Search Results (2,317)

Understanding visitor sentiment is essential for developing effective tourism strategies, particularly as Google Maps reviews have become a key channel for public feedback on tourist attractions. Yet, the unstructured format and dialectal diversity of Arabic reviews pose significant challenges for extracting actionable insights at scale. This study evaluates the performance of traditional machine learning and transformer-based models for aspect-based sentiment analysis (ABSA) on Arabic Google Maps reviews of tourist sites across Saudi Arabia. A manually annotated dataset of more than 3500 reviews was constructed to assess model effectiveness across six tourism-related aspects: price, cleanliness, facilities, service, environment, and overall experience. Experimental results demonstrate that multi-head BERT architectures, particularly AraBERT, consistently outperform traditional classifiers in identifying aspect-level sentiment. Ara-BERT achieved an F1-score of 0.97 for the cleanliness aspect, compared with 0.91 for the best-performing classical model (LinearSVC), indicating a substantial improvement. The proposed ABSA framework facilitates automated, fine-grained analysis of visitor perceptions, enabling data-driven decision-making for tourism authorities and contributing to the strategic objectives of Saudi Vision 20300. Full article

Depth completion aims to achieve high-quality dense depth prediction from a pair of synchronized sparse depth map and RGB image, and it plays an important role in many intelligent applications, including urban mapping, scene understanding, autonomous driving, and augmented reality. Although the existing convolutional neural network (CNN)-based deep learning architectures have obtained state-of-the-art depth completion results, depth ambiguities in large areas with extremely sparse depth measurements remain a challenge. To address this problem, an efficient hierarchical feature fusion network (HFF-Net) is proposed for producing complete and accurate depth completion results. The key components of HFF-Net are the hierarchical depth completion architecture for predicting a robust initial depth map, and the multi-level spatial propagation network (MLSPN) for progressively refining the predicted initial depth map in a coarse-to-fine manner to generate a high-quality depth completion result. Firstly, the hierarchical feature extraction subnetwork is adopted to extract multi-scale feature maps. Secondly, the hierarchical depth completion architecture that incorporates a hierarchical feature fusion module and a progressive depth rectification module is utilized to generate an accurate and reliable initial depth map. Finally, the MLSPN-based depth map refinement subnetwork is adopted, which progressively refines the initial depth map utilizing multi-level affinity weights to achieve a state-of-the-art depth completion result. Extensive experiments were undertaken on two widely used public datasets, i.e., the KITTI depth completion and NYUv2 datasets, to validate the performance of HFF-Net. The comprehensive experimental results indicate that HFF-Net produces robust depth completion results on both datasets. Full article

Accurate monitoring of orchard pests is vital for green and efficient apple production. Yet images captured by intelligent pest-monitoring lamps often contain small targets, weak boundaries, and crowded scenes, which hamper detection accuracy. We present MDAS-YOLO, a lightweight detection framework tailored for smart pest monitoring in apple orchards. At the input stage, we adopt the LIME++ enhancement to mitigate low illumination and non-uniform lighting, improving image quality at the source. On the model side, we integrate three structural innovations: (1) a C3k2-MESA-DSM module in the backbone to explicitly strengthen contours and fine textures via multi-scale edge enhancement and dual-domain feature selection; (2) an AP-BiFPN in the neck to achieve adaptive cross-scale fusion through learnable weighting and differentiated pooling; and (3) a SimAM block before the detection head to perform zero-parameter, pixel-level saliency re-calibration, suppressing background redundancy without extra computation. On a self-built apple-orchard pest dataset, MDAS-YOLO attains 95.68% mAP, outperforming YOLOv11n by 6.97 percentage points while maintaining a superior trade-off among accuracy, model size, and inference speed. Overall, the proposed synergistic pipeline—input enhancement, early edge fidelity, mid-level adaptive fusion, and end-stage lightweight re-calibration—effectively addresses small-scale, weak-boundary, and densely distributed pests, providing a promising and regionally validated approach for intelligent pest monitoring and sustainable orchard management, and offering methodological insights for future multi-regional pest monitoring research. Full article

