Results 131 to 140 of about 67,939 (291)

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Allocation rules for museum pass programs [PDF]

We consider natural axioms for allocating the income of museum pass programs. Two allocation rules are characterized and are shown to coincide with the Shapley value and the equal division solution of the associated TU-game introduced by Ginsburgh and ...
Béal, Sylvain, Solal, Philippe
core   +1 more source

A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
A novel hybrid transfer learning approach for brain tumor classification achieves 99.47% accuracy using magnetic resonance imaging (MRI) images. By combining image preprocessing, ensemble deep learning, and explainable artificial intelligence (XAI) techniques like gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP), the ...
Sadia Islam Tonni, Md. Alif Sheakh, Mst. Sazia Tahosin, Md. Zahid Hasan, Taslima Ferdaus Shuva, Touhid Bhuiyan, Muhammad Ali Abdullah Almoyad, Nabil Anan Orka, Md. Tanvir Rahman, Risala Tasin Khan, M. Shamim Kaiser, Mohammad Ali Moni   +11 more
wiley   +1 more source

Allocation of fixed costs: characterization of the (dual) weighted Shapley value. [PDF]

The weighted value was introduced by Shapley in 1953 as an asymmetric version of his value. Since then several axiomatizations have been proposed including one by Shapley in 1981 specifically addressed to cost allocation, a context in which weights ...
Pierre Dehez
core  

FW-Shapley: Real-Time Estimation of Weighted Shapley Values

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Accepted at ICASSP ...
Panda, Pranoy, Tandon, Siddharth, Balasubramanian, Vineeth N   +2 more
openaire   +2 more sources

Machine Learning‐Driven Digital Twin of a Field‐Effect Transistor‐Based Hormone Biosensor for Real‐Time Estradiol Monitoring

Advanced Intelligent Systems, EarlyView.
A machine learning‐driven digital twin simulates an aptamer‐functionalized BioFET measuring 17β‐estradiol. Real‐time Isd signals are processed, features are extracted, and trained models estimate hormone concentration. In parallel, a one‐step‐ahead forward model learns biosensor dynamics and generates realistic synthetic signals, enabling in silico ...
Anastasiia Gorelova, Alexandra Parichenko, Shirong Huang, Santiago Melia, Gianaurelio Cuniberti   +4 more
wiley   +1 more source

Integrating Artificial Intelligence With Droplet‐Based Microfluidics: Advances, Challenges, and Emerging Opportunities

Advanced Intelligent Systems, EarlyView.
Droplet‐based microfluidics enables precise, high‐throughput microscale reactions but continues to face challenges in scalability, reproducibility, and data complexity. This review examines how artificial intelligence enhances droplet generation, detection, sorting, and adaptive control and discusses emerging opportunities for clinical and industrial ...
Junyan Lai, Sukrit Gaira, Rongfeng Wang, Qingtian Zhang, Ming Li, Liao Wu, Diangeng Li, Yi Li, Chengchen Zhang, Jinhong Guo, Shi‐Yang Tang   +10 more
wiley   +1 more source

Allocation of fixed costs and the weighted Shapley value [PDF]

The weighted value was introduced by Shapley in 1953 as an asymmetric version of his value. Since then several approximations have been proposed including one by Shapley in 1981 specifically addressed to cost allocation, a context in which weights appear
DEHEZ, Pierre
core  

Data‐Driven Review and Machine Learning Prediction of Diamond Vacancy Center Synthesis

Advanced Intelligent Systems, EarlyView.
A machine learning framework is applied to photoluminescence spectra to extract linewidths and uncover how NV, SiV, GeV, and SnV centers evolve with growth and processing conditions. Unified normalization and k‐fold validation reveal cross‐method trends and enable rapid prediction of defect size and fabrication parameters, offering a data‐driven route ...
Zhi Jiang, Marco Peres, Carlo Bradac, Gil Gonçalves   +3 more
wiley   +1 more source

Machine Learning Accelerates Crystallization for Structure Determination

Angewandte Chemie, EarlyView.
Single‐crystal x‐ray diffraction (SCXRD) is often constrained by the difficulty of obtaining suitable crystals. Here, a machine learning‐accelerated co‐crystal discovery workflow is established for a crystalline mate strategy that achieves over 95% prediction accuracy and experimentally delivers 114 co‐crystals from 120 candidates.
Cui‐Zhou Luan, Xue‐Zhi Wang, Jian‐Guo Song, Yu Gu, Jing Wu, Ye‐Ting Wang, Jin‐Feng Liang, Jia‐Le Rao, Mo Xie, Jonathan R. Nitschke, Dan Li   +10 more
wiley   +2 more sources