Backbone comparison in crack detection. by Wenhao Ren (2561731)

“…<div><p>Developing an efficient and accurate algorithm for detecting building cracks, especially micro-cracks, is essential for ensuring structural integrity and safety. …”

RealBench: A Repo-Level Code Generation Benchmark Aligned with Real-World Software Development Practices by RealBench RealBench (22275393)

“…<br>│ └── level4/ # Level 4 projects (expert complexity).<br>├── src/ # Main source code for the RealBench framework.…”

The eleven ‘best’ candidate OBIA algorithms used in this study to identify beluga whale locations. by John Iacozza (5787789)

Comparative classification performance using VGG, ResNet, and ViT models, with each model applied in two different training approaches on segmented cells: trained from scratch and... by Oriana Presacan (22232246)

Data Sheet 1_Assisting human annotation of marine images with foundation models.pdf by Eric C. Orenstein (21776879)

“…Taking inspiration from large language models, computer vision researchers are now thinking in terms of “foundation models” that can yield reasonable zero- and few-shot detection and segmentation performance with human prompting. …”

Mean values of different benchmark functions. by Jalal- ud-Din (21474307)

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Detail of benchmark functions comparison. by Jalal- ud-Din (21474307)

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Average execution time (in seconds) of different benchmark functions. by Jalal- ud-Din (21474307)

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Stander deviation values of different benchmark functions. by Jalal- ud-Din (21474307)

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Comparison of different crossover operators. by Jalal- ud-Din (21474307)

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Comparison of segmentation outcomes between pre-trained and fine-tuned MedSAM, DeepLabV3+, and U-Net++ models applied to cropped cell images using bounding boxes. by Oriana Presacan (22232246)

(a) Microscopic Image of a sample MP in the glass slide; (b) Image Segmented into 3 Clusters and the general shape of MP is highlighted in Yellow (Please refer to the Online Versio... by Claudia Barile (20678698)

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Hair removal preprocessing using the DullRazor algorithm before image generation. by Mujung Kim (22367223)

<b>Codes for "Fatigue analysis of wind turbine and load reduction through wind-farm-level yaw control"</b> by Yize Wang (19535173)

“…<p dir="ltr">The farm-level yaw control optimization algorithm Codes for "Fatigue analysis of wind turbine and load reduction through wind-farm-level yaw control"</p>…”

Accuracy of different electrical and optical neural networks for various input noise levels. by Reyhane Ahmadi (14857363)

Temperature measurement at the wire tip during TSE MRI using the standard bases (“Channel coordinate”), low-SAR bases and safe-mode PTX MRI with channel settings determined in the... by Maryam Arianpouya (12299222)

Data Sheet 3_VanillaNet-YOLOv8 segment: detection of nano-iron oxide regulation on rice seedling growth vitality under salt stress.zip by Weihang Jiang (22261666)

“…The image quality was enhanced through preprocessing with a super-resolution algorithm (SSN). In order to address the challenges associated with detecting rice seedlings, which are characterised by their diminutive size and dense growth patterns, the YOLOv8-seg model has been enhanced. …”

Data Sheet 5_VanillaNet-YOLOv8 segment: detection of nano-iron oxide regulation on rice seedling growth vitality under salt stress.zip by Weihang Jiang (22261666)

“…The image quality was enhanced through preprocessing with a super-resolution algorithm (SSN). In order to address the challenges associated with detecting rice seedlings, which are characterised by their diminutive size and dense growth patterns, the YOLOv8-seg model has been enhanced. …”

Data Sheet 1_VanillaNet-YOLOv8 segment: detection of nano-iron oxide regulation on rice seedling growth vitality under salt stress.zip by Weihang Jiang (22261666)

“…The image quality was enhanced through preprocessing with a super-resolution algorithm (SSN). In order to address the challenges associated with detecting rice seedlings, which are characterised by their diminutive size and dense growth patterns, the YOLOv8-seg model has been enhanced. …”

Data Sheet 4_VanillaNet-YOLOv8 segment: detection of nano-iron oxide regulation on rice seedling growth vitality under salt stress.zip by Weihang Jiang (22261666)

“…The image quality was enhanced through preprocessing with a super-resolution algorithm (SSN). In order to address the challenges associated with detecting rice seedlings, which are characterised by their diminutive size and dense growth patterns, the YOLOv8-seg model has been enhanced. …”