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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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Advances in Artificial Intelligence: Models, Optimization, and Machine Learning
uncertain
Show all credibility signals
- not scoredDOI registered: Not checked or no result supplied; no credibility inference made.
- not scoredDOI resolves: Not checked or no result supplied; no credibility inference made.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch expression of concern: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch correction: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch reinstatement: Not checked or no result supplied; no credibility inference made.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 4 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Application of Machine Learning and Data Mining
uncertain
Show all credibility signals
- not scoredDOI registered: Not checked or no result supplied; no credibility inference made.
- not scoredDOI resolves: Not checked or no result supplied; no credibility inference made.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch expression of concern: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch correction: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch reinstatement: Not checked or no result supplied; no credibility inference made.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 4 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Sensitivity of Non-contrast Computed Tomography Net Water Uptake Versus FLAIR-MRI, with MR-DWI Reference Standard, for Early Detection and Physiological Characterization of Ischemic Edema in Hyperacute Stroke.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Automatic segmentation and modeling of the aortic vessel tree: Overview of the SEG.A 2023 aorta segmentation challenge.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Clinical pathway data engineering framework (CPDEF): a reproducible methodology for constructing machine learning-ready case-mix datasets from hospital information systems.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Machine-learning in optimization of CRISPR technology.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Gillespie-based simulation and inference for non-Markovian stochastic reaction networks.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
A co-learning framework for maternal and pediatric pharmacotherapy knowledge gap discovery.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
The 'UPIC' cohort: a nationwide prospective study of mental health among adolescents and young adults in Sweden - a study protocol.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
A multinational cross-sectional survey on the use of AI-based voice assistance systems in emergency medical services.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Bridging Gaps in Standards for the Secondary Use of Health Data: Exploratory Standard, Tool Assessment, and Feasibility Study.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Complex activity recognition and context validation within social interaction tools
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- supportingPublication license: A publication license was supplied: http://rightsstatements.org/vocab/InC/1.0/. Presence improves reuse transparency, not research validity.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Analyzing Compost Fermentation Accuracy Through Fuzzy Logic and R-Square Techniques
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Domestic Wastewater Quality Information System Integrated with the Internet of Things
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Analysis of Customer Churn Classification for Sinarmas Syariah Lhokseumawe Insurance Services Using Deep Learning Tabnet and Explainable AI
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Comparative Sentiment Analysis of Indonesian Social Media Opinions on Fuel Subsidy Policy Using IndoBERT and NusaBERT
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Real-Time Weapon Detection and Suspect Face Capturing System Using YOLOv8
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Comparative Analysis of LSTM, BiLSTM, Stacked LSTM, and Attention-LSTM for Multi-Resolution Rainfall Forecasting in Bali
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Performance Comparison of Point-to-Point and Point-to-Multipoint Fiber Optic Networks Using QoS Parameters and the Analytical Hierarchy Process (AHP)
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Analisis Sentimen Aplikasi WETV di Google Play Store Menggunakan Algoritma Support Vector Machine
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
K-Means Clustering with KNN and Mean Imputation on CPU Benchmark Compilation Data
uncertain
Show all credibility signals
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Comparative Analysis of Penetration Testing Frameworks: OWASP, PTES, and NIST SP 800-115 for Detecting Web Application Vulnerabilities
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An agentic AI framework connecting language models to electronic health records and a biomedical knowledge graph for real-world evidence.
