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Background/Objectives: Accurate detection and classification of blood cell types in microscopic images are crucial for diagnosing various hematological conditions. This study aims to develop and evaluate advanced architectures for automating blood cell detection and classification using the newly proposed YOLOv10 and YOLOv11 models, with a specific focus on identifying red blood cells (RBCs), white blood cells (WBCs), and platelets in microscopic images as a preliminary step of the complete blood count (CBC). Methods: The Blood Cell Count Detection (BCCD) dataset was enriched using data augmentation techniques to improve model robustness and diversity. Extensive experiments were performed, including complete weight initialization, advanced optimization strategies, and meticulous hyperparameter tuning for the YOLOv11 architecture. Results: The YOLOv11-l model achieved an overall mean Average Precision (mAP) of 93.8%, reflecting its robust accuracy across multiple blood cell types. Conclusions: The findings underscore the efficacy of the YOLOv11 architecture in automating blood cell classification with high precision, demonstrating its potential to enhance hematological analyses and support clinical diagnosis. [ABSTRACT FROM AUTHOR]

Mastering SQL programming skills is fundamental in computer science education, and Online Judging Systems (OJS) play a critical role in automatically assessing SQL codes, improving the accuracy and efficiency of evaluations. However, these systems are vulnerable to manipulation by students who can submit "cheating codes" that pass the tests without genuinely solving programming problems or demonstrating authentic SQL skills. This study analyzed over 5.8 million SQL codes validated by OJS and identified four types of cheating codes: Explicit Result Output, Quantitative Output Manipulation, Data-Observed Clause Manipulation, and DML-Driven Test Case Distortion. The initial experiment treated SQL codes as plain text using the Bag of Words vector model and processed them with six machine learning models to detect cheating. The results showed an average recall of 74.73% and precision of 97.10%, confirming the efficacy of automated detection. In the subsequent experiments, the first of these used 12 syntactic and semantic features of SQL codes, achieving a recall rate of 59.55% and precision of 87.26%. The final experiment added two more characteristic features of cheating codes to these models, significantly improving recall to 89.35% and precision to 95.25%. This highlights the importance of characteristic cheating features in identifying cheating codes. The study's findings deepen our understanding of cheating codes and contribute to enhancing online programming education and assessment quality. [ABSTRACT FROM AUTHOR]

The increasing availability and resolution of LiDAR data are revolutionizing landscape archaeology, enabling unprecedented large-scale studies. However, the time-intensive nature of manual analysis has posed significant challenges. This research tackles the complexities of large-scale archaeological site detection by integrating Artificial Intelligence (AI) with LiDAR data, focusing on hillforts across diverse European landscapes. A semi-automated workflow employing Convolutional Neural Networks (CNNs) was developed and tested across three regions – England, Hesse (Germany), and Molise (Italy) – covering a total area of 180,000 km2. The methodology utilized the Atlas of Hillforts of Britain and Ireland to train a CNN on LiDAR datasets and tested the model’s transferability to Germany and Italy. Techniques such as pseudo-labelling and fine-tuning addressed the “Model Drift” problem, improving region-specific performance. The AI classifier achieved F1 scores ranging from 34–38%, demonstrating its adaptability to diverse landscapes, including the Mediterranean terrain of Molise and Hesse’s densely forested regions. Case studies identified new potential hillforts in England and promising candidates in Hesse and Molise, underscoring the effectiveness of the approach. While automation significantly reduces manual workload, human verification remains critical for refining AI predictions and addressing false positives. This study also applies different expert validation workflows, emphasizing their efficiency and adaptability to regional differences. By combining automated detection with expert review, the research showcases the potential for scalable, AI-assisted archaeological discovery across diverse landscapes, providing a valuable tool for academic research and heritage management.

The global expansion of shrimp aquaculture has positioned this sector as a significant contributor to seafood supply. Efficient production hinges on effective feeding management. This study presents Python-based computational tools for the acoustic monitoring of white shrimp (Penaeus vannamei) feeding activities. We compare the proposed Python implementation for automatic detection of shrimp feeding sounds with a reference detection method using Raven Pro®, examining various shrimp sizes and stocking densities. Our comparative analysis shows substantial agreement between the two methods, particularly for shrimp weighing more than 1 g across different density groups. Although Raven Pro® is a well-established bioacoustic software tested under laboratory conditions, the Python-based methods proposed here offer practical applications in real farming scenarios and can be integrated into dedicated hardware. These techniques not only enhance feeding practices but also demonstrate the potential of computational tools in advancing aquaculture management. • A Python method for detection of shrimp acoustic feeding activity is presented. • The proposed method was compared with a reference detector of Raven Pro®. • An agreement was found between click detectors across shrimp sizes and densities. • Python-based detector enables computation in resource-limited aquaculture settings. [ABSTRACT FROM AUTHOR]