Results 131 to 140 of about 106,398 (294)

A Generalized Linear Model and Machine Learning Approach for Predicting the Frequency and Severity of Cargo Insurance in Thailand’s Border Trade Context

Risks
The study compares model approaches in predictive modeling for claim frequency and severity within the cross-border cargo insurance domain. The aim is to identify the optimal model approach between generalized linear models (GLMs) and advanced machine ...
Praiya Panjee, Sataporn Amornsawadwatana
doaj   +1 more source

Gradient boosting MUST taggers for highly-boosted jets

The European Physical Journal Plus
AbstractThe Mass Unspecific Supervised Tagging (MUST) method has proven to be successful in implementing generic jet taggers capable of discriminating various signals over a wide range of jet masses. We implement the MUST concept by using eXtreme Gradient Boosting () classifiers instead of neural networks (NNs) as previously done.
J. A. Aguilar-Saavedra, E. Arganda, F. R. Joaquim, R. M. Sandá Seoane, J. F. Seabra   +4 more
openaire   +3 more sources

Resistance to Overdoping Allows Over 2000 S cm−1 Conductivity in P(g3BTTT) With Anion‐Exchange Doping

Advanced Materials, EarlyView.
Anion‐exchange doping of conjugated polymers is an effective way to achieve high conductivities. Here, we report over 2000 S cm−1 electrical conductivity for doped P(g3BTTT). In addition, we show that P(g3BTTT) sustains exceptionally high doping levels without any drop in the charge mobility.
Basil Hunger, Maximilian M. Horn, Eva Röck, Diego Rosas Villalva, Lize Bynens, Jochen Vanderspikken, Christina Kousseff, Silène Gobeil, Olivier Bardagot, Nesibe Akmanşen‐Kalayci, Sarah H. Tolbert, Iain McCulloch, Wouter Maes, Demetra Tsokkou, Natalie Banerji   +14 more
wiley   +1 more source

Gradient boosting for parsimonious additive covariance matrix modelling

, 2023
Gradient boosting algorithms are attractive for effect selection in multi-parameter generalized additive models. Due to the high-dimensionality of the problem, a parsimonious covariance matrix model is required for modelling multivariate Gaussian data ...
Ruggero Bellio, Vincenzo Gioia, Matteo Fasiolo   +2 more
core  

AI–Guided 4D Printing of Carnivorous Plants–Inspired Microneedles for Accelerated Wound Healing

Advanced Materials, EarlyView.
This work presents an artificial intelligence (AI)‐guided 4D‐printed microneedle platform inspired by carnivorous plants for wound healing. A thermo‐responsive shape memory polymer enables body temperature–triggered self‐coiling for autonomous wound closure.
Hyun Lee, Moon‐Jo Kim, DongEung Kim, Chan Ho Moon, Seojoon Bang, Hyeong Seok Kang, Ju Yeong Gwon, Jong Hwa Seo, Junhyub Jeon, Junhyuk Son, Munwon Lim, Minho Kang, Dong Yun Lee, Donghyun Lim, Jung‐Hoon Park, Gi Doo Cha, Soo‐Hong Lee, Tae‐Sik Jang, Kisuk Yang, Yunho Jeong, Youngho Eom, Hyun‐Do Jung   +21 more
wiley   +1 more source

Supervised Score-Based Modeling by Gradient Boosting

Score-based generative models can effectively learn the distribution of data by estimating the gradient of the distribution. Due to the multi-step denoising characteristic, researchers have recently considered combining score-based generative models with
Zhao, Changyuan, Du, Hongyang, Liu, Guangyuan, Niyato, Dusit   +3 more
core   +1 more source

Neural-NGBoost: Natural gradient boosting with neural network base learners

ICT Express
NGBoost has shown promising results in probabilistic and point estimation tasks. However, it is vague still whether this method can be scalable to neural architecture system since its base learner is based on decision trees.
Jamshidjon Ganiev, Deok-Woong Kim, Seung-Hwan Bae   +2 more
doaj   +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

High‐Performance Transparent, Deformable, and Recoverable Biomimetic Stevia–PVA Hydrogel Triboelectric Nanogenerator with Machine Learning‐Assisted Motion Recognition

Advanced Materials, EarlyView.
A transparent, deformable stevia–PVA hydrogel triboelectric nanogenerator delivers significantly enhanced mechanical strength and electrical output through biomimetic hydrogen‐bonded networks. Coupled with machine learning–assisted signal recognition, the self‐powered hydrogel enables accurate human‐motion sensing for intelligent wearable and IoT ...
Thien Trung Luu, Bui Minh Quang, Toan Minh Pham, Jinsoo Kim, Kyungwho Choi, Dukhyun Choi   +5 more
wiley   +1 more source

Phase Engineering of Nanomaterials (PEN): Evolution, Current Challenges, and Future Opportunities

Advanced Materials, EarlyView.
This review summarizes the synthesis, phase transition, advanced characterization spanning ex situ to in situ and operando techniques, and diverse applications of phase engineering of nanomaterials (PEN). It further outlines key challenges and future opportunities, such as phase stability, architecture control, and artificial intelligence (AI)‐driven ...
Ye Chen, Zhanxi Fan, Jingjie Ge, Qiyuan He, Zhuangchai Lai, Jiawei Liu, Chaoliang Tan, Hua Zhang   +7 more
wiley   +1 more source