As time progresses and technology improves, biological data sets are continuously increasing in size. New methods and new implementations of existing methods are needed to keep pace with this increase. In this paper, we present a high-performance com...
Due to the rapid development of phasor measurement units (PMUs) and the wide area of interconnection of modern power systems, the security of power systems is confronted with severe challenges. A novel framework based on data for static voltage stabi...
The mapping of invasive plant species is essential for effective ecosystem control and planning, especially in protected areas. One of the widespread invasive plants that threatens the species richness of Natura 2000 habitats in Europe is the large-l...
Changes to ice cover on lakes throughout the northern landscape has been established as an indicator of climate change and variability, expected to have implications for both human and environmental systems. Monitoring lake ice cover is also required...
Highly accurate rice cultivation distribution and area extraction are essential to food security. Moreover, Inner Mongolia, whose slogan is “from scientific rice to world rice”, is an essential national rice production base. However, high...
A proliferation of invasive species is displacing native species, occupying their habitats and degrading biodiversity. One of these is the invasive goldenrod (Solidago spp.), characterized by aggressive growth that results in habitat disruption as it...
In this study, in order to solve the difficulty of the inversion of soil arsenic (As) content using laboratory and field reflectance spectroscopy, we examined the transferability of the prediction method. Sixty-three soil samples from the Daye city a...
Edge-deployed machine learning systems for industrial fault detection face two practical constraints: limited computational and memory resources on edge devices, and the demand for transparent, interpretable decision support. Tree ensembles such as R...
Sensor arrays serve a crucial role in various fields such as environmental monitoring, industrial process control, and medical diagnostics, yet their reliability remains challenged by sensor drift and noise contamination. This study presents a novel...
This study aimed to accurately map burned forest areas and analyze the spatial distribution of forest fires under complex terrain conditions. This study integrates Landsat 8, Sentinel-2, and MODIS data to map burned forest areas in the complex terrai...
With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and...
Forest fire events in coastal eastern Guangdong are strongly associated with human activities and often occur as small-scale, short-lived early-stage fire events. Given uneven thermal monitoring infrastructure, a key challenge is leveraging data-rich...
To better address the problem of the low prediction accuracy of used car prices under a large number of features and big data and improve the accuracy of existing deep learning models, an iterative framework combining XGBoost and LightGBM is proposed...
The first research attempt to dynamically optimize the CORDIC algorithm’s iteration count using artificial intelligence is presented in this paper. Conventional approaches depend on a certain number of iterations, which frequently results in ex...
Creating a national baseline for natural resources, such as mangrove forests, and monitoring them regularly often requires a consistent and robust methodology. With freely available satellite data archives and cloud computing resources, it is now mor...
With the application and development of Internet technology, network traffic is growing rapidly, and the situation of network security is becoming more and more serious. As an important way to protect network security, abnormal traffic detection has...
The accurate and timely access to the spatial distribution information of crops is of great importance for agricultural production management. Although widely used, supervised classification mapping requires a large number of field samples, and is co...
A common problem with matching algorithms, in photogrammetry and computer vision, is the imperfection of finding all correct corresponding points, so-called inliers, and, thus, resulting in incorrect or mismatched points, so-called outliers. Many alg...
This paper presents a failure prediction model using iterative feature selection, which aims to accurately predict the failure occurrences in industrial Internet of Things (IIoT) environments. In general, vast amounts of data are collected from vario...
Diffusion is a commonly used technique for spreading information from point to point on a graph. The rationale behind diffusion is not clear. The multi-type Galton–Watson forest is a random model of population growth without space or any other...
The main research question addressed in the paper is related to the possibility of medium-term forecasting of the public debts of the EU member states. The analysis focuses on a broad range of indicators (macroeconomic, fiscal, monetary, global, and...
Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest cl...
Burn wound depth is a significant determinant of patient treatment. Typically, the evaluation of burn depth relies heavily on the clinical experience of doctors. Even experienced surgeons may not achieve high accuracy and speed in diagnosing burn dep...
In recent years, the semantic segmentation model has been widely applied in fields such as the extraction of crops due to its advantages such as strong discrimination ability, high accuracy, etc. Currently, there is no standard set of ground true lab...
The presence of missing values in a time-series dataset is a very common and well-known problem. Various statistical and machine learning methods have been developed to overcome this problem, with the aim of filling in the missing values in the data....
The pervasive challenge of missing data in scientific research forces a critical trade-off: discarding incomplete observations, which risks significant information loss, while conventional imputation methods struggle to maintain accuracy in high-dime...
Machine-learning algorithms have made significant strides, achieving high accuracy in many applications. However, traditional models often need large datasets, as they typically peel substantial portions of the data in each iteration, complicating th...
The classification of stream waters using parameters such as fecal coliforms into the classes of body contact and recreation, fishing and boating, domestic utilization, and danger itself is a significant practical problem of water quality prediction...
In this paper, we introduce a refined three-stage inversion algorithm (TSIA) for forest height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR). Specifically, the iterative extraction of the boundary of the coherence...
Cocoa is the economic backbone of Côte d’Ivoire and Ghana, making them the leading cocoa-producing countries in the world. However, cocoa farming has been a major driver of deforestation and landscape degradation in West Africa. Various s...
Multispectral remote sensing (RS) data and synthetic aperture radar (SAR) data can provide horizontal and vertical information about forest AGB under different stand conditions. With the abundance of RS features extracted from multispectral and SAR d...
This research aims to detect subtle changes by combining binary change analysis, the Iteratively Reweighted Multivariate Alteration Detection (IRMAD), over dual polarimetric Advanced Land Observing Satellite (ALOS) backscatter with augmented data for...
Accurate placement and geometry of simulation plots are essential for spatially explicit modeling of forest ecosystems. This study introduces a Monte Carlo-based approach for optimizing the spatial alignment of simulation plots with their source poly...
Land use/cover change (LUCC) models are essential for studying the profound impact of land use/cover dynamics on various aspects of the natural and social environment. Cellular Automata (CA) is widely used in the dynamic modeling of complex LUCC syst...
As a severe inflammatory response syndrome, sepsis presents complex challenges in predicting patient outcomes due to its unclear pathogenesis and the unstable discharge status of affected individuals. In this study, we develop a machine learning-base...
Forest biomass change monitoring is essential for climate change. Synthetic aperture radar (SAR) and optimal remote sensing (RS) data are two very helpful data sources for forest biomass monitoring and estimation. During the procedure of biomass esti...
This study developed an integrated approach for estimating tropical forest aboveground biomass (AGB) by combining UAV–LiDAR structural metrics and Sentinel-2B spectral data, optimized through successive projections algorithm (SPA) feature selec...
The Coordinate Rotation Digital Computer (CORDIC) algorithm is widely employed in digital signal processing and hardware accelerators because it computes a broad range of elementary functions using iterative shift-and-add operations. Conventional COR...
The California Bearing Ratio (CBR) is a critical parameter in pavement design and building foundation assessment; however, it requires labor intensive laboratory testing, including a 96 h soaking period. This study evaluated nine machine learning alg...
Sustainable urban growth advocates the implementation of transit-oriented development (TOD) to optimize urban spatial structure. The bilateral planning concept of TOD emphasizes the importance of discovering areas with existing TOD features but poor...
As carbon dioxide (CO2) concentrations continue to rise, climate change, characterized by global warming, presents a significant challenge to global sustainable development. Currently, most global shortwave infrared CO2 retrievals rely on fully physi...
Many tropical forest landscapes are now complex mosaics of intact forests, recovering forests, tree crops, agroforestry, pasture, and crops. The small patch size of each land cover type contributes to making them difficult to separate using satellite...
As autonomous driving moves toward large-scale deployment, controllable and efficient simulation testing has become a primary means of ensuring system safety. However, in open-world environments, existing scenario catalogs often fail to cover the ful...
Non-destructive detection of the pH value of kiwifruit has important practical significance for its quality classification. In this study, hyperspectral fluorescence imaging technology was proposed to quantitatively predict the pH value of kiwifruit...
In the case of low frequencies (e.g., P-band) radar observations, the Gaussian Vertical Backscatter (GVB) model, a model that takes into account the vertical heterogeneity of the wave-canopy interactions, can describe the forest vertical backscatter...
Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to the success or failure of a project. The risk should be predicted earlier to make a software project successful. A model is...
Populus pruinosa Schrenk is a keystone species in arid riparian ecosystems, where its physiological status is critical for biodiversity and soil stabilization. In this study, spectral reflectance, leaf chlorophyll density (CHD), and leaf water conten...
Ensuring transparency and security in digital recruitment systems remains a critical challenge. This study proposes BC-XAIA, a unified framework that integrates blockchain, smart contracts, explainable artificial intelligence (XAI), and agile methodo...
Random Forests are powerful machine learning models widely applied in classification and regression tasks due to their robust predictive performance. Nevertheless, traditional Random Forests face computational challenges during tree construction, par...
Postal traffic and transport face challenges related to the rapid growth of parcel volumes, increasing demands for sustainability, and the need for integration into the smart transportation concept. This study explores the application of machine lear...
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