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Paper Citation Record · LEDGER

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification

As of 22 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2507.01778.

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

pith.paper-citation-record.v1
2507.01778 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:48:02.125869Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

21 of 21 outbound references displayed

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  • unresolved1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a1385646-1ca4-441f-bdfb-a1cabbd8e33b · outbound

This paper cites PV array soiling detection using machine learning,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification PV array soiling detection using machine learning,

Reference 1

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Source-reported events for the cited work

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Observation dd305fa5-5d2c-47be-9edd-65855a2f27c7 · outbound

This paper cites Solar panel hotspot localization and fault classification using deep learning approa ch,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification Solar panel hotspot localization and fault classification using deep learning approa ch,

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation ae9c0c74-449e-4d70-b7ed-691628c537b5 · outbound

This paper cites Improved solar panel efficiency th rough dust detection using the InceptionV3 transfer learning m odel,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification Improved solar panel efficiency th rough dust detection using the InceptionV3 transfer learning m odel,

Reference 3

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Source-reported events for the cited work

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Observation 0f0a4577-6551-42b7-b9a5-4e18b2671a7f · outbound

This paper cites SolNet: A convolutional neural network for detecting dust on solar panel,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification SolNet: A convolutional neural network for detecting dust on solar panel,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation b46f1dcf-555f-4136-b2b5-41b19b3ca90b · outbound

This paper cites Detection of soiling on PV module using deep learning,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification Detection of soiling on PV module using deep learning,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 26310dd9-cc79-4332-8619-9f35e769ad0b · outbound

This paper cites PV module soiling detec tion using visible spectrum imaging and machine learning,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification PV module soiling detec tion using visible spectrum imaging and machine learning,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation f0fd4d06-5ea2-4c1a-8b67-c5bf6a5f21dc · outbound

This paper cites Automatic soiling and partial shading asse ssment on PV modules through RGB images analysis,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification Automatic soiling and partial shading asse ssment on PV modules through RGB images analysis,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 817b3fef-936c-4f75-b703-c4dacf605ea8 · outbound

This paper cites Deep-learning-based probabilistic estimation of solar PV soiling loss,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification Deep-learning-based probabilistic estimation of solar PV soiling loss,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 31748a2b-1c18-4ae1-b02a-c3c17b3145e1 · outbound

This paper cites Application of deep learning based detector YOLOv5 for soiling recognition in photovoltaic modules,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification Application of deep learning based detector YOLOv5 for soiling recognition in photovoltaic modules,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 4cd54f87-995d-4e3d-a60c-f958860d424f · outbound

This paper cites Non- invasive health status diagnosis of solar PV panel using ensemble classifier,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification Non- invasive health status diagnosis of solar PV panel using ensemble classifier,

Reference 10

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Source-reported events for the cited work

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Observation bff6e04f-edcc-45b8-b223-84a32953e510 · outbound

This paper cites A supervised ensemble le arning method for fault diagnosis in photovoltaic strings,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification A supervised ensemble le arning method for fault diagnosis in photovoltaic strings,

Reference 11

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Source-reported events for the cited work

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Observation 3c637823-3c16-4393-95ce-0fa453b3c838 · outbound

This paper cites Two-stage s elective ensemble of CNN via deep tree training for medical image classification,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification Two-stage s elective ensemble of CNN via deep tree training for medical image classification,

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 37b507a7-5190-4d4e-a0e3-5ae1f459f230 · outbound

This paper cites Ensemble of convolutional neural networks for bioimage classification,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification Ensemble of convolutional neural networks for bioimage classification,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 8a6ac8c2-5a75-4d1a-9e41-d77eb3add787 · outbound

This paper cites Deepsolareye: Power loss prediction and weakly supervised soiling localization via fully convoluti onal networks for solar panels,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification Deepsolareye: Power loss prediction and weakly supervised soiling localization via fully convoluti onal networks for solar panels,

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 3c972420-a115-43fc-b500-414d910f9069 · outbound

This paper cites Bagging predictors,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification Bagging predictors,

Reference 15

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Source-reported events for the cited work

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Observation a2b21fa6-87b9-4c91-9d16-e471236457d2 · outbound

This paper cites A decision-theoretic generalization of on-line learning and an application to boosting,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification A decision-theoretic generalization of on-line learning and an application to boosting,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation b7897203-6543-402e-b161-aa15303164fd · outbound

This paper cites an unresolved cited work.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification Unresolved cited work

Reference 17

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Source-reported events for the cited work

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Observation 42e74e63-57aa-4d61-8451-c90093b73b1c · outbound

This paper cites Zhou, Ensemble Methods: Foundations and Algorithms , 1st ed.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification Zhou, Ensemble Methods: Foundations and Algorithms , 1st ed

Reference 18

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Source-reported events for the cited work

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Observation e5df0c8d-50bf-4216-b154-46462951fcc2 · outbound

This paper cites Blending ensemble machine learning wi th Python,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification Blending ensemble machine learning wi th Python,

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 9d6db2a0-93d5-4d50-9f71-191fc174cb42 · outbound

This paper cites Combination of bagging and boosting ensemble learning methods: A solution to model underfitting and overfitting,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification Combination of bagging and boosting ensemble learning methods: A solution to model underfitting and overfitting,

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 072c8fff-a095-40dd-9753-5a35894d19aa · outbound

This paper cites A dynami c ensemble learning algorithm for neural networks,.

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification A dynami c ensemble learning algorithm for neural networks,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Pith citing papers

No inbound Pith citation observations are available.