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28 papers · ranked by Valyu relevance

01
PPubMed23 Nov 2025
Logging-data-driven lithology identification of conglomerate reservoir by the assistance of integrated machine learning methods
Jiming Liu, Dongjin Xu
Lithology is a key parameter in reservoir fine description and evaluation. It is difficult to directly identify reservoir lithology using a single logging curve or conventional cross-plot method due to the mud-gravel mixing in complex reservoirs. The accurate identification of conglomerate reservoir lithology has…
90relevance
02
AarXiv17 Nov 2025
MorphBoost: Self-Organizing Universal Gradient Boosting with Adaptive Tree Morphing
Boris Kriuk
—Traditional gradient boosting algorithms employ static tree structures with fixed splitting criteria that remain unchanged throughout training, limiting their ability to adapt to evolving gradient distributions and problem-specific characteristics across different learning stages. This work introduces MorphBoost, a…
90relevance
03
PPubMed13 Feb 2026
Accurate and interpretable prediction of chemical oxygen demand using explainable boosting algorithms with SHAP analysis
Khaled Merabet, Sungwon Kim, Salim Heddam, Fabio Di Nunno + 5 more
Accurate prediction of Chemical Oxygen Demand (COD) is vital for effective water quality management and pollution control. This study compares six ensemble boosting models, AdaBoost, CatBoost, XGBoost, LightGBM, HistGBRT, and NGBoost, for estimating COD from multiple water quality parameters, including pH, dissolved…
88relevance
04
PPubMed26 Mar 2026
Advancing Colorimetric Analysis in Enzyme-Linked Immunosorbent Assays: Harnessing Nonlinear Regression for Improved Accuracy and Predictive Performance
Shaghayegh Mirhosseini, Aryanaz Faghih Nasiri, Fatemeh Khatami, Akram Mirzaei + 3 more
Analysis in Enzyme-Linked Immunosorbent Assays: Harnessing Nonlinear Regression for Improved Accuracy and Predictive Performance Authors: Shaghayegh Mirhosseini, Aryanaz Faghih Nasiri, Fatemeh Khatami, Akram Mirzaei, Seyed Mohammad Kazem Aghamir, Nathan S. Swami, Mohammadreza Kolahdouz Smartphone-based colorimetric…
88relevance
05
PPubMed12 Dec 2025
Design a model to predict incomplete immunization among Ethiopian children using ensemble machine learning algorithms
Birku Getie Mihret, Betelhem Nega Belay, Jenberu Mekurianew Kelkay, Ayalew Melese Amare + 8 more
Immunization is a cost-effective public health intervention globally, including in Ethiopia. However, the study focused on children aged 0-59 months and analyzed factors influencing incomplete immunization using ensemble machine learning techniques. A total of 16,394 EDHS datasets were used, with 80% for training and…
87relevance
06
PPubMed10 Nov 2025
Comparative performance of bagging and boosting ensemble models for predicting lumpy skin disease with multiclass-imbalanced data
Hagar F. Gouda, Fatma D. M. Abdallah
Ensemble machine learning (ML) algorithms, such as bagging and boosting, are powerful decision-support tools that enhance disease prediction and risk management in the veterinary field. Lumpy Skin Disease (LSD) poses a significant threat to livestock health and results in substantial economic losses. This study aims to…
86relevance
07
AarXiv7 Jul 2026
Boosting with List-Decodable Codes
Addison Prairie, Li-Yang Tan
Boosting is a fundamental technique for generically improving the accuracy of learning algorithms (Schapire 1989). Existing boosting algorithms construct a strong learner using $O(\log(\frac{1}ε)/γ^2)$ calls to a $γ$-advantage weak learner, and this round complexity is known to be optimal for generic boosters that…
85relevance
08
AarXiv25 Jan 2026
Boosting methods for interval-censored data with regression and classification
Yuan Bian, Grace Y. Yi, Wenqing He
Boosting has garnered significant interest across both machine learning and statistical communities. Traditional boosting algorithms, designed for fully observed random samples, often struggle with real-world problems, particularly with interval-censored data. This type of data is common in survival analysis and…
84relevance
09
AarXiv21 Feb 2026
Boosting for Vector-Valued Prediction and Conditional Density Estimation
