Search Results (443)

Background/Objectives: Vertebral degenerative features are promising but often subjectively scored indicators for adult age estimation. We evaluated an objective surface roughness metric, the “average distance to the fitted ellipse” score (DS), calculated automatically for every vertebra from C7 to S1 on routine CT images. Methods: CT scans of 176 adults (94 males, 82 females; 21–94 years) were retrospectively analyzed. For each vertebra, the mean orthogonal deviation of the anterior superior endplate from an ideal ellipse was extracted. Sex-specific multiple linear regression served as a baseline; support vector regression (SVR), random forest (RF), k-nearest neighbors (k-NN), and Gaussian naïve-Bayes pseudo-regressor (GNB-R) were tuned with 10-fold cross-validation and evaluated on a 20% hold-out set. Performance was quantified with the standard error of the estimate (SEE). Results: DS values correlated moderately to strongly with age (peak r = 0.60 at L3–L5). Linear regression explained 40% (males) and 47% (females) of age variance (SEE ≈ 11–12 years). Non-parametric learners improved precision: RF achieved an SEE of 8.49 years in males (R² = 0.47), whereas k-NN attained 10.8 years (R² = 0.45) in women. Conclusions: Automated analysis of vertebral cortical roughness provides a transparent, observer-independent means of estimating adult age with accuracy approaching that of more complex deep learning pipelines. Streamlining image preparation and validating the approach across diverse populations are the next steps toward forensic adoption. Full article

High-quality multispectral LiDAR (MSL) data are crucial for land cover (LC) classification. However, the Titan MSL system encounters challenges of inconsistent spatial–spectral information due to its unique scanning and data saving method, restricting subsequent classification accuracy. Existing spectral reconstruction methods often require empirical parameter settings and involve high computational costs, limiting automation and complicating application. To address this problem, we introduce the dual attention spectral optimization reconstruction network (DossaNet), leveraging an attention mechanism and spatial–spectral information. DossaNet can adaptively adjust weight parameters, streamline the multispectral point cloud acquisition process, and integrate it into classification models end-to-end. The experimental results show the following: (1) DossaNet exhibits excellent generalizability, effectively recovering accurate LC spectra and improving classification accuracy. Metrics across the six classification models show some improvements. (2) Compared with the method lacking spectral reconstruction, DossaNet can improve the overall accuracy (OA) and average accuracy (AA) of PointNet++ and RandLA-Net by a maximum of 4.8%, 4.47%, 5.93%, and 2.32%. Compared with the inverse distance weighted (IDW) and k-nearest neighbor (KNN) approach, DossaNet can improve the OA and AA of PointNet++ and DGCNN by a maximum of 1.33%, 2.32%, 0.86%, and 2.08% (IDW) and 1.73%, 3.58%, 0.28%, and 2.93% (KNN). The findings further validate the effectiveness of our proposed method. This method provides a more efficient and simplified approach to enhancing the quality of multispectral point cloud data. Full article

Increasing road congestion is the main constraint that may influence the economic development of cities and urban freight transport efficiency because it generates additional costs related to delay, influences social life, increases environmental emissions, and decreases service quality. This may result from several factors, including an increase in logistics activities in the urban core. Therefore, this paper aims to define the relationship between the logistics sprawl phenomenon and congestion level. In this sense, we explored the literature to summarize the phenomenon of logistics sprawl in different cities and defined the dependent and independent variables. Congestion level was defined as the dependent variable, while the increasing distance resulting from logistics sprawl, along with city and operational flow characteristics, was treated as independent variables. We compared the performance of several models, including decision tree, support vector machine, gradient boosting, k-nearest neighbor, logistic regression and random forest. Among all the models tested, we found that the random forest algorithm delivered the best performance in terms of prediction. We combined both logistic regression—for its interpretability—and random forest—for its predictive strength—to define, explain, and interpret the relationship between the studied variables. Subsequently, we collected data from the literature and various databases, including transit city sources. The resulting dataset, composed of secondary and open-source data, was then enhanced through standard augmentation techniques—SMOTE, mixup, Gaussian noise, and linear interpolation—to improve class balance and data quality and ensure the robustness of the analysis. Then, we developed a Python code and executed it in Colab. As a result, we deduced an equation that describes the relationship between the congestion level and the defined independent variables. Full article

