This study re-evaluates ROC-AUC for binary classification under severe class imbalance (<3% positives). Despite its widespread use, ROC-AUC can mask operationally salient differences among classifiers when the costs of false positives and false ne...
The search for novel metabolic biomarkers is intense but has had limited practical outcomes for medicine. Part of the problem is that we lack knowledge of how different comorbidities influence biomarkers’ performance. In this study, 49 metaboli...
Compared to various optical remote sensing data, studies on the performance of dual-pol Synthetic aperture radar (SAR) on lithology discrimination are scarce. This study aimed at using Sentinel-1 data to distinguish dolomite, andesite, limestone, san...
Chest X-ray evaluation is challenging due to its high demand and the complexity of diagnoses. In this study, we propose an optimized deep learning model for the multi-label classification of chest X-ray images. We leverage pretrained convolutional ne...
The prediction of election outcomes is a critical area in political analysis and decision-making. Accurate forecasting models can significantly influence electoral strategies and policy formulation. Existing models, however, face challenges in handli...
This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and...
The occurrence of flash floods is a natural yet unavoidable occurrence over time. In addition to harming people, property, and resources, it also undermines a country’s economy. This paper attempts to identify areas of flood vulnerability using...
Effective network intrusion detection using anomaly scores from unsupervised machine learning models depends on the performance of the models. Although unsupervised models do not require labels during the training and testing phases, the assessment o...
Cancer remains a deadly disease. We developed a lightweight, accurate, general-purpose deep learning algorithm for skin cancer classification. Squeeze-MNet combines a Squeeze algorithm for digital hair removal during preprocessing and a MobileNet dee...
Predicting rehabilitation outcomes is essential for guiding clinical decisions and improving patient care. Traditional machine learning methods, while effective, are often limited in their ability to capture complex, nonlinear relationships in data....
Reports of Rift Valley fever (RVF), a highly climate-sensitive zoonotic disease, have been rather frequent in Kenya. Although multiple empirical analyses have shown that machine learning methods outperform time series models in forecasting time serie...
Screening models for diabetes can look deceptively strong when the same variables used to define the label are also allowed into the predictors. To avoid that shortcut, we built a leakage-controlled diabetes screening pipeline using National Health a...
The Receiver Operating Characteristic (ROC) is widely used for assessing the performance of classification algorithms. In GIScience, ROC has been applied to assess models aimed at predicting events, such as land use/cover change (LUCC), species distr...
Cardiovascular risk assessment remains a central challenge in both population-based prevention and high-risk clinical settings. We benchmarked a previously developed mechanistic model of cardiovascular ageing against machine-learning baselines across...
Background/Objectives: This study examines the effectiveness of different resampling methods and classifier models for handling imbalanced datasets, with a specific focus on critical healthcare applications such as cancer diagnosis and prognosis. Met...
Drought monitoring in arid regions commonly relies on climatic indices, yet the accumulation timescale that best represents surface soil moisture drought may vary spatially. This study evaluated the Standardized Precipitation Evapotranspiration Index...
Flood susceptibility mapping is essential for effective flood mitigation and land management, particularly in rapidly urbanizing watersheds. This study proposes a multi-criteria framework for improving flood susceptibility modeling through conditioni...
(1) Background: In this research, we aimed to identify and validate a set of relevant features to distinguish between benign nevi and melanoma lesions. (2) Methods: Two datasets with 70 melanomas and 100 nevi were investigated. The first one containe...
4 February 2021
The paper aims to adjust the Taiwanese version of Internet gaming disorder-short form Likert scale with Likert (IGD-SF-T-L) based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria to a Likert scale model and test its psych...
29 September 2026
Bridge structural health monitoring (SHM) depends on sensor streams that may contain missing blocks, noise, spikes, drift, and recorded faults. We evaluated a frozen, group-aware stress testing protocol on 64 Vänersborg bridge opening events and...
ROC (Receiver Operating Characteristic) analyses are considered under a variety of assumptions concerning the distributions of a measurement X in two populations. These include the binormal model as well as nonparametric models where little is assume...
Although the immune system has been implicated in the pathophysiology of gestational diabetes mellitus (GDM) and postpartum abnormal glucose tolerance (AGT), little is known about the transcriptional response of inflammation-related genes linked to m...
Diagnostic biomarkers play a critical role in biomedical research, particularly for the diagnosis and prediction of diseases, etc. To enhance diagnostic accuracy, extensive research about combining multiple biomarkers has been developed based on the...
Accurate waveform classification in noisy environments is an important task in modern communications, radar signal analysis, biomedical signal interpretation, industrial monitoring and other signal processing systems. This paper investigates the perf...
Background: Acute appendicitis in girls presenting with lower abdominal pain remains a frequent diagnostic dilemma because of the overlap in clinical presentation with gynaecological and non-surgical causes. This study aimed to evaluate the diagnosti...
A significant problem in Massive Open Online Courses (MOOCs) is the high rate of student dropout in these courses. An effective student dropout prediction model of MOOC courses can identify the factors responsible and provide insight on how to initia...
