Results 41 to 50 of about 123,621 (240)

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

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

Augmented Semi-naive Bayes Classifier

, 2013
The naive Bayes is a competitive classifier that makes strong conditional independence assumptions. Its accuracy can be improved by relaxing these assumptions. One classifier which does that is the semi-naive Bayes. The state-of-the-art algorithm for learning a semi-naïve Bayes from data is the backward sequential elimination and joining (BSEJ ...
Mihaljevic, Bojan, Larrañaga Múgica, Pedro María, Bielza Lozoya, María Concepción   +2 more
openaire   +2 more sources

Explaining Naive Bayes Classifications

Education and Research Archive, 2003
Technical report TR03-09. Naive Bayes classifiers, a popular tool for predicting the labels of query instances, are typically learned from a training set. However, since many training sets contain noisy data, a classifier user may be reluctant to blindly trust a predicted label.
Greiner, Russ, Poulin, B., Lu, Paul, Anvik, J., Lu, Z., Macdonell, Cam, Wishart, David, Eisner, Roman, Szafron, Duane   +8 more
openaire   +3 more sources

Search Strategies for Binary Feature Selection for a Naive Bayes Classifier [PDF]

, 2015
We compare in this paper several feature selection methods for the Naive Bayes Classifier (NBC) when the data under study are described by a large number of redundant binary indicators.
Cottrell, Marie, Lacaille, Jérôme, Rabenoro, Tsirizo, Rossi, Fabrice   +3 more
core   +3 more sources

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

Patient‐Reported Outcomes with Selpercatinib Among Patients with RET Fusion–Positive Non‐Small Cell Lung Cancer in the Phase I/II LIBRETTO‐001 Trial

The Oncologist, EarlyView., 2021
Abstract Background LIBRETTO‐001 is an ongoing, global, open‐label, phase I/II study of selpercatinib in patients with advanced or metastatic solid tumors. We report interim patient‐reported outcomes in patients with RET fusion–positive non‐small cell lung cancer (NSCLC).
Anna Minchom, Aaron C. Tan, Erminia Massarelli, Vivek Subbiah, Valentina Boni, Bruce Robinson, Lori J. Wirth, Lisa M. Hess, Min‐Hua Jen, Jennifer Kherani, Elizabeth Olek, Caroline E. McCoach   +11 more
wiley   +1 more source

Perbandingan Jaringan Syaraf Tiruan Dan Naive Bayes Dalam Deteksi Seseorang Terkena Penyakit Stroke [PDF]

, 2014
Tujuan penelitian ini adalah membuat aplikasi Jaringan Syaraf Tiruan dan Naive Bayes untuk memprediksi penyakit stroke dan membandingkan tingkat akuratan dari kedua metode yang digunakan.
Arifudin, R. (R), Rohmana, I. (I)
core   +2 more sources

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

Patient‐Reported Outcomes with Selpercatinib Treatment Among Patients with RET‐Mutant Medullary Thyroid Cancer in the Phase I/II LIBRETTO‐001 Trial

The Oncologist, EarlyView., 2021
Abstract Background Medullary thyroid cancer (MTC) standard of care includes multikinase inhibitors (MKIs), which can exacerbate disease‐related diarrhea, primarily because of non‐RET kinase inhibition. We report diarrhea and other patient‐reported outcomes (PROs) with selpercatinib, a highly selective RET inhibitor, among patients with RET‐mutant MTC ...
Lori J. Wirth, Bruce Robinson, Valentina Boni, Daniel S.W. Tan, Caroline McCoach, Erminia Massarelli, Lisa M. Hess, Min‐Hua Jen, Jennifer Kherani, Elizabeth Olek, Vivek Subbiah   +10 more
wiley   +1 more source