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Paper Citation Record · LEDGER

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer

As of 21 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2501.10980.

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

pith.paper-citation-record.v1
2501.10980 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:48:50.600357Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

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  • verified fuzzy24
  • unresolved19
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 674578c1-bc9c-4bdb-96ea-5f96d5d58432 · outbound

This paper cites Performance analysis of various machine learning -based approaches for detection and classification of lung cancer in humans,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Performance analysis of various machine learning -based approaches for detection and classification of lung cancer in humans,

Reference 1

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Observation 356ade9e-0080-4571-82b5-6759828c3be7 · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 2

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Observation 8df96906-15ea-4f7a-b240-2ef5be3f9f90 · outbound

This paper cites Prediction lung cancer–in machine learning perspective,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Prediction lung cancer–in machine learning perspective,

Reference 3

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This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 4

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Source-reported events for the cited work

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Observation 222d9444-e8f3-416e-b982-bd4479a162b5 · outbound

This paper cites Predicting outcomes of nonsmall cell lung cancer using CT image features,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Predicting outcomes of nonsmall cell lung cancer using CT image features,

Reference 5

Resolution
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Source-reported events for the cited work

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Observation fbec45df-7b1e-4ae5-8a2d-b574280440cd · outbound

This paper cites M., Barari, M., & Zarrabi, H.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer M., Barari, M., & Zarrabi, H

Reference 6

Resolution
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Source-reported events for the cited work

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Observation ac9c7145-3aea-4489-a7de-9b191f34fe06 · outbound

This paper cites Multi -stage lung cancer detection and prediction using multi -class svm classifie,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Multi -stage lung cancer detection and prediction using multi -class svm classifie,

Reference 7

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Observation 319faaaa-3292-4281-9b7f-572b505f98b4 · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 8

Resolution
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Source-reported events for the cited work

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Observation 54feefd4-1160-47bc-a494-2b5457686669 · outbound

This paper cites AI -based smart prediction of clinical disease using random forest classifier and Naive Bayes,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer AI -based smart prediction of clinical disease using random forest classifier and Naive Bayes,

Reference 9

Resolution
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Source-reported events for the cited work

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Observation fc031d6c-b5e2-445e-bc61-d079554f3c4b · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 10

Resolution
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Source-reported events for the cited work

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Observation 2070870c-bd20-4560-8ae4-02cdff1626d2 · outbound

This paper cites Early lung cancer detection using nucleus segementation based features,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Early lung cancer detection using nucleus segementation based features,

Reference 11

Resolution
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Source-reported events for the cited work

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Observation a9b31105-da88-497e-b4e1-885342cf5eef · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 12

Resolution
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Source-reported events for the cited work

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Observation dd5253f6-6e2e-4fdf-a7ec-91833007a1c8 · outbound

This paper cites Using an hebbian learning rule for multi-class svm classifiers,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Using an hebbian learning rule for multi-class svm classifiers,

Reference 13

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Source-reported events for the cited work

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Observation 7215dcbe-537e-4f9c-953e-b7ee1c739a65 · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 14

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Source-reported events for the cited work

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Observation 945ef75d-53a0-472b-a9eb-6ca11e2530f9 · outbound

This paper cites Nonlinear component analysis as a kernel eigenvalue problem,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Nonlinear component analysis as a kernel eigenvalue problem,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 8f4e1497-e356-4db5-b940-da885dad9ce3 · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 16

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Source-reported events for the cited work

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Observation 89d58bfa-8e14-4191-a854-7fc32720d1e4 · outbound

This paper cites Cristianini and J.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Cristianini and J

Reference 17

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Source-reported events for the cited work

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Observation c541928b-d814-41bd-b008-ba9cf2d03127 · outbound

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An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer J., & Mirzaei, A

Reference 18

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Observation 8dac0a2f-2876-4311-93a0-b02e0c3ed165 · outbound

This paper cites Fuzzy kernel k - medoids algorithm for multiclass multidimensional data classification,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Fuzzy kernel k - medoids algorithm for multiclass multidimensional data classification,

Reference 19

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Source-reported events for the cited work

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Observation 09c9b259-f858-4015-aaf3-93204c0d5b1d · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 20

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Observation 6a0d65f3-6fb0-46ee-b85b-81f0b56eb702 · outbound

This paper cites Development of two -stage SVM -RFE gene selection strategy for microarray expression data analysis,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Development of two -stage SVM -RFE gene selection strategy for microarray expression data analysis,

Reference 21

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Observation aa14e1e2-6bb9-4497-a20d-78049f156a5b · outbound

This paper cites An Optimized Multi-Layer Resource Management in Mobile Edge Computing Networks: A Joint Computation Offloading and Caching Solution.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer An Optimized Multi-Layer Resource Management in Mobile Edge Computing Networks: A Joint Computation Offloading and Caching Solution

Reference 22

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Source-reported events for the cited work

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Observation 44420b3f-b5c9-46fa-b132-174e6a42cc0d · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 23

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Source-reported events for the cited work

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Observation 4245ed4e-4ad4-4ec2-b1cc-9ef24082714a · outbound

This paper cites Multiple SVM -RFE for gene selection in cancer classification with expression data,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Multiple SVM -RFE for gene selection in cancer classification with expression data,

Reference 24

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Source-reported events for the cited work

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Observation 70dfa637-b76a-4564-a0a0-01f4375c6e4b · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 25

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Observation ecbbd1f0-d5ac-41ce-904f-2abc36d69dca · outbound

