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25 papers · ranked by Valyu relevance

01
AarXiv15 Oct 2025
Continuous-variable photonic quantum extreme learning machines for fast collider-data selection
Benedikt Maier, Michael Spannowsky, Simon Williams
We study continuous-variable photonic quantum extreme learning machines as fast, low-overhead front-ends for collider data processing. Data is encoded in photonic modes through quadrature displacements and propagated through a fixed-time Gaussian quantum substrate. The final readout occurs through Gaussian-compatible…
90relevance
02
PPubMed4 May 2026
Explainable AI-driven diagnosis model for early glaucoma detection using grey-wolf optimized extreme learning machine approach Explainable AI-driven diagnosis for early glaucoma detection using grey-wolf optimization
Debendra Muduli, Santosh Kumar Sharma, Sujata Dash, Bernardo Lemos + 2 more
Glaucoma is a leading global cause of blindness, making early detection essential. This paper introduces GlaucoXAI (Glaucoma Explainable AI), an advanced computer-aided diagnosis (CAD) model that integrates machine learning and explainable AI for glaucoma detection using retinal fundus images. The proposed model…
89relevance
03
AarXiv3 Nov 2025
Entanglement estimation of Werner states with a quantum extreme learning machine
Assil, Hajar, Allati, Abderrahim El + 2 more
Hajar Assil, 1 Abderrahim El Allati,1, 2 and Gian Luca Giorgi 3 1 Laboratory of R&D in Engineering Sciences, Faculty of Sciences and Techniques Al-Hoceima, Abdelmalek Essaadi University, Tetouan, Morocco 2 Max Planck Institute for the Physics of Complex Systems, N¨othnitzer Str. 38, D-01187 Dresden, Germany 3 Institute…
86relevance
04
AarXiv28 Oct 2025
Physics-Informed Extreme Learning Machine (PIELM): Opportunities and Challenges
Yang He, Ren Fei, Calabrò, Francesco + 4 more
- 1. School of Qilu Transportation, Shandong University - 2. School of Mechanical Engineering, Shandong Key Laboratory of CNC Machine Tool Functional Components, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China; - 3. Dipartimento di Matematica e Applicazioni, University of Naples…
83relevance
05
AarXiv25 Jan 2026
Over-The-Air Extreme Learning Machines with XL Reception via Nonlinear Cascaded Metasurfaces
Kyriakos Stylianopoulos, Mattia Fabiani, Giulia Torcolacci, Davide Dardari + 1 more
—The recently envisioned goal-oriented communications paradigm calls for the application of inference on wirelessly transferred data via Machine Learning (ML) tools. An emerging research direction deals with the realization of inference ML models directly in the physical layer of Multiple-Input Multiple-Output (MIMO)…
81relevance
06
AarXiv9 Jul 2026
Spectral Stability of Pseudoinverse-Based Extreme Learning Machine
Bich Van Nguyen, Ngoc Anh Khong
Extreme Learning Machine (ELM) computes output weights analytically using the Moore-Penrose pseudoinverse. Although this leads to fast training, its numerical stability depends strongly on the conditioning of the hidden layer matrix. This paper studies pseudoinverse-based ELM from a spectral perspective. We show that…
81relevance
07
PPubMed30 Apr 2026
IoT-aware extreme machine learning for efficient health monitoring
Nithya Rekha Sivakumar
Urban Health monitoring systems significantly improve public well-being. Internet technologies have enabled a new dimension of healthcare technology, generating a large amount of data for informed decision-making. Many researchers used machine learning-based classification because of their physical feature extraction.…
79relevance
08
AarXiv24 Nov 2025
Validity in machine learning for extreme event attribution
Cassandra C. Chou, Scott L. Zeger, Benjamin Q. Huynh
Extreme event attribution (EEA), an approach for assessing the extent to which disasters are caused by climate change, is crucial for informing climate policy and legal proceedings. Machine learning is increasingly used for EEA by modeling rare weather events otherwise too complex or computationally intensive to model…
