Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review | Paper Citation Record

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This paper cites Image-based surface defect detection using deep learning: A review,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Image-based surface defect detection using deep learning: A review,

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This paper cites State of the art in defect detection based on machine vision,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review State of the art in defect detection based on machine vision,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Industrial defect detection through computer vision: A survey,

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This paper cites China’s manufacturing locus in 2025: With a comparison of “made-in-china 2025.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review China’s manufacturing locus in 2025: With a comparison of “made-in-china 2025

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This paper cites Salgues, Society 5.0: industry of the future, technologies, methods and tools.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Salgues, Society 5.0: industry of the future, technologies, methods and tools

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This paper cites Recent advances in industrial internet: insights and challenges,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Recent advances in industrial internet: insights and challenges,

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This paper cites The literature review of manufacturing industry 4.0 using mapping design with gis approach,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review The literature review of manufacturing industry 4.0 using mapping design with gis approach,

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This paper cites Md-yolo: Surface defect detector for industrial complex environments,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Md-yolo: Surface defect detector for industrial complex environments,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Smart manufacturing,

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This paper cites Visual computing as a key enabling technology for industrie 4.0 and industrial internet,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Visual computing as a key enabling technology for industrie 4.0 and industrial internet,

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This paper cites Automated visual defect detection for flat steel surface: A survey,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Automated visual defect detection for flat steel surface: A survey,

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Observation 6e9d7c59-70cc-4f30-9bac-cd13fca6a9fa · outbound

This paper cites Machine learning for industrial applications: A comprehensive literature review,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Machine learning for industrial applications: A comprehensive literature review,

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This paper cites Deep learning for automatic vision-based recognition of industrial surface defects: A survey,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Deep learning for automatic vision-based recognition of industrial surface defects: A survey,

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Observation 5525ecea-e48a-41bd-82da-0b08b4f1be38 · outbound

This paper cites Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Unresolved cited work

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Defect detection in atomic-resolution images via unsupervised learning with translational invariance,

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Observation 8cc4b16f-5fa4-426a-8072-54bb37d4ed52 · outbound

This paper cites Contrastive self-supervised representation learning framework for metal surface defect detection,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Contrastive self-supervised representation learning framework for metal surface defect detection,

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This paper cites Automatic defect classification using semi-supervised learning with defect localization,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Automatic defect classification using semi-supervised learning with defect localization,

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Observation c5deadbc-9c2a-47d2-8b41-335d5ea16419 · outbound

This paper cites Efficient and accurate semi-supervised semantic segmentation for industrial surface defects,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Efficient and accurate semi-supervised semantic segmentation for industrial surface defects,

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This paper cites Deep learning methods for object detection in smart manufacturing: A survey,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Deep learning methods for object detection in smart manufacturing: A survey,

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This paper cites Deep learning for unsupervised anomaly localization in industrial images: A survey,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Deep learning for unsupervised anomaly localization in industrial images: A survey,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Aircraft visual inspection: A systematic literature review,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Aero-engine blade defect detection: A systematic review of deep learning models,

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This paper cites A technology maturity assessment framework for industry 5.0 machine vision systems based on systematic literature review in automotive manufacturing,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review A technology maturity assessment framework for industry 5.0 machine vision systems based on systematic literature review in automotive manufacturing,

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This paper cites Deep cnn-based visual defect detection: Survey of current literature,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Deep cnn-based visual defect detection: Survey of current literature,

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This paper cites Deep industrial image anomaly detection: A survey,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Deep industrial image anomaly detection: A survey,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review A systematic review of deep learning approaches for surface defect detection in industrial applications,

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This paper cites Self-supervised anomaly detection in computer vision and beyond: A survey and outlook,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Self-supervised anomaly detection in computer vision and beyond: A survey and outlook,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Anomaly detection based on artificial intelligence of things: A systematic literature mapping,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Real-time defect detection in electronic components during assembly through deep learning,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review A lightweight deep- learning algorithm for welding defect detection in new energy vehicle battery current collectors,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review A comprehensive survey on machine learning driven material defect detection: Challenges, solutions, and future prospects,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Unresolved cited work