Soil heavy metal contamination poses significant risks to ecosystems and human health, necessitating accurate prediction methods for effective monitoring and remediation. We propose a multi-model and variable combination framework to improve the prediction of soil heavy metal content by integrating diverse environmental and spatial features. The methodology incorporates environmental variables (e.g., soil properties, remote sensing indices), spatial autocorrelation measures based on nearest-neighbor distances, and spatial regionalization variables derived from interpolation techniques such as ordinary kriging, inverse distance weighting, and trend surface analysis. These variables are systematically combined into six distinct sets to evaluate their predictive performance. Three advanced models—Partial Least Squares Regression, Random Forest, and a Deep Forest variant (DF21)—are employed to assess the robustness of the approach across different variable combinations. Experimental results demonstrate that the inclusion of spatial autocorrelation and regionalization variables consistently enhances prediction accuracy compared to using environmental variables alone. Furthermore, the proposed framework exhibits strong generalizability, as validated through subset analyses with reduced training data. The study highlights the importance of integrating spatial dependencies and multi-source data for reliable heavy metal prediction, offering practical insights for environmental management and policy-making. Compared to using environmental variables alone, the full framework incorporating spatial features achieved relative improvements of 18–23% in prediction accuracy (R²) across all models, with the Deep Forest variant (DF21) showing the most substantial enhancement. The findings advance the field by providing a flexible and scalable methodology adaptable to diverse geographical contexts and data availability scenarios. Full article

Many researchers focus on improving reproductive health in sows and ensuring successful breeding by accurately identifying the optimal time of ovulation through estrus detection. One promising non-contact technique involves using computer vision to analyze temperature variations in thermal images of the sow’s vulva. However, variations in camera distance during dataset collection can significantly affect the accuracy of this method, as different distances alter the resolution of the region of interest, causing pixel intensity values to represent varying areas and temperatures. This inconsistency hinders the detection of the subtle temperature differences required to distinguish between estrus and non-estrus states. Moreover, failure to maintain a consistent camera distance, along with external factors such as atmospheric conditions and improper calibration, can distort temperature readings, further compromising data accuracy and reliability. Furthermore, without addressing distance variations, the model’s generalizability diminishes, increasing the likelihood of false positives and negatives and ultimately reducing the effectiveness of estrus detection. In our previously proposed methodology for estrus detection in sows, we utilized YOLOv8 for segmentation and keypoint detection, while monocular depth estimation was used for camera calibration. This calibration helps establish a functional relationship between the measurements in the image (such as distances between labia, the clitoris-to-perineum distance, and vulva perimeter) and the depth distance to the camera, enabling accurate adjustments and calibration for our analysis. Estrus classification is performed by comparing new data points with reference datasets using a three-nearest-neighbor voting system. In this paper, we aim to enhance our previous method by incorporating the mean pixel intensity of the region of interest as an additional factor. We propose a detailed four-step methodology coupled with two stages of evaluation. First, we carefully annotate masks around the vulva to calculate its perimeter precisely. Leveraging the advantages of deep learning, we train a model on these annotated images, enabling segmentation using the cutting-edge YOLOv9 algorithm. This segmentation enables the detection of the sow’s vulva, allowing for analysis of its shape and facilitating the calculation of the mean pixel intensity in the region. Crucially, we use monocular depth estimation from the previous method, establishing a functional link between pixel intensity and the distance to the camera, ensuring accuracy in our analysis. We then introduce a classification approach that differentiates between estrus and non-estrus regions based on the mean pixel intensity of the vulva. This classification method involves calculating Euclidean distances between new data points and reference points from two datasets: one for “estrus” and the other for “non-estrus”. The classification process identifies the five closest neighbors from the datasets and applies a majority voting system to determine the label. A new point is classified as “estrus” if the majority of its nearest neighbors are labeled as estrus; otherwise, it is classified as “non-estrus”. This automated approach offers a robust solution for accurate estrus detection. To validate our method, we propose two evaluation stages: first, a quantitative analysis comparing the performance of our new YOLOv9 segmentation model with the older U-Net and YOLOv8 models. Secondly, we assess the classification process by defining a confusion matrix and comparing the results of our previous method, which used the three nearest points, with those of our new model that utilizes five nearest points. This comparison allows us to evaluate the improvements in accuracy and performance achieved with the updated model. The automation of this vital process holds the potential to revolutionize reproductive health management in agriculture, boosting breeding success rates. Through thorough evaluation and experimentation, our research highlights the transformative power of computer vision, pushing forward more advanced practices in the field. Full article