Jian Qian, Shu Ge
We formalize this stability property as (α, β)-boostability. We show that geometric median aggregation achieves (α, β)-boostability for a broad class of divergences, with tradeoffs that depend on the underlying geometry. For vector-valued prediction and conditional density estimation, we characterize boostability under…
83relevance
10
PPubMed11 Dec 2025
A multidimensional analysis of the 21 st century competencies scale through ai-driven data mining techniques
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…
82relevance
11
AarXiv8 Jul 2026
path_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting
Claudio Meggio, Johan Pensar, Riccardo De Bin
We present path_boost, a Python package for interpretable supervised learning on graph-structured input data. The package implements PathBoost, a gradient boosting algorithm that automatically discovers predictive labeled paths within graphs during the learning process. Unlike graph neural networks, which are generally…
81relevance
12
AarXiv7 May 2026
BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification
Yi-Siang Wang, Kuan-Yu Chen, Yu-Chen Den, Darby Tien-Hao Chang
Large language models (LLMs) have recently been adapted to tabular prediction by serializing structured features into natural language, but their performance in low-data regimes remains limited compared to gradient-boosted decision trees (GBDTs). In this work, we revisit the boosting paradigm, traditionally associated…
79relevance
13
CchemRxiv24 Oct 2025
XGBoost and Physical-Informed Neural Networks as Surrogate Models for Vapor-Liquid Equilibrium in PC-SAFT
Authors not listed
Phase equilibrium calculations are crucial in chemical engineering design and optimization processes. The PC-SAFT equation of state (EoS) can precisely calculate phase equilibrium, but is relatively complex and computationally intensive. Surrogate models are mathematically simple models that map or regress the…
77relevance
14
AarXiv25 Jun 2026
Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients
Abrar Alotaibi, Lujain Alnajrani, Nawal Alsheikh, Alhatoon Alanazy + 4 more
Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually damages the liver, often leading to permanent scarring, known as cirrhosis. Patients sometimes have moderate or no symptoms of liver illness for decades before developing…
77relevance
15
AarXiv27 Dec 2025
Collaborative Optimization of Multiclass Imbalanced Learning: Density-Aware and Region-Guided Boosting
Chuantao Li, Zhi Li, Jiahao Xu, Jie Li + 1 more
Numerous studies attempt to mitigate classification bias caused by class imbalance. However, existing studies have yet to explore the collaborative optimization of imbalanced learning and model training. This constraint hinders further performance improvements. To bridge this gap, this study proposes a collaborative…
77relevance
16
PPubMed31 Oct 2025
Machine learning algorithms for predicting and identifying the influencing predictors of antenatal care visits among women in Bangladesh: Evidence from BDHS 2022 data Predicting and Identifying the influencing predictors of antenatal care visits
Md. A. Salam, Md. Merajul Islam, Md. Rezaul Karim, Md Hasinur Rahaman Khan
This study applied ten widely used machine learning algorithms, namely decision tree (DT), RF, artificial neural network (ANN), logistic regression (LR), adaptive boosting (AdaB), extreme gradient boosting (XGB), gradient boosting (GB), k-nearest neighbors (KNN), ranger (RG), and support vector machine (SVM). These…
76relevance
17
BbioRxiv2 Mar 2026
Interpretable AI–driven materiomics to decode microenvironmental cues for stem cell immunomodulation
Chenxi Pan, Yi He, Kaiwen Chen, Qifan Wang + 4 more
Hydrogels that mimic the extracellular matrix can create a microenvironment with various physicochemical cues, which significantly influence stem cell fate with particular emphasis on their immunoregulatory and pro-regenerative functions. However, elucidating how biomaterial cues regulate cell fate is invariably…
72relevance
18
BbioRxiv10 Aug 2026
Gaussian Accelerated Molecular Dynamics in GROMACS
Yuefeng Yang
