Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T20:46:20.180963Z
Paper Citation Record · LEDGER
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection
As of 7 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2507.01924.
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 2026-08-07T06:34:17.273281+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
55 of 55 outbound references displayed
- verified exact18
- verified fuzzy3
- unresolved16
- parse uncertain0
- malformed identifier4
- metadata mismatch14
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b4800460-0c26-4044-b9a4-a17b66655f75 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 1
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T20:46:24.259934Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 687f6063-3759-47e5-b544-73b38cddb3d4 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 2
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:46:24.338114Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 75cf2742-49dc-4842-bdb4-3704ba8fd047 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 3
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:46:24.330717Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e6837568-9c1d-4f0f-afe0-76438e0d50e8 · outbound
This paper cites Ashtiani and Bijan Raahemi.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Ashtiani and Bijan Raahemi
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:46:15.717242Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 106ed2b6-8a3a-4693-8109-d8ffc68791e9 · outbound
This paper cites Oyedele, Muhammad Bilal, Taofeek Dolapo Akinosho, Juan Manuel Davila Delgado, and Lukman Adewale Akanbi.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Oyedele, Muhammad Bilal, Taofeek Dolapo Akinosho, Juan Manuel Davila Delgado, and Lukman Adewale Akanbi
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:46:15.775783Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5f3e4d62-62e3-4e36-bcb9-15b3c559e945 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 6
- Resolution
- verified exact
- doi, observed 2026-08-06T20:46:22.632483Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 17ee1cc9-49d5-43d1-89b1-4be19d134a00 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 7
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:46:24.323343Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 79f154c3-06c2-43b5-b36d-c75ee3d8ff72 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 8
- Resolution
- verified exact
- doi, observed 2026-08-06T20:46:22.623681Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 884f82d0-8c3f-4ad2-aff9-2c2f0b7c8146 · outbound
This paper cites Sahand Mohammadi Ziabari, and Amr Elsherbini.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Sahand Mohammadi Ziabari, and Amr Elsherbini
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:46:24.315718Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation b93f7796-074f-4476-8363-0bde93083b2b · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 10
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T20:46:24.109103Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4f732cc5-9034-42fd-92ac-939af6ace6db · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 11
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T20:46:24.039070Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e6aa6cf5-dd1f-402a-b09b-fda933cbe630 · outbound
This paper cites Sahand Mohammadi Ziabari, and Marc van Houten.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Sahand Mohammadi Ziabari, and Marc van Houten
Reference 12
- Resolution
- verified exact
- doi, observed 2026-08-06T20:46:22.615389Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ff7ada1c-b7d3-4bf0-8194-66a8ad435c1d · outbound
This paper cites Bayan Bruss, and Leman Akoglu.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Bayan Bruss, and Leman Akoglu
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:46:24.307924Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f1f6e326-099a-426f-8817-6720ff78c928 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:46:16.672507Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bf3b41c4-df16-452a-baa2-894bfdc4a3ea · outbound
This paper cites From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management
Reference 15
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T20:46:22.510725Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 375a4185-ea51-4081-98ba-0bcf159bfbd2 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 16
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-06T20:46:16.913232Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c8a92ba5-4e80-48ea-a091-fa016fdbdb37 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 17
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T20:46:23.911749Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2bdfea6f-a451-489e-8cdf-d833d1eff6c8 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 18
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T20:46:23.848256Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 440fd610-5995-414a-ab20-146fdaacdf13 · outbound
This paper cites On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning
Reference 19
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T20:46:22.325761Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f10578c2-f62c-4f5a-b8a0-d7e8a12ea4e4 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 20
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:46:24.299968Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 32229d58-ab22-45dd-a400-384be5bfd217 · outbound
This paper cites FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data
Reference 21
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T20:46:22.170831Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e3c6433c-b6c5-43e6-862a-835221cf1fe6 · outbound
This paper cites Challenges and Complexities in Machine Learning based Credit Card Fraud Detection.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Challenges and Complexities in Machine Learning based Credit Card Fraud Detection
Reference 22
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T20:46:21.978336Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 3e0853b9-6b95-46a7-b025-b3d1fd26c832 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:46:17.401942Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 18bc474e-4b16-46cf-9257-5f8e96f88bca · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 24
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T20:46:23.709900Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d81319d9-bff1-484e-a311-9cac4b2e940c · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 25
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T20:46:23.775839Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 66fb0b8a-6957-42a3-9f25-214c27091336 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 26
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:46:24.292487Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 612c18ca-89ca-4b49-9a16-fb007ab6144c · outbound
This paper cites Deep Anomaly Detection under Labeling Budget Constraints.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Deep Anomaly Detection under Labeling Budget Constraints
Reference 27
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T20:46:21.726601Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 208c0000-f502-4ffa-8ebe-a3635c61d892 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 28
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:46:24.284906Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e5eaa13f-dbf9-4289-8409-99544a3100a4 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:46:18.273728Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d8a109c7-fc67-46da-baf9-fe33983a2433 · outbound
This paper cites A Unified Approach to Interpreting Model Predictions.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection A Unified Approach to Interpreting Model Predictions
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:46:18.122895Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d8603381-4cca-4d16-87a4-308dd3351a5d · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 31
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T20:46:23.459929Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation da41f0e0-4070-4c42-9495-af4ebec3394b · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 32
- Resolution
- verified exact
- doi, observed 2026-08-06T20:46:21.529313Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 60e15af2-a21c-407e-be46-1a622eb55d35 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:46:18.349890Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 54f2acad-0553-46f4-af77-fb8bb17d4107 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 34
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-06T20:46:23.336470Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 57f8fae3-3145-4eed-ad5f-316a09c99051 · outbound
This paper cites Jaleed Khan.