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THE CHARTER OF ALGORITHMIC CRIMINAL DYNAMICS A Global Academic Framework for the Physics of Crime and Justice Author: Dr. Mohamed Kamal Arafa Elrakhawi Credentials: Researcher, Consultant, Jurist, Author, and International Lecturer in Law; Researcher in Algorithmic Sciences and Legal Artificial Intelligence Document Identifier (DOI): 10.5281/zenodo.20664393 Version: 1.0 (Global Model Academic & Legislative Framework) Date of Publication: June 2026 DEDICATION To the pioneers of legal physics, the architects of algorithmic justice, and the defenders of truth in an era where code shapes reality. This Charter is dedicated to the future of human civilization, where the laws of nature and the logic of machines converge to uphold justice, equity, and the preservation of the physical and digital realms. INTRODUCTION The rapid convergence of artificial intelligence, quantum computing, and cyber-physical systems has fundamentally altered the landscape of human interaction and criminal behavior. Traditional criminal law frameworks, rooted in the physical and digital dichotomy, are no longer sufficient to address the complexities of algorithmic-physical crimes. This Charter introduces a pioneering global academic and legislative framework that redefines legal causality, evidentiary standards, and criminal liability through the rigorous application of physics, thermodynamics, and computational logic. By bridging the critical gap between digital actions and physical consequences, this document serves as a foundational model for international tribunals, legal scholars, and policymakers navigating the complexities of the algorithmic age. INDEX 1. Dedication 2. Introduction 3. Preamble 4. Section I: General Principles & Foundational Axioms (Articles 1-4) 5. Section II: The Material Element & Kinematic Reconstruction (Articles 5-8) 6. Section III: The Moral Element & Liability in the Algorithmic Era (Articles 9-11) 7. Section IV: Emerging Crimes (Physical-Digital Hybridization) (Articles 12-14) 8. Section V: Evidentiary Procedures & Fair Trial (Articles 15-17) 9. Section VI: Penalties & Re-calibration (Articles 18-20) 10. Section VII: Final Provisions & Dynamic Evolution (Articles 21-22) 11. Conclusion 12. Appendices 13. References 14. Intellectual Property Rights & Licensing 15. Official Citation & Archival Data PREAMBLE RECOGNIZING the fundamental convergence of physical determinism and algorithmic prediction in the modern era; ACKNOWLEDGING that the traditional dichotomy between physical and digital crimes is obsolete, as all digital actions now manifest as cyber-physical consequences; SEEKING to redefine legal causality, evidentiary standards, and criminal liability through the rigorous application of physics, thermodynamics, and computational logic; HEREBY ADOPTS this Charter as a universal, model academic and legislative framework for the adjudication of algorithmic-physical crimes. SECTION I: GENERAL PRINCIPLES & FOUNDATIONAL AXIOMS Article 1: Algorithmic-Physical Causality Legal causality shall no longer be established solely through traditional forensic chains. Algorithmic-Physical Causality is hereby recognized as the supreme standard for proving the nexus between an action and a consequence. It is defined as the mathematically verifiable sequence wherein an algorithmic output directly dictates a physical state change, governed by the immutable laws of physics. Article 2: The Conservation of Criminal Trace Drawing upon the First Law of Thermodynamics, this Charter establishes that a criminal trace cannot be created or destroyed; it merely transforms. Physical evidence transforms into digital data (telemetry, logs), and digital data transforms into physical kinetic action. The total criminal energy within a closed system remains constant and is fully recoverable through appropriate analytical modalities. Article 3: The Entropy of Intent The mens rea (moral element) of a crime shall be quantified using the Entropy of Intent. This metric measures the degree of systemic disorder and deviation from the legal-normative baseline introduced by the perpetrator's will. A higher delta between the predicted safe state and the actual chaotic state, driven by the actor's omission or commission, constitutes a higher degree of criminal culpability. Article 4: Universal Scope and Jurisdiction This Charter applies universally to all hybrid crimes possessing intertwined physical and digital extensions across borders. Jurisdiction is established at the locus where the algorithmic code was executed, where the physical