Search Results (7,365)

This study uses Gonjo County, Chamdo City, Tibet, as the study area and addresses the challenges of lithological complexity and low efficiency of conventional geological surveys in the Qinghai–Tibet Plateau. This study applies the first systematic application of Chinese GF-5 AHSI data to conduct detailed lithological classification in a plateau environment. Three types of datasets were constructed, including the full-band (FB) dataset, shortwave infrared diagnostic bands (SWIR), and feature-selected bands (FS). Four classification models—Support Vector Machine (SVM), Long Short-Term Memory network (LSTM), Multi-Scale Convolutional Neural Network (MSCNN), and Spectral-Spatial Unified Network (SSUN)—were comparatively evaluated to systematically assess the performance of spectral feature selection and deep learning methods for hyperspectral lithological classification. The experimental results explicitly demonstrate the superiority of spectral-spatial feature extraction. Specifically, compared to the baseline Support Vector Machine (SVM) model, which achieved an overall accuracy of 74.67% and a kappa coefficient of 0.6952, the proposed SSUN model demonstrated an advantage, reaching an overall accuracy of 90.94% and a kappa coefficient of 0.8917. By jointly extracting spectral sequence features and spatial contextual information, SSUN effectively suppresses noise and enhances the spatial continuity of lithological boundaries. The results demonstrate the high practical applicability and spectral fidelity of GF-5 AHSI data for lithological identification in plateau stratigraphic environments. The shortwave infrared region is confirmed to be a critical spectral domain for lithological discrimination, and spectral-spatial deep learning models can maintain high classification accuracy after feature dimensionality reduction, achieving a balance between classification efficiency and accuracy. This study provides reliable methodological support for remote sensing lithological mapping and mineral resource exploration in complex plateau geological environments. Full article

The aim of this study was to examine the potential of multispectral imaging derived from unmanned aerial vehicles (UAVs) for detecting the spread of mistletoe (Viscum album ssp. austriacum L.) in Scots pine stands and to assess the information potential of selected vegetation indices in mistletoe detection. UAV campaigns were performed in the Niepołomice Primeval Forest (Niepołomice Forest District, Regional Directorate of the Polish State Forests National Holding, Kraków, Poland). A fixed-wing UAV Trinity F90+ (Quantum Systems GmbH) equipped with a five-band multispectral MicaSense RedEdge-M camera and an RGB Sony UMC-R10C camera was employed. The number of trees infected by mistletoe, as well as the quantity and area of mistletoe biogroups, were derived based on the classification of true multispectral orthophotos using a support vector machine (SVM) classifier. The spectral information potential assessment identified NIR (B5) as the most important single spectral source of information, while the greatest information potential among vegetation indices was found in NormG, CIG, and GRVI. The mistletoe classification of the 22.5-ha compartment revealed 1735 mistletoe biogroups covering a total area of 489 m², with 58.6% of the 2917 detected tree crowns identified as infected (Kappa = 0.74). The results confirm that UAV-based multispectral data, particularly when combined with green-sensitive vegetation indices, enable effective differentiation of mistletoe from host tree crowns. The integration of the near-infrared (NIR) band further enhanced classification performance. This study evaluates UAV-based multispectral and RGB imagery for detecting common mistletoe (Viscum album ssp. austriacum) in Scots pine stands. The information potential of 22 vegetation indices was assessed to identify the most effective spectral features for mistletoe classification. Full article

Reliable potato quality monitoring during postharvest handling requires compact sensing systems that can acquire chemically relevant information while operating on irregular tuber surfaces. In this study, a Flexible Spectral Sensing Gripper (FSSG) was developed by integrating a low-cost 12-channel visible/near-infrared (Vis/NIR) spectral sensor array, electronic components, and an ESP32-S microcontroller onto a flexible printed circuit (FPC) substrate encapsulated with PDMS. By embedding the sensing units into the grasping interface, the FSSG enables conformal, multi-point spectral acquisition during potato handling, reducing optical-coupling uncertainty associated with unstable contact. Spectral reflectance data were collected from potato tubers, and dry matter content (DMC) and starch content (SC) were determined by standard chemical analysis as reference values. Multiple linear regression (MLR) and partial least squares regression (PLSR) models were compared under Norm, SNV, MSC, SNV-Norm, and MSC-Norm preprocessing conditions, and support vector machine (SVM) classification was used to distinguish healthy and artificially induced deteriorated samples. Normalization combined with MLR provided the best performance among the evaluated regression approaches, achieving cross-validation coefficients of determination (<!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ --> Full article

