Paper Citation Record · LEDGER

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction

As of 16 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 8 inbound Pith citation observations for arXiv:2412.02211.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.02211 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:46:09.712602Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:05:02.109885Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-08T21:44:20.420362Z

Reference resolution

20 of 20 outbound references displayed

verified exact3
verified fuzzy7
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0f2a16f0-ade6-42b7-b762-2fe64c7e9711 · outbound

This paper cites GLMAE: Graph Representation Learning Method Combining Generative Learning and Masking Autoencoder,.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction GLMAE: Graph Representation Learning Method Combining Generative Learning and Masking Autoencoder,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:46:09.964264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7ebc8dae-15cf-4103-bb63-82362d3ccfa5 · outbound

This paper cites Calibration Learning for Few-shot Novel Product Description,.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Calibration Learning for Few-shot Novel Product Description,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T23:46:09.638847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72d89e98-546b-46a8-82e5-2280430a52aa · outbound

This paper cites Sahand: A Software Fault- Prediction Method Using Autoencoder Neural Network and K-Means Algorithm,.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Sahand: A Software Fault- Prediction Method Using Autoencoder Neural Network and K-Means Algorithm,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:46:09.945489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 98499f9b-eb03-44fe-907e-10fc3927b260 · outbound

This paper cites Detecting Network Anomalies Using the Rain Optimization Algorithm and Hoeffding Tree-Based Autoencoder,.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Detecting Network Anomalies Using the Rain Optimization Algorithm and Hoeffding Tree-Based Autoencoder,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:46:09.932822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6316fff1-1ca7-435c-b7a9-bb9e32207cb4 · outbound

This paper cites Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T23:46:09.652163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6209d80-9121-4ce2-81d0-d021b5fe3940 · outbound

This paper cites Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:46:09.857731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4c03a04d-914f-4e68-8cbc-75067386f5a2 · outbound

This paper cites Dual-Branch Dynamic Graph Convolutional Network for Robust Multi-Label Image Classification,.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Dual-Branch Dynamic Graph Convolutional Network for Robust Multi-Label Image Classification,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:46:09.921514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f9cf7631-fd64-4b64-8ef8-1db0df988a53 · outbound

This paper cites Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T23:46:09.664961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7bbcce2-f219-4b52-a5b4-0addeb400b27 · outbound

This paper cites Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T23:46:09.668971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2018affd-7c04-42a5-8a29-c90c66ae80ab · outbound

This paper cites Adaptive Cache Management for Complex Storage Systems Using CNN-LSTM-Based Spatiotemporal Prediction.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Adaptive Cache Management for Complex Storage Systems Using CNN-LSTM-Based Spatiotemporal Prediction

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T23:46:09.672963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4de66fa-744b-467e-9cfc-1104b66b1e74 · outbound

This paper cites A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T23:46:09.676987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05607bbf-da7e-42bf-b59a-d15541884b7f · outbound

This paper cites ALBERT-Driven Ensemble Learning for Medical Text Classification,.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction ALBERT-Driven Ensemble Learning for Medical Text Classification,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:46:09.910014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1c98f373-a999-4404-8a25-779b4f7eb50a · outbound

This paper cites Time-Series Load Prediction for Cloud Resource Allocation Using Recurrent Neural Networks,.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Time-Series Load Prediction for Cloud Resource Allocation Using Recurrent Neural Networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:46:09.898567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8b928585-fbc5-400c-8b34-9e87fe7017ac · outbound

This paper cites Robust Graph Neural Networks for Stability Analysis in Dynamic Networks.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Robust Graph Neural Networks for Stability Analysis in Dynamic Networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T23:46:09.688673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3739982-d96f-49cc-8937-291783f462d9 · outbound

This paper cites Enhancing Recommendation Systems with Multi-Modal Transformers in Cross-Domain Scenarios,.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Enhancing Recommendation Systems with Multi-Modal Transformers in Cross-Domain Scenarios,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:46:09.886266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b3571ff7-b80e-41c0-8362-315702b650a4 · outbound

This paper cites A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Mining.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Mining

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T23:46:09.696688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa6cb56b-616e-4fa9-af72-c042d2ea9fe0 · outbound

This paper cites Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T23:46:09.700841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5799050-c428-467a-8e0a-869ea1edd821 · outbound

This paper cites Comparison of Norm-Based Feature Selection Methods on Biological Omics Data,.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Comparison of Norm-Based Feature Selection Methods on Biological Omics Data,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T23:46:09.704895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90de52a0-50ef-49e5-89e8-1ddf0ae436ef · outbound

This paper cites Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:46:09.766959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 342d2b29-6344-4ff1-a341-dc6784cf2aaa · outbound

This paper cites Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management.

An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:46:09.750780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 00184f4f-c900-483a-a3ed-5d5aa1c9499d · inbound

Accurate Medical Named Entity Recognition Through Specialized NLP Models cites this paper.

Accurate Medical Named Entity Recognition Through Specialized NLP Models An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T18:05:02.109885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41645374-c522-46f9-b2c4-63937f67d4c9 · inbound

Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Mining cites this paper.

Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Mining An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T11:18:31.030693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcc0e983-26b5-417c-8934-0a91da081bb1 · inbound

Adaptive User Interface Generation Through Reinforcement Learning: A Data-Driven Approach to Personalization and Optimization cites this paper.

Adaptive User Interface Generation Through Reinforcement Learning: A Data-Driven Approach to Personalization and Optimization An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T10:18:14.620040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e54c311d-f24b-4d45-9774-7d8be4e674c2 · inbound

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments cites this paper.

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T05:40:19.787105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d345ab3f-9398-4968-ac1b-b6559926fccf · inbound

Computer Vision-Driven Gesture Recognition: Toward Natural and Intuitive Human-Computer cites this paper.

Computer Vision-Driven Gesture Recognition: Toward Natural and Intuitive Human-Computer An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T04:50:46.901299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f08225f2-6c1d-4a96-a5bf-32c59ee5002d · inbound

A Matrix Logic Approach to Efficient Frequent Itemset Discovery in Large Data Sets cites this paper.

A Matrix Logic Approach to Efficient Frequent Itemset Discovery in Large Data Sets An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T00:39:35.504950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ba3ceaa-bc0e-4e59-8dd1-ced6f4fc9ccd · inbound

A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Prediction cites this paper.

A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Prediction An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-08T21:44:20.425255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 49628f68-ec01-4dc1-866c-78d823006aa4 · inbound

Feature Space Topology Control via Hopkins Loss cites this paper.

Feature Space Topology Control via Hopkins Loss An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T17:04:15.920968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.