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Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series

Lecture Notes in Computer Science(2019)10.1007/978-3-030-30490-4Source: DataRank Database

Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series is a research paper published in Lecture Notes in Computer Science (2019). On theSindex it has a DataRank of 1.6. It has been cited 37 times, with 37 citing works in its 1-hop citation network.

N/A

1.6DataRank · unranked

Scored on demand from live citation data

37 citations · 37 citing works in its network

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DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Impact (FWCI 0.98)OA: closedTopics (11)IDs (PubMed, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

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DataRank Breakdown

Base Score 33%Citation Network 67%

Base Score Contribution

0.546

From this paper's citation signal

Citation Network Contribution

1.1

From 30 citing papers with measurable signal

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Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 37 citers.

Complete quadruple extraction using a two-stage neural model for aspect-based sentiment analysis
Neurocomputing202231 citations35 referencesContributes 0.099
SUSAN: A Deep Learning based anomaly detection framework for sustainable industry
Sustainable Computing Informatics and Systems202331 citations49 referencesContributes 0.071
Utilizing correlation in space and time: Anomaly detection for Industrial Internet of Things (IIoT) via spatiotemporal gated graph attention network
Alexandria Engineering Journal202413 citations39 referencesContributes 0.068
Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges
Information Fusion2022328 citations89 referencesContributes 0.065
WordTransABSA: Enhancing Aspect-based Sentiment Analysis with masked language modeling for affective token prediction
Expert Systems with Applications202346 citations62 referencesContributes 0.062

Authors (5)

Věra Kůrková ORCID,Pavel Karpov ORCID,Fabian Joachim Theis ORCID,Theis, Fabian,Igor V. Tetko ORCID

Live enrichment

PubMed:23304386 OpenAlex:https://openalex.org/W4248132034

FWCI

0.98

Citation Percentile

0.8%

Citation Trend

2021

2022

2023

2024

2025

closedLicense: http://www.springer.com/tdm

publisherhttp://link.springer.com/content/pdf/10.1007/978-3-030-30490-4.pdf

publisherhttp://link.springer.com/content/pdf/10.1007/978-3-030-30490-4

book serieshttps://doi.org/10.1007/978-3-030-30490-4

Fields of Study

Neural Networks and Reservoir ComputingNeural Networks and Applications

Keywords

Computer scienceArtificial neural networkArtificial intelligenceComputationSeries (stratigraphy)Deep learningVolume (thermodynamics)Machine learningAlgorithm

Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series

FAIR Checklist

DataRank Breakdown

Top 5 citers driving the network score

Authors (5)

Impact

Open Access

Topics