75relevance
09
PPubMed7 Jan 2026
Machine learning-based prediction of the axial load capacity of UHPC strengthened reinforced concrete columns: A comparative analysis Machine learning-based prediction of the axial load capacity of UHPC strengthened reinforced concrete columns
Viet Hai Hoang, Minh Quang Tran, Van Thuc Ngo, Parthiban Kathirvel
This study develops and evaluates machine learning (ML) models to predict the axial load capacity (Pu) of reinforced concrete (RC) columns strengthened with ultra-high-performance concrete (UHPC) jackets. A comprehensive experimental database containing 105 test samples with 17 key input parameters was compiled from…
64relevance
10
BbioRxiv3 Jun 2026
AdventML: Advanced Enzyme Temperature Prediction with Transformer-Based Embeddings and Resampling Strategies
Jaldert François, Bart De Moor, Vera van Noort
Accurate prediction of enzymes’ optimal catalytic temperature (T_opt_) is crucial in biotechnology, as enzymes with extreme T_opt_ values are highly desirable for reactions at extreme temperatures and for their general stability. However, experimental determination of T_opt_ is costly, labor-intensive, and…
64relevance
11
PPubMed8 Oct 2025
Machine learning-based forecasting of air quality index under long-term environmental patterns: A comparative approach with XGBoost, LightGBM, and SVM Machine learning-based forecasting of air quality index under long-term environmental patterns
Sevtap Tırınk, Takayuki Mizuno
Air pollution is a global problem that threatens environmental sustainability and severely affects public health. Monitoring air quality and predicting future pollution levels are critical for creating effective environmental policies and enabling individuals to take precautions against air pollution. This study…
64relevance
12
AarXiv29 May 2026
Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach
Baptiste Leroux, Clément Dombry, Anne Sabourin
We study quantile regression in an extrapolation regime where the covariate takes unusually large values. Under regular variation assumptions, extreme observations can be effectively characterized through their angular components, enabling learning strategies that focus on the angle of the most extreme observations.…
63relevance
13
PPubMed8 May 2026
Advanced and explainable machine learning model for prediction of surface roughness of tempered steel AISI 1060
Firi Ziyad, Habtamu Alemayehu, Desalegn Wogaso, Getachew Semegn + 4 more
This research examined the performance of a machine learning algorithm when predicting the surface roughness of tempered steel AISI 1060. Different machine learning algorithms, such as decision tree (DT), random forest (RF), adaptive boosting (ADB), gradient boosting (GB), and extreme gradient boosting (XGB), were…
62relevance
14
PPubMed4 Nov 2025
Hybrid Wavelet–ML models for regional drought forecasting in Norway
Türker Tuğrul, Sertaç Oruç, Jessica Louise Hall, Ali Ulvi Galip Şenocak + 1 more
Drought is a natural disaster that often remains unnoticed until ecosystem impacts become severe. Therefore, monitoring and detecting droughts are important research topics. Consequently, drought indices with different focuses, such as precipitation or soil moisture, have been developed. Yet, the utility of the indices…
62relevance
15
BbioRxiv14 Nov 2025
Discovery and optimization of antimicrobial peptides from extreme environments on global scale
Zixin Kang, Haohong Zhang, Qize Zhou, Jiayi Liu + 4 more
Novel antibiotics to combat global antimicrobial resistance (AMR) in human and animal pathogens are urgently required [1]. Antimicrobial peptides (AMPs) are a class of small molecules inhibiting growth of various microorganism, including both Gram-negative and Gram-positive bacteria, fungi and viruses [2]. These…
61relevance
16
CchemRxiv13 Oct 2025
Classical Machine Learning vs Quantum Machine Learning in Computational Chemistry: Hype, Hope, and Horizons
Authors not listed
Computational chemistry has entered a new era where machine learning (ML) models—particularly graph neural networks and machine learning force fields—routinely deliver quantum mechanical accuracy at classical speeds, scaling to millions of atoms and reshaping workflows in drug discovery, catalysis, and materials…
59relevance
17
CchemRxiv13 Jan 2026
A Rigorous Statistical Benchmark of the Tsetlin Machine: A Novel Rule-Based Method Exceeding SOTA for QSAR Prioritisation
Authors not listed
Quantitative Structure Activity Relationship (QSAR) remains an effective tool for early-stage chemical modelling and virtual screening in drug design. The advancements in this field are led by two core paradigms, 1) descriptor engineering, where complex fixed-length vectors of compounds are generated and conventional…
57relevance
18
PPubMed26 May 2026
Progress in the Application of Machine Learning in the Field of Single-Cell and Spatial Transcriptomics
Yan Zhu, Ziling Hao, Li Zhu, Linyuan Shen + 3 more
The rapid evolution of transcriptome sequencing technologies has driven significant breakthroughs across the life sciences. The advent of single-cell RNA-sequencing (scRNA-seq) has enabled gene expression profiling at single-cell resolution, whereas spatial transcriptomics further contextualizes these transcriptional…
56relevance
19
CchemRxiv21 Oct 2025
Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
54relevance
20
PPubMed31 Dec 2025
Applications of Machine Learning for Cognitive Health in Older Individuals With HIV: Rapid Systematic Review
Hwayoung Cho, Jiyoun Song, Hannah Cho, Lin Li + 5 more
Background More than half of people with HIV are now older than 50 years, and they face an approximately 60% higher risk of developing dementia compared with the general population. In recent years, the application of artificial intelligence, particularly machine learning, combined with the growing availability of…
54relevance
21
BbioRxiv10 Feb 2026
PRIZM: Combining Low-N Data and Zero-shot Models to Design Enhanced Protein Variants
David Harding-Larsen, Brianna Marie Lax, Martina Escorial García, Catarina Mendonça + 3 more
Machine learning has repeatedly shown the ability to accelerate protein engineering, but many approaches demand large amounts of robust, high-quality training data as well as substantial computational expertise. While large pre-trained models can function as zero-shot proxies for predicting variant effects, selecting…
53relevance
22
CchemRxiv18 Dec 2025
Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes
Authors not listed
Accurate extrapolation in data-scarce scientific systems remains a central challenge for machine intelligence. In microbial bioprocessing, kinetic parameters change non-monotonically with reactor volume due to interacting hydrodynamic, oxygen-transfer, and mixing effects, rendering classical empirical scaling laws…
53relevance
23
CchemRxiv23 Oct 2025
Machine Learning Prediction of Enantioselectivity in Asymmetric Catalysis from Small, Curated Datasets: Case Studies in Magnesium-Catalyzed Epoxidation and Thia-Michael Addition
Authors not listed
Theoretical prediction of enantioselectivity for broad range of substrates in a given reaction has long been a formidable challenge, traditionally replaced by labor-intensive screening of multiple conditions. Until recently this remained an unaddressed problem in asymmetric catalysis under data-limited scenarios, yet…
53relevance
24
CchemRxiv14 Nov 2025
Bringing ML to the real world: rewards are all we need
Authors not listed
Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…
52relevance
25
BbioRxiv17 Mar 2026
Toward defining loss functions in neuroscience: an XOR-based neuronal mechanism
María Peña Fernández, Lara Lloret Iglesias, Jesús Marco de Lucas
One of the most compelling ideas for bridging neuroscience and artificial neural networks is the establishment of a framework based on three main components: network architecture, optimization mechanism, and loss (or objective) function to be minimized. While the first two components have been extensively explored, the…
51relevance

