YOLO11. by Junyan Wang (4738518)

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SCI-YOLO11. by Junyan Wang (4738518)

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Structure of the SCI-YOLO11 network. by Junyan Wang (4738518)

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Significant inter-relationships between baseline variables and MSCC or MCS. by Harshit Arora (22008280)

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Inter-assay parameters correlations. by Ekaterina M. Koltsova (5464523)

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The inter-regional Gini coefficient in CCD of HER and DIC. by Ying Xie (50016)

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Differences in inter-regional synergy of HER and DIC in China. by Ying Xie (50016)

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Data_Sheet_1_Physiological synchrony in electrodermal activity predicts decreased vigilant attention induced by sleep deprivation.docx by Ivo V. Stuldreher (11897870)

Intra- and inter-day precision and accuracy. by Ewa Paszkowska (21246702)

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Shuffling identity decreases behavior trajectory uniqueness. by Catalin Mitelut (5587493)

The network structure of FasterNet--YOLO. by Shiwei Yu (6060308)

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Multiplication rate of J774 and RAW cells in relation to the intracellular growth of ITMAP-263 (not showing significant intracellular multiplication in PECs) and LEM3049 (showing r... by Sarah Hendrickx (321143)

Inter-operator reliability^b'*'. by Lotte van Dam (17347733)

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Detection effect diagram of YOLO11. by Jianming Meng (22209039)

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Trends in inter-provincial agricultural carbon emission Dagum Gini coefficient rankings, 2010–2020. by Lingzhi Tan (19146572)

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PR diagram of YOLOv8n and BGS-YOLO: (a) YOLOv8n and (b) BGS-YOLO. by Zeyu Liang (4813593)

The architecture of the proposed BGM-YOLO model. by Chenghai Yu (21440036)

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Overall network architecture of FCMI-YOLO. by Junjie Lu (160350)

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Structure of UAS-YOLO network. by Ju Liang (4277062)

“…These characteristics impose high demands on detection algorithms in terms of fine-grained feature extraction, cross-scale fusion capability, and occlusion resistance.The YOLOv11s model has significant limitations in practical applications: its feature extraction module has a single semantic representation, the traditional feature pyramid network has limited capability to detect multi-scale targets, and it lacks an effective feature compensation mechanism when targets are occluded.To address these issues, we propose a UAV aerial small target detection algorithm named UAS-YOLO (Universal Inverted Bottleneck with Adaptive BiFPN and Separated and Enhancement Attention module YOLO), which incorporates three key optimizations. …”

Comparison of YOLO-DSTD model with other models. by Jianming Meng (22209039)

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