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An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Scheduling algorithm for bidirectional LPT

Reference 26

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Source-reported events for the cited work

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An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Recursive fuzzy granulation for gene subsets extraction and cancer classification,

Reference 27

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Observation 6ab995c9-ef85-4d71-840b-f18619a8170c · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 28

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Observation c2cf0075-e0fd-4175-8546-7208fb8fe7de · outbound

This paper cites Gene selection for cancer classification using support vector machines,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Gene selection for cancer classification using support vector machines,

Reference 29

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Source-reported events for the cited work

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Observation 41b86efc-332a-4ffa-b422-7522cd82ada3 · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 30

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Source-reported events for the cited work

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An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer & Mirzapour, R

Reference 31

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Source-reported events for the cited work

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Observation fae78f8c-5e74-493d-87ff-ceeb8cfa6fe5 · outbound

This paper cites Comparison of support vector machine recursive feature elimination and kernel function as feature selection using support vector machine for lung cancer classification,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Comparison of support vector machine recursive feature elimination and kernel function as feature selection using support vector machine for lung cancer classification,

Reference 32

Resolution
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Source-reported events for the cited work

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Observation f7cb6534-6479-44d8-93fd-022f7d3e4bff · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 33

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Source-reported events for the cited work

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Observation 735a1fc8-e047-4351-9c54-01748605eec7 · outbound

This paper cites Application of machine learning techniques for the diagnosis of lung cancer with ANT colony optimization,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Application of machine learning techniques for the diagnosis of lung cancer with ANT colony optimization,

Reference 34

Resolution
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Source-reported events for the cited work

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Observation 1d6d8b76-32f2-4293-9acf-b0a83c4dd217 · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 35

Resolution
unresolved
raw_fallback, observed 2026-08-10T18:48:50.877854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation b4369296-bff9-4f1b-b749-e86ec9ae4d38 · outbound

This paper cites Early Detection of Lung Cancer from CT Scan Images Using Binarization Technique,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Early Detection of Lung Cancer from CT Scan Images Using Binarization Technique,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:48:50.861300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 683a1c5e-3a1b-45ed-844e-ff9e6defa375 · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 37

Resolution
unresolved
raw_fallback, observed 2026-08-10T18:48:50.842508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation d5304baa-12f4-403d-98bf-73c588768580 · outbound

This paper cites Feature selection using stochastic diffusion search,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Feature selection using stochastic diffusion search,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:48:50.824457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 6ec86caf-5ae0-4b6d-b380-ddcbb895b6ed · outbound

This paper cites A Novel Approach for Establishing Connectivity in Partitioned Mobile Sensor Networks Using Beamforming Techniques.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer A Novel Approach for Establishing Connectivity in Partitioned Mobile Sensor Networks Using Beamforming Techniques

Reference 39

Resolution
verified exact
local_arxiv, observed 2026-08-10T18:48:50.679787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T18:48:50.553088Z digest=sha256:8775d0cd958d3b7f07635807435eec460d2f5bd1417997d17ec7ed4d65e4f639

Observation 715290ac-ccd0-44ec-95cf-5955e44765b2 · outbound

This paper cites Comparative study of K -NN, naive Bayes and decision tree classification techniques,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Comparative study of K -NN, naive Bayes and decision tree classification techniques,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:48:50.806798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T18:48:50.563488Z digest=sha256:fe4b998df50ae0a06a1233868b787014b94aa3e884661312c38a6ab71daf4247

Observation a012e0f4-a5e3-4f0f-98fc-0f34aeb29865 · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 41

Resolution
unresolved
raw_fallback, observed 2026-08-10T18:48:50.787609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation e124582d-4943-4a8b-bcd1-42b05a23831f · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-10T18:48:50.770834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T18:48:50.576147Z digest=sha256:90568de996997ef6266f4ffdfad98bb13c6e44e1c5419fc51ff9e4fb3b94e7e6

Observation 7d96bf4c-29a4-4cc4-ab20-91116eef066f · outbound

This paper cites Lung cancer prediction using feed forward back propagation neural networks with optimal features,.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Lung cancer prediction using feed forward back propagation neural networks with optimal features,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T18:48:50.753634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T18:48:50.581794Z digest=sha256:e61e792d6a1ac65f1c03a6c8950a43531e7f6046801d27493e782825124ce5a6

Observation 9045b39a-f111-4de1-bdba-f04bfee78f68 · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 44

Resolution
unresolved
raw_fallback, observed 2026-08-10T18:48:50.736633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T18:48:50.588580Z digest=sha256:aefbe889cbb657d76647b30a2aaab68f68cc16d880e7e4e94137b023004ca957

Observation 0806311d-3bea-4e89-8ec7-a44dcc219049 · outbound

This paper cites an unresolved cited work.

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Unresolved cited work

Reference 45

Resolution
unresolved
raw_fallback, observed 2026-08-10T18:48:50.720438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-10T18:48:50.594924Z digest=sha256:5a3ca7ab48a4b99a48145904dab00efe74c019c5805c559c55fd69e4c6a0e521

Observation a27532cf-2a7f-4132-b7ee-c098647a3a47 · outbound

This paper cites Presenting a new approach in security in inter-vehicle networks (VANET).

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer Presenting a new approach in security in inter-vehicle networks (VANET)

Reference 46

Resolution
verified exact
local_arxiv, observed 2026-08-10T18:48:50.653095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Pith citing papers

No inbound Pith citation observations are available.