To improve the efficiency and accuracy of steel surface defect detection while reducing computational complexity, this paper proposes a lightweight detection algorithm—Finite Element Method-YOLO (FEM-YOLO). The algorithm aims to enhance defect detection performance by optimizing the network architecture and minimizing computational overhead. Methodologically, FEM-YOLO is based on the YOLOv8n architecture, incorporating a lightweight Feature Net network as the backbone for feature extraction. Through efficient parameter sharing and feature extraction mechanisms, the algorithm reduces its complexity. Additionally, FEM-YOLO innovatively combines Enhance Conv with the C2f module to form the C2f-Enhance module, thereby improving the representation of fine details and edges within the feature maps. To further enhance detection performance, a multi-path shared convolutional detection head is designed. This design significantly reduces the number of parameters through parameter sharing, thereby improving detection accuracy while maintaining the lightweight nature of the algorithm. The experimental results demonstrate that, on the NEU-DET enhanced dataset, the FEM-YOLO algorithm achieves a parameter count of 1.4 M, which is reduced to 43.7% of the baseline algorithm, with a computational complexity of 4.6 GFLOPs, 52.9% lower than the baseline. Furthermore, the FPS reaches 256. When employing the Focal_EIoU loss function, the mean Average Precision (mAP) reaches 83.3%, validating the algorithm’s efficiency and accuracy in steel surface defect detection. Full article

Fine-grained identification of crop planting structures provides key data for precision agriculture, thereby supporting scientific production and evidence-based policy making. This study selected a representative experimental farmland in Qingyang, Gansu Province, and acquired Unmanned Aerial Vehicle (UAV) multi-temporal data (six epochs) from multiple sensors (multispectral [visible–NIR], thermal infrared, and LiDAR). By fusing 59 feature indices, we achieved high-accuracy extraction of cropland and planting structures and identified the key feature combinations that discriminate among crops. The results show that (1) multi-source UAV data from April + June can effectively delineate cropland and enable accurate plot segmentation; (2) July is the optimal time window for fine-scale extraction of all planting-structure types in the area (legumes, millet, maize, buckwheat, wheat, sorghum, maize–legume intercropping, and vegetables), with a cumulative importance of 72.26% for the top ten features, while the April + June combination retains most of the separability (67.36%), enabling earlier but slightly less precise mapping; and (3) under July imagery, the SAM (Segment Anything Model) segmentation + RF (Random Forest) classification approach—using the RF-selected top 10 of the 59 features—achieved an overall accuracy of 92.66% with a Kappa of 0.9163, representing a 7.57% improvement over the contemporaneous SAM + CNN (Convolutional Neural Network) method. This work establishes a basis for UAV-based recognition of typical crops in the Qingyang sector of the Loess Plateau and, by deriving optimal recognition timelines and feature combinations from multi-epoch data, offers useful guidance for satellite-based mapping of planting structures across the Loess Plateau following multi-scale data fusion. Full article

The diameter of the sunflower flower head and the thickness of its margins are important crop phenotypic parameters. Traditional, single-dimensional two-dimensional imaging methods often struggle to balance precision with computational efficiency. This paper addresses the limitations of the YOLOv11n-seg model in the instance segmentation of floral disk fine structures by proposing the MBLA-YOLO instance segmentation model, achieving both lightweight efficiency and high accuracy. Building upon this foundation, a non-contact measurement method is proposed that combines an improved model with three-dimensional point cloud analysis to precisely extract key structural parameters of the flower head. First, image annotation is employed to eliminate interference from petals and sepals, whilst instance segmentation models are used to delineate the target region; The segmentation results for the disc surface (front) and edges (sides) are then mapped onto the three-dimensional point cloud space. Target regions are extracted, and following processing, separate models are constructed for the disc surface and edges. Finally, with regard to the differences between the surface and edge structures, targeted methods are employed for their respective calculations. Whilst maintaining lightweight characteristics, the proposed MBLA-YOLO model achieves simultaneous improvements in accuracy and efficiency compared to the baseline YOLOv11n-seg. The introduced CKMB backbone module enhances feature modelling capabilities for complex structural details, whilst the LADH detection head improves small object recognition and boundary segmentation accuracy. Specifically, the CKMB module integrates MBConv and channel attention to strengthen multi-scale feature extraction and representation, while the LADH module adopts a tri-branch design for classification, regression, and IoU prediction, structurally improving detection precision and boundary recognition. This research not only demonstrates superior accuracy and robustness but also significantly reduces computational overhead, thereby achieving an excellent balance between model efficiency and measurement precision. This method avoids the need for three-dimensional reconstruction of the entire plant and multi-view point cloud registration, thereby reducing data redundancy and computational resource expenditure. Full article

Understanding streamflow dynamics in watersheds affected by human activity and climate variability is important for sustainable water and environmental resource management. This study evaluates the vulnerability of Alabama watersheds to anthropogenic and climatic changes using an integrated framework combining GIS, remote sensing, hydrological modeling, and machine learning (ML). Three Soil and Water Assessment Tool (SWAT) models, differing in spatial resolution and soil inputs, were developed to simulate streamflow under baseline and land-use/land cover (LULC) scenarios from 1990 to 2023. The model, built with consistent 100 × 100 m rasters and fine-resolution SSURGO (Soil Survey Geographic Database) soil data, achieved the best calibration and was selected for detailed analysis. Streamflow trends were assessed over two periods (1993–2009 and 2010–2023) to help isolate climate variability (from LULC effects), while LULC changes were evaluated using 1992, 2011, and 2021 maps. A Long Short-Term Memory (LSTM) model further enhanced simulation accuracy by integrating partially calibrated SWAT outputs. Watershed vulnerability was ranked using a multi-criteria framework. Two watersheds were classified as highly vulnerable, nine as moderately vulnerable, and three as having low vulnerability. Basin-level contrasts revealed moderate climate impacts in the Tombigbee Basin, greater climate sensitivity in the Black Warrior Basin, and LULC-dominated impacts in the Alabama Basin. Overall, LULC change exerted stronger and more spatially variable effects on streamflow than climate variability. This study introduces a transferable SWAT–ML vulnerability ranking framework to guide watershed and environmental management in data-scarce, human-modified regions. Full article

Mangroves are important blue carbon coastal ecosystems and play a crucial role in mitigating global climate change. However, fine spatial patterns of mangrove biomass carbon hotspots and protection gaps in Guangdong have not been quantified. In this study, we mapped mangrove biomass carbon by integrating Sentinel-2 satellite imagery and field survey data from Guangdong’s coastlines acquired in 2023 for the first time. Using the Getis-Ord Gi* spatial statistic method, we identified the mangrove biomass carbon hotspots and highlighted protection gaps in mangrove conservation. The total mangrove biomass carbon of Guangdong was estimated to be 1,209,305.68 Mg C (with a mean density of 80.56 Mg C/ha), with Zhanjiang containing the highest carbon stock, accounting for over half of the total. Nature reserves supported higher mean biomass carbon (83.03 Mg C/ha), compared with areas outside nature reserves (77.99 Mg C/ha), underscoring their important role in enhancing mangrove carbon storage. The overlapping area between the mangrove biomass carbon stock hotspot areas and the nature reserves is 71.62 km², accounting for 51.13% of the total hotspot area. In terms of mangrove biomass carbon stocks, the main protection gaps in Guangdong are distributed in Anpu Gang, the region south of Zhanjiang, Shuidong Harbor, Pearl River Estuary, Kaozhou Yang, and Yifengxi Port. Our findings reveal the spatial heterogeneity of mangrove carbon stocks in Guangdong and provide novel insights for optimizing mangrove management and spatial planning of nature reserves for conservation and restoration. Full article

The limited availability of pixel-level annotated medical images complicates training supervised segmentation models, as these models require large datasets. To deal with this issue, SemiSeg-CAW, a semi-supervised segmentation framework that leverages class-level information and an adaptive multi-loss function, is proposed to reduce dependency on extensive annotations. The model combines segmentation and classification tasks in a multitask architecture that includes segmentation, classification, weight generation, and ClassElevateSeg modules. In this framework, the ClassElevateSeg module is initially pre-trained and then fine-tuned jointly with the main model to produce auxiliary feature maps that support the main model, while the adaptive weighting strategy computes a dynamic combination of classification and segmentation losses using trainable weights. The proposed approach enables effective use of both labeled and unlabeled images with class-level information by compensating for the shortage of pixel-level labels. Experimental evaluation on two public ultrasound datasets demonstrates that SemiSeg-CAW consistently outperforms fully supervised segmentation models when trained with equal or fewer labeled samples. The results suggest that incorporating class-level information with adaptive loss weighting provides an effective strategy for semi-supervised medical image segmentation and can improve the segmentation performance in situations with limited annotations. Full article

The rapid advancement of large language models (LLMs) has significantly transformed the development and deployment of generative AI chatbots across various domains. This systematic literature review (SLR) analyzes 39 primary studies published between 2020 and 2025 to explore how these models are utilized, the sectors in which they are deployed, and the broader trends shaping their use. The findings reveal that models such as GPT-3.5, GPT-4, and LLaMA variants have been widely adopted, with applications spanning education, healthcare, business services, and beyond. As adoption increases, research continues to emphasize the need for more adaptable, context-aware, and responsible chatbot systems. The insights from this review aim to guide the effective integration of LLM-based chatbots, highlighting best practices such as domain-specific fine-tuning, retrieval-augmented generation (RAG), and multi-modal interaction design. This review maps the current landscape of LLM-based chatbot development, explores the sectors and primary use cases in each domain, analyzes the types of generative AI models used in chatbot applications, and synthesizes the reported limitations and future directions to guide effective strategies for their design and deployment across domains. Full article

Ensuring global food security requires accurate and robust solutions for crop health monitoring, weed detection, and large-scale land-cover classification. To this end, we propose AgroVisionNet, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for local feature extraction, Vision Transformers (ViTs) for capturing long-range global dependencies, and Graph Neural Networks (GNNs) for modeling spatial relationships between image regions. The framework was evaluated on five diverse benchmark datasets—PlantVillage (leaf-level disease detection), Agriculture-Vision (field-scale anomaly segmentation), BigEarthNet (satellite-based land-cover classification), UAV Crop and Weed (weed segmentation), and EuroSAT (multi-class land-cover recognition). Across these datasets, AgroVisionNet consistently outperformed strong baselines including ResNet-50, EfficientNet-B0, ViT, and Mask R-CNN. For example, it achieved 97.8% accuracy and 95.6% IoU on PlantVillage, 94.5% accuracy on Agriculture-Vision, 92.3% accuracy on BigEarthNet, 91.5% accuracy on UAV Crop and Weed, and 96.4% accuracy on EuroSAT. These results demonstrate the framework’s robustness across tasks ranging from fine-grained disease detection to large-scale anomaly mapping. The proposed hybrid approach addresses persistent challenges in agricultural imaging, including class imbalance, image quality variability, and the need for multi-scale feature integration. By combining complementary architectural strengths, AgroVisionNet establishes a new benchmark for deep learning applications in precision agriculture. Full article