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:46:18.846243Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 554bf08d-c7b9-4cd3-b6fc-b043eba4fc60 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 36
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-06T20:46:18.649255Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7d58c1a1-f5b7-47b0-ad58-27da3ee350a5 · outbound
This paper cites Gnaneswar, R.
Reference 37
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T20:46:23.153496Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 9c81d75b-ad5b-4b81-bd06-3ea6f6cd497a · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 38
- Resolution
- verified exact
- doi, observed 2026-08-06T20:46:21.423696Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5b6f63f8-0ee3-4f4e-8638-d093fcf06c63 · outbound
This paper cites Samuthira Pandi, S Alamelu Alias Rajasree, and Dr.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Samuthira Pandi, S Alamelu Alias Rajasree, and Dr
Reference 39
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T20:46:23.217111Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f69a9d71-d596-4da4-8ef2-4ac2ce59f519 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 40
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T20:46:23.094839Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a9e35ece-7809-484e-ae84-5eb25cc65d31 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 41
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:46:24.276674Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 539b2860-851c-4692-88b9-6479cafe7e87 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 42
- Resolution
- verified exact
- doi, observed 2026-08-06T20:46:21.228675Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation b257206d-ddb6-42dc-a725-4b9c810e2835 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 43
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T20:46:23.022532Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2482d14d-3e1f-4a70-b039-270d00f9a29c · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 44
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T20:46:22.949870Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a60efe30-bee8-4bbf-8004-445c33ca77e4 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 45
- Resolution
- verified exact
- doi, observed 2026-08-06T20:46:21.011666Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 69fdaedc-fafd-4131-a6ba-58bff800d5a4 · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 46
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T20:46:22.881683Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8aca39f2-a3b4-4013-af28-42466ea5e9dc · outbound
This paper cites Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:46:19.838825Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d251a9a3-7112-4fab-9754-9b0a9b206058 · outbound
This paper cites Augmenting data-driven models for energy systems through feature engineering: A Python framework for feature engineering.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Augmenting data-driven models for energy systems through feature engineering: A Python framework for feature engineering
Reference 48
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T20:46:20.800055Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 1f5d740a-158e-49c2-99a8-07484183b25b · outbound
This paper cites Arik, and Tomas Pfister.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Arik, and Tomas Pfister
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:46:24.267549Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5ba19010-6eee-449f-9c5a-58ee2fe87852 · outbound
This paper cites Credit Card Fraud Detection Using Advanced Transformer Model.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Credit Card Fraud Detection Using Advanced Transformer Model
Reference 50
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T20:46:20.377536Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 0a685f7f-df31-48e2-a70e-f4f1b286cb8d · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 51
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-06T20:46:19.895271Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eabb2d5f-3ae1-4495-b68d-ef78956f33cd · outbound
This paper cites SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
Reference 53
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T20:46:20.559786Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 7cb1d4b8-7d4f-4541-821a-4c91c83c523d · outbound
This paper cites an unresolved cited work.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection Unresolved cited work
Reference 55
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T20:46:22.731030Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e3f461c8-334f-44e4-94b7-8c4a57b39c51 · outbound
This paper cites 2022 International Conference on Big Data, Information and Computer Network (BDICN), 306–310.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection 2022 International Conference on Big Data, Information and Computer Network (BDICN), 306–310
Reference 2022
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T20:46:23.603631Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 65078e74-f68c-4081-8d1b-8d5b551197a6 · outbound
This paper cites A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection
Reference 2023
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T20:46:22.650414Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Pith citing papers
No inbound Pith citation observations are available.