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10 merged results for "Neural Network Technologies"

Partial results: at least one source did not answer. Available results are shown rather than treating an upstream outage as zero matches.

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  1. Machine-learning prediction of Williamson nanofluid flow over a nonlinear stretching sheet with velocity and concentration slip and melting effects using nonlinear activation function

    Mehdi Mahboobtosi · 2026 · Physics Open

    uncertain Transparent signal score 53/100 · policy 1.0.0

    Found in doaj · DOI 10.1016/j.physo.2026.100427

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    • cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
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    • supportingDirectory of Open Access Journals: A matching record was returned by DOAJ. Source: DOAJ; license: CC0
    • not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

  2. A machine learning approach predicts future risk to suicidal ideation from social media data

    Arunima Roy, Katerina Nikolitch, Rachel McGinn, Safiya Jinah · 2020 · npj Digital Medicine

    uncertain Transparent signal score 53/100 · policy 1.0.0

    Found in doaj · DOI 10.1038/s41746-020-0287-6

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    • cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
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    • supportingDirectory of Open Access Journals: A matching record was returned by DOAJ. Source: DOAJ; license: CC0
    • not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

  3. Exploring Color Features With Nasnet CNN and Improved Water Cycle Algorithm for Product Emotional Design

    Xie Chaoyang · 2025 · IEEE Access

    uncertain Transparent signal score 53/100 · policy 1.0.0

    Found in doaj · DOI 10.1109/access.2025.3586024

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    • cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 4 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

  4. Intelligent diagnosis with Chinese electronic medical records based on convolutional neural networks

    Xiaozheng Li, Huazhen Wang, Huixin He, Jixiang Du · 2019 · BMC Bioinformatics

    uncertain Transparent signal score 53/100 · policy 1.0.0

    Found in doaj · DOI 10.1186/s12859-019-2617-8

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    • cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
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    • not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

  5. CULTIVATING RESILIENCE: A TRANSFORMATIVE APPROACH TO ENHANCING CLOUD DATA SECURITY WITH TRANSFORMER-BASED TECHNIQUES

    Gantela Prabhakar, Bobba Basaveswara Rao · 2024 · Proceedings on Engineering Sciences

    uncertain Transparent signal score 53/100 · policy 1.0.0

    Found in doaj · DOI 10.24874/pes06.04.014

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    • cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
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    • supportingDirectory of Open Access Journals: A matching record was returned by DOAJ. Source: DOAJ; license: CC0
    • not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

  6. A Review of Power System Fault Diagnosis with Spiking Neural P Systems

    Yicen Liu, Ying Chen, Prithwineel Paul, Songhai Fan · 2021 · Applied Sciences

    uncertain Transparent signal score 53/100 · policy 1.0.0

    Found in doaj · DOI 10.3390/app11104376

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    • cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
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    • supportingDirectory of Open Access Journals: A matching record was returned by DOAJ. Source: DOAJ; license: CC0
    • not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

  7. Real-Time Cardiac Arrhythmia Classification Using TinyML on Ultra-Low-Cost Microcontrollers: A Feasibility Study for Resource-Constrained Environments

    Misael Zambrano-de la Torre, Sebastian Guzman-Alfaro, Andrea Acuña-Correa, Manuel A. Soto-Murillo · 2026 · Bioengineering

    uncertain Transparent signal score 53/100 · policy 1.0.0

    Found in doaj · DOI 10.3390/bioengineering13050532

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    • cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
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    • not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

  8. Prediction of Dimensional Changes of Low-Cost Metal Material Extrusion Fabricated Parts Using Machine Learning Techniques

    Zhicheng Zhang, James Femi-Oyetoro, Ismail Fidan, Muhammad Ismail · 2021 · Metals

    uncertain Transparent signal score 53/100 · policy 1.0.0

    Found in doaj · DOI 10.3390/met11050690

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    • cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
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    • supportingDirectory of Open Access Journals: A matching record was returned by DOAJ. Source: DOAJ; license: CC0
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    • not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

  9. The evaluation of renewable energy power using hybrid model of neural network and data envelopment analysis (neuro - DEA)

    Fershteh Poorahangaryan, Ali Shahbi, Esmaeel Nabiee · 2014 · Journal of Applied Research on Industrial Engineering

    uncertain Transparent signal score 58/100 · policy 1.0.0

    Found in doaj · no DOI

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    • not scoredDOI registered: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

  10. Using different computing systems to solve the automatic cloud classification problem according to MODIS satellite data by probabilistic neural network

    Aleksey Viktorovich Skorokhodov, Sergey Vladimirovich Aksenov, Andrey Vladimirovich Aksenov, Dmitriy Nikolaevich Laykom · 2017 · Известия Томского политехнического университета: Инжиниринг георесурсов

    uncertain Transparent signal score 58/100 · policy 1.0.0

    Found in doaj · no DOI

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    • not scoredDOI registered: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
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    • supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
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    • not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
    • cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer