Results 91 to 100 of about 102,648 (335)

Machine Learning‐Driven Prediction of Microplastic Aging Processes and Environmental Risk Assessment Across Multi‐Media Systems

Advanced Science, EarlyView.
This perspective proposes a cohesive machine learning strategy to decode microplastic aging. It advocates for Federated Learning to dismantle global data silos and introduces the TRACE framework (TRansport, Aging, Corona, Ecotoxicity). By integrating physics‐informed modeling with causal discovery, this approach bridges the laboratory‐field gap to ...
Yaping Lyu, Xinran Qiu, Xing Li, Tianhuan Yang, Xuetao Guo, Hao Qiu, Peng Zhang   +6 more
wiley   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

Klasifikasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayesian Classification

IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2013
Abstrak Setiap hari server Twitter menerima data tweet dengan jumlah yang sangat besar, dengan demikian, kita dapat melakukan data mining yang digunakan untuk tujuan tertentu. Salah satunya adalah untuk visualisasi kemacetan lalu lintas di sebuah kota.
Sandi Fajar Rodiyansyah, Edi Winarko
doaj   +3 more sources

A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays

BMC Bioinformatics, 2006
Background Uncertainty often affects molecular biology experiments and data for different reasons. Heterogeneity of gene or protein expression within the same tumor tissue is an example of biological uncertainty which should be taken into account when ...
Piergiorgi Paolo, Magni Paolo, Demichelis Francesca, Rubin Mark A, Bellazzi Riccardo   +4 more
doaj   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Chemical named entity recognition in the texts of scientific publications using the naïve Bayes classifier approach. [PDF]

J Cheminform, 2022
Tarasova OA, Rudik AV, Biziukova NY, Filimonov DA, Poroikov VV.   +4 more
europepmc   +2 more sources

Classifying with the Fine Structure of Distributions: Leveraging Distributional Information for Robust and Plausible Naïve Bayes

Machine Learning and Knowledge Extraction
In machine learning, the Bayes classifier represents the theoretical optimum for minimizing classification errors. Since estimating high-dimensional probability densities is impractical, simplified approximations such as naïve Bayes and k-nearest ...
Quirin Stier, Jörg Hoffmann, Michael C. Thrun   +2 more
doaj   +1 more source

Locally Weighted Learning for Naive Bayes Classifier

CoRR, 2014
As a consequence of the strong and usually violated conditional independence assumption (CIA) of naive Bayes (NB) classifier, the performance of NB becomes less and less favorable compared to sophisticated classifiers when the sample size increases. We learn from this phenomenon that when the size of the training data is large, we should either relax ...
Kim-Hung Li, Cheuk Ting Li
openaire   +2 more sources

Improved Accuracy of Naive Bayes Classifier for Determination of Customer Churn Uses SMOTE and Genetic Algorithms

Journal of Soft Computing Exploration, 2020
With increasing competition in the business world, many companies use data mining  techniques to determine the level of customer loyalty. The customer data used in this  study is the german credit dataset obtained from UCI.
Afifah Ratna Safitri, M. A. Muslim
semanticscholar   +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