Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:55:50.511271Z
Paper Citation Record · LEDGER
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study
As of 20 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2505.11424.
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 2026-08-20T06:33:59.587034+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
48 of 48 outbound references displayed
- verified exact0
- verified fuzzy41
- unresolved7
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation af8eaeda-343d-4f73-b919-ea2573dc1fbb · outbound
This paper cites Moving object detection based on enhanced YOLO -V2 model.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Moving object detection based on enhanced YOLO -V2 model
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.409950Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation cfe20bf3-a6b6-4946-9faf-bef9a6309f59 · outbound
This paper cites Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.395199Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 961cd562-bb3a-4c9e-9a29-77d773dea082 · outbound
This paper cites From moving objects detection to classification and recognition: A review for smart environments.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study From moving objects detection to classification and recognition: A review for smart environments
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.379619Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0afd017d-21e6-4dd8-a36d-5ef971f62d08 · outbound
This paper cites Semantic analysis of moving objects in video sequences.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Semantic analysis of moving objects in video sequences
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.364393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 823a0126-558d-459c-83b5-e7cd557cffa9 · outbound
This paper cites An overview of GAN -DeepFakes detection: Proposal improvement and evaluation.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study An overview of GAN -DeepFakes detection: Proposal improvement and evaluation
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.348354Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e0465e77-b364-4f0b-8dab-d24300df1b06 · outbound
This paper cites Fer -YOLO: Detection and classification based on facial expressions.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Fer -YOLO: Detection and classification based on facial expressions
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.332296Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 58850aa0-4671-48fe-89c9-52e3b6035de1 · outbound
This paper cites A review and comparative study on probabilistic object detection in autonomous driving.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study A review and comparative study on probabilistic object detection in autonomous driving
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.203275Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2630e588-d794-4275-9bc3-89fd47c41deb · outbound
This paper cites Recent advances in small object detection based on deep learning: A review.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Recent advances in small object detection based on deep learning: A review
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.186658Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation ec5eaa9a-c73d-4575-bc05-75be41348908 · outbound
This paper cites Exudate regeneration for automated exudate detection in retinal fundus images.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Exudate regeneration for automated exudate detection in retinal fundus images
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.170412Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 8859e495-2e78-48cc-8b08-a0141b831dbc · outbound
This paper cites Deployment of AI -based RBF network for photovoltaics fault detection procedure.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Deployment of AI -based RBF network for photovoltaics fault detection procedure
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.153515Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a3ca556b-3ded-40c6-b32e-dae44c7f7516 · outbound
This paper cites Automated surface defect detection framework using machine vision and convolutional neural networks.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Automated surface defect detection framework using machine vision and convolutional neural networks
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.138344Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 1a5009a4-a532-4c2b-b2bb-311a09504600 · outbound
This paper cites A review of machine learning for the optimization of production processes.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study A review of machine learning for the optimization of production processes
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.122984Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 51449329-15b2-4b55-a8b6-e7c7f73f034c · outbound
This paper cites Deep learning for smart manufacturing: Methods and applications.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Deep learning for smart manufacturing: Methods and applications
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.108240Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d5347c49-8707-4f04-bde3-ce22e9e5b706 · outbound
This paper cites Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.092969Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7b5d68f6-c140-4cda-86f3-f17edb4e9541 · outbound
This paper cites Experiments with neural net object detection system YOLO on small training datasets for intelligent robotics.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Experiments with neural net object detection system YOLO on small training datasets for intelligent robotics
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.076967Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d7115bbb-a7b1-4c18-846f-8126c8fc8b11 · outbound
This paper cites Using deep learning to detect defects in manufacturing: A comprehensive survey and current challenges.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Using deep learning to detect defects in manufacturing: A comprehensive survey and current challenges
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.062151Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 411aabdb-9c75-4510-9598-1904bd06d86f · outbound
This paper cites Optimizing the trade -off between single -stage and two -stage deep object detectors using image difficulty prediction.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Optimizing the trade -off between single -stage and two -stage deep object detectors using image difficulty prediction
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.047376Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation cbaf0ebb-8f97-445a-a736-19a23261dbd0 · outbound
This paper cites Overview of two -stage object detection algorithms.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Overview of two -stage object detection algorithms
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.032898Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a32c09c9-dffb-4534-a6c8-4a32d610043e · outbound
This paper cites A review of object detection models based on convolutional neural network.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study A review of object detection models based on convolutional neural network
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.016343Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 12886163-686c-4f40-9408-9474e09e6326 · outbound
This paper cites YOLO: A Brief History; 2023.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study YOLO: A Brief History; 2023
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:51.000425Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation cefaf406-16b4-4e5f-8d28-7db6c336e913 · outbound
This paper cites YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:55:50.365602Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b417712b-70e7-4c98-ab0e-ceff16668db1 · outbound
This paper cites Real-time visual detection and tracking system for traffic monitoring.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Real-time visual detection and tracking system for traffic monitoring
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.984728Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 51488679-6869-4901-9df1-2b8bfe5b1582 · outbound
This paper cites A background subtraction algorithm for detecting and tracking vehicles.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study A background subtraction algorithm for detecting and tracking vehicles
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.969705Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e742347e-94ab-45ab-ae5f-064e612b34f1 · outbound
