Results 111 to 120 of about 36,209 (267)

Analysis of Gradient Boosting Algorithms with Optuna Optimization and SHAP Interpretation for Phishing Website Detection

Journal of Applied Informatics and Computing
Phishing remains a persistent cybersecurity threat, evolving rapidly to bypass traditional blacklist-based detection systems. Machine Learning (ML) approaches offer a promising solution, yet finding the optimal balance between detection accuracy and ...
Rahmat Fauzi Abu Bakar, Majid Rahardi
doaj   +1 more source

Bayesian Exploration of Metal‐Organic Framework‐Derived Nanocomposites for High‐Performance Supercapacitors

Advanced Intelligent Discovery, EarlyView.
An AI‐assisted approach is introduced to decode synthesis–performance relationships in metal‐organic framework‐derived supercapacitor materials using Bayesian optimization and predictive modeling, streamlining the search for optimal energy storage properties.
David Gryc, Pritam Banerjee, Felipe Jacques Lima Duarte, Joerg R. Jinschek, Premkumar Murugesan, Babak Rezaei, Stephan Sylvest Keller, Roland A. Fischer, Mian Zahid Hussain   +8 more
wiley   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

A Robust and Interpretable Ensemble Learning Framework for Early Mortality Risk Stratification in Heart Failure

Journal of Intelligent Computing and Health Informatics
Heart failure remains a formidable global health challenge, frequently complicated by cardiorenal syndrome, which necessitates early and dynamic mortality risk stratification.
Agustiyar Agustiyar, Roman Tsarev, Elham Pishgar   +2 more
doaj   +1 more source

Explaining ML predictions with SHAP

As machine learning models become increasingly accurate and complex, explainability has become essential to ensure trust, transparency, and informed decision-making. SHapley Additive exPlanations (SHAP) provide a rigorous and intuitive approach for interpreting model predictions, delivering consistent and theoretically grounded feature attributions ...
openaire   +2 more sources

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Machine Learning Model Optimization and Interpretability Analysis for Classifying Student Stress Levels

Sistemasi: Jurnal Sistem Informasi
This study aims to compare and analyze the performance of several algorithms in classifying student stress levels. The dataset used in this research is the Student Lifestyle Dataset obtained from the Kaggle repository, consisting of 2,000 records with ...
Samuel Wijayadi Sugiharto, Yessica Nataliani   +1 more
doaj   +1 more source

Interpretable Diagnostics with SHAP-Rule: Fuzzy Linguistic Explanations from SHAP Values

Mathematics
This study introduces SHAP-Rule, a novel explainable artificial intelligence method that integrates Shapley additive explanations with fuzzy logic to automatically generate interpretable linguistic IF-THEN rules for diagnostic tasks. Unlike purely numeric SHAP vectors, which are difficult for decision-makers to interpret, SHAP-Rule translates feature ...
Alexandra I. Khalyasmaa, Pavel V. Matrenin, Stanislav A. Eroshenko   +2 more
openaire   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Post-marketing safety of tarlatamab in small cell lung cancer based on FAERS and WHO-VigiAccess with SHAP-based interpretable machine learning analysis of immune-related adverse events

Frontiers in Pharmacology
BackgroundTarlatamab is a DLL3-targeted bispecific T-cell engager approved for previously treated extensive-stage small cell lung cancer (ES-SCLC).
Yingyong Ou, Xianxian Zhong, Mei Mei, Xiao Gu, Shanshan Luo, Gaoyu Chen, Xuqin Xie, Desheng Sun   +7 more
doaj   +1 more source