Synthetic aperture radar (SAR) data from Sentinel-1 enable land cover classification independent of cloud cover and illumination; however, classification performance is affected by inherent speckle noise. This study evaluates the influence of eight speckle filtering algorithms on classification accuracy using Sentinel-1 Ground Range Detected (GRD) data across five contrasting terrain types in eastern Slovakia (mountain, forest, urban, cropland, and water). Speckle suppression was assessed using Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Equivalent Number of Looks (ENL). Classification performance was quantified using Support Vector Machine (SVM), Random Forest (RF), and Histogram-based Gradient Boosting (HistGB) under VV, VH, and dual-polarization (VV + VH) configurations with repeated balanced sampling. Classification accuracy varies across terrain types. In croplands, Lee Sigma combined with SVM in VV + VH mode achieved Overall Accuracy (OA) = 0.746 ± 0.010, whereas in mountainous areas, OA = 0.838 ± 0.005 was achieved with Intensity-Driven Adaptive Neighborhood (IDAN) filtering. Urban areas achieved OA = 0.890 ± 0.006, whereas forest classification remained limited (best OA = 0.582 ± 0.011). Water surfaces approached saturation accuracy (OA ≈ 0.9998). Dual polarization improved performance in heterogeneous environments but had a limited effect in homogeneous classes. The results show that terrain structure influences the interaction between speckle filtering and classification performance. Full article

Ultra-wide field (UWF) fundus image classification is an important part of the entire process of medical screening and decision support. However, the discrimination of various retinal disease classes is difficult due to the similarity between classes, class imbalance, and the indeterminacy of visual patterns. In our research, an explainable neutrosophic knowledge distillation (NKD) model for UWF fundus image classification is proposed. In the proposed model, the teacher model is a ResNet50 architecture that provides the student model with supervisory information that is aware of the indeterminacy of predictions. The proposed model combines the CLAHE-based preprocessing method with the neutrosophic distillation method to enable the student model to learn from the hard labels as well as the teacher model. The experimental results were evaluated using the 5-fold cross-validation method with an additional hold-out evaluation. The experimental results show that the proposed NKD model has a mean accuracy of 84.00%, specificity of 97.33%, precision of 84.99%, recall of 84.00%, and F1-score of 84.02%. The proposed model also has an accuracy of 87.86% with specificity of 97.48% and AUC of 97.48% in the ablation-based full model evaluation. It outperformed classical machine learning baselines based on Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and LBP + HOG features with Support Vector Machines (SVM) classifiers, as well as the baseline student, fuzzy student, and teacher Convolutional Neural Network (CNN) models. For improved interpretability, the Grad-CAM++ technique was used to analyze the proposed NKD model. This analysis showed that the network attended to relevant retinal regions during classification. These results suggest that the proposed model can be an effective tool for UWF fundus image classification. Full article

Background/Objectives: Esophageal cancer is one of the major global causes of cancer mortality, and the 5-year survival rate remains below 20% because many cases are detected late. In this study, a Spectral-Aided Vision Enhancer (SAVE) algorithm was utilized to convert conventional white-light endoscopic images (WLI) into hyperspectral-like narrow-band imaging (NBI) images for machine-learning classification of Dysplasia, Normal, and Squamous Cell Carcinoma (SCC). Methods: A total of 762 WLI images obtained from Kaohsiung Medical University were augmented to 1074 using the Al bumentations library, employing vertical flipping, horizontal flipping, and rotations. The SAVE conversion pipeline employs a 24-patch Macbeth color checker for calibration, γ-correction, CIE XYZ transformation, and multivariate regression to interpolate spectral bands, yielding an average color difference of 2.79 (CIEDE2000) from true NBI. The training outcomes and performance metrics illustrate the versatility of the machine learning/deep learning models—Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)—which were trained and evaluated on both the original WLI and SAVE datasets. Performance metrics were analyzed based on precision, recall, accuracy, and F1-score. Results: The CNN sample achieved an accuracy of 100 percent on SAVE data, compared to 93 percent for WLI. The accuracy of RF improved, with WLI at 91% and SAVE at 96%, while SVM increased from 79% to 84%. These improvements indicate the diagnostically valuable spectral variations that can be amplified with SAVE, resulting in significant enhancements in pre-cancer/SCC sensitivity. Conclusions: The proposed SAVE method demonstrates significant potential for enhancing endoscopic imaging and advancing computer-aided diagnosis in esophageal cancer screening, with applicability in other gastrointestinal imaging scenarios as well. Full article

Accurate landslide susceptibility assessment remains challenging in mountainous regions with complex terrain, heterogeneous geology, and clustered landslide inventories. This study develops a slope–unit–based landslide susceptibility assessment framework for Tongren County, Qinghai Province, China, using a landslide inventory of 217 events, multi–source environmental data, Certainty Factor (CF)–based conditioning–factor analysis, and machine learning models. Eighteen conditioning factors derived from remote sensing, geological survey, and meteorological datasets were extracted at the slope–unit scale, and their collinearity was evaluated using Pearson’s correlation and the Variance Inflation Factor (VIF). Eight models—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), AdaBoost, Decision Tree (DT), XGBoost, K–Nearest Neighbors (KNN), and Convolutional Neural Network (CNN)—were evaluated under a 70:30 train/test split. The results show clear performance differences among the tested models: SVM achieved the best overall balance between discrimination and landslide detection (AUC = 0.9489; recall = 0.879). The tested CNN baseline showed relatively weak performance under the current slope–unit–based tabular–data setting. Susceptibility zoning results showed that high– and very–high–susceptibility zones were mainly concentrated along the Longwu River and its tributaries, where middle–elevation dissected terrain, weak lithological materials, river–valley erosion, and human engineering activities spatially coincide. These results provide a practical basis for slope monitoring and land–use planning in Tongren County. Full article

Moldy pear core constitutes a severe internal defect that compromises fruit quality. This study proposes a nondestructive detection method for Korla pear moldy core using Vis/NIR spectral signals, aimed at supporting post-harvest quality control and automated industrial sorting. We collected spectral signals from pears and quantified the moldy pear core area to classify samples into healthy (S = 0%), slightly moldy (0 < S ≤ 10%), and severely moldy (S > 10%) categories. We constructed a three-tier comparative framework to evaluate the progression from conventional machine learning to advanced deep learning: traditional methods using univariate selection (US) and random forest (RF) for feature extraction followed by support vector machine (SVM) classification; 1D-ResNet for direct processing of spectral signals; and two-dimensional approaches transforming signals into improved gramian angular field (IGAF) or Laplacian pyramid Markov transition field (LPMTF) images processed through deep belief network (DBN), MobileNetv3, and Vision Transformer (ViT). The LPMTF-ViT combination delivered the best performance with 98.98% test accuracy and 94.44% external validation accuracy, significantly exceeding traditional approaches and 1D-ResNet. This innovative approach delivers effective technical support for early-stage, nondestructive detection of internal fruit defects. It also establishes a scalable foundation for automated industrial inspection systems, potentially reducing post-harvest losses while ensuring premium quality control in modern fruit supply chains. Full article

Flanged connections are a critical joining method in modern industrial production, making the detection of bolt loosening in flanges a vital step to ensure industrial safety. Current research on bolt loosening detection in flanges mainly focuses on flat-face flanges without gaskets, while studies on bolted pipe flanges containing gaskets are relatively limited. To achieve bolt loosening detection in such gasketed pipe flanges, this paper analyzes the influence of bolt loosening on wave propagation in the gasket based on the stress wave principle and finite element simulation, and employs the hammer impact method to realize the detection of bolt loosening degree in pipeline flanges. The optimal knock force and hammer head material for the bolt loosening detection experiments were determined experimentally. Through comparative experiments, the Support Vector Machine—Recursive Feature Elimination (SVM-RFE) model was identified as being more accurate and efficient in assessing the degree of bolt loosening. Furthermore, the model was optimized by incorporating feature enhancement and cost-sensitive learning, thereby providing a reliable methodological solution for the rapid identification of bolt loosening severity in pipeline flanges. Full article

Reliable onboard fault detection and diagnosis (FDD) is essential for autonomous small-satellite constellation operations. The satellite telemetry streams are typically high-dimensional, strongly time-correlated, and severely imbalanced. These characteristics make rare but critical faults hard to recognize. To address these issues, this paper proposes an imbalance-aware spatiotemporal diagnostic framework based on three-dimensional convolutional neural networks (3D-CNNs). Multivariate telemetry is first converted into structured spatiotemporal volumes via sliding-window segmentation and grid-based embedding. This enables the model to jointly learn temporal evolution and cross-parameter coupling patterns. A lightweight residual 3D-CNN is developed to enable end-to-end multi-class classification. In addition, a class-balanced focal objective function is introduced to mitigate class-imbalance issues and enhance sensitivity to minority fault modes. The Lumelite series satellite telemetry dataset, comprising 23 fault types, is constructed for training and evaluation. The proposed lightweight residual 3D-CNN is benchmarked against long short-term memory–random forest (LSTM-RF), support vector machine (SVM), 2D-CNN, CNN-LSTM, and residual neural network models. Experimental results show that the proposed algorithm has the highest overall accuracy and Macro-F1 score. It also obtains higher Recall for low-frequency faults. The computational complexity studies indicate that the proposed algorithm has promising potential for real-time satellite health monitoring. Full article

Accurate and timely information on forest disturbance drivers is important for sustainable forest management, global carbon cycle accounting, and climate change response. However, forest disturbance classification is difficult due to two major challenges: limited labeled samples and highly imbalanced disturbance class distribution. In this article, a new framework for multi-type forest disturbance classification based on collaborative semi-supervised learning and sample generation was proposed. First, forest disturbance is detected using long-term remote sensing time series data and disturbance detection algorithms. Spatiotemporal, spectral and terrain features of different disturbance types are extracted. On this basis, to address the problem of imbalanced and small-sample conditions, a collaborative classification strategy is developed. Based on a small number of labeled samples, Support Vector Machine (SVM) and Random Forest (RF) are used to build dual base classifiers. A confident learning (CL) framework is applied to select high-confidence pseudo-labeled samples from unlabeled data. Then, a latent diffusion model (LDM) is introduced to generate high-fidelity pseudo-samples. This increases the sample size and balances the class distribution. Based on the augmented dataset, the dual classifiers are iteratively optimized using a co-training strategy, which improves model generalization under complex conditions. The results show that the proposed framework could generate high-quality pseudo-samples and effectively reduce class imbalance. The overall accuracy (OA) of the proposed framework reaches 93.2%, which is 5.7% and 4.4% higher than single classifier baselines, respectively. After introducing the LDM-based balancing mechanism, performance is further improved by 1.8% compared with the pure semi-supervised framework. This study provides an efficient and reliable solution for large-scale forest ecosystem monitoring. Full article

Efficient spectrum sensing remains a main challenge for Cognitive Radio Networks (CRNs), especially in a wireless environment where methods like energy detection have high uncertainty. This work proposes an entropy-based spectrum-sensing system enhanced with machine-learning algorithms and implemented on a Software-Defined Radio (SDR) platform for real scenario testing. Entropy measures, such as Shannon and Rényi entropies, are used as discriminative features to distinguish occupied and idle frequency bands and release the channel if needed. Machine learning classifiers have achieved good results. In this research, Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), and Random Forests (RFs) are used with data captured via a GNU Radio and the Universal Software Radio Peripheral (USRP)-based SDR testbed. The experimental results demonstrate a probability of detection (P_d) above 0.9 and a false alarm rate (P_fa) below 0.1, indicating a substantial improvement over the classical energy detector of more than 20% for some signal-to-noise ratio (SNR) values. The integration of entropy metrics with machine learning (ML) models enables a dynamic detection in variable spectral environments, providing a practical framework for CRNs. Full article

