Results 191 to 200 of about 67,619 (257)

Interpretable machine learning for predicting moderate-to-severe insomnia risk in Chinese adults with autism spectrum disorder. [PDF]

Front Psychiatry
Lin Z, ShangGuan Y, Sim YJ, Jia X, Deng X, Qing Y, Huang Y, Yao Y, Zhang W.   +8 more
europepmc   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Predicting extubation failure in patients with cardiorespiratory decompensation: development and validation of an interpretable machine learning-based risk calculator. [PDF]

Front Cardiovasc Med
Zhang T, Lu T, Yang S.
europepmc   +1 more source

When Poor Exciton Dissociation Limits Photocurrents in Organic Solar Cells: Why Low Offset Non‐Fullerene Acceptor Blends Can't Be Efficient

Advanced Materials, EarlyView.
The energetic offset between the donor and the acceptor components in organic photoactive layers is central to the tradeoff between photovoltage and photocurrent losses. This Perspective covers the most important issues surrounding this topic in non‐fullerene acceptor blends, from the difficulty of accurately determining state energies and driving ...
Dieter Neher, Manasi Pranav
wiley   +1 more source

Using interpretable machine learning models to predict the occurrence of severe influenza in hospitalized children: a retrospective cohort study. [PDF]

BMC Med Inform Decis Mak
Wu J, Qian W, Xie S, Yan Y, Chen G, Wang Y, Liu X, Ma X, Du Y, Liang Y, Gu S, Li W, Chen A, Yi C, Tian M.   +14 more
europepmc   +1 more source

Leaftronics: Bio‐Fractal Scaffolds From Leaf Venation for Low‐Waste Electronics

Advanced Materials, EarlyView.
“Leaftronics” transforms naturally evolved leaf venation into quasi‐fractal scaffolds for sustainable electronics. Polymer‐infiltrated leaf skeletons can be used to fabricate ultra‐smooth, reflow‐ and thin‐film‐compatible decomposable substrates, while making the same lignocellulose networks conducting results in flexible transparent electrodes.
Rakesh Rajendran Nair, Hrisheekesh Thachoth Chandran, Karl Leo, Hans Kleemann   +3 more
wiley   +1 more source

When more is not always better: exercise dose and children's cognitive development from an interpretable machine learning perspective. [PDF]

Front Psychiatry
Zhu W, Zhang W, Yong L.
europepmc   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

An interpretable machine learning model for predicting prognosis in acute ischemic stroke with large vessel occlusion. [PDF]

Front Neurol
Li M, Wei J, Lu G, Zhao H, Meng L, Bao H.   +5 more
europepmc   +1 more source