Results 51 to 60 of about 498 (210)
ABSTRACT Urban bus accidents present major safety and operational challenges, particularly in densely populated metropolitan areas. This study develops a machine learning‐based analytical framework to identify, quantify, and interpret the factors associated with severe bus accidents.
Bowei Chen +3 more
wiley +1 more source
Mining High Utility Itemsets Based on Pattern Growth without Candidate Generation
Mining high utility itemsets (HUIs) has been an active research topic in data mining in recent years. Existing HUI mining algorithms typically take two steps: generating candidates and identifying utility values of these candidate itemsets.
Yiwei Liu, Le Wang, Lin Feng, Bo Jin
doaj +1 more source
FTKHUIM: A Fast and Efficient Method for Mining Top-K High-Utility Itemsets
High-utility itemset mining (HUIM) is an important task in the field of knowledge data discovery. The large search space and huge number of HUIs are the consequences of applying HUIM algorithms with an inappropriate user-defined minimum utility threshold
Vinh V. Vu +8 more
doaj +1 more source
The purpose of this study was to explore the risk factors for autonomous vehicle (AV) crashes and their interdependencies. A total of 659 AV crash data were collected between 2018 and July 2024 from AV crash reports published by the California Department of Motor Vehicles.
Tao Wang +4 more
wiley +1 more source
Identifying the Focus Word in Natural Language Questions Based on Association Rules
Knowledge base‐based intelligent question‐answering systems have insufficient understanding of the questions. In the early stages of research, it is effective in most cases that the existing natural language question‐understanding methods can answer questions by connecting entities and relationships when ignoring the identification of focus words ...
Xin Hu +5 more
wiley +1 more source
PUC: parallel mining of high-utility itemsets with load balancing on spark
Distributed programming paradigms such as MapReduce and Spark have alleviated sequential bottleneck while mining of massive transaction databases. Of significant importance is mining High Utility Itemset (HUI) that incorporates the revenue of the items ...
Brahmavar Anup Bhat +2 more
doaj +1 more source
mHUIMiner: A Fast High Utility Itemset Mining Algorithm for Sparse Datasets
High utility itemset mining is the problem of finding sets of items whose utilities are higher than or equal to a specific threshold. We propose a novel technique called mHUIMiner, which utilises a tree structure to guide the itemset expansion process to
Peng, AY +5 more
core +1 more source
ABSTRACT Machine learning techniques are increasingly used for high‐stakes decision‐making, such as college admissions, loan attribution, or recidivism prediction. Thus, it is crucial to ensure that the models learnt can be audited or understood by human users, do not create or reproduce discrimination or bias and do not leak sensitive information ...
Julien Ferry +4 more
wiley +1 more source
High Utility Itemset Mining by Using Binary PSO Algorithm
The goal of pattern mining is to find some novel patterns from a given database. High utility itemset mining (HUIM) is a research direction of the pattern mining as a sub-domain of data mining.
TAO BODONG
core
Abstract Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer, thereby facilitating the formulation of power outage maintenance plans and power dispatch strategies. However, existing prediction methods based on the structure of ‘splicing prediction and diagnosis method’ suffer ...
Peng Zhang +5 more
wiley +1 more source

