Results 51 to 60 of about 36,481,271 (303)
Advanced Functional Materials, EarlyView.ABSTRACT Electronic waste has emerged as a major environmental challenge, driven by the massive consumption and a limited lifetime of modern electronic devices, stimulating the development of sustainable electronics. Here, an all‐biomaterial gelatin‐choline‐citric acid ([Ch][CA]) ionogel is developed as an active binder to realize self‐sintered ...
Lin Guo +10 more
wiley +1 more source
IEEE Access, 2019 Deep Neural Networks (DNNs) has been dominating recent data mining related tasks with better performances. This article proposes a hybrid approach that exploits the unique predictive power of DNN and classical time series regression models, including ...
Shancheng Jiang +7 more
doaj +1 more source
A data mining framework for time series estimation
Journal of Biomedical Informatics, 2010 Time series estimation techniques are usually employed in biomedical research to derive variables less accessible from a set of related and more accessible variables. These techniques are traditionally built from systems modeling approaches including simulation, blind decovolution, and state estimation.
Xiao Hu 0002 +4 more
openaire +3 more sources
Neural Network Ensembles for Time Series Prediction [PDF]
, 2007 Rapidly evolving businesses generate massive amounts of time-stamped data sequences and defy a demand for massively multivariate time series analysis. For such data the predictive engine shifts from the historical auto-regression to modelling complex
Ruta, Dymitr +3 more
core +1 more source
Advanced Materials, EarlyView.Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu +3 more
wiley +1 more source
Detecting anomalies in multivariate time series from automotive systems [PDF]
, 2013 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the automotive industry test drives are conducted during the development of new vehicle models or as a part of quality assurance for series vehicles ...
Theissler, Andreas
core
Spatiotemporal Data Mining Problems and Methods
Analytics, 2023 Many scientific fields show great interest in the extraction and processing of spatiotemporal data, such as medicine with an emphasis on epidemiology and neurology, geology, social sciences, meteorology, and a great interest is also observed in the study
Eleftheria Koutsaki +2 more
doaj +1 more source
T-Time: Threshold-Based Data Mining on Time Series [PDF]
2008 IEEE 24th International Conference on Data Engineering, 2008 Mining time series data is an important approach for the analysis in many application areas as diverse as biology, environmental research, medicine, or stock chart analysis. As nearly all data mining tasks on this kind of data depend on a distance function between two time series, a huge number of such functions has been developed during the last ...
Johannes Aßfalg +5 more
openaire +1 more source
catch22: CAnonical time-series CHaracteristics [PDF]
, 2019 Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry.
Knaute, Philip +5 more
core +1 more source
Breaking Bottlenecks in Solid Electrolyte Discovery With Large Artificial Intelligence Models
Advanced Materials, EarlyView.Large AI models are presented as a route to break key bottlenecks in solid electrolyte discovery by linking materials databases, machine‐learning interatomic potentials, large language models, and autonomous experiments. This perspective highlights how AI can accelerate the identification of solid electrolytes with fast ion transport, stable interfaces,
Eric Jianfeng Cheng +24 more
wiley +1 more source