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Background: Oral diseases such as caries, gingivitis, and periodontitis are highly prevalent worldwide and often arise from plaque. This study focuses on detecting three plaque stages—new, mature, and over-mature—using state-of-the-art YOLO architectures to enhance early intervention and reduce reliance on manual visual assessments. Methods: We compiled a dataset of 531 RGB images from 177 individuals, captured via multiple mobile devices. Each sample was treated with disclosing gel to highlight plaque types, then preprocessed for lighting and color normalization. YOLOv9, YOLOv10, and YOLOv11, in various scales, were trained to detect plaque categories, and their performance was evaluated using precision, recall, and mean Average Precision (mAP@50). Results: Among the tested models, YOLOv11m achieved the highest mAP@50 (0.713), displaying superior detection of over-mature plaque. Across all YOLO variants, older plaque was generally easier to detect than newer plaque, which can blend with gingival tissue. Applying the O'Leary index indicated that over half of the study population exhibited severe plaque levels. Conclusions: Our findings demonstrate the feasibility of automated plaque detection with advanced YOLO models in varied imaging conditions. This approach offers potential to optimize clinical workflows, support early diagnoses, and mitigate oral health burdens in low-resource communities. [ABSTRACT FROM AUTHOR]

This study presents a comparative analysis of YOLO detection models for the accurate identification of bean leaf diseases caused by Coleoptera pests in natural environments. By using a manually collected dataset of healthy and infected bean leaves in natural conditions, we labeled at the leaf level and evaluated the performance of the YOLOv5, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 models. Mean average precision (mAP) was used to assess the performance of the models. Among these, YOLOv9e exhibited the best performance, effectively balancing precision and recall for datasets with limited size and variability. In addition, we integrated the Sophia optimizer and PolyLoss function into YOLOv9e and enhanced it, providing even more accurate detection results. This paper highlights the potential of advanced deep learning models, optimized with second-order optimizers and custom loss functions, in improving pest detection, crop management, and overall agricultural yield. [ABSTRACT FROM AUTHOR]

This review explores the use of machine learning (ML) techniques for detecting pests and diseases in crops, which is a significant challenge in agriculture, leading to substantial yield losses worldwide. This study focuses on the integration of ML models, particularly Convolutional Neural Networks (CNNs), which have shown promise in accurately identifying and classifying plant diseases from images. By analyzing studies published from 2019 to 2024, this work summarizes the common methodologies involving stages of data acquisition, preprocessing, segmentation, feature extraction, and prediction to develop robust ML models. The findings indicate that the incorporation of advanced image processing and ML algorithms significantly enhances disease detection capabilities, leading to the early and precise diagnosis of crop ailments. This can not only improve crop yield and quality but also reduce the dependency on chemical pesticides, contributing to more sustainable agricultural practices. Future research should focus on enhancing the robustness of these models to varying environmental conditions and expanding the datasets to include a wider variety of crops and diseases. CNN-based models, particularly specialized architectures like ResNet, are the most widely used in the studies reviewed, making up 42.36% of all models, with ResNet alone contributing 7.65%. This highlights ResNet's appeal for tasks that demand deep architectures and sophisticated feature extraction. Additionally, SVM models account for 9.41% of the models examined. The prominence of both ResNet and MobileNet reflects a trend toward architectures with residual connections for deeper networks, alongside efficiency-focused designs like MobileNet, which are well-suited for mobile and edge applications. [ABSTRACT FROM AUTHOR]

In traditional farming, fertilizers are often used without precision, resulting in unnecessary expenses and potential damage to the environment. This study introduces a new method for accurately identifying macronutrient deficiencies in Rhodena lettuce crops. We have developed a four-stage process. First, we gathered two sets of data for lettuce seedlings: one is composed of color images and the other of point clouds. In the second stage, we employed the interactive closest point (ICP) method to align the point clouds and extract 3D morphology features for detecting nitrogen deficiencies using machine learning techniques. Next, we trained and compared multiple detection models to identify potassium deficiencies. Finally, we compared the outcomes with traditional lab tests and expert analysis. Our results show that the decision tree classifier achieved 90.87% accuracy in detecting nitrogen deficiencies, while YOLOv9c attained an mAP of 0.79 for identifying potassium deficiencies. This innovative approach has the potential to transform how we monitor and manage crop nutrition in agriculture. [ABSTRACT FROM AUTHOR]

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The automotive industry has recently adopted multicore processors and microcontrollers to meet the requirements of new features, such as autonomous driving, and comply with the latest safety standards. However, inter-core communication poses a challenge in ensuring real-time requirements such as time determinism and low latencies. Concurrent access to shared buffers makes predicting the flow of data difficult, leading to decreased algorithm performance. This study explores the integration of Logical Execution Time (LET) and Time-Division Multiple Access (TDMA) models in multicore embedded systems to address the challenges in inter-core communication by synchronizing read/write operations across different cores, significantly reducing latency variability and improving system predictability and consistency. Experimental results demonstrate that this integrated approach eliminates data loss and maintains fixed operation rates, achieving a consistent latency of 11 ms. The LET-TDMA method reduces latency variability to approximately 1 ms, maintaining a maximum delay of 1.002 ms and a minimum delay of 1.001 ms, compared to the variability in the LET-only method, which ranged from 3.2846 ms to 8.9257 ms for different configurations. [ABSTRACT FROM AUTHOR]