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review A novel weakly supervised ensemble learning framework for automated pixel-wise industry anomaly detection,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Bigdatasetgan: Synthesizing imagenet with pixel-wise annotations,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Defectgan: Synthetic data generation for emu defects detection with limited data,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Meta-transfer learning for few-shot learning,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Challenges, opportunities and future directions of smart manufacturing: A state of art review,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Transfer learning methods for fractographic detection of fatigue crack initiation in additive manufac- turing,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review A nondestructive automatic defect detection method with pixelwise segmentation,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Auto-annotated deep segmentation for surface defect detection,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Deep Semi-Supervised Anomaly Detection

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Image-based defect detection in lithium-ion battery electrode using convolutional neural networks,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Defect classification and detection using a multitask deep one-class cnn,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Surface defect detection of industrial components based on improved yolov5s,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review A real-time automated defect detection system for ceramic pieces manufacturing process based on computer vision with deep learning,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Sewer pipeline fault identification using anomaly detection algorithms on video sequences,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review A generic semi-supervised deep learning-based approach for automated surface inspection,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Image based quality inspection in smart manufacturing systems: A literature review,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Identification and classi- fication of materials using machine vision and machine learning in the context of industry 4.0,

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This paper cites A cascading fuzzy logic with image processing algorithm–based defect detection for automatic visual inspection of industrial cylindrical object’s surface,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review A cascading fuzzy logic with image processing algorithm–based defect detection for automatic visual inspection of industrial cylindrical object’s surface,

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This paper cites A systematic review of machine-vision-based leather surface defect 16 IEEE SENSORS JOURNAL, VOL. XX, NO. XX, XXXX 2024 inspection,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review A systematic review of machine-vision-based leather surface defect 16 IEEE SENSORS JOURNAL, VOL. XX, NO. XX, XXXX 2024 inspection,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Multi-view surface inspection using a rotating table,

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Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Omnidirectional vision,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:42.033707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d5d0b4f9-f6cf-4b52-9a7e-dfa412ffef7f · outbound

This paper cites Omnidirectional imaging sensor based on conical mirror for pipelines,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Omnidirectional imaging sensor based on conical mirror for pipelines,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:42.015694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation af1eb87e-fb6f-4b57-8d42-292b9b57dd63 · outbound

This paper cites Automatic detection and identification of defects by deep learning algorithms from pulsed thermography data,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Automatic detection and identification of defects by deep learning algorithms from pulsed thermography data,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.989447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a4b36143-8fb0-49f9-bdc8-e0c5da7a63bf · outbound

This paper cites Progress in active infrared imaging for defect detection in the renewable and electronic industries,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Progress in active infrared imaging for defect detection in the renewable and electronic industries,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.974175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ca94f7b6-53b7-4d11-bc15-936b195680e8 · outbound

This paper cites Infrared imaging of photovoltaic modules a review of the state of the art and future challenges facing gigawatt photovoltaic power stations,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Infrared imaging of photovoltaic modules a review of the state of the art and future challenges facing gigawatt photovoltaic power stations,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.949636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e4271d82-5246-4be3-8dbb-3db448b890c9 · outbound

This paper cites Laser-induced thermography: An effective detection approach for multiple-type defects of printed circuit boards (pcbs) multilayer complex structure,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Laser-induced thermography: An effective detection approach for multiple-type defects of printed circuit boards (pcbs) multilayer complex structure,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.927304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 98584e2b-7497-41c5-aa53-0d8e7855578b · outbound

This paper cites The need for precision lighting control in machine vision,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review The need for precision lighting control in machine vision,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.906820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2061cda8-ac53-45a5-9239-16d775d880f2 · outbound

This paper cites 3d vision: Mvtec software.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review 3d vision: Mvtec software