As a crucial component of the Beijing–Hangzhou Grand Canal’s hydraulic engineering, the Jiangnan Canal has historically played a pivotal role in China’s development as a key hydraulic infrastructure. This water conservancy project, connecting northern and southern water systems, not only facilitated regional economic integration but also nurtured unique cultural landscapes along its course. The Jiangnan Canal and its adjacent cities were selected as the study area to systematically investigate 334 tangible cultural heritage (TCH) sites and 420 intangible cultural heritage (ICH) elements. Through integrated Geographical Information System (GIS) spatial analyses—encompassing nearest neighbor index, kernel density estimation, standard deviation ellipse assessment, multi-ring buffer zoning, and Geodetector modeling, the spatiotemporal distribution features of cultural heritage were quantitatively characterized, with a focus on identifying the underlying driving factors shaping its spatial configuration. The analysis yields four main findings: (1) both TCH and ICH exhibit significant spatial clustering patterns across historical periods, with TCH distribution displaying an axis-core structure centered on the canal, whereas ICH evolved from dispersed to clustered configurations. (2) The center of gravity of TCH is primarily around Taihu Lake, while that of ICH is mainly on the south side of Taihu Lake, and the direction of distribution of both is consistent with the direction of the canal. (3) Multi-ring buffer analysis indicates that 77.2% of TCH and 49.8% of ICH clusters are concentrated within 0–10 km of the canal, demonstrating distinct spatial patterns: TCH exhibits a gradual canal-dependent density decrease with distance, whereas ICH reveals multifactorial spatial dynamics. (4) Human activity factors, particularly nighttime light intensity, are identified as predominant drivers of heritage distribution patterns, with natural environmental factors exerting comparatively weaker influence. These findings provide empirical support for developing differentiated conservation strategies for canal-related cultural heritage. The methodology offers replicable frameworks for analyzing heritage corridors in complex historical landscapes, contributing to both applied conservation practices and theoretical advancements in cultural geography. Full article

The purpose of this paper is twofold. On a technical side, we propose an extension of the Hausdorff distance from metric spaces to spaces equipped with asymmetric distance measures. Specifically, we focus on extending it to the family of Bregman divergences, which includes the popular Kullback–Leibler divergence (also known as relative entropy). The resulting dissimilarity measure is called a Bregman–Hausdorff divergence and compares two collections of vectors—without assuming any pairing or alignment between their elements. We propose new algorithms for computing Bregman–Hausdorff divergences based on a recently developed Kd-tree data structure for nearest neighbor search with respect to Bregman divergences. The algorithms are surprisingly efficient even for large inputs with hundreds of dimensions. As a benchmark, we use the new divergence to compare two collections of probabilistic predictions produced by different machine learning models trained using the relative entropy loss. In addition to the introduction of this technical concept, we provide a survey. It outlines the basics of Bregman geometry, and motivated the Kullback–Leibler divergence using concepts from information theory. We also describe computational geometric algorithms that have been extended to this geometry, focusing on algorithms relevant for machine learning. Full article

The track-to-track association (T2TA) problem is a fundamental and critical challenge in information fusion, situational awareness, and target tracking. Existing algorithms based on statistical mathematics, fuzzy mathematics, gray theory, and artificial intelligence suffer from several limitations that are hard to solve, such as over-idealized models, unrealistic assumptions, insufficient real-time performance, and high computational complexity due to pairwise matching requirements. Considering these limitations, we propose a self-adaptive step-2-based K-nearest neighbor density peak clustering (SAS-KNN-DPC) algorithm to address T2TA problem. Firstly, the step-2 temporal neighborhood affinity matrix under a non-registration framework is defined and the calculation methods for multi-feature track-point fusion similarity matrix are given. Secondly, the proposed self-adaptive multi-feature similarity truncation matrix is defined to measure the multidimensional distance between track points and the self-adaptive step-2 truncation distance is also defined to enhance the adaptivity of the algorithm. Finally, we propose improved definitions of local distance and global relative distance to complete both cluster center selection and association assignment. The proposed algorithm eliminates the need for exhaustive pairwise matching between track sequences and avoids time alignment, significantly improving the real-time performance of T2TA. Simulation results demonstrate that compared to other algorithms, the proposed algorithm achieves higher accuracy, reduced computational time, and better real-time performance in complex scenarios. Full article

The wear loss and frictional characteristics of magnesium-based hybrid composites reinforced with boron carbide (B₄C) particles and graphite filler were the main subjects of the investigation. Key parameters, including reinforcement content (0–10 wt%), applied load (5–30 N), sliding speed (0.5–3 m/s), and sliding distance (500–3000 m), were varied. Data-driven machine learning (ML) algorithms were utilized to identify complex patterns and predict relationships between input variables and output responses. Five distinct machine learning algorithms, Artificial Neural Network (ANN), Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting Machine (GBM), and Support Vector Machine (SVM), were employed to analyze experimental tribological data for predicting wear loss and coefficients of friction (COFs). The performance evaluation showed that ML models effectively predicted friction behavior and wear behavior of magnesium-based hybrid composites using tribological test data. A comparison of model performances revealed that the Gradient Boosting Machine (GBM) provided superior accuracy compared to other machine learning models in predicting both wear loss and the coefficient of friction. Additionally, feature importance analysis indicated that the graphite weight percentage was the most significant influence in predicting the coefficient of friction and wear loss characteristics. Full article

Natural human–human communication consists of multiple modalities interacting together. When an intelligent robot or wheelchair is being developed, it is important to consider this aspect. One of the most common modality pairs in multimodal human–human communication is speech–hand gesture interaction. However, not all the hand gestures that can be identified in this type of interaction are useful. Some hand movements can be misinterpreted as useful hand gestures or intentional hand gestures. Failing to filter out these unintentional gestures could lead to severe faulty identifications of important hand gestures. When speech–hand gesture multimodal systems are designed for disabled/elderly users, the above-mentioned issue could result in grave consequences in terms of safety. Gesture identification systems developed for speech–hand gesture systems commonly use hand features and other gesture parameters. Hence, similar gesture features could result in the misidentification of an unintentional gesture as a known gesture. Therefore, in this paper, we have proposed an intelligent system to filter out these unnecessary gestures or unintentional gestures before the gesture identification process in multimodal navigational commands. Timeline parameters such as time lag, gesture range, gesture speed, etc., are used in this filtering system. They are calculated by comparing the vocal command timeline and gesture timeline. For the filtering algorithm, a combination of the Locally Weighted Naive Bayes (LWNB) and K-Nearest Neighbor Distance Weighting (KNNDW) classifiers is proposed. The filtering system performed with an overall accuracy of 94%, sensitivity of 97%, and specificity of 90%, and it had a Cohen’s Kappa value of 88%. Full article

Forest aboveground biomass (AGB) serves as a crucial quantitative indicator that reflects the carbon sequestration capacity of forests, and accurately mapping AGB is pivotal for assessing forest ecosystem stability. However, mapping AGB in subtropical evergreen broadleaf forests in southern China presents challenges due to their complex canopy structure, stand heterogeneity, and spectral signal saturation. The phenological features reflecting seasonal vegetation dynamics are conducive to over-coming these challenges. By analyzing differential spectral reflectance patterns during the non-growing (Jan–Mar, Nov–Dec) versus growing (Apr–Oct) seasons, this study established a phenological feature-based methodology for improving AGB estimation in subtropical evergreen broadleaf forests. Subsequently, four time series vegetation indices (VI), namely NDVI, EVI2, NDPI, and IRECI were employed to extract phenological features (PFs) for mapping forest AGB using a multiple linear regression model (MLR), K-nearest neighbor model (KNN), support vector machine model (SVM), and random forest model (RF). The results demonstrated significant differences in Sentinel-2 spectral reflectance (740–1610 nm bands) between the growing and non-growing seasons. The PFs demonstrated the highest distance correlation coefficient (0.57), significantly outperforming other baseline feature types (0.44). Furthermore, seasonal changes in NDVI and NDPI were found to better reflect AGB accumulation in evergreen broadleaf forests compared to EVI2 and IRECI. Incorporating diverse PFs derived from all four VI significantly enhanced the accuracy of AGB mapping by yielding rRMSE values ranging from 21.01% to 25.06% and R² values ranging from 0.40 to 0.58. The results inferred that PFs can be considered a key factor for alleviating spectral signal saturation problems while effectively improving the accuracy of AGB estimation. Full article