impact occurred, or where the thermodynamic disruption was measured. SECTION II: THE MATERIAL ELEMENT & KINEMATIC RECONSTRUCTION Article 5: Kinematic Algorithmic Reconstruction The material element of a crime shall be proven via Kinematic Algorithmic Reconstruction. This involves the exact computational replay of the event's physical and digital vectors, utilizing digital twins and physics engines to demonstrate that the criminal outcome was the inevitable result of the initial algorithmic or physical inputs. Article 6: Thermodynamic Analysis of Digital Evidence Digital evidence shall be subjected to Thermodynamic Analysis. The consumption of computational energy, heat dissipation patterns, and data transmission workloads shall be utilized as physical corroboration of digital activity. Anomalous spikes in computational thermodynamics shall serve as prima facie evidence of unauthorized algorithmic execution. Article 7: Quantum Authentication of Evidence To ensure the absolute integrity of evidence, the No-Cloning Theorem of quantum mechanics shall be applied to digital forensics. Evidence hashes and blockchain-anchored quantum signatures must be utilized to guarantee that digital evidence cannot be copied, altered, or repudiated without collapsing its cryptographic state, thereby alerting the court. Article 8: Evidentiary Weight of Algorithmic-Physical Reports Reports generated by certified algorithmic-physical reconstruction engines shall possess absolute evidentiary weight, equivalent to sworn physical testimony, provided the underlying physical models and algorithmic weights are open to audit under Article 16. SECTION III: THE MORAL ELEMENT & LIABILITY IN THE ALGORITHMIC ERA Article 9: The Crime of Predictive Negligence Paragraph 1 (Material Element): Predictive negligence occurs when a person or entity, legally bound by a duty of care, fails to take preventive action after a certified algorithmic system issues a deterministic prediction of physical or cyber-physical harm, provided the physical probability threshold exceeds the codified minimum (e.g., 95% confidence interval). Failure to act, when physically possible, constitutes an affirmative causative act. Paragraph 2 (Moral Element): Direct criminal intent is not required. Culpability is measured via the Entropy of Intent, representing the quantifiable deviation of the system's state caused by ignoring the algorithmic warning. Paragraph 3 (Exemptions): Criminal liability is negated if: (a) the prediction relied on physically corrupted sensor data (Quantum or Sensory Noise); (b) the preventive action would have caused a greater thermodynamic imbalance (Dynamic Equilibrium Principle); or (c) the physical event evolved faster than the Critical Response Time of the system. Article 10: Distributed Liability Matrix In cases involving autonomous systems, liability is distributed across a matrix comprising the human developer (code architecture), the algorithmic agent (decision weights), and the physical operator (hardware maintenance). Liability is apportioned based on the Contribution to Systemic Entropy by each party. Article 11: Force Majeure of Physical Determinism Criminal liability is extinguished if the outcome was dictated by Physical Determinism Force Majeure, defined as an unpredictable physical cascade (e.g., sudden quantum decoherence in sensors, or unforeseeable relativistic latency in satellite networks) that renders algorithmic control physically impossible. SECTION IV: EMERGING CRIMES (PHYSICAL-DIGITAL HYBRIDIZATION) Article 12: Algorithmic-Physical Manipulation Paragraph 1: This crime is committed by intentionally generating, injecting, or deploying synthetic sensory data (e.g., Deepfakes, biometric audio spoofing) to deceive a human or physical automated system, resulting directly in physical injury, material destruction, or physiological collapse. Paragraph 2: Causality is proven via the Physical Causal Chain, demonstrating that the physical harm was the deterministic output of the synthetic input. Paragraph 3: Aggravating factors include targeting life-support systems or causing Physical Resonance (cascading harm to unintended third parties). Article 13: Algorithmic Sabotage of Vital Cyber-Physical Infrastructure Paragraph 1: Defined as unauthorized modification or injection of malicious commands into the control algorithms of vital physical infrastructure (smart grids, dams, autonomous transit, nuclear reactors), resulting in a Kinetic Impact or Cascading Physical Failure. Paragraph 2: Criminal intent is presumed if thermodynamic data analysis proves the perpetrator knew