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.888437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a0c5e804-6179-4fb2-b274-3577ff90f9cd · outbound

This paper cites An omnidirectional vision sensor based on a spherical mirror catadioptric system,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review An omnidirectional vision sensor based on a spherical mirror catadioptric system,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.864458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e5c73934-458c-4b7e-b330-46238c6eff10 · outbound

This paper cites Infrared thermography for condition monitoring – a review,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Infrared thermography for condition monitoring – a review,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.837925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b75dcf9b-dc0e-4da0-b77d-79ebd1602ba1 · outbound

This paper cites Color image to grayscale image conversion,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Color image to grayscale image conversion,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.813067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 55c39aed-90ff-411e-b837-fe88adc5b445 · outbound

This paper cites Crack defect detection processing algorithm and method of mems devices based on image processing technology,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Crack defect detection processing algorithm and method of mems devices based on image processing technology,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.791765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f7e61391-61b7-4821-b0e7-03fb2bc940f5 · outbound

This paper cites Exploring deep learning and machine learning approaches for brain hemorrhage detection,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Exploring deep learning and machine learning approaches for brain hemorrhage detection,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.774163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d8ef9575-a162-4bc2-8b57-129f0bc75550 · outbound

This paper cites Automatic thresholding for defect detection,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Automatic thresholding for defect detection,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.751742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 69ec4f5f-4f7f-4e73-b632-4bcafc49c548 · outbound

This paper cites Novel image processing method inspired by wavelet transform,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Novel image processing method inspired by wavelet transform,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.728157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e579b840-c20b-4e0f-a13c-48be1e06ce80 · outbound

This paper cites A comprehensive survey on support vector machine classification: Applications, challenges and trends,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review A comprehensive survey on support vector machine classification: Applications, challenges and trends,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.707467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7e1bdad4-60c5-4d07-9ad0-b815e545dfd6 · outbound

This paper cites A brief review of nearest neighbor algorithm for learning and classification,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review A brief review of nearest neighbor algorithm for learning and classification,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.681580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c2527ecc-635a-455a-922a-4f409ee94cc3 · outbound

This paper cites Naive bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation indika,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Naive bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation indika,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.647208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e6c643e1-82b0-470f-a1b4-433a0e80bbd3 · outbound

This paper cites Decision tree algorithm in machine learning,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Decision tree algorithm in machine learning,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.620583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 109d536c-51ec-46c9-8fe7-a36f1349b885 · outbound

This paper cites Random Forests,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Random Forests,

Reference 76

Resolution: malformed identifier; no resolver link, observed 2026-08-10T18:27:39.297022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba701d78-bbf3-482d-93e0-517b92812316 · outbound

This paper cites Convolutional neural networks: an overview and application in radiology,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Convolutional neural networks: an overview and application in radiology,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.597185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 75862c99-f1b7-4bb8-8fe3-54b15899f665 · outbound

This paper cites A survey of defect detection applications based on generative adversarial networks,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review A survey of defect detection applications based on generative adversarial networks,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.572298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 041e272c-e502-49b9-91d2-f6541c87c25d · outbound

This paper cites You only look once: Unified, real-time object detection,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review You only look once: Unified, real-time object detection,

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-10T18:27:39.322306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 165b963e-6a57-453e-b4f1-ba019bf6d3df · outbound

This paper cites A review of yolo algorithm developments,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review A review of yolo algorithm developments,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.534646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4bc394e5-99f1-436f-b6c7-295e580ac170 · outbound

This paper cites Research on real-time detection system of rail surface defects based on deep learning,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Research on real-time detection system of rail surface defects based on deep learning,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.495435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5df8bca2-5666-4fa4-9d5d-edd3e425b645 · outbound

This paper cites Casdd: Automatic surface defect detection using a complementary adversarial network,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Casdd: Automatic surface defect detection using a complementary adversarial network,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.474422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation de587a5e-18e0-4a24-b27d-f574da1a9305 · outbound