Gravity Recovery and Climate Experiment (GRACE) satellites primarily monitor changes in land water storage, including groundwater, soil moisture, lake and river surface water, and canopy and snow water. However, its coarse spatial resolution of 0.25 degrees limits its ability to observe smaller basins. To assess aquifer depletion and evaluate a long-term water resource management framework, GRACE data are crucial. It remains rare for GRACE-focused studies to be conducted in great depth. A comprehensive review of 80 articles published between 2011 and 2025 was conducted using the Scopus and Web of Science databases. These articles focused on downscaling GRACE data using machine learning (ML) methods. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines were used in this review. This study highlights the attributes of ML models, the input variables used, the evaluation metrics, and the output resolution. Based on the analysis of the articles, random forest (RF) methods were used in the majority of the papers. Gradient boosting (GB), artificial neural networks (ANN), support vector machines (SVM), support vector regression (SVR), and long short-term memory (LSTM) were the most widely used ML methods. As input variables, rainfall (Pr), soil moisture (SM), and runoff (Qs) are essential. In 2011, there were very few journal articles; since 2021, the number has increased. The number of published studies from China was the highest (24), followed by the USA (12) and Iran (9). A total of 38 journals published reviewed articles. In terms of articles, Remote Sensing generates 19%, Journal of Hydrology has 10%, and Journal of Hydrology: Regional Studies has 8%. The paper also discusses limitations, challenges, recommendations, and potential future directions for improving the accuracy of the GWS change prediction model. Full article

Because textured contact lenses obscure the iris’s natural texture, they pose a serious threat to the accuracy of iris recognition systems and may make identity theft possible. Therefore, this work proposes a reliable method for textured contact lens detection that uses efficient global texture descriptors and effective feature selection with classification techniques. Run-Length and Zernike Moments are effective global texture descriptors that have been extracted from preprocessed iris images that were acquired from the IIIT-D CLI dataset. To improve classification performance, Ant Colony Optimization (ACO) was used to decrease the dimensionality of the feature vectors. Support Vector Machine (SVM) and Logistic Regression (LOG), two classifiers, have been evaluated with different descriptor pairings. According to findings from experiments, Zernike features optimized by ACO and paired with LOG produced the greatest accuracy of 98.04%, greatly surpassing previous methods. The efficacy of the presented approach for safe and dependable iris-based biometric systems is demonstrated by its exceptional results with regard to accuracy, recall, precision, and F1-score. Full article

This systematic literature review explores the intersection of Artificial Intelligence (AI) and Predictive Maintenance (PdM) within Industry 4.0. Using a PRISMA-based methodology, 123 studies published between 2014 and April 2024 were analyzed to characterize technological trends, algorithmic choices, industrial applications, and evaluation practices. The review reveals a consistent growth of research interest, driven by the widespread adoption of Internet of Things (IoT) devices and increased data availability. The manufacturing sector dominates the literature, although most studies rely on standardized datasets rather than real industrial environments. Among the identified AI methods, Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT) and K-Nearest Neighbors (KNNs) represent the most frequently applied algorithms for tasks such as failure prediction, fault detection, and remaining useful life (RUL) estimation. Model performance is commonly evaluated with Accuracy (Acc), Precision, Recall, F1-Score, and Root Mean Square Error (RMSE), reflecting the prevalence of both classification and regression-based PdM analyses. Despite significant advances, this review identifies persistent gaps, including limited domain diversity, scarce long-term real-world validation, and insufficient use of eXplainable AI (XAI) techniques. The findings highlight the need for broader domain coverage, improved interpretability, and validation under realistic industrial conditions. Overall, this review consolidates current knowledge on AI-enabled PdM and outlines critical directions to enhance reliability, transparency, and industrial relevance in the context of Industry 4.0. Full article