.org/article/a68e9bb8f0bd42d6b8c9842c6321237e

A graph-based Gaussian Mixture Variational Autoencoder improves metagenome binning for short contigs

Autoren: Lu Zhang Zhenmiao Zhang Mingxing Rao et al.

MODAPro: Explainable Heterogeneous Networks with Variational Graph Autoencoder for Mining Disease-Specific Functional Molecules and Pathways from Omics Data.

Autoren: Zhao J He J Guan P et al.

Quelle: Analytical chemistry [Anal Chem] 2025 Oct 28; Vol. 97 (42), pp. 23222-23233. Date of Electronic Publication: 2025 Oct 19.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: American Chemical Society Country of Publication: United States NLM ID: 0370536 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1520-6882 (Electronic) Linking ISSN: 00032700 NLM ISO Abbreviation: Anal Chem Subsets: MEDLINE

MeSH-Schlagworte: Data Mining* , Deep Learning*, Humans ; Autoencoder

Self-Organizing Maps-Assisted Variational Autoencoder for Unsupervised Network Anomaly Detection.

Autoren: Huang, Hailong Yang, Jiahong Zeng, Hang et al.

Quelle: Symmetry (20738994). Apr2025, Vol. 17 Issue 4, p520. 15p.

Schlagwörter: *DETECTION algorithms, *SELF-organizing maps, *FEATURE selection, *ANOMALY detection (Computer security), *AUTOENCODERS

In-chip artificial intelligence technology for generating and self-correcting the topology of low-consumption RC filters

Autoren: Zainab Al-Araji

Quelle: Engineering and Technology Journal, Vol 43, Iss 8, Pp 705-715 (2025)

Schlagwörter: genrlcirc resistor, capacitor filter self-healing electronics q-learning optimization graph-based variational autoencoder, Science, Technology

Dateibeschreibung: electronic resource

Relation: https://etj.uotechnology.edu.iq/article_189015_57b69986ab6fe5662e4bd747327a38ac.pdf; https://doaj.org/toc/1681-6900; https://doaj.org/toc/2412-0758

Zugangs-URL: https://doaj.org/article/832d76cdb37342a1b483df7fca08cd50

GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction

Autoren: Hui Cui Toshiya Nakaguchi Ping Xuan et al.

Quelle: Briefings in Bioinformatics. 23

Schlagwörter: 0301 basic medicine, 0303 health sciences, 03 medical and health sciences, Proteins, Drug Interactions, Neural Networks, Computer

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/34718408
https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbab453/40907009/bbab453.pdf
https://pubmed.ncbi.nlm.nih.gov/34718408/

A Transductive Zero-Shot Learning Framework for Ransomware Detection Using Malware Knowledge Graphs.

Autoren: Wang, Ping Li, Hao-Cyuan Lin, Hsiao-Chung et al.

Quelle: Information. Jun2025, Vol. 16 Issue 6, p458. 35p.

Schlagwörter: Knowledge graphs, Receiver operating characteristic curves, Deep learning, Behavioral assessment, Autoencoder

Enhancing Facial Reconstruction Using Graph Attention Networks

Autoren: Hyeong Geun Lee Jee Sic Hur Yeo Chan Yoon et al.

Quelle: IEEE Access, Vol 11, Pp 136680-136691 (2023)

Schlagwörter: Graph convolution network, convolution neural network, variational autoencoder, Electrical engineering. Electronics. Nuclear engineering, facial reconstruction, graph attention network, TK1-9971

Zugangs-URL: https://doaj.org/article/1fa1fd658fe342bdbe3ba655b30ff048

Variational Graph Convolutional Networks for Dynamic Graph Representation Learning

Autoren: Aabid A. Mir Megat F. Zuhairi Shahrulniza Musa et al.

Quelle: IEEE Access, Vol 12, Pp 161697-161717 (2024)

Schlagwörter: network security, deep learning, graph neural networks, Anomaly detection, complex networks, Electrical engineering. Electronics. Nuclear engineering, heterogeneous networks, TK1-9971

Zugangs-URL: https://doaj.org/article/205888aa585544ec8d0e7f0c49e14058

A multimodal fusion network based on variational autoencoder for distinguishing SCLC brain metastases from NSCLC brain metastases.

Autoren: Linyan X Jie C Kexuan Z et al.