This paper cites Deep residual learning for image recognition,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Deep residual learning for image recognition,

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-10T18:27:39.358574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27d663fa-6259-4c21-ac75-87d82f46e6d7 · outbound

This paper cites Learning from Few Examples: A Summary of Approaches to Few-Shot Learning.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Learning from Few Examples: A Summary of Approaches to Few-Shot Learning

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-10T18:27:39.368250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15d8e9ba-5b03-456d-8296-6c82dd66eeb9 · outbound

This paper cites Few-shot steel surface defect detection,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Few-shot steel surface defect detection,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.391640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3239c086-810a-49f8-b394-ffe643365571 · outbound

This paper cites Winclip: Zero-/few-shot anomaly classification and segmentation,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Winclip: Zero-/few-shot anomaly classification and segmentation,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.369943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8ecbac01-f8b6-4760-bd69-06d5b5c22222 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Learning Transferable Visual Models From Natural Language Supervision

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-10T18:27:39.393856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4dbba1a-91f0-46f0-a54c-f6bfc0997921 · outbound

This paper cites Towards total recall in industrial anomaly detection,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Towards total recall in industrial anomaly detection,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.349692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0810ac7e-c9a5-47c3-b162-53e60d0e959d · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Distilling the Knowledge in a Neural Network

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-10T18:27:39.408904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a5b6239-316f-4c03-b9dc-e4ea6be5beec · outbound

This paper cites Teacher-student architecture for knowledge distillation: A survey,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Teacher-student architecture for knowledge distillation: A survey,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.330827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2ed9b4bc-de49-4742-9569-fa7ae2244d1b · outbound

This paper cites Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.313752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 600f7153-0633-405d-b6e4-9968154c97c4 · outbound

This paper cites Softpatch: Unsupervised anomaly detection with noisy data,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Softpatch: Unsupervised anomaly detection with noisy data,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.297140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f980bca1-4d93-46b2-aee2-03a5d8266091 · outbound

This paper cites Automatic industry pcb board dip process defect detection system based on deep ensemble self-adaption method,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Automatic industry pcb board dip process defect detection system based on deep ensemble self-adaption method,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.272748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0234fb84-fdd8-41b0-80c8-6f9d63199d5e · outbound

This paper cites Augmented hybrid learning for visual defect inspection in real-world hydrogen storage manufacturing scenarios,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Augmented hybrid learning for visual defect inspection in real-world hydrogen storage manufacturing scenarios,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.251620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4cbd67bb-0478-429c-bdcb-6ddbc8515d8d · outbound

This paper cites Cannizzaro, A.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Cannizzaro, A

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.229176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 400fb1a1-675c-462d-8692-ad8dd806b652 · outbound

This paper cites Segmentation-based deep-learning approach for surface-defect detection,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Segmentation-based deep-learning approach for surface-defect detection,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.210093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f98555ed-1540-41b7-9ef4-97ce2815466f · outbound

This paper cites Towards real-time in-situ monitoring of hot-spot defects in l-pbf: A new classification-based method for fast video-imaging data analysis,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Towards real-time in-situ monitoring of hot-spot defects in l-pbf: A new classification-based method for fast video-imaging data analysis,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.190419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 44c2e485-5683-4ce5-a3bd-dddab248493f · outbound

This paper cites One class based feature learning approach for defect detection using deep autoencoders,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review One class based feature learning approach for defect detection using deep autoencoders,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.158979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 07fc71b7-e31e-432b-a1f2-fffb5b9aa9ad · outbound

This paper cites Vision based defects detection for keyhole tig welding using deep learning with visual explanation,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Vision based defects detection for keyhole tig welding using deep learning with visual explanation,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.135008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d815e55f-5c69-40af-81b1-e3e53135fb91 · outbound

This paper cites Automated inspection in robotic additive manufacturing using deep learning for layer deformation detection,.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review Automated inspection in robotic additive manufacturing using deep learning for layer deformation detection,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:27:41.112831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers