Groundwater management and protection has been facilitated by computational modeling of aquifer vulnerability and monitoring aquifers using groundwater sampling. The DRASTIC (Depth to water, Recharge, Aquifer media, Soil media, Topography, Impact of...
Probability calibration is commonly utilized to enhance the reliability and interpretability of probabilistic classifiers, yet its potential for reducing algorithmic bias remains under-explored. In this study, the role of probability calibration tech...
Deep learning has recently demonstrated the ability to predict long-term patient risk and its stratification when trained on imaging data such as chest radiographs. However, existing methods formulate estimating patient risk as a binary classificatio...
Multi-epoch point-cloud analysis requires rigid alignment without allowing genuine geometric change to bias the estimated pose. This study presents a reliability-guided iterative closest point (ICP) formulation in which a compact classifier assigns e...
Alkaloids are naturally occurring metabolites with a wide variety of pharmacological activities and applications in science, particularly in medicinal chemistry as anti-inflammatory drugs. Because they can be labelled as active or inactive compounds...
This article presents an empirical comparison of four automatic face recognition methods: Principal Component Analysis (PCA), Local Binary Patterns (LBP), the deep neural network FaceNet, and an original hybrid approach proposed by the authors, refer...
Background/Objectives: A four-class stage classifier can assign a pressure ulcer stage to an erroneous detection. We evaluated supervised candidate verification and its rejection costs. Methods: This retrospective challenge-set study used a frozen de...
Stress is subjective and is manifested differently from one person to another. Thus, the performance of generic classification models that classify stress status is crude. Building a person-specific model leads to a reliable classification, but it re...
Páramos host more than 3500 vascular plant species and are crucial water providers for millions of people in the northern Andes. Monitoring species distribution at large scales is an urgent conservation priority in the face of ongoing climatic...
Removing redundant features and improving classifier performance necessitates the use of meta-heuristic and deep learning (DL) algorithms in feature selection and classification problems. With the maturity of DL tools, many data-driven polarimetric s...
Recently, we reported on the in vivo application of a miniaturized measuring device for 3D visualization of the superior vocal fold vibrations from high-speed recordings in combination with a laser projection unit (LPU). As a long-term vision for thi...
Background/Objectives: Skeletal maturity assessment is essential for timing orthodontic growth-modification treatment. The cervical vertebral maturation (CVM) method is widely used, but manual staging is subjective and prone to inter-observer variabi...
Background/Objectives: Antibiotic-resistant Pseudomonas aeruginosa and limited experimental validation capacity motivate efficient prioritization of antibacterial candidates from large chemical libraries. Quantitative structure–activity relatio...
The textile industry is generating great environmental concerns due to the exponential growth of textile products’ consumption (fast fashion) and production. The textile value chain today operates as a linear system (textile products are produc...
Background: Early detection of dysglycemia in young adults is important but underexplored. This study aimed to (1) predict long-term changes in fasting plasma glucose (δ-FPG) and (2) classify future prediabetes using complementary machine learn...
Football match outcomes are influenced by a complex interplay of dynamic team strategies and stochastic match events, posing a significant challenge for predictive analytics. In this paper, we present a unified benchmarking study for football match o...
Background/Objectives: Electrocardiogram (ECG)-based myocardial infarction (MI) classifiers may achieve high internal validation performance but show reduced performance when applied to data from another source. The task is a controlled binary health...
Flood susceptibility modeling helps understand the relationship between influencing factors and occurrence of urban flooding and further provides spatial distribution of flood risk, which is critical for flood-risk reduction. Machine learning methods...
The application of Empirical Line Method (ELM) for hyperspectral Atmospheric Compensation (AC) premises the underlying linear relationship between a material’s reflectance and appearance. ELM solves the Radiative Transfer (RT) equation under sp...
Effusive volcanic eruptions pose significant hazards to populated areas, making the accurate dating of lava flows essential for reconstructing eruptive histories and assessing volcanic risk. Traditional dating techniques, such as radiometric, paleoma...
In global cold regions and seasonal frozen soil areas, frost heave failure of rock slopes severely endangers infrastructure safety, particularly along China’s Sichuan–Tibet and Qinghai–Tibet Railways. To address this, a meshless num...
Anomaly detection systems deployed in critical applications require not only high accuracy but also reliable uncertainty quantification and coverage guarantees. This paper is an empirical study—rather than a contribution of new conformal-predic...
In recent decades, forest disturbances have increased in both frequency and intensity, driven by global warming and urbanization. Remote sensing, together with forest disturbance algorithms, offers broad opportunities for forest disturbance monitorin...
Background/Objectives: Obstructive sleep apnea (OSA) is a prevalent, yet underdiagnosed, disorder associated with cardiovascular and cognitive risks. While overnight polysomnography (PSG) remains the diagnostic gold standard, it is resource-intensive...
As digital payment infrastructures expand, the incidence of card-not-present fraud has become a major source of operational and financial risk for banks, payment processors, and merchants. In response, financial institutions increasingly rely on data...
Background. Radiographic bone loss is a primary determinant of periodontitis stage. Intraoral radiographs (periapical and bitewing) are the standard projections for assessing interproximal bone levels, yet their interpretation is subjective and poorl...
There is not enough data and computational power for conventional flood mapping methods in many parts of the world, thus fast and low-data-demanding methods are very useful in facing the disaster. This paper presents an innovative procedure for estim...
The growing scale of photovoltaic (PV) installations creates an urgent need for automated fault detection systems that operate in real time on resource-constrained monitoring hardware. Although deep learning methods achieve high classification accura...
Background/Objectives: Vision–language models such as BiomedCLIP are increasingly investigated for their diagnostic potential in medical imaging. Although these foundation models show promise in general radiographic interpretation, their applic...
Background: Periodontitis is a common inflammatory disease characterized by progressive loss of alveolar bone. Cone-beam computed tomography (CBCT) can visualize 3D periodontal bone defects, but its interpretation is time-consuming and examiner-depen...
Background: Automated brain-MRI analysis is usually judged by accuracy at a single operating point, yet a model that is accurate on average may still fail without signalling that it is about to fail. Selective prediction addresses this by allowing a...
While publicly available, cost-free coarse and medium spatial resolution satellite data such as MODIS and Landsat perform well in characterizing industrial cropping systems, commercial high spatial resolution satellite data are often preferred altern...
Background/Objectives: Depression is a leading cause of disability among middle-aged and older adults, with its burden markedly amplified by multimorbidity. In rapidly aging China, distinct multimorbidity phenotypes interact with depressive symptoms...
Background/Objectives: Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart defect, and cardiac computed tomography (CT) is increasingly central to its anatomical and pre-procedural assessment. Artificial-intelligence research in TO...