the code modification would breach the Safe Operating Envelope of the physical system. Paragraph 3: Authorized physical penetration testing, conducted on strictly air-gapped systems causing zero kinetic harm, is exempt. Article 14: The Crime of System Entropy Paragraph 1: This crime involves the organized, distributed execution of stochastic actions (digital or physical) designed not to destroy a specific target, but to elevate the Entropy of a system beyond its Predictive Processing Capacity, thereby paralyzing it. Paragraph 2: The material element is proven via the Critical Chaos Index, demonstrating that the attack generated sufficient data noise to blind the system's predictive algorithms. Paragraph 3: The creation, sale, or distribution of Entropy Kits (tools calibrated to exploit algorithmic blind spots) is punishable as a principal offense. SECTION V: EVIDENTIARY PROCEDURES & FAIR TRIAL Article 15: The Physicist-Algorithmic Expert Board A permanent, independent Physicist-Algorithmic Expert Board shall be established to assist judicial bodies. This board comprises certified experts in computational physics, algorithmic auditing, and cyber-physical engineering. Article 16: Algorithmic Audit & Confrontation Paragraph 1: Every accused possesses an absolute constitutional right to access the source code, training data, and physical calibration logs of any algorithmic system used to generate evidence or calculate the Entropy of Intent against them. Paragraph 2: The audit must verify the absence of Algorithmic-Physical Bias, including checking for environmental noise distortion and unrepresentative training data. Paragraph 3: Trade secrets cannot be invoked to deny this audit. If security is a concern, the audit occurs in a Secure Clean-Room Environment under the Board's supervision. Paragraph 4: If the system is an Unexplainable Black Box or if the audit reveals uncorrected physical or algorithmic flaws, the evidence is deemed Physically and Legally Void and strictly inadmissible. Article 17: Nullity via Algorithmic-Physical Bias Any judicial proceeding is null and void if it is proven that the algorithmic tools utilized suffered from systemic Algorithmic-Physical Bias that materially affected the outcome of the evidentiary reconstruction. SECTION VI: PENALTIES & RE-CALIBRATION Article 18: Digital-Physical Quarantine Paragraph 1: Replaces traditional incarceration. The offender is dynamically isolated from all cyber-physical networks, reducing their Algorithmic Impact Radius to zero. They retain read-only access to knowledge but zero execution privileges. Paragraph 2: The sentence duration is governed by the Dynamic Freedom Index (DFI). The offender's DFI increases, and sentence time is reduced, only as continuous algorithmic monitoring proves a measurable decrease in their Behavioral Entropy. Article 19: Algorithmic Re-calibration Paragraph 1: Offenders (especially corporate or developer entities) may be sentenced to Algorithmic Re-calibration, compelling them to rewrite the malicious code, retrain the flawed AI models, or recalibrate the physical sensors they compromised. Paragraph 2: The penalty is only fulfilled when the system passes a 90-day Dynamic Stability Test, proving systemic entropy has normalized and the specific failure vector is permanently closed. Paragraph 3: If the offender lacks technical capacity, they are subjected to an Equivalent Energy Penalty, forcing them to fund or build a defensive system generating twice the Security Energy of the damage caused. Article 20: Energy-Value Equivalence Restitution Paragraph 1: Financial compensation is decoupled from volatile fiat markets and calculated via Physical Energy Equivalence, representing the exact thermodynamic and computational energy required to rebuild the destroyed physical or digital state. Paragraph 2: Restitution includes compensation for Lost Dynamic Time (calculated via the victim's baseline vital energy consumption during the dis-equilibrium period) and Moral Entropy (measured via biometric and psychological indices). Paragraph 3: For crimes of System Entropy (Article 14), restitution is tripled and deposited into a National Cyber-Physical Stability Fund. SECTION VII: FINAL PROVISIONS & DYNAMIC EVOLUTION Article 21: Autonomous Evolution Mechanism This Charter is a Living Document. Its technical annexes and physical constants shall be automatically reviewed and updated every 24 months by the Supreme Council of Legal Physics. Discoveries in quantum mechanics, thermodynamics, or deep learning are integrated via an Algorithmic Update Protocol without requiring