Search Results (2,388)

The conventional farming system, which predominates in most countries, is based on the use of agrochemical deep ploughing and other special methods. However, intensive farming has several negative impacts, including soil and water pollution and reduced biodiversity. The microbial community plays a crucial role in maintaining the health of agricultural ecosystems. In this context, we need to study how different agricultural practices affect the structural and functional characteristics of agricultural ecosystems. This study assessed the diversity, structure, and functional characteristics of the soil bacterial community in two different cropping systems. The subjects of the study were soil samples from Chernozem, which had been cultivated using the organic method for 11 years and the conventional method for 20 years. The fields are located in the southern part of the Russian Federation. Our results indicated minimal differences in the microbial diversity and soil community composition between the two systems studied. The profiling of the soil bacterial community revealed differences in the abundances of Proteobacteria, Bacteroidota, and Cyanobacteria, which were predominated in the conventional farming system (CFS), while Methylomirabilota and Fusobacteriota were more abundant in the organic farming system (OFS). Bacterial taxa and functional genes associated with nitrogen, phosphorus, and sulphur cycling were found to be more abundant in CFS soils than in OFS soils. The instrumental measurement of soil metabolic activity and microbial biomass content showed that CFS soils had higher microbiome activity than OFS soils. Overall, the study found that the agronomic practices used in conventional farming not only help to maintain the functional properties of the soil microbiome, but also significantly increase its microbiological activity and nutrient bioconversion, compared to organic farming practices. Full article

A well-educated midwifery workforce is critical to providing quality health services. However, the quality of midwifery education in Nigeria is identified as a factor contributing to the country’s poor maternal and neonatal health outcomes and inability to meet global development goals. This study aimed to analyse the process used to strengthen the quality of midwifery education with the aim of generating a middle-range model to prepare competent and confident midwifery graduates. The Strauss and Corbin version of the Grounded Theory approach that is underpinned by the Social Constructivism Paradigm was adopted for this qualitative study. Strengthening the quality of midwifery education (SQME) emerged as the model’s core phenomenon. Major concepts, including the midwifery education context, nature of the curriculum, SQME process, pillars, and outcomes, supported the core phenomenon. Strengthening the quality of midwifery education can be achieved over a long time provided the pillars of SQME are deep-rooted to sustain the process of strengthening the quality of midwifery education. The model can be used to strengthen the quality of midwifery education and may be adapted to nursing/allied health programmes in Nigeria and other developing countries. Full article

This study investigated the ability of artificial neural networks (ANNs) to resolve the nonlinear dynamics inherent in the behavior of complex fluid flows, which often exhibit multifaceted characteristics that challenge traditional analytical or numerical methods. By employing flow profile pairs that are generated through high-fidelity numerical simulations, encompassing both the one-dimensional benchmark problems and the more intricate three-dimensional boundary layer transition problem, this research convincingly demonstrates that neural networks possess a remarkable capacity to effectively capture the discontinuities and the subtle wave characteristics that occur at small scales within complex fluid flows, thereby showcasing their robustness in handling intricate fluid dynamics phenomena. Furthermore, even in the context of challenging three-dimensional problems, this study reveals that the average velocity profiles can be predicted with a high degree of accuracy, utilizing a limited number of input profiles during the training phase, which underscores the efficiency and efficacy of the model in understanding complex systems. The findings of this study significantly underscore the immense potential that artificial neural networks, along with deep learning methodologies, hold in advancing our comprehension of the fundamental physics that govern complex fluid dynamics systems, while concurrently demonstrating their applicability across a variety of flow scenarios and their capacity to yield insightful revelations regarding the nonlinear relationships that exist among diverse flow parameters, thus paving the way for future research in this critical area of study. Full article

An unmanned aerial vehicle (UAV) swarm has emerged as a powerful tool for mission execution in a variety of applications supported by deep neural networks (DNNs). In the context of UAV swarms, conventional methods for efficient data processing involve transmitting data to cloud and edge servers. However, these methods often face limitations in adapting to real-time applications due to the low latency of cloud-based approaches and weak mobility of edge-based approaches. In this paper, a new system called deep reinforcement learning-based resilient layer distribution (DRL-RLD) for distributed inference is designed to minimize end-to-end latency in UAV swarm, considering the resource constraints of UAVs. The proposed system dynamically allocates CNN layers based on UAV-to-UAV and UAV-to-ground communication links to minimize end-to-end latency. It can also enhance resilience to maintain mission continuity by reallocating layers when inoperable UAVs occur. The performance of the proposed system was verified through simulations in terms of latency compared to the comparison baselines, and its robustness was demonstrated in the presence of inoperable UAVs. Full article

