Results 81 to 90 of about 119,353 (308)

The Third‐Generation Magnetic Super‐Stable Mineralizer: Complete Removal and Separation of Multiple Heavy Metal Pollutants

open access: yesAdvanced Science, EarlyView.
The 3rd‐generation magnetic super‐stable mineralizer M‐MgAl‐700 features a core‐shell structure, strong superparamagnetism, large surface area, and abundant oxygen defects. It achieves exceptional Cd(II) and As(V) mineralization capacities, reducing both pollutants in co‐contaminated water and soil to meet national standards.
Haoran Wang   +9 more
wiley   +1 more source

TEXT MINING – PREREQUISITE FOR KNOWLEDGE MANAGEMENT SYSTEMS [PDF]

open access: yes
Text mining is an interdisciplinary field with the main purpose of retrieving new knowledge from large collections of text documents. This paper presents the main techniques used for knowledge extraction through text mining and their main areas of ...
Dragoº Marcel VESPAN
core  

Phase Fraction Modulation Enhances Li+/Na+ Diffusion Disparity in Spent LiFePO4 Cathodes for Efficient Lithium Extraction from Brine

open access: yesAdvanced Science, EarlyView.
A direct cascaded utilization strategy for spent LiFePO4 batteries efficiently extracts lithium from brine, embodying the “urban mining” concept. Phase fraction modulation revealed its significant impact on local bond lengths of crystal structure and the Li/Na diffusion energy barrier, identifying the best material (Li0.19FePO4) for lithium extraction.
Ruiqi Yin   +7 more
wiley   +1 more source

A Review of Text Corpus-Based Tourism Big Data Mining

open access: yesApplied Sciences, 2019
With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism ...
Qin Li   +4 more
doaj   +1 more source

Ten tips for a text-mining-ready article: How to improve automated discoverability and interpretability.

open access: yesPLoS Biology, 2020
Data-driven research in biomedical science requires structured, computable data. Increasingly, these data are created with support from automated text mining.
Robert Leaman   +3 more
doaj   +1 more source

A new rule pruning text categorisation method [PDF]

open access: yes, 2010
Associative classification integrates association rule and classification in data mining to build classifiers that are highly accurate than that of traditional classification approaches such as greedy and decision tree.
Hadi, Wa'el   +9 more
core   +1 more source

Grade Prediction via Prior Grades and Text Mining on Course Descriptions: Course Outlines and Intended Learning Outcomes

open access: yes, 2022
Academic grades in assessments are predicted to determine if a student is at risk of failing a course. Sequential models or graph neural networks that have been employed for grade prediction do not consider relationships between course descriptions.
S. Supraja   +7 more
core   +1 more source

Environmental Insights and Sustainability Opportunities for Scaled‐Up MXene Production Without Etching

open access: yesAdvanced Science, EarlyView.
Through a systematic and comprehensive analysis of the environmental impacts for the emerging MXene synthesis pathways, this study presents process transformation and optimization opportunities for low‐carbon MXene production from laboratory to industrial scales.
Yushuai Huang   +6 more
wiley   +1 more source

How Advanced Artificial Intelligence Technologies Shape Drug–Drug and Drug–Target Interaction Modeling

open access: yesAdvanced Science, EarlyView.
This review explores the convergence of artificial intelligence technologies in modeling drug–drug and drug–target interactions. By evaluating advanced feature engineering, architectural innovations, and learning paradigms reveals shared evolutionary trends and critical challenges, such as cold‐start settings and shortcut learning.
Xin Sun, Tong Wang
wiley   +1 more source

Clustering documents with active learning using Wikipedia

open access: yes, 2008
Wikipedia has been applied as a background knowledge base to various text mining problems, but very few attempts have been made to utilize it for document clustering. In this paper we propose to exploit the semantic knowledge in Wikipedia for clustering,
Ian H. Witten   +7 more
core   +1 more source

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