Exploring stable isotope patterns in monthly precipitation across Southeast Asia using contemporary deep learning models and SHapley Additive exPlanations (SHAP) techniques [0.03%]
基于深度学习模型和SHAP技术探讨东南亚地区月降水稳定同位素特征
Mojtaba Heydarizad,Nathsuda Pumijumnong,Masoud Minaei et al.
Mojtaba Heydarizad et al.
Keywords: Precipitation; SHAP analysis; Southeast Asia; machine-learning models; simulation; validation.
Mike Pols,Geert Brocks,Sofía Calero et al.
Mike Pols et al.
Using on-the-fly machine-learning force fields trained against density functional theory calculations, we confirm the presence of chiral phonons, a potential key factor for these effects....Keywords: angular momentum; chirality; density functional theory; machine-learning force fields; metal halide perovskites; phonons, chiral phonons.
Clinical characteristics of COVID-19 in children and adolescents: insights from an Italian paediatric cohort using a machine-learning approach [0.03%]
意大利儿童和青少年新型冠状病毒肺炎临床特征及机器学习研究视角
Stefania Fiandrino,Daniele Donà,Carlo Giaquinto et al.
Stefania Fiandrino et al.
It aims to identify patterns in COVID-19 morbidity by clustering individuals based on symptom similarities and duration of symptoms and develop a machine-learning tool to classify new cases into risk groups....First, we apply an unsupervised machine-learning algorithm to cluster individuals into groups.
Illuminating the black box: Machine learning enhances preoperative prediction in intrahepatic cholangiocarcinoma [0.03%]
开启黑箱：机器学习提高术前预测肝内胆管癌的能力
Eyad Gadour,Mohammed S AlQahtani
Eyad Gadour
The study by Huang et al, published in the World Journal of Gastroenterology, advances intrahepatic cholangiocarcinoma (ICC) management by developing a machine-learning model to predict textbook outcomes (TO) based on preoperative factors.
Illuminating the black box: Machine learning enhances preoperative prediction in intrahepatic cholangiocarcinoma [0.03%]
开启黑箱：机器学习增强术前预测肝内胆管癌的可能性
Eyad Gadour,Mohammed S AlQahtani
Eyad Gadour
The study by Huang et al, published in the World Journal of Gastroenterology, advances intrahepatic cholangiocarcinoma (ICC) management by developing a machine-learning model to predict textbook outcomes (TO) based on preoperative factors.
Comprehensive smartphone image dataset for fish species identification in Bangladesh's freshwater ecosystems [0.03%]
用于识别孟加拉国淡水生态系统鱼类物种的全面智能手机图像数据集
Saieef Sunny,Shiam Prodhan,Nazmuj Shakib et al.
Saieef Sunny et al.
By providing an extensive collection of labeled, high-quality images, this dataset serves as a valuable foundation for research on aquatic biodiversity, fishery management, and the development of precise machine-learning models for species recognition.
Prediction of the classification, labelling and packaging regulation H-statements with confidence using conformal prediction with N-grams and molecular fingerprints [0.03%]
基于N元语法和分子指纹的符合预测法自信地预测分类、标签和包装条例H项标准
Ulf Norinder,Ziye Zheng,Ian Cotgreave
Ulf Norinder
Effective chemical hazard labelling systems are essential for safeguarding human health and the environment as a result of widespread chemical use, and machine-learning models can be used to predict hazard labels efficiently and reduce the use of animal tests.
Integrating explainable machine learning and transcriptomics data reveals cell-type specific immune signatures underlying macular degeneration [0.03%]
可解释机器学习与转录组数据的整合揭示年龄相关黄斑变性背后细胞特异性免疫特征
Khang Ma,Hosei Nakajima,Nipa Basak et al.
Khang Ma et al.
Here, we develop an explainable machine-learning pipeline (ML) using transcriptome data of 453 donor retinas, identifying 81 genes distinguishing AMD from controls (AUC-ROC of 0.80, CI 0.70-0.92).
Development of Machine-Learning-Based Models for Detection of Cognitive Impairment in Patients Receiving Maintenance Hemodialysis [0.03%]
基于机器学习的检测维持性血液透析患者认知障碍模型的研究开发
Tsai-Chieh Ling,Chiung-Chih Chang,Jia-Ling Wu et al.
Tsai-Chieh Ling et al.
Conclusions: Our study demonstrates that using machine-learning models, we can identify patients with impaired cognition with only several questions in CASI and MMSE within 5 min.
Observational Study
European journal of neurology. 2025 Jun;32(6):e70246. DOI:10.1111/ene.70246 2025
Machine-learning single-photodetector receiver for the simultaneous detection of hybrid RF and baseband fiber-wireless signals [0.03%]
基于机器学习的单光子探测器接收机同时检测混合射频和基带光纤无线信号
Guo Hao Thng,Lumeng Xu,Said Mikki
Guo Hao Thng
As mobile communication networks advance through each generation, the number and frequency of utilized frequency bands increase. If each supported frequency range is detected using a separate photodetector in a centralized radio access netw...
耗时 0.15936 秒，为您在
48229835
条记录里面共找到 10000 篇文章 [XML]