Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T13:27:41.272230Z
Paper Citation Record · LEDGER
Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models
As of 15 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2508.00898.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
16 of 16 outbound references displayed
- verified exact0
- verified fuzzy11
- unresolved5
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation af3774e1-b47b-4b0e-bb0a-d5475f29422d · outbound
This paper cites Sánchez Velázquez1, Mingbo Cai2, 3, Andrew Coney1, Álvaro J.
Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models Sánchez Velázquez1, Mingbo Cai2, 3, Andrew Coney1, Álvaro J
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:27:43.695896Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 9fe6f95a-43a7-47cf-a96c-d7c3a6b68fef · outbound
This paper cites This observation aligns with the bias-variance trade-off, as described by (Belkin et al., 2019).
Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models This observation aligns with the bias-variance trade-off, as described by (Belkin et al., 2019)
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:27:41.502582Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 69abe739-0a59-481d-bf22-40a8438c5fee · outbound
This paper cites 2 Related works This paper aims to evaluate hybrid Deep Learning models for video frame predic-tion.
Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models 2 Related works This paper aims to evaluate hybrid Deep Learning models for video frame predic-tion
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:27:43.102341Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 7b7922e6-44b0-4f5e-bad9-9878fa653b74 · outbound
This paper cites As can be seen in the previous Figure, the workflow implies different Deep Learning models that are defined as follows.
Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models As can be seen in the previous Figure, the workflow implies different Deep Learning models that are defined as follows
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:27:42.263461Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 1099aa3e-361a-450a-be48-b85399e8835f · outbound
This paper cites excellent prediction.
Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models excellent prediction
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:27:41.486305Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 3e4704ef-ae6a-4c5c-a040-3615ccea5f33 · outbound
This paper cites ∑(𝑦#− ŷ#)%.
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:27:41.738490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 44144c3c-77ed-4a1c-829f-9d451648ecbe · outbound
This paper cites First, before training the models, the creation of the training, validation, and test subsets is needed.
Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models First, before training the models, the creation of the training, validation, and test subsets is needed
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:27:41.545804Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 4651710e-f1f2-4ab9-87c7-857b8b784767 · outbound
This paper cites an unresolved cited work.
Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models Unresolved cited work
Reference 11
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T13:27:41.466348Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 8a4eea92-e932-4668-8930-aad5677cda4a · outbound
This paper cites This significant reduction trans-lates directly into a smaller carbon footprint and longer battery life when the models are deployed on embedded or edge devices.
Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models This significant reduction trans-lates directly into a smaller carbon footprint and longer battery life when the models are deployed on embedded or edge devices
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:27:41.442151Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 05777608-911b-4b66-8907-d9871a267be5 · outbound
This paper cites an unresolved cited work.
Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models Unresolved cited work
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T13:27:41.272230Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 554a6c8a-4632-4f44-96a9-6a6e23a2183d · outbound
This paper cites V., Al-Shehari, T., Alsadhan, N.
Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models V., Al-Shehari, T., Alsadhan, N
Reference 846
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T13:27:41.269296Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ed4b7bee-44a1-4cd0-b9f0-5994ac61d11d · outbound
This paper cites Regarding (Kemal Pola & Saban Öztürk, 2023), GRUs simplify the internal structure of LSTM cells by reducing the number of gates, thereby decreasing the model's time complexity.
Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models Regarding (Kemal Pola & Saban Öztürk, 2023), GRUs simplify the internal structure of LSTM cells by reducing the number of gates, thereby decreasing the model's time complexity
Reference 2014
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:27:42.044108Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation c2c4544d-6e07-4867-9b16-3823735a149d · outbound
This paper cites ∑∣𝑦#− ŷ#∣.
Reference 2017
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:27:41.870473Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 7d7d421e-6c20-4051-a6b7-26312647d4aa · outbound
This paper cites an unresolved cited work.
Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models Unresolved cited work
Reference 2021
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T13:27:42.579716Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 406af1d9-9be7-4dea-a8e5-7c3f8f07409b · outbound
This paper cites They find that the ConvLSTM model, which explicitly captures temporal and spatial patterns, outperforms GANs in predicting fu-ture frames.
Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models They find that the ConvLSTM model, which explicitly captures temporal and spatial patterns, outperforms GANs in predicting fu-ture frames
Reference 2022
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:27:42.818635Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation c5350249-2d5f-41e5-a7c1-13a36a74ecb4 · outbound
This paper cites an unresolved cited work.
Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models Unresolved cited work
Reference 2024
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T13:27:43.349950Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Pith citing papers
No inbound Pith citation observations are available.