Gaussian accelerated molecular dynamics (GaMD) enhances conformational sampling by adding a smooth boost potential without requiring predefined collective variables, but an engine-integrated implementation has not been available in GROMACS. Here, we implement total-, dihedral-, and dual-boost GaMD in GROMACS 2025.4…
70relevance
19
BbioRxiv8 Oct 2025
Boosting the speed and accuracy of protein quantification algorithms in mass spectrometry-based proteomics
Thang V Pham, Chau TM Tran, Alex A Henneman, Long HC Pham + 5 more
Current methods for protein level quantification in mass spectrometry-based proteomics do not scale with the increasing number of samples because of limited system memory and algorithmic complexities. Here we propose a new data structure that supports parsing of input as data stream, improve state of the art…
67relevance
20
CchemRxiv17 Dec 2025
Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis
Authors not listed
Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
67relevance
21
BbioRxiv7 Jun 2026
Multi-level, multi-body atomic interaction graphs for machine learning-based prediction of protein-ligand binding energies
Tram TH. Le, Huy Vo, Nhat HM. Nguyen, Binh T. Nguyen + 1 more
Accurate prediction of binding affinity is crucial for rational drug design and discovery. Traditional computational methods often rely on complex scoring functions that incorporate a multitude of physical and chemical descriptors, leading to high computational demands and sometimes limited generalizability. In this…
67relevance
22
CchemRxiv29 Oct 2025
What drives property prediction for solid-state hydrogen storage? Data or smart features?
Authors not listed
Metal hydrides play a pivotal role in a wide range of applications, including hydrogen storage, compression, heat management, and catalysis, making them a central focus of interdisciplinary research spanning chemistry, materials science, and engineering. The performance of the metal hydride based systems is strongly…
65relevance
23
BbioRxiv25 Aug 2026
Artificial intelligence model: optimizing cancer risk level predictions using machine learning and deep learning approaches
Areen Arabiat, Hamza Abu Owida, Suhaila Abuowaida, Nawaf Alshdaifat + 2 more
This study emphasizes the potential of computational techniques in cancer risk assessment, highlighting opportunities for specific and data-driven healthcare solutions. It examines the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches to improve cancer risk assessment using a…
64relevance
24
BbioRxiv18 Feb 2026
Resolving Genome-to-Phenotype Links in Bacteria: Machine-Learned Inference from Downsampled k-mer Representations
T. Regueira, C. Quaglia, O. Lund
Standard approaches to bacterial phenotyping often treat the entire genome as the fundamental unit of information, resulting in high-dimensional inputs that may contain significant redundancy. Consequently, current bacterial phenotyping techniques typically rely on the assumption that entire sequences are required for…
63relevance
25
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…
59relevance
26
CchemRxiv23 Dec 2025
Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models
Authors not listed
Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published…
58relevance
27
CchemRxiv17 Oct 2025
Causal Feature Selection for Materials Optimization: Identifying Causal Process Parameters via Machine Learning and High-Dimensional Hypothesis Testing
Authors not listed
High-throughput experimentation (HTE) in materials science generates vast, high-dimensional datasets relating synthesis parameters to material properties. While machine learning (ML) models excel at predicting properties from these parameters, they often fail to distinguish causal drivers from merely correlated…
56relevance
28
CchemRxiv10 Dec 2025
Opportunities and Challenges of Machine Learning in Atomic Layer Deposition
Authors not listed
The integration of machine learning methods is transforming many areas of research by, for instance, accelerating molecular dynamics simulations and enabling improved prediction and optimization of chemical reactions. However, despite this progress, the adoption of data-driven approaches in atomic layer deposition…
55relevance