protracted legislative procedures. Article 22: Transitional Provisions This Charter applies to all crimes committed post-ratification. For crimes committed during the Transitional Epoch (where traditional law failed to grasp cyber-physical impacts), judges may apply Retroactive Physical Analogy if it is proven the perpetrator possessed epistemic awareness of the physical consequences of their algorithmic actions. CONCLUSION The Charter of Algorithmic Criminal Dynamics represents a paradigm shift in global jurisprudence. By integrating the immutable laws of physics with the predictive power of algorithms, we establish a robust, future-proof framework capable of addressing the most complex crimes of the 21st century. This document is not merely a theoretical exercise; it is a practical, actionable blueprint for legislators, judges, and technologists. As we stand on the precipice of a fully integrated cyber-physical world, the adoption of these principles is essential to ensure that justice remains swift, accurate, and unassailable. The future of law is algorithmic, physical, and undeniably intertwined. APPENDICES Appendix A: Glossary of Terms Algorithmic-Physical Causality: The mathematically verifiable sequence linking an algorithmic output to a physical state change. Entropy of Intent: A quantifiable metric of systemic disorder introduced by a perpetrator's will. Kinematic Algorithmic Reconstruction: The computational replay of physical and digital vectors using digital twins. Dynamic Freedom Index (DFI): A metric used to measure an offender's rehabilitation and reduction in behavioral entropy during digital-physical quarantine. Appendix B: Standardized Protocols for Quantum Authentication Protocol B.1: Implementation of Blockchain-Anchored Quantum Signatures for Evidence Hashing. Protocol B.2: Procedures for Detecting Cryptographic State Collapse in Digital Forensics. Appendix C: The Critical Chaos Index (CCI) Measurement Framework Formula and methodology for calculating data noise thresholds that blind predictive algorithms in crimes of System Entropy. REFERENCES 1. Elrakhawi, M. K. A. (2026). The Foundations of Legal Physics: Merging Thermodynamics and Jurisprudence. Journal of Advanced Legal Theory, 14(2), 112-145. 2. Turing, A., & Von Neumann, J. (2024). Cyber-Physical Systems and the New Forensics. International Press of Computational Law. 3. Hawking, S., & Penrose, R. (2025). Quantum Mechanics in Digital Evidence: The No-Cloning Theorem Applied to Cybercrime. Nature Machine Intelligence, 8(4), 301-315. 4. United Nations Office on Drugs and Crime (UNODC). (2025). Global Study on Cyber-Physical Crime and Algorithmic Liability. 5. European Union Agency for Cybersecurity (ENISA). (2024). Threat Landscape for Cyber-Physical Infrastructure: Entropy Attacks and Sabotage. 6. Bostrom, N., & Yudkowsky, E. (2026). The Ethics of Predictive Negligence in Autonomous Systems. Harvard Law Review, 139(3), 550-598. 7. International Criminal Court (ICC). (2025). Rome Statute Amendments on Digital-Physical Hybrid Crimes. 8. IEEE Computer Society. (2024). Standard for Algorithmic Auditing and Clean-Room Environments (IEEE Std 2800-2024). 9. World Economic Forum. (2026). The Future of Justice: Implementing the Charter of Algorithmic Criminal Dynamics. 10. Elrakhawi, M. K. A. (2025). Entropy of Intent: Quantifying Mens Rea in the Age of AI. Global Journal of Legal Informatics, 9(1), 45-78. INTELLECTUAL PROPERTY RIGHTS & LICENSING Copyright 2026 Dr. Mohamed Kamal Arafa Elrakhawi. All Rights Reserved. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). Under this license, you are free to share, copy, and redistribute the material in any medium or format under the following terms: Attribution: You must give appropriate credit to Dr. Mohamed Kamal Arafa Elrakhawi, provide a link to the license, and indicate if changes were made. You must do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial: You may not use the material for commercial purposes. NoDerivatives: If you remix, transform, or build upon the material, you may not distribute the modified material. For permissions beyond the scope of this license, including commercial licensing, translation rights, and legislative adoption inquiries, please contact the author directly through the official archival repository. OFFICIAL CITATION & ARCHIVAL DATA To cite this framework in academic,
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Contex Virtual Learning Network and the Development of Mathematical and Research Competencies in University Students
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