The presence of water accumulation on aircraft surfaces constitutes a considerable hazard to both performance and safety, necessitating vigilant inspection and maintenance protocols. In this study, we introduce an innovative semantic segmentation model, grounded in deep learning principles, for the precise identification and delineation of water accumulation areas within infrared images of aircraft exteriors. Our proposed model harnesses the robust features of ResNet, serving as the foundational architecture for U-Net, thereby augmenting the model’s capacity for comprehensive feature characterization. The incorporation of channel attention mechanisms, spatial attention mechanisms, and depthwise separable convolution further refines the network structure, contributing to enhanced segmentation performance. Through rigorous experimentation, our model surpasses existing benchmarks, yielding a commendable 22.44% reduction in computational effort and a substantial 38.89% reduction in parameter count. The model’s outstanding performance is particularly noteworthy, registering a 92.67% mean intersection over union and a 97.97% mean pixel accuracy. The hallmark of our innovation lies in the model’s efficacy in the precise detection and segmentation of water accumulation areas on aircraft skin. Beyond this, our approach holds promise for addressing analogous challenges in aviation and related domains. The enumeration of specific quantitative outcomes underscores the superior efficacy of our model, rendering it a compelling solution for precise detection and segmentation tasks. The demonstrated reductions in computational effort and parameter count underscore the model’s efficiency, fortifying its relevance in broader contexts. Full article

Central Nervous System (CNS) tumors represent a significant public health concern due to their high morbidity and mortality rates. Magnetic Resonance Imaging (MRI) has emerged as a critical non-invasive modality for the detection, diagnosis, and management of brain tumors, offering high-resolution visualization of anatomical structures. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown potential in augmenting MRI-based diagnostic accuracy for brain tumor detection. In this study, we evaluate the diagnostic performance of six fundamental MRI sequences in detecting tumor-involved brain slices using four distinct CNN architectures enhanced with transfer learning techniques. Our dataset comprises 1646 MRI slices from the examinations of 62 patients, encompassing both tumor-bearing and normal findings. With our approach, we achieved a classification accuracy of 98.6%, underscoring the high potential of CNN-based models in this context. Additionally, we assessed the performance of each MRI sequence across the different CNN models, identifying optimal combinations of MRI modalities and neural networks to meet radiologists’ screening requirements effectively. This study offers critical insights into the integration of deep learning with MRI for brain tumor detection, with implications for improving diagnostic workflows in clinical settings. Full article

Few-shot learning (FSL) is a learning paradigm which aims to address the issue of machine/deep learning techniques which traditionally need huge amounts of labelled data to work out. The remote sensing (RS) community has explored this paradigm with numerous published studies to date. Nevertheless, there is still a need for clear pieces of evidence on FSL-related issues in the RS context, such as which of the inference approaches is more suitable: inductive or transductive? Moreover, how does the number of epochs used during training, based on the meta-training (base) dataset, relate to the number of unseen classes during inference? This study aims to address these and other relevant questions in the context of FSL for scene classification in RS images. A comprehensive evaluation was conducted considering eight FSL approaches (three inductive and five transductive) and six scene classification databases. Some conclusions of this research are as follows: (1) transductive approaches are better than inductive ones. In particular, the transductive technique Transductive Information Maximisation (TIM) presented the best overall performance, where in 20 cases it got the first place; (2) a larger number of training epochs is more beneficial when there are more unseen classes during the inference phase. The most impressive gains occurred particularly considering the AID (6-way) and RESISC-45 (9-way) datasets. Notably, in the AID dataset, a remarkable 58.412% improvement was achieved in 1-shot tasks going from 10 to 200 epochs; (3) using five samples in the support set is statistically significantly better than using only one; and (4) a higher similarity between unseen classes (during inference) and some of the training classes does not lead to an improved performance. These findings can guide RS researchers and practitioners in selecting optimal solutions/strategies for developing their applications demanding few labelled samples. Full article

