Results 11 to 20 of about 22,816,084 (277)

Graph-based representation for identifying individual travel activities with spatiotemporal trajectories and POI data

Scientific Reports, 2022
Individual daily travel activities (e.g., work, eating) are identified with various machine learning models (e.g., Bayesian Network, Random Forest) for understanding people’s frequent travel purposes.
Xinyi Liu, Meiliu Wu, Bo Peng, Qunying Huang   +3 more
doaj   +1 more source

Data mining

, 2018
Data mining support for priority ranking of SLC family members for target selection in WP4.
Daniel T. Larose, Chantal D. Larose
  +5 more sources

CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories

IEEE Transactions on Knowledge and Data Engineering, 2021
CRISP-DM(CRoss-Industry Standard Process for Data Mining) has its origins in the second half of the nineties and is thus about two decades old. According to many surveys and user polls it is still the de facto standard for developing data mining and ...
Fernando Martínez-Plumed, Lidia Contreras-Ochando, C. Ferri, José Hernández Orallo, Meelis Kull, N. Lachiche, M. J. R. Quintana, P. Flach   +7 more
semanticscholar   +1 more source

A Scheme for Quickly Simulating Extraterrestrial Solar Radiation over Complex Terrain on a Large Spatial-Temporal Span—A Case Study over the Entirety of China

Remote Sensing, 2022
Extraterrestrial solar radiation (ESR) is the essential basic background for solar radiation, which determines the occurrence of the weather and atmospheric phenomena.
Siwei Lin, Nan Chen, Qianqian Zhou, Tinmin Lin, Huange Li   +4 more
doaj   +1 more source

Prediction of Inbound and Outbound Passenger Flow in Urban Rail Transit Based on Spatio-Temporal Attention Residual Network

Applied Sciences, 2023
Passenger flow prediction is a critical approach to ensure the effective functioning of urban rail transit. However, there are few studies that combine multiple influencing factors for short-term passenger flow prediction.
Jun Yang, Xueru Dong, Huifan Yang, Xiao Han, Yan Wang, Jiayue Chen   +5 more
doaj   +1 more source

Data-mining chess databases [PDF]

, 2010
This is a report on the data-mining of two chess databases, the objective being to compare their sub-7-man content with perfect play as documented in Nalimov endgame tables. Van der Heijden’s ENDