Results 41 to 50 of about 29,456 (261)

Predicting severe stunting and its determinants among under-five in Eastern African Countries: A machine learning algorithms.

PLoS ONE
IntroductionSevere stunting is one of the primary public health challenges in LMIC including Eastern African Countries, which affects millions of children.
Halid Worku Jemil, Sonia Worku Semayneh, Altaseb Beyene Kassaw, Kassahun Dessie Gashu   +3 more
doaj   +1 more source

What Are the Preferences of Chinese Farmers for Drones (UAVs): Machine Learning in Technology Adoption Behavior

Drones
With the continuous advancement of sustainable agriculture, drone technology has become a focus of attention. Current research primarily relies on classical models for questionnaire surveys and analyses within specific regions, rather than implementing ...
Fanhao Yang, Jianya Zhao, Jinteng Liu, Zijia Luo, Xingchen Gu, Shu Wang   +5 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

Optimizing NiMo Electrocatalyst Coatings for Hydrogen Evolution in Alkaline Electrolysis With The Machine Learning‐Guided Autonomous Platform CatBot

Advanced Functional Materials, EarlyView.
This work presents a closed‐loop optimization workflow integrating CatBot, an automated synthesis and testing system, to discover NiMo hydrogen‐evolution catalysts. CatBot synthesised and evaluated 228 unique samples, identifying the best performing catalysts for transfer to nickel‐foam electrodes. The best candidate is highly active, reaching 1 Acm−2${
Paolo Vincenzo Freiesleben de Blasio, Jonas Elsborg, Elif Coşkun, Andreas Burger, Pradipkumar Manubhai Leuaa, Thu Pham Anh Phan, Jin Hyun Chang, Christodoulos Chatzichristodoulou   +7 more
wiley   +1 more source

Enhancing Reservoir Simulation History Matching Using SHAP Value for Parameter Range Selection

Energies
Accurate reservoir history matching is indispensable for reliable subsurface forecasting and optimal field development; however, it remains a formidable challenge due to the high dimensionality of geological parameter spaces, strong nonlinear dynamics ...
Grachik Eremyan, Adel M. Magdeev, Mohammed Al-Shargabi, Ivan V. Matveev, Ivan E. Smirnov, Gleb Y. Shishaev, Shadfar Davoodi   +6 more
doaj   +1 more source

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

Predictors of Professional Responses in Nonprofit Mental Health Forums: Interpretable Machine Learning Analysis

Journal of Medical Internet Research
BackgroundOnline mental health communities increase access and equity for patients seeking psychological support. User demand and professional contributions are key to their sustainability.
Shuang Geng, Yanghui Li, Jie Wang, Peixuan Chen, Xusheng Wu, Zhiqun Zhang   +5 more
doaj   +1 more source

Interpreting direct sales’ demand forecasts using SHAP values

Production, 2023
Abstract Paper aims Several concerns regarding the lack of interpretability of machine learning models obstruct the implementation of machine learning projects as part of the demand forecasting process. This paper presents a methodology to support the introduction of machine learning into the forecasting process of a traditional direct sales company by
Mariana Arboleda-Florez, Carlos Castro-Zuluaga   +1 more
openaire   +2 more sources

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Shap-CAM: Visual Explanations for Convolutional Neural Networks Based on Shapley Value

, 2022
Explaining deep convolutional neural networks has been recently drawing increasing attention since it helps to understand the networks' internal operations and why they make certain decisions. Saliency maps, which emphasize salient regions largely connected to the network's decision-making, are one of the most common ways for visualizing and analyzing ...
Quan Zheng, ZiWei Wang 0001, Jie Zhou 0001, Jiwen Lu   +3 more
openaire   +3 more sources