Quelle: Medical physics [Med Phys] 2025 Jul; Vol. 52 (7), pp. e17816. Date of Electronic Publication: 2025 May 02.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: John Wiley and Sons, Inc Country of Publication: United States NLM ID: 0425746 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2473-4209 (Electronic) Linking ISSN: 00942405 NLM ISO Abbreviation: Med Phys Subsets: MEDLINE

MeSH-Schlagworte: Brain Neoplasms*/secondary , Brain Neoplasms*/diagnostic imaging , Carcinoma, Non-Small-Cell Lung*/pathology , Carcinoma, Non-Small-Cell Lung*/diagnostic imaging , Lung Neoplasms*/pathology , Small Cell Lung Carcinoma*/diagnostic imaging , Small Cell Lung Carcinoma*/pathology , Image Processing, Computer-Assisted*/methods, Humans ; Diagnosis, Differential ; Magnetic Resonance Imaging ; Autoencoder

TV-CCANM: a transformer variational inference in confounding cascade additive noise model for causal effect estimation.

Autoren: Ahmad, Sohail Wang, Hong

Quelle: Journal of Statistical Computation & Simulation. Sep2025, Vol. 95 Issue 14, p3048-3076. 29p.

Schlagwörter: *CAUSAL inference, *CAUSAL models

A densely connected framework for cancer subtype classification

Autoren: Yu Li Denggao Zheng Kaijie Sun et al.

Quelle: BMC Bioinformatics, Vol 26, Iss 1, Pp 1-18 (2025)

Schlagwörter: Cancer subtypes, Multi-omics, Kidney cancer, Variational autoencoder, DenseNet, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1471-2105

Zugangs-URL: https://doaj.org/article/8da5bd516b704b0bac71cb95d2d46a12

Signed versus isomorphic graph autoencoders for microRNA-single-nucleotide polymorphisms network reconstruction in periodontal osteo-genomic landscapes.

Alternate Title: Autocodificadores de grafos isomorfos versus firmados para la reconstrucción de redes de polimorfismos de un solo nucleótido de microARN en paisajes osteogenómicos periodontales. (Spanish)

Autoren: Sharma, Sarvagya Yadalam, Pradeep Kumar Ardila, Carlos M.

Quelle: Gaceta Médica de Caracas; jul-sep2025, Vol. 133 Issue 3, p675-685, 11p

Causal Structure Representation Learning of Unobserved Confounders in Latent Space for Recommendation.

Autoren: Xu, Hangtong Xu, Yuanbo Li, Chaozhuo et al.

Quelle: ACM Transactions on Information Systems. Nov2025, Vol. 43 Issue 6, p1-29. 29p.

Schlagwörter: *Recommender systems, Causal models, Weather, Noise

Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies

Autoren: Kim, Jeehong Kim, Minchan Ju, Jaeseong et al.

Index Begriffe: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/2502.14197

Effective Treatment of Drug-Resistant Cancers (Pancreatic, Glioblastoma, Metastatic) — Vaccine and Drug Development through the Hamzah Model

Autoren: JALALI, SEYED RASOUL

Schlagwörter: cancer, oncology, drug resistance, multidrug resistance, tumour heterogeneity, pancreatic cancer, glioblastoma, metastatic cancer, resistant tumours, adaptive resistance, tumour relapse, chemotherapy, radiotherapy, immunotherapy, targeted therapy, nanomedicine, nanotechnology, nanoparticle drug delivery, liposomal delivery, lipid nanoparticles, PLGA nanoparticles, PEGylated nanoparticles, controlled release, targeted release, tumour microenvironment, hypoxia, angiogenesis, vascularisation, tumour stem cells, immune evasion, T–cell activation, CTL response, Th1 induction, immune checkpoint inhibition, PD–1, PD–L1, CTLA–4, CAR–T therapy, NK cells, macrophages, dendritic cells, adaptive immunity, innate immunity, cytokines, interleukins, interferons, chemokines, tumour–associated macrophages, tumour infiltrating lymphocytes, tumour–associated fibroblasts, stromal interactions, tumour signalling, EGFR resistance, PI3K/AKT pathway, MAPK pathway, mTOR signalling, tyrosine kinase inhibition, DNA repair mechanisms, homologous recombination, non–homologous end joining, mismatch repair, DNA repair overdrive, genomic instability, chromosomal instability, mutational landscape, oncogenic mutations, tumour suppressor loss, TP53, BRCA1, BRCA2, KRAS, NRAS, BRAF, EGFR mutations, HER2 amplification, MYC activation, cell cycle dysregulation, apoptosis evasion, senescence bypass, autophagy regulation, metabolic rewiring, Warburg effect, glycolysis, oxidative phosphorylation, mitochondrial dysfunction, lactate export, pH regulation, proton transporters, ion channels, tumour acidity, ROS signalling, redox balance, antioxidant defences, glutathione, thioredoxin, peroxiredoxin, ferroptosis, necroptosis, pyroptosis, cell death pathways, fractional calculus, fractal derivatives, fractional order models, complex integration, multiscale integration, matter–energy transformation, quantum biology, quantum noise, stochastic modelling, chaos theory, bifurcation analysis, attractors, strange attractors, phase transitions, metastability, dynamic equilibrium, complex adaptive systems, systems biology, integrative modelling, computational oncology, mathematical oncology, bioinformatics, omics integration, genomics, transcriptomics, proteomics, metabolomics, epigenomics, pharmacogenomics, pharmacokinetics, pharmacodynamics, PK–PD modelling, absorption, distribution, metabolism, excretion, clearance, plasma volume, bioavailability, half–life, dose–response, EC50, IC50, Emax, Hill coefficient, drug synergy, drug antagonism, combination therapy, polytherapy, cocktail therapy, adaptive dosing, dose escalation, therapeutic window, therapeutic index, off–target effects, side effect minimisation, toxicity reduction, safety margin, quality of life, survival rate, progression–free survival, overall survival, relapse–free survival, clinical trials, preclinical trials, in vivo models, in vitro models, xenografts, organoids, patient–derived xenografts, spheroids, co–culture systems, 3D models, microfluidics, lab–on–chip, organ–on–chip, bioreactors, lyophilisation, freeze drying, nanofabrication, nanocarriers, smart nanoparticles, responsive nanoparticles, stimuli–responsive release, pH–sensitive release, temperature–sensitive release, redox–sensitive release, enzyme–sensitive release, targeted ligands, antibodies, aptamers, peptides, folate targeting, transferrin targeting, EGFR targeting, HER2 targeting, integrins, RGD peptides, nuclear localisation, mitochondrial targeting, lysosomal escape, endosomal release, exosomes, extracellular vesicles, vesicular trafficking, clathrin mediated endocytosis, caveolin mediated endocytosis, macropinocytosis, receptor mediated endocytosis, intracellular trafficking, pharmacological modelling, biophysics, thermodynamics, statistical mechanics, entropy, enthalpy, free energy, Gibbs free energy, Helmholtz free energy, partition functions, state transitions, molecular dynamics, Monte Carlo simulations, lattice models, Ising model, spin systems, neural networks, deep learning, convolutional neural networks, recurrent neural networks, reinforcement learning, generative adversarial networks, variational autoencoders, XGBoost, random forest, decision trees, ensemble learning, predictive modelling, classification, regression, clustering, dimensionality reduction, PCA, t–SNE, UMAP, feature engineering, feature selection, biomarkers, predictive biomarkers, prognostic biomarkers, diagnostic biomarkers, liquid biopsy, circulating tumour DNA, circulating tumour cells, exosomal RNA, tumour markers, CA19–9, CEA, AFP, PSA, CA125, imaging biomarkers, radiomics, PET imaging, MRI, CT imaging, functional imaging, diffusion weighted imaging, perfusion imaging, metabolic imaging, nuclear imaging, image–guided therapy, image processing, signal processing, time series analysis, Fourier analysis, Laplace transforms, fractional Laplacian, differential equations, partial differential equations, integro–differential equations, fractional differential equations, boundary conditions, initial conditions, stability criteria, numerical analysis, discretisation, finite element analysis, spectral methods, operator theory, eigenvalues, eigenvectors, Hilbert space, Banach space, functional analysis, operator semigroups, semigroup theory, stochastic differential equations, Ito calculus, Stratonovich calculus, Langevin dynamics, Fokker–Planck equation, Kolmogorov equations, path integrals, quantum field theory, string theory, cosmological models, general relativity, special relativity, spacetime curvature, energy density, dark matter, dark energy, entropy flow, holographic principle, quantum entanglement, decoherence, superposition, wavefunction collapse, Schrödinger equation, Dirac equation, Heisenberg uncertainty, Planck constant, Boltzmann constant, Avogadro constant, dimensional analysis, scaling laws, renormalisation group, universality, critical phenomena, nonlinearity, nonlocality, long–range interactions, fractional Brownian motion, Lévy flights, anomalous diffusion, subdiffusion, superdiffusion, percolation theory, fractal geometry, Hausdorff dimension, Minkowski dimension, Cantor sets, Sierpinski structures, Koch curve, fractal networks, scale–free networks, small–world networks, network theory, graph theory, adjacency matrices, Laplacian matrices, spectral graph theory, network robustness, percolation threshold, epidemics on networks, cancer networks, signalling networks, metabolic networks, gene regulatory networks, protein–protein interaction networks, immune networks, dynamical networks, multiscale networks, hierarchical clustering, modularity, community detection, information theory, Shannon entropy, mutual information, Kullback–Leibler divergence, Jensen–Shannon divergence, complexity theory, algorithmic complexity, Kolmogorov complexity, Turing machines, computational complexity, NP–hard problems, optimisation, convex optimisation, non–convex optimisation, gradient descent, stochastic gradient descent, Adam optimiser, evolutionary algorithms, genetic algorithms, particle swarm optimisation, ant colony optimisation, simulated annealing, Bayesian optimisation, Markov chain Monte Carlo, Gibbs sampling, variational inference, probabilistic programming, causal inference, counterfactual analysis, sensitivity analysis, robustness analysis, uncertainty quantification, error propagation, variance reduction, surrogate modelling, reduced order models, high performance computing, parallel computing, distributed computing, GPU acceleration, CUDA programming, MPI, OpenMP, quantum computing, quantum algorithms, Grover's algorithm, Shor's algorithm, quantum annealing, quantum machine learning, hybrid quantum–classical models, future contingencies, scenario planning, stress testing, resilience modelling, adaptive systems, evolutionary dynamics, co–evolution, arms race, predator–prey models, ecological modelling, fitness landscapes, landscape ruggedness, adaptive peaks, valley crossing, tumour evolution, clonal evolution, immune surveillance, immune escape, therapeutic resistance, evolutionary game theory, replicator dynamics, Nash equilibria, evolutionary stable strategies, cancer ecosystems, tumour ecology, competition, cooperation, parasitism, resource allocation, niche construction, tumour dormancy, relapse mechanisms, latency, minimal residual disease, eradication thresholds, cure probability, survival probability, remission, remission durability, patient stratification, personalised medicine, precision medicine, systems oncology, integrative oncology, translational oncology, computational biomedicine, mathematical medicine, fractal oncology, ψ–Hamzah Equation, Hamzah model, Seyed Rasoul Jalali, ψ–Hamzah multiscale framework, ψ–Hamzah fractional–fractal derivative, ψ–Hamzah complex integration, ψ–Hamzah cancer resistance model, ψ–Hamzah pharmacokinetics, ψ–Hamzah pharmacodynamics, ψ–Hamzah adaptive dosing, ψ–Hamzah entropy analysis, ψ–Hamzah intelligence index, ψ–Hamzah validation, ψ–Hamzah field simulation, ψ–Hamzah therapeutic design, ψ–Hamzah vaccine, ψ–Hamzah drug, ψ–Hamzah intellectual property, ψ–Hamzah universal cure

Multi-Channel Causal Variational Autoencoder

Autoren: Al-Ali, Safaa Balelli, Irene E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE) et al.

Weitere Verfasser: Al-Ali, Safaa Balelli, Irene E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE) et al.

Quelle: https://hal.science/hal-04666466 ; 2024.

Schlagwörter: Multi-channel, Causal discovery, Variational Autoencoder, [MATH]Mathematics [math], [INFO]Computer Science [cs], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [STAT]Statistics [stat], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]

Verfügbarkeit: https://hal.science/hal-04666466
https://hal.science/hal-04666466v1/document
https://hal.science/hal-04666466v1/file/Preprint_MC2VAE_SA_IB.pdf

Physically Interpretable Representation Learning with Gaussian Mixture Variational AutoEncoder (GM-VAE)

Autoren: Fan, Tiffany Cutforth, Murray D'Elia, Marta et al.

Schlagwörter: Machine Learning

Relation: http://arxiv.org/abs/2511.21883

Verfügbarkeit: http://arxiv.org/abs/2511.21883

A Novel Approach for Capturing Consumer Behavior and Preference - MulVAEK.

Autoren: Li, Yijing Tang, Jing Gonul, Sinan et al.

Quelle: IEEE Computational Intelligence Magazine; May2025, Vol. 20 Issue 2, p33-44, 12p