Results for Boosting algorithm

Logging-data-driven lithology identification of conglomerate reservoir by the assistance of integrated machine learning methods

MorphBoost: Self-Organizing Universal Gradient Boosting with Adaptive Tree Morphing

Accurate and interpretable prediction of chemical oxygen demand using explainable boosting algorithms with SHAP analysis

Advancing Colorimetric Analysis in Enzyme-Linked Immunosorbent Assays: Harnessing Nonlinear Regression for Improved Accuracy and Predictive Performance

Design a model to predict incomplete immunization among Ethiopian children using ensemble machine learning algorithms

Comparative performance of bagging and boosting ensemble models for predicting lumpy skin disease with multiclass-imbalanced data

Boosting with List-Decodable Codes

Boosting methods for interval-censored data with regression and classification

Boosting for Vector-Valued Prediction and Conditional Density Estimation

A multidimensional analysis of the 21 st century competencies scale through ai-driven data mining techniques

path_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting

BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification

XGBoost and Physical-Informed Neural Networks as Surrogate Models for Vapor-Liquid Equilibrium in PC-SAFT

Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients

Collaborative Optimization of Multiclass Imbalanced Learning: Density-Aware and Region-Guided Boosting

Machine learning algorithms for predicting and identifying the influencing predictors of antenatal care visits among women in Bangladesh: Evidence from BDHS 2022 data Predicting and Identifying the influencing predictors of antenatal care visits

Interpretable AI–driven materiomics to decode microenvironmental cues for stem cell immunomodulation

Gaussian Accelerated Molecular Dynamics in GROMACS

Boosting the speed and accuracy of protein quantification algorithms in mass spectrometry-based proteomics

Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis

Multi-level, multi-body atomic interaction graphs for machine learning-based prediction of protein-ligand binding energies

What drives property prediction for solid-state hydrogen storage? Data or smart features?

Artificial intelligence model: optimizing cancer risk level predictions using machine learning and deep learning approaches

Resolving Genome-to-Phenotype Links in Bacteria: Machine-Learned Inference from Downsampled k-mer Representations

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

Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models

Causal Feature Selection for Materials Optimization: Identifying Causal Process Parameters via Machine Learning and High-Dimensional Hypothesis Testing

Opportunities and Challenges of Machine Learning in Atomic Layer Deposition

Logging-data-driven lithology identification of conglomerate reservoir by the assistance of integrated machine learning methods

MorphBoost: Self-Organizing Universal Gradient Boosting with Adaptive Tree Morphing

Accurate and interpretable prediction of chemical oxygen demand using explainable boosting algorithms with SHAP analysis

Advancing Colorimetric Analysis in Enzyme-Linked Immunosorbent Assays: Harnessing Nonlinear Regression for Improved Accuracy and Predictive Performance

Design a model to predict incomplete immunization among Ethiopian children using ensemble machine learning algorithms

Comparative performance of bagging and boosting ensemble models for predicting lumpy skin disease with multiclass-imbalanced data

Boosting with List-Decodable Codes

Boosting methods for interval-censored data with regression and classification

Boosting for Vector-Valued Prediction and Conditional Density Estimation

A multidimensional analysis of the 21 st century competencies scale through ai-driven data mining techniques

path_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting

BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification

XGBoost and Physical-Informed Neural Networks as Surrogate Models for Vapor-Liquid Equilibrium in PC-SAFT

Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients

Collaborative Optimization of Multiclass Imbalanced Learning: Density-Aware and Region-Guided Boosting

Machine learning algorithms for predicting and identifying the influencing predictors of antenatal care visits among women in Bangladesh: Evidence from BDHS 2022 data Predicting and Identifying the influencing predictors of antenatal care visits

Interpretable AI–driven materiomics to decode microenvironmental cues for stem cell immunomodulation

Gaussian Accelerated Molecular Dynamics in GROMACS

Boosting the speed and accuracy of protein quantification algorithms in mass spectrometry-based proteomics

Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis

Multi-level, multi-body atomic interaction graphs for machine learning-based prediction of protein-ligand binding energies

What drives property prediction for solid-state hydrogen storage? Data or smart features?

Artificial intelligence model: optimizing cancer risk level predictions using machine learning and deep learning approaches

Resolving Genome-to-Phenotype Links in Bacteria: Machine-Learned Inference from Downsampled k-mer Representations

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

Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models

Causal Feature Selection for Materials Optimization: Identifying Causal Process Parameters via Machine Learning and High-Dimensional Hypothesis Testing

Opportunities and Challenges of Machine Learning in Atomic Layer Deposition