Artificial intelligence has significant applications in computer vision studies for cultural heritage. In this research field, visual inspection of historical buildings and the digitization of heritage using machine learning models stand out. However, the literature still lacks datasets for the classification and identification of Brazilian religious buildings using deep learning, particularly with images from the historic town of Ouro Preto. It is noteworthy that Ouro Preto was the first Brazilian World Heritage Site recognized by UNESCO in 1980. In this context, this paper aims to address this gap by proposing a new image dataset, termed ImageOP: The Image Dataset with Religious Buildings in the World Heritage Town of Ouro Preto for Deep Learning Classification. This new dataset comprises 1613 images of facades from 32 religious monuments in the historic town of Ouro Preto, categorized into five classes: fronton (pediment), door, window, tower, and church. The experiments to validate the ImageOP dataset were conducted in two stages: simulations and computer vision using smartphones. Furthermore, two deep learning structures (MobileNet V2 and EfficientNet B0) were evaluated using Edge Impulse software. MobileNet V2 and EfficientNet B0 are architectures of convolutional neural networks designed for computer vision applications aiming at low computational cost, real-time classification on mobile devices. The results indicated that the models utilizing EfficientNet achieved the best outcomes in the simulations, with accuracy = $94.5\%$, precision = $96.0\%$, recall = $96.0\%$, and F-score = $96.0\%$. Additionally, superior accuracy values were obtained in detecting the five classes: fronton ($96.4\%$), church ($97.1\%$), window ($89.2\%$), door ($94.7\%$), and tower ($95.4\%$). The results from the experiments with computer vision and smartphones reinforced the effectiveness of the proposed dataset, showing an average accuracy of $88.0\%$ in detecting building elements across nine religious monuments tested for real-time mobile device application. The dataset is available in the Mendeley Data repository. Full article

Background: Cephalometric analysis is important in diagnosing and planning treatments for patients, traditionally relying on 2D cephalometric radiographs. With advancements in 3D imaging, automated landmark detection using deep learning has gained prominence. However, 3D imaging introduces challenges due to increased network complexity and computational demands. This study proposes a multi-scale 3D CNN-based approach utilizing direct regression to improve the accuracy of maxillofacial landmark detection. Methods: The method employs a coarse-to-fine framework, first identifying landmarks in a global context and then refining their positions using localized 3D patches. A clinical dataset of 150 CT scans from maxillofacial surgery patients, annotated with 30 anatomical landmarks, was used for training and evaluation. Results: The proposed method achieved an average RMSE of 2.238 mm, outperforming conventional 3D CNN architectures. The approach demonstrated consistent detection without failure cases. Conclusions: Our multi-scale-based 3D CNN framework provides a reliable method for automated landmark detection in maxillofacial CT images, showing potential for other clinical applications. Full article

Deep learning-based autoencoders represent a promising technology for use in network-based attack detection systems. They offer significant benefits in managing unknown network traces or novel attack signatures. Specifically, in the context of critical infrastructures, such as power supply systems, AI-based intrusion detection systems must meet stringent requirements concerning model accuracy and trustworthiness. For the intrusion response, the activation of suitable countermeasures can greatly benefit from additional transparency information (e.g., attack causes). Transformers represent the state of the art for learning from sequential data and provide important model insights through the widespread use of attention mechanisms. This paper introduces a two-stage transformer-based autoencoder for learning meaningful information from network traffic at the packet and sequence level. Based on this, we present a sequential attention weight perturbation method to explain benign and malicious network packets. We evaluate our method against benchmark models and expert-based explanations using the CIC-IDS-2017 benchmark dataset. The results show promising results in terms of detecting and explaining FTP and SSH brute-force attacks, highly outperforming the results of the benchmark model. Full article

Deep learning has become the preferred method for automated object detection, but the accurate detection of small objects remains a challenge due to the lack of distinctive appearance features. Most deep learning-based detectors do not exploit the temporal information that is available in video, even though this context is often essential when the signal-to-noise ratio is low. In addition, model development choices, such as the loss function, are typically designed around medium-sized objects. Moreover, most datasets that are acquired for the development of small object detectors are task-specific and lack diversity, and the smallest objects are often not well annotated. In this study, we address the aforementioned challenges and create a deep learning-based pipeline for versatile small object detection. With an in-house dataset consisting of civilian and military objects, we achieve a substantial improvement in YOLOv8 (baseline mAP = 0.465) by leveraging the temporal context in video and data augmentations specifically tailored to small objects (mAP = 0.839). We also show the benefit of having a carefully curated dataset in comparison with public datasets and find that a model trained on a diverse dataset outperforms environment-specific models. Our findings indicate that small objects can be detected accurately in a wide range of environments while leveraging the speed of the YOLO architecture. Full article

This research study investigates and predicts the obesity prevalence in Missouri, utilizing deep neural visual features extracted from medium-resolution satellite imagery (Sentinel-2). By applying a deep convolutional neural network (DCNN), the study aims to predict the obesity rate of census tracts based on visual features in the satellite imagery that covers each tract. The study utilizes Sentinel-2 satellite images, processed using the ResNet-50 DCNN, to extract deep neural visual features (DNVF). Obesity prevalence data, sourced from the CDC’s 2022 estimates, is analyzed at the census tract level. The datasets were integrated to apply a machine learning model to predict the obesity rates in 1052 different census tracts in Missouri. The analysis reveals significant associations between DNVF and obesity prevalence. The predictive models show moderate success in estimating and predicting obesity rates in various census tracts within Missouri. The study emphasizes the potential of using satellite imagery and advanced machine learning in public health research. It points to environmental factors as significant determinants of obesity, suggesting the need for targeted health interventions. Employing DNVF to explore and predict obesity rates offers valuable insights for public health strategies and calls for expanded research in diverse geographical contexts. Full article