This paper cites Moving vehicle detection by optimal segmentation of the dynamic stixel world.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Moving vehicle detection by optimal segmentation of the dynamic stixel world
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.954329Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e27e80c9-ec8b-4819-911f-3c50a5aeb753 · outbound
This paper cites Faster R -CNN: Towards real -time object detection with region proposal networks.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Faster R -CNN: Towards real -time object detection with region proposal networks
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.939078Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation c4e20317-fe33-467a-88af-0f89b818306d · outbound
This paper cites Ssd: Single shot multibox detector.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Ssd: Single shot multibox detector
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.924016Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6863baca-6dbd-4a57-b0b4-6cb726bfe95f · outbound
This paper cites Deep residual learning for image recognition.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Deep residual learning for image recognition
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.908316Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation c6aae737-f421-40b2-92a8-c3e28bd360cc · outbound
This paper cites BoltVision: A Comparative Analysis of CNN, CCT, and ViT in Achieving High Accuracy for Missing Bolt Classification in Train Components.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study BoltVision: A Comparative Analysis of CNN, CCT, and ViT in Achieving High Accuracy for Missing Bolt Classification in Train Components
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.893496Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 228c3d21-3782-4c31-8927-f09ce98aec1a · outbound
This paper cites Lightweight Convolutional Network with Integrated Attention Mechanism for Missing Bolt Detection in Railways.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Lightweight Convolutional Network with Integrated Attention Mechanism for Missing Bolt Detection in Railways
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.876688Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 88e5b49e-b038-4f48-b8a1-f296a046cf22 · outbound
This paper cites Attention-Based Automated Pallet Racking Damage Detection.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Attention-Based Automated Pallet Racking Damage Detection
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.858588Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b91bad79-8cbf-42d3-b14d-878393eabe00 · outbound
This paper cites YOLO -v5 Variant Selection Algorithm Coupled with Representative Augmentations for Modelling Production-Based Variance in Automated Lightweight Pallet Racking Inspection.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study YOLO -v5 Variant Selection Algorithm Coupled with Representative Augmentations for Modelling Production-Based Variance in Automated Lightweight Pallet Racking Inspection
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.841156Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 348ed89c-55e0-4cf6-b8bc-8ab93844fede · outbound
This paper cites Lightweight convolutional network for automated photovoltaic defect detection.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Lightweight convolutional network for automated photovoltaic defect detection
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.825072Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a012958d-07ee-46f4-8cc8-9ad457d35a07 · outbound
This paper cites State-of-the-Art Bangla Handwritten Character Recognition Using a Modified Resnet-34 Architecture.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study State-of-the-Art Bangla Handwritten Character Recognition Using a Modified Resnet-34 Architecture
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.810188Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2bceb9b7-b537-42ea-ab86-a9cc6a0dbfe4 · outbound
This paper cites An improved YOLOv2 for vehicle detection.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study An improved YOLOv2 for vehicle detection
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.795884Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 8ef8b4b2-26e1-4dbb-8d53-07f245df9678 · outbound
This paper cites The real -time detection of traffic participants using YOLO algorithm.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study The real -time detection of traffic participants using YOLO algorithm
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.780724Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 38a810ed-3d62-4b08-8142-ba3150079be5 · outbound
This paper cites A video streaming vehicle detection algorithm based on YOLOv4.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study A video streaming vehicle detection algorithm based on YOLOv4
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.765289Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7e4209a2-c008-4fe5-9d32-a746eddabf2f · outbound
This paper cites Detecting heavy goods vehicles in rest areas in winter conditions using YOLOv5.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Detecting heavy goods vehicles in rest areas in winter conditions using YOLOv5
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.748737Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 73d53d93-806a-4d3e-a7f9-6e4c9d789b6a · outbound
This paper cites an unresolved cited work.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Unresolved cited work
Reference 38
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T20:55:50.733187Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b6184ebb-cf94-42fc-a39e-10ed99bcdb56 · outbound
This paper cites A., Negaresh, M., Abdollahi, J., Mohammadi, M., Ghobadi, H., Mohammadzadeh, B., & Amani, F.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study A., Negaresh, M., Abdollahi, J., Mohammadi, M., Ghobadi, H., Mohammadzadeh, B., & Amani, F
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.718351Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7a3595e8-f15c-4bfb-a9e7-082c940006ef · outbound
This paper cites (2024, February).
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study (2024, February)
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.702722Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7b438693-2d8a-40d8-90f2-684f27097755 · outbound
This paper cites an unresolved cited work.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Unresolved cited work
Reference 41
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T20:55:50.687803Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e8168835-507f-424e-bdb4-41533cd422eb · outbound
This paper cites (2024, February).
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study (2024, February)
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.670481Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e1498d4d-a90b-4d0d-83c1-4a3b3f6793b9 · outbound
This paper cites (2024, February).
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study (2024, February)
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.651198Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d2cda0aa-0489-46f5-8ef2-f7feb78a8d0a · outbound
This paper cites I., & Naji, H.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study I., & Naji, H
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:55:50.635398Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2eefd427-e6c3-4f7d-bfdc-f83509e8d526 · outbound
This paper cites an unresolved cited work.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Unresolved cited work
Reference 45
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T20:55:50.619048Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation ad2c2cab-c8c0-400a-bf24-7a84d7aadf68 · outbound
This paper cites Optimizing RPL Routing Using Tabu Search to Improve Link Stability and Energy Consumption in IoT Networks.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Optimizing RPL Routing Using Tabu Search to Improve Link Stability and Energy Consumption in IoT Networks
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:55:50.501717Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7f22b865-03d1-4c10-8f40-e737f3ff45cc · outbound
This paper cites an unresolved cited work.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Unresolved cited work
Reference 47
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T20:55:50.602632Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 4c7a6c2e-b06e-452f-bd37-0f7484324a10 · outbound
This paper cites an unresolved cited work.
Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study Unresolved cited work
Reference 48
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T20:55:50.586846Z
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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
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