A comparison of several statistical techniques common in species distribution modeling was developed during this study to evaluate and obtain the statistical model most accurate to predict the distribution of different forest tree species (in our cas...
This work proposes a new method to classify multi-spectral satellite images based on multivariate adaptive regression splines (MARS) and compares this classification system with the more common parallelepiped and maximum likelihood (ML) methods. We a...
Wireless communication channel scenario classification is crucial for new modern wireless technologies. Reducing the time consumed by the data preprocessing phase for such identification is also essential, especially for multiple-scenario transitions...
This study proposes a deep learning-based framework for the automated classification of photovoltaic solar cells as defective or normal using electroluminescence (EL) imaging. A balanced dataset containing 20,400 EL images, comprising 10,200 defectiv...
The dynamics of cardiac signals can be studied using methods for nonlinear analysis of heart rate variability (HRV). The methods that are used in the article to investigate the fractal, multifractal and informational characteristics of the intervals...
Background: At the time of cancer diagnosis, it is crucial to accurately classify malignant gastric tumors and the possibility that patients will survive. Objective: This study aims to investigate the feasibility of identifying and applying a new fea...
Background: In view of the high incidence of infantile hemangioma (IH) in infants and young children, a comprehensive and reasonable evaluation scale for referral is urgently needed. This study compared the influence of the Hemangioma Severity Scale...
Floods are among the most frequent and destructive natural hazards in Kazakhstan, particularly in the Abai Region, Kazakhstan, where topographic, hydrological, and climatic factors strongly influence flood occurrence. This study presents a comprehens...
Spoofing attacks remain among the most critical security threats in Connected and Autonomous Vehicles (CAVs). This work introduces a lightweight, two-level spoofing detection framework based on Kalman filtering, designed for real-time deployment in v...
We propose a distribution-aware framework for unsupervised outlier detection that transforms multivariate data into one-dimensional neighborhood statistics and identifies anomalies through fitted parametric distributions. This directly addresses cent...
In many African nations, including Nigeria, groundwater remains the most readily available source of clean water. However, finding and developing these resources in heterogeneous terrain, such as the Federal Capital Territory (FCT), Abuja, is challen...
The Sikkim region of the Eastern Himalayas is highly susceptible to Glacial Lake Outburst Floods (GLOFs), a risk that has increased significantly due to rapid glacial retreat driven by climate change in recent years. This study presents a comprehensi...
Groundwater is essential for drinking and irrigation to ensure a stable life and economic development, especially in semi-arid to arid rural areas such as the Lagnadiz district in central Morocco. However, its occurrence is highly variable in discont...
Background/Objective: This study aimed to assess the prognostic significance of pretreatment nutritional and systemic inflammatory indices (IIs), and body composition parameters in patients with locally advanced cervical cancer (LACC) treated with ch...
Background/Objectives: Colorectal cancer (CRC) is one of the most common and deadly types of cancer. Compared with the classical histopathological approach, this study discusses the application of 1H NMR and FT-IR techniques for the fast evaluation d...
Diabetes develops through a mix of clinical, metabolic, lifestyle, demographic, and environmental factors. Most current classification models focus on traditional biomedical indicators and do not include environmental exposure biomarkers. In this stu...
Software-Defined Networking (SDN) centralizes control logic, improving programmability but exposing the controller to volumetric and low-rate Distributed Denial of Service (DDoS) attacks. Entropy-based detectors often raise late alarms or require sig...
Diabetes mellitus is strongly associated with cardiovascular dysfunction and remains one of the leading contributors to morbidity and mortality worldwide. Early identification of diabetes-related cardiovascular alterations is essential for timely ris...
Background: Listeria monocytogenes (LM) is a facultative anaerobe, Gram-positive bacillus which is widely distributed in nature, and can be separated from soil, water, and rotten vegetables. Immunocompetent people are less likely to suffer from LM in...
The monitoring of pre-weaned calf behavior is crucial for ensuring health, welfare, and optimal growth. This study aimed to develop and validate a machine learning-based technique for the simultaneous monitoring of multiple behaviors in pre-weaned be...
Real-world datasets are heavily skewed where some classes are significantly outnumbered by the other classes. In these situations, machine learning algorithms fail to achieve substantial efficacy while predicting these underrepresented instances. To...
Background/Objectives: This study evaluated the role of a discrete multi-wavelength near-infrared spectroscopic (DMW-NIRS) imaging device for rapid breast lesion differentiation. Methods: A total of 62 women (mean age, 49.9 years) with ultrasound (US...
Background/Objectives: Functional lumen imaging probe (FLIP) panometry allows real-time assessment of the esophagogastric junction opening and esophageal body contractile activity during an endoscopic procedure. Despite the development of the Dallas...
Objective: To determine how predictive performance varies across artificial intelligence model architectures for predicting unplanned readmission and mortality in the intensive care unit (ICU), evaluated either as separate or composite outcomes. Meth...
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