The contamination of marine ecosystems by oil spills poses a significant threat to the marine environment, necessitating the prompt and effective implementation of measures to mitigate the associated damage. Satellites offer a spatial and temporal advantage over aircraft and unmanned aerial vehicles (UAVs) in oil spill detection due to their wide-area monitoring capabilities. While oil spill detection has traditionally relied on synthetic aperture radar (SAR) images, the combined use of optical satellite sensors alongside SAR can significantly enhance monitoring capabilities, providing improved spatial and temporal coverage. The advent of deep learning methodologies, particularly convolutional neural networks (CNNs) and Transformer models, has generated considerable interest in their potential for oil spill detection. In this study, we conducted a comprehensive and objective comparison to evaluate the suitability of CNN and Transformer models for marine oil spill detection. High-resolution optical satellite images were used to optimize DeepLabV3+, a widely utilized CNN model; Swin-UPerNet, a representative Transformer model; and Mask2Former, which employs a Transformer-based architecture for both encoding and decoding. The results of cross-validation demonstrate a mean Intersection over Union (mIoU) of 0.740, 0.840 and 0.804 for all the models, respectively, indicating their potential for detecting oil spills in the ocean. Additionally, we performed a histogram analysis on the predicted oil spill pixels, which allowed us to classify the types of oil. These findings highlight the considerable promise of the Swin Transformer models for oil spill detection in the context of future marine disaster monitoring. Full article

This paper addresses the critical challenge of secure computing in the context of deep learning, focusing on the pressing need for effective data privacy protection during transmission and storage, particularly in sensitive fields such as finance and healthcare. To tackle this issue, we propose a novel deep learning model that integrates a hash tree structure with a skip attention mechanism. The hash tree is employed to ensure data integrity and security, enabling the rapid verification of data changes, while the skip attention mechanism enhances computational efficiency by allowing the model to selectively focus on important features, thus minimizing unnecessary processing. The primary objective of our research is to develop a secure computing model that not only safeguards data privacy but also optimizes feature extraction capabilities. Our experimental results on the CIFAR-10 dataset demonstrate significant improvements over traditional models, achieving a precision of 0.94, a recall of 0.89, an accuracy of 0.92, and an F1-score of 0.91, notably outperforming standard self-attention and CBAM. Additionally, the visualization of results confirms that our approach effectively balances efficient feature extraction with robust data privacy protection. This research contributes a new framework for secure computing, addressing both the security and efficiency concerns prevalent in current methodologies. Full article

Traditional unmanned aerial vehicle (UAV) detection methods struggle with multi-scale variations during flight, complex backgrounds, and low accuracy, whereas existing deep learning detection methods have high accuracy but high dependence on equipment, making it difficult to detect small UAV targets efficiently. To address the above challenges, this paper proposes an improved lightweight high-precision model, YOLOv8-E (Enhanced YOLOv8), for the fast and accurate detection and identification of small UAVs in complex environments. First, a Sobel filter is introduced to enhance the C2f module to form the C2f-ESCFFM (Edge-Sensitive Cross-Stage Feature Fusion Module) module, which achieves higher computational efficiency and feature representation capacity while preserving detection accuracy as much as possible by fusing the SobelConv branch for edge extraction and the convolution branch to extract spatial information. Second, the neck network is based on the HSFPN (High-level Screening-feature Pyramid Network) architecture, and the CAA (Context Anchor Attention) mechanism is introduced to enhance the semantic parsing of low-level features to form a new CAHS-FPN (Context-Augmented Hierarchical Scale Feature Pyramid Network) network, enabling the fusion of deep and shallow features. This improves the feature representation capability of the model, allowing it to detect targets of different sizes efficiently. Finally, the optimized detail-enhanced convolution (DEConv) technique is introduced into the head network, forming the LSCOD (Lightweight Shared Convolutional Object Detector Head) module, enhancing the generalization ability of the model by integrating a priori information and adopting the strategy of shared convolution. This ensures that the model enhances its localization and classification performance without increasing parameters or computational costs, thus effectively improving the detection performance of small UAV targets. The experimental results show that compared with the baseline model, the YOLOv8-E model achieved (mean average precision at IoU = 0.5) an [email protected] improvement of 6.3%, reaching 98.4%, whereas the model parameter scale was reduced by more than 50%. Overall, YOLOv8-E significantly reduces the demand for computational resources while ensuring high-precision detection. Full article