Results 71 to 80 of about 8,833 (199)
Data‐Driven Materials Research and Development for Functional Coatings
Advanced Science, Volume 11, Issue 42, November 13, 2024.Functional coatings play a vital role in various industries for their protective and functional properties. However, its design often involves time‐consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data‐driven approaches are gaining traction in materials science. This review provides an overview
Kai Xu +8 more
wiley +1 more source
A Fast Approach for Up-Scaling Frequent Itemsets
IEEE Access, 2020 With the rapid growth of data scale and diversification of demand, people have an urgent desire to extract useful frequent itemset from datasets of different scales. It is no doubt that the traditional method can solve the problem.
Runzi Chen, Shuliang Zhao, Mengmeng Liu
doaj +1 more source
IET Generation, Transmission &Distribution, Volume 18, Issue 13, Page 2313-2324, July 2024.This paper proposes an electric vehicle (EV) load diagnosis algorithm considering data privacy. The validity of the algorithm in this paper is verified by using the real collected EV load data. Abstract Accurate forecasting of electric vehicle (EV) load is essential for grid stability and energy management. EV load forecasting is influenced by multiple
Ruien Bian +3 more
wiley +1 more source
Reconstructing thicket clump formation using association rules analysis
Journal of Vegetation Science, Volume 35, Issue 3, May/June 2024.Association rules (or market basket) analysis was effective in eliciting common associations between species and size classes across different stages of thicket clump formation in a savanna. Vachellia karroo established alone in open grassland, whereas a suite of clump‐initiating species recruited in close association with large V.
Rhys Nell +2 more
wiley +1 more source
An efficient closed frequent itemset miner for the MOA stream mining system [PDF]
, 2013 Mining itemsets is a central task in data mining, both in the batch and the streaming paradigms. While robust, efficient, and well-tested implementations exist for batch mining, hardly any publicly available equivalent exists for the streaming scenario ...
Bifet Figuerol, Albert Carles +2 more
core +1 more source
IET Intelligent Transport Systems, Volume 18, Issue 3, Page 517-527, March 2024.Abstract This study aims to explore the relationship between traffic flow states and crash type/severity in the scenarios of normal crashes, primary crashes, and secondary crashes using the association rules mining approach. The crash data and real‐time traffic data were collected from the I‐880 freeway for five years in California, USA.
Bo Yang +4 more
wiley +1 more source
JUTI: Jurnal Ilmiah Teknologi Informasi, 2002 Data transaksi tiruan yang menyerupai transaksi nyata pada lingkungan ritel dibutuhkan dalam pengetesan teknik data mining untuk pencarian pola asosiasi dan pola sekuensial dari basis data berskala besar.
Arif Djunaidy +2 more
doaj +1 more source
DLLog: An Online Log Parsing Approach for Large‐Scale System
International Journal of Intelligent Systems, Volume 2024, Issue 1, 2024.Syslog is a critical data source for analyzing system problems. Converting unstructured log entries into structured log data is necessary for effective log analysis. However, existing log parsing methods demonstrate promising accuracy on limited datasets, but their generalizability and precision are uncertain when applied to diverse log data ...
Hailong Cheng +4 more
wiley +1 more source
Frequent Itemset Mining for Big Data Using Greatest Common Divisor Technique
Data Science Journal, 2017 The discovery of frequent itemsets is one of the very important topics in data mining. Frequent itemset discovery techniques help in generating qualitative knowledge which gives business insight and helps the decision makers. In the Big Data era the need
Mohamed A. Gawwad +2 more
doaj +1 more source
A Model for Predicting IoT User Behavior Based on Bayesian Learning and Neural Networks
Journal of Computer Networks and Communications, Volume 2024, Issue 1, 2024.To facilitate the allocation of energy and resources in the Internet of Things system, this paper presents a model for predicting user behavior in Internet of Things environments. The model is based on Bayesian learning and neural networks and is designed to provide insights into the future behavior of users, allowing for the allocation of resources in
Xin Xu +3 more
wiley +1 more source