Results for Extreme Learning Machine

Continuous-variable photonic quantum extreme learning machines for fast collider-data selection

Explainable AI-driven diagnosis model for early glaucoma detection using grey-wolf optimized extreme learning machine approach Explainable AI-driven diagnosis for early glaucoma detection using grey-wolf optimization

Entanglement estimation of Werner states with a quantum extreme learning machine

Physics-Informed Extreme Learning Machine (PIELM): Opportunities and Challenges

Over-The-Air Extreme Learning Machines with XL Reception via Nonlinear Cascaded Metasurfaces

Spectral Stability of Pseudoinverse-Based Extreme Learning Machine

IoT-aware extreme machine learning for efficient health monitoring

Validity in machine learning for extreme event attribution

Machine learning-based prediction of the axial load capacity of UHPC strengthened reinforced concrete columns: A comparative analysis Machine learning-based prediction of the axial load capacity of UHPC strengthened reinforced concrete columns

AdventML: Advanced Enzyme Temperature Prediction with Transformer-Based Embeddings and Resampling Strategies

Machine learning-based forecasting of air quality index under long-term environmental patterns: A comparative approach with XGBoost, LightGBM, and SVM Machine learning-based forecasting of air quality index under long-term environmental patterns

Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach

Advanced and explainable machine learning model for prediction of surface roughness of tempered steel AISI 1060

Hybrid Wavelet–ML models for regional drought forecasting in Norway

Discovery and optimization of antimicrobial peptides from extreme environments on global scale

Classical Machine Learning vs Quantum Machine Learning in Computational Chemistry: Hype, Hope, and Horizons

A Rigorous Statistical Benchmark of the Tsetlin Machine: A Novel Rule-Based Method Exceeding SOTA for QSAR Prioritisation

Progress in the Application of Machine Learning in the Field of Single-Cell and Spatial Transcriptomics

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Applications of Machine Learning for Cognitive Health in Older Individuals With HIV: Rapid Systematic Review

PRIZM: Combining Low-N Data and Zero-shot Models to Design Enhanced Protein Variants

Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes

Machine Learning Prediction of Enantioselectivity in Asymmetric Catalysis from Small, Curated Datasets: Case Studies in Magnesium-Catalyzed Epoxidation and Thia-Michael Addition

Bringing ML to the real world: rewards are all we need

Toward defining loss functions in neuroscience: an XOR-based neuronal mechanism

Continuous-variable photonic quantum extreme learning machines for fast collider-data selection

Explainable AI-driven diagnosis model for early glaucoma detection using grey-wolf optimized extreme learning machine approach Explainable AI-driven diagnosis for early glaucoma detection using grey-wolf optimization

Entanglement estimation of Werner states with a quantum extreme learning machine

Physics-Informed Extreme Learning Machine (PIELM): Opportunities and Challenges

Over-The-Air Extreme Learning Machines with XL Reception via Nonlinear Cascaded Metasurfaces

Spectral Stability of Pseudoinverse-Based Extreme Learning Machine

IoT-aware extreme machine learning for efficient health monitoring

Validity in machine learning for extreme event attribution

Machine learning-based prediction of the axial load capacity of UHPC strengthened reinforced concrete columns: A comparative analysis Machine learning-based prediction of the axial load capacity of UHPC strengthened reinforced concrete columns

AdventML: Advanced Enzyme Temperature Prediction with Transformer-Based Embeddings and Resampling Strategies

Machine learning-based forecasting of air quality index under long-term environmental patterns: A comparative approach with XGBoost, LightGBM, and SVM Machine learning-based forecasting of air quality index under long-term environmental patterns

Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach

Advanced and explainable machine learning model for prediction of surface roughness of tempered steel AISI 1060

Hybrid Wavelet–ML models for regional drought forecasting in Norway

Discovery and optimization of antimicrobial peptides from extreme environments on global scale

Classical Machine Learning vs Quantum Machine Learning in Computational Chemistry: Hype, Hope, and Horizons

A Rigorous Statistical Benchmark of the Tsetlin Machine: A Novel Rule-Based Method Exceeding SOTA for QSAR Prioritisation

Progress in the Application of Machine Learning in the Field of Single-Cell and Spatial Transcriptomics

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Applications of Machine Learning for Cognitive Health in Older Individuals With HIV: Rapid Systematic Review

PRIZM: Combining Low-N Data and Zero-shot Models to Design Enhanced Protein Variants

Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes

Machine Learning Prediction of Enantioselectivity in Asymmetric Catalysis from Small, Curated Datasets: Case Studies in Magnesium-Catalyzed Epoxidation and Thia-Michael Addition

Bringing ML to the real world: rewards are all we need

Toward defining loss functions in neuroscience: an XOR-based neuronal mechanism