Plant height is a key agronomic trait influencing both seed production and yield in hybrid rice. In the elite japonica hybrid ‘Shenyou 26’, optimal plant height differences between the restorer line (‘Shenhui 26’) and the male sterile line (‘Shen 9A’) are critical for efficient pollination. In this study, we dissected the genetic basis of plant height variation using a doubled haploid (DH) population derived from ‘Shenyou 26’. Multi-environment phenotyping and QTL mapping identified seven QTLs associated with plant height, among which qPH1.1 and qPH9.1 were validated. qPH1.1 co-localized with the semi-dwarf gene SD1, and ‘Shen 9A’ carries a rare SD1-EQH allele that potentially confers reduced height relative to the SD1-EQ allele in ‘Shenhui 26’. qPH9.1 also contributed significantly to plant height variation, with the Shenhui26 allele increasing plant height in backcross validation. These findings indicate that plant height variation in ‘Shenyou 26’ is controlled by multiple loci, including SD1 allelic variants and other complementary QTLs, providing valuable resources for fine-tuning plant architecture in rice breeding. Full article

Hydroxyapatite (HA) coatings improve implant bioactivity but suffer from brittleness and limited functionality. Here, we report a hybrid coating strategy combining flame-sprayed HA/TiO₂ with in situ hydrogel growth. TiO₂ incorporated into the HA matrix acted as a photocatalytic initiator for acrylamide polymerization under UV. Unlike conventional hydrogel coatings that require added photoinitiators or separate surface modification steps, TiO₂ incorporated into the HA layer serves as a built-in photocatalytic initiator, enabling direct polymerization of acrylamide monomers on the sprayed surface. The resulting HA/TiO₂–hydrogel coatings exhibited a continuous hydrogel layer with intimate contact to the ceramic surface, as evidenced by SEM cross-sections and elemental mapping. The HA/TiO₂ 1% coating produced a continuous coverage in close contact with the surface, while excessive TiO₂(5%) led to uncontrolled hydrogel growth and partial coating failure. SEM cross-sections revealed a dense, well-adhered coating with homogeneously distributed Ca, P, O, and finely dispersed Ti. Upon immersion in simulated body fluid (SBF), submicron globular deposits progressively developed on the coating surface. EDS showed an increase in Ca/P ratio from ~1.66 (as-sprayed) to ~1.92 (14 days). These findings highlight a straightforward approach for combining flame-sprayed ceramics with photocatalytic hydrogel growth, providing a practical route toward multifunctional implant surface modification. Full article

Industrial Internet of Things (IIoT) systems are increasingly exposed to sophisticated and rapidly evolving cyber threats. In response, this work proposes a proactive threat detection framework that leverages pretrained transformer-based language models to identify emerging attack patterns within IIoT ecosystems. This work introduces a transformer-based framework that fine-tunes domain-specific pretrained models (SecBERT, SecRoBERTa, CyBERT), derives potential attack-path patterns from vulnerability–tactic mappings, and incorporates a retrieval-based fallback mechanism. The fallback not only improves robustness under uncertainty, but also provides a practical solution to the absence of labeled datasets linking ICS-specific MITRE ATT&CK tactics with vulnerabilities, thereby filling a key research gap. Experiments show that the fine-tuned models substantially outperform traditional machine learning baselines; SecBERT achieves the best balance while maintaining high inference efficiency. Overall, the framework advances vulnerability-driven threat modeling in IIoT and offers a foundation for the proactive identification of attack patterns. Full article

Drones offer a promising solution for automating distribution tower inspection, but real-time defect detection remains challenging due to limited computational resources and the small size of critical defects. This paper proposes TDD-YOLO, an optimized model based on YOLOv11n, which enhances small defect detection through four key improvements: (1) SPD-Conv preserves fine-grained details, (2) CBAM amplifies defect salience, (3) BiFPN enables efficient multi-scale fusion, and (4) a dedicated high-resolution detection head improves localization precision. Evaluated on a custom dataset, TDD-YOLO achieves an mAP@0.5 of 0.873, outperforming the baseline by 3.9%. When deployed on a Jetson Orin Nano at 640 × 640 resolution, the system achieves an average frame rate of 28 FPS, demonstrating its practical viability for real-time autonomous inspection. Full article