With new high-performance server technology in data centers and bunkers, optimizing search engines to process time and resource consumption efficiently is necessary. The database query system, upheld by the standard SQL language, has maintained the same functional design since the advent of PL/SQL. This situation is caused by recent research focused on computer resource management, encryption, and security rather than improving data mining based on AI tools, machine learning (ML), and artificial neural networks (ANNs). This work presents a projected methodology integrating a multilayer perceptron (MLP) with Kmeans. This methodology is compared with traditional PL/SQL tools and aims to improve the database response time while outlining future advantages for ML and Kmeans in data processing. We propose a new corollary: h k → H = S S E (C) , w h e r e k > 0 a n d ∃ X , executed on application software querying data collections with more than 306 thousand records. This study produced a comparative table between PL/SQL and MLP-Kmeans based on three hypotheses: line query, group query, and total query. The results show that line query increased to 9 ms, group query increased from 88 to 2460 ms, and total query from 13 to 279 ms. Testing one methodology against the other not only shows the incremental fatigue and time consumption that training brings to database query but also that the complexity of the use of a neural network is capable of producing more precision results than the simple use of PL/SQL instructions, and this will be more important in the future for domain-specific problems. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to examine the implementation of active methodologies in the teaching–learning process in computer science. To achieve this objective, a systematic review using the PRISMA method was performed; the search for articles was conducted through the Scopus and Web of Science databases and the scientific search engine Google Scholar. By establishing inclusion and exclusion criteria, 15 research papers were selected addressing the use of various active methodologies which have had a positive impact on students' learning processes. Among the principal active methodologies highlighted are problem-based learning, flipped classrooms, and gamification. The results of the review show how active methodologies promote significant learning, in addition to fostering more outstanding commitment, participation, and motivation on the students' part. It was observed that active methodologies contribute to the development of fundamental cognitive and socio-emotional skills for their professional growth. [ABSTRACT FROM AUTHOR]

The purpose of this research is to analyze the acquisition of teachers' digital competencies through the use of virtual environments and information and communication technologies to improve the teaching-learning process. A systematic review was conducted through the search and compilation of empirical studies over five years (2019 to 2023), in the specialized databases: Scopus, Redalyc, SciELO, and the academic search engine Google Scholar. Based on the PRISMA statement, and through inclusion and exclusion criteria, a total of fourteen research papers were selected. After examining them, it was possible to identify how digital literacy through the use of virtual learning environments influences the development of teaching work and performance and consequently also contributes to student learning. Among the digital skills most used by teachers that have allowed innovation and improvement of the teaching process are mainly the management of information and mastery of digital tools for the creation and management of educational resources. [ABSTRACT FROM AUTHOR]

This paper introduces a novel background subtraction method that utilizes texture-level analysis based on the Gabor filter bank and statistical moments. The method addresses the challenge of accurately detecting moving objects that exhibit similar color intensity variability or texture to the surrounding environment, which conventional methods struggle to handle effectively. The proposed method accurately distinguishes between foreground and background objects by capturing different frequency components using the Gabor filter bank and quantifying the texture level through statistical moments. Extensive experimental evaluations use datasets featuring varying lighting conditions, uniform and non-uniform textures, shadows, and dynamic backgrounds. The performance of the proposed method is compared against other existing methods using metrics such as sensitivity, specificity, and false positive rate. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and robustness. It effectively handles scenarios with complex backgrounds, lighting changes, and objects that exhibit similar texture or color intensity as the background. Our method retains object structure while minimizing false detections and noise. This paper provides valuable insights into computer vision and object detection, offering a promising solution for accurate foreground detection in various applications such as video surveillance and motion tracking. [ABSTRACT FROM AUTHOR]

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO's evolution, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with transformers. We start by describing the standard metrics and postprocessing; then, we discuss the major changes in network architecture and training tricks for each model. Finally, we summarize the essential lessons from YOLO's development and provide a perspective on its future, highlighting potential research directions to enhance real-time object detection systems. [ABSTRACT FROM AUTHOR]

This work aims to offer an analysis of empirical research on the automatic learning methods used in detecting microRNA (miRNA) as potential markers of breast cancer. To carry out this study, we consulted the sources of Google Scholar, IEEE, PubMed, and Science Direct using appropriate keywords to meet the objective of the research. The selection of interesting articles was carried out using exclusion and inclusion criteria, as well as research questions. The results obtained in the search were 36 articles, of which PubMed = 14, IEEE = 8, Science Direct = 4, Google Scholar = 10; among them, six were selected, since they met the search perspective. In conclusion, we observed that the machine learning methods frequently mentioned in the reviewed studies were Support Vector Machine (SVM) and Random Forest (RF), the latter obtaining the best performance in terms of precision. [ABSTRACT FROM AUTHOR]