16 papers · ranked by Valyu relevance
QingWei Chen, ShuMei Li, Liang Huang, XiangYu Yu + 3 more
Background Antimicrobial peptides (AMPs) are short peptides with diverse biological activities and playing a crucial role in various biological processes. Due to the widespread misuse of traditional antibiotics and the increasing resistance of microorganisms to these drugs, AMPs have emerged as a promising alternative.…
Emanuel Casmiry, Neema Mduma, Ramadhani Sinde
In the face of increasing cyberattacks, Structured Query Language (SQL) injection remains one of the most common and damaging types of web threats, accounting for over 20% of global cyberattack costs. However, due to its dynamic and variable nature, the current detection methods often suffer from high false positive…
Osvaldo Velazquez-Gonzalez, Antonio Alarcón-Paredes, Cornelio Yañez-Marquez
Classification is a central task in machine learning, underpinning applications in domains such as finance, medicine, engineering, information technology, and biology. However, machine learning pattern classification can become a complex or even inexplicable task for current robust models due to the complexity of…
Shisheng Cao, Ran Pang, Yongqiang Chen, Langdi Zhong + 3 more
Introduction Antimicrobial resistance (AMR) continues to rise globally, highlighting the need for rapid, label-free, and cost-effective bacterial identification methods. In this proof-of-concept study, portable Raman spectroscopy combined with machine learning was used to identify five clinically relevant bacterial…
Nasim Mahmud Nayan, Al Mamun Rana, Md. Monirul Islam, Jia Uddin + 3 more
Parkinson’s disease (PD) is a progressive neurological disorder that affects millions globally, posing significant challenges in early and accurate diagnosis. Recent advancements in machine learning (ML) offer promising approaches for addressing these challenges by enabling more precise and efficient PD predictions.…
Marie-Luise Leitner, Martin Arendasy
Logistic regression is one of the most widely used and foundational models in both psychological research and statistical classification. As a generalized linear model (GLM), it provides a robust framework for estimating the probability of a binary outcome based on one or more predictor variables (; ). Its enduring…
Shubham Tomar, Karita C.F. Lidani, Matthew Dekker, Aline A.I. Moraes + 10 more
Title: Highlights 1. • Epidemiology of myocardial injury events beyond myocardial infarction (MI) as a binary entity is lacking. 2. • The Universal Definition of MI classifies a spectrum of myocardial injury events based on etiology. 3. • MI is classified by etiology in clinical practice. 4. • Etiological…
Nigmet Koklu
In recent years, evaluating competencies such as knowledge, practical skills, character traits, and meta-learning capabilities has gained increasing importance in educational research. As educational datasets grow larger and more complex, machine learning offers promising tools for analyzing student responses and…
Md. Julkar Nain Siam, Tanvir Ahsan Showrov, Md. Sakir Hossain, Najmus Shakif Ayaan + 3 more
Motor imagery (MI)-based brain-computer interfaces (BCIs) enable users to control external devices using EEG signals, offering great potential in assistive and rehabilitation technologies. However, MI recognition remains challenging due to EEG’s low signal-to-noise ratio (SNR), inter-subject variability, and complex…
Andrea Lo Sasso, Nicola Amoroso, Domenico Diacono, Marianna La Rocca + 6 more
Breath analysis is emerging as a non-invasive and promising diagnostic approach capable of assessing a patient’s metabolic state by detecting volatile organic compounds in exhaled breath. This study investigates the potential of breath analysis for the early detection of lung cancer, respiratory and gastrointestinal…
Julie R. Pivin-Bachler, Egon L. van den Broek
Title: Summary Ranging from health to cybersecurity, real-world data are heavily imbalanced. Handling imbalance is among the formidable challenges of machine learning (ML), as it deteriorates ML’s performance, yielding biased results toward majority classes. However, finding an adequate measure to assess the impact of…
Daniela-Maria Cristea, Ioan Sima, Laszlo Barna Iantovics
Introduction Classifying gastrointestinal (GI) polyps detected in colonoscopy images is a critical task in colorectal cancer prevention. Given the diagnostic ambiguity of serrated polyps, which share morphological features with both hyperplastic and adenomatous lesions, this study focuses on multiclass classification…
Jichong Lei, Cannan Yi, Hong Hu, Tao Qing + 8 more
Real-time monitoring of operator fatigue is critical for ensuring operational safety and reliability in dynamic industrial environments, especially under swing conditions such as offshore floating nuclear power platforms. This study proposes a multimodal fatigue monitoring framework based on surface electromyography…
Guiqin Liang, Jian Zhang, Dave Winkler
An accurate classification of material stability often requires fusing multiple features under uncertainty. Dempster-Shafer (D-S) theory is a powerful framework for multi-source information fusion under uncertainty. However, its effectiveness critically depends on the quality of basic probability assignments (BPAs)…
Abdelmonem M. Ibrahim, Doaa A. Fakhry, Fares Al-Shargie, Jan Cornelis
Feature selection is crucial for high-dimensional sensor and biomedical data because it reduces redundancy, improves generalization, and supports interpretable biomarker discovery. In this study, we propose a Binary Chaos-Enhanced Newton-Raphson-Based Optimizer (BCNRBO) for wrapper-based feature selection. The method…
Rijul Gupta, Catherine Madill, Craig Jin
Introduction As machine-learning models for vocal pathology advance, their performance is increasingly constrained not by modelling techniques but by the taxonomic structures used to define the classification task itself. Conventional clinical frameworks, while grounded in diagnostic practice, often reflect conceptual…