Results 101 to 110 of about 44,463 (307)

PVE and SHAP: Application of SHAP and Machine Learning to PVE Quantitative Data

, 2023
A relatively novel approach of public participation is the Participatory Value Evaluation (PVE) in which a dilemma of a policymaker is provided to citizens (Mouter, Shortall, et al., 2021).
van de Ven, Mart (author)
core  

Machine Learning‐Assisted Design and Performance Prediction of a Compact Dual‐Band Polarization‐Insensitive THz Metamaterial Absorber for Skin‐Cancer‐Related Refractive‐Index Sensing

Advanced Electronic Materials, EarlyView.
A compact QASRR‐based THz metamaterial absorber enables polarization‐insensitive dual‐band absorption and skin‐cancer‐related refractive‐index sensing through measurable resonance shifts. Field, surface‐current, and circuit analyses clarify the dual‐resonance mechanism, while StackNet‐assisted prediction accurately estimates the simulated absorption ...
Md. Murad Kabir Nipun, S. M. Rifatur Rana, Md. Golam Morshed, Imrose Jahan, F. A. Sabbir Ahamed, Md. Jahedul Islam   +5 more
wiley   +1 more source

SHAP Informed Neural Network

In the context of neural network optimization, this study explores the performance and computational efficiency of learning rate adjustment strategies applied with Adam and SGD optimizers.
Sheng, Victor S., Jarrod Graham, Graham, Jarrod, Victor S. Sheng   +3 more
core   +1 more source

Supervised clustering with SHAP values

, 2023
Mestrado Bolonha em Data Analytics for BusinessIn the last years, data has grown at a fast rate. Not only growing in size, data is also becoming far more complex then what it used to be.
Conceição, Rodrigo Queirós
core   +1 more source

SHAP summary plot.

, 2022
Each point in the plot represents the SHAP value for a feature in an individual record of the dataset. The color represents the feature value from high (red) to low (blue). Features are ordered by importance.
Aziz Guergachi (7504544), Alberto Carlevaro (14134885), Maurizio Mongelli (14134888), Marta Lenatti (14134882), Alessia Paglialonga (5576102), Karim Keshavjee (7504541)   +5 more
core   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

Evaluation of Similarity of Image Explanations Produced by SHAP, LIME and Grad-CAM

Кібернетика та комп'ютерні технології
Introduction. Convolutional neural networks (CNNs) are a subtype of neural networks developed specifically to work with images [1]. They have achieved great success both in research and in practical applications in recent years, however, one of the major
Vladyslav Yavtukhovskyi, Violeta Tretynyk   +1 more
doaj   +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

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Machine Learning-Based Predictive Model for Mortality in Female Breast Cancer Patients Considering Lifestyle Factors

Cancer Management and Research
Meixin Zhen,1,* Haibing Chen,1,* Qing Lu,1 Hui Li,2 Huang Yan,2 Ling Wang2 1Xiangya College of Nursing, Central South University, Changsha, Hunan, 410013, People’s Republic of China; 2Nursing Department, The Third Xiangya Hospital, Central South ...
Zhen M, Chen H, Lu Q, Li H, Yan H, Wang L   +5 more
doaj