This study investigates the AI-assisted analyses of radiation disinfection effects on the mechanical properties of recycled date kernel powder–epoxy composites for medical applications, utilizing Euclidean distances and the k-nearest neighbor (KNN) algorithm. Tensile and compression tests were conducted on twenty specimens following ASTM standards, with the data analyzed using a t-test to evaluate the impact of the UVC disinfection process on the material’s mechanical properties. The application of AI through the KNN algorithm successfully identified the three most representative curves out of five for both tensile and compression tests. This targeted curve selection minimized variability and focused on the most relevant data, enhancing the reliability of the analysis. Following the application of UVC and AI, tensile tests showed a 20–30% increase in ultimate stress. Similarly, compression tests revealed a 25% increase in transition stress, an 18–22% improvement in ultimate stress, and approximately a 12% rise in fracture stress. This research underscores the potential of combining AI, sustainable materials, and UVC technology to develop advanced composites for medical applications. The proposed methodology offers a robust framework for evaluating material performance while promoting the creation of eco-friendly, high-performance materials that meet the stringent standards of medical use. Full article

A new machine learning algorithm, BoxRF, was developed specifically for estimating grades from drillhole datasets. The method combines the features of classical estimation methods, such as search boxes, search direction, and estimation based on inverse distance methods, with the robustness of random forest (RF) methods that come from forming numerous random groups of data. The method was applied to a porphyry copper deposit, and results were compared to various ML methods, including XGBoost (XGB), k-nearest neighbors (KNN), neural nets (NN), and RF. Scikit-learn RF (SRF) performed the best (R² = 0.696) among the ML methods but underperformed BoxRF (R² = 0.751). The results were confirmed through a five-fold cross-validation exercise where BoxRF once again outperformed SRF. The box dimensions that performed the best were similar in length to the ranges indicated by variogram modeling, thus demonstrating a link between machine learning and traditional methods. Numerous combinations of hyperparameters performed similarly well, implying the method is robust. The inverse distance method was found to better represent the grade–space relationship in BoxRF than median values. The superiority of BoxRF over SRF in this dataset is encouraging, as it opens the possibility of improving machine learning by incorporating domain knowledge (principles of geology, in this case). Full article

This paper proposes ParDP, an algorithm and concrete tool for unsupervised clustering, which belongs to the class of density peaks-based clustering methods. Such methods rely on the observation that cluster representative points (centroids) are points of higher local density surrounded by points of lesser density. Candidate centroids, though, are to be far from each other. A key factor of ParDP is adopting a k-Nearest Neighbors (kNN) technique for estimating the density of points. Complete clustering depends on densities and distances among points. ParDP uses principal component analysis to cope with high-dimensional data points. The current implementation relies on Java parallel streams and the built-in lock-free fork/join mechanism, enabling the exploitation of the computing power of commodity multi/many-core machines. This paper demonstrates ParDP’s clustering capabilities by applying it to several benchmark and real-world datasets. ParDP’s operation can either be directed to observe the number of clusters in a dataset or to finalize clustering with an assigned number of clusters. Different internal and external measures can be used to assess the accuracy of a resultant clustering solution. Full article

Controlling forage quality and grazing are crucial for sustainable livestock production, health, productivity, and animal performance. However, the limited availability of reliable handheld sensors for timely pasture quality prediction hinders farmers’ ability to make informed decisions. This study investigates the in-field dynamics of Mombasa grass (Megathyrsus maximus) forage biomass production and quality using optical techniques such as visible imaging and near-infrared (VIS-NIR) hyperspectral proximal sensing combined with machine learning models enhanced by covariance-based error reduction strategies. Data collection was conducted using a cellphone camera and a handheld VIS-NIR spectrometer. Feature extraction to build the dataset involved image segmentation, performed using the Mahalanobis distance algorithm, as well as spectral processing to calculate multiple vegetation indices. Machine learning models, including linear regression, LASSO, Ridge, ElasticNet, k-nearest neighbors, and decision tree algorithms, were employed for predictive analysis, achieving high accuracy with R² values ranging from 0.938 to 0.998 in predicting biomass and quality traits. A strategy to achieve high performance was implemented by using four spectral captures and computing the reflectance covariance at NIR wavelengths, accounting for the three-dimensional characteristics of the forage. These findings are expected to advance the development of AI-based tools and handheld sensors particularly suited for silvopastoral systems. Full article