Results 81 to 90 of about 31,305 (267)

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

Shapley Additive Explanation for Local Class Differentiation: Local Explainability for Class Differentiation in Classification Models

Advanced Intelligent Systems
Explainable artificial intelligence (XAI) seeks to provide transparency to machine learning models, enabling users tounderstand how and why predictions are generated.
Roxana M. Romero Luna, Hugo A. Ordoñez Erazo, Carlos A. Cobos Lozada   +2 more
doaj   +1 more source

Interpretable AI integrated with near-infrared spectroscopy for precise assessment of key factors in flue-cured tobacco leaf quality formation: A case study of moisture

Industrial Crops and Products
Moisture content (MC) during tobacco curing is critical to final sensory quality, yet conventional methods cannot satisfy the need for rapid, non-destructive monitoring.
Yapeng Kang, Jun Jiang, Lingfeng Meng, Yuhang Zhao, Yunfei Ma, Shengjiang Wu, Yingpeng Dai, Kesu Wei   +7 more
doaj   +1 more source

Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization

Advanced Electronic Materials, EarlyView.
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali, David Lehninger, Florencio Sánchez, Ignasi Fina   +3 more
wiley   +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

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

A novel environmental indicator: Compound wind droughts and heat waves for assessing climate-driven ecological and energy sustainability

Ecological Indicators
Compound extremes, specifically concurrent low wind power (wind droughts) and heat waves, threaten ecological stability and renewable energy. However, their dynamics and impacts remain poorly understood.
Jiewen You, Fangxu Yin, Boen Zhang, Mo Zhou, Yamin Qing, Ying Chen, Lu Gao   +6 more
doaj   +1 more source

Can Machine Learning Reduce Unnecessary Surgeries? A Retrospective Analysis Using Threshold Optimization to Prevent Negative Appendectomies in Adults

Annals of Gastroenterological Surgery, EarlyView.
Threshold‐optimized machine learning models using routine clinical and laboratory data in 623 adults undergoing appendectomy. Logistic regression (AUC = 0.765) and random forest (AUC = 0.785) were the best‐performing models for appendicitis detection and complicated appendicitis prediction, respectively.
Ivan Males, Marko Kumric, Zvonimir Boban, Josip Vrdoljak, Dario Pecenkovic, Matej Ivanda, Marko Grahovac, Zenon Pogorelic, Josko Bozic   +8 more
wiley   +1 more source

Machine learning-based prediction of recurrent extrahepatic bile duct stones after common bile duct exploration: a comparative study of models and SHAP-driven interpretability analysis

Frontiers in Medicine
PurposeThis study aimed to construct and compare machine learning models for predicting recurrent extrahepatic bile duct stones after common bile duct exploration and to clarify the contribution of key risk factors using SHAP analysis, thereby providing ...
Yugang Cao, Xun Hu, Jun Guo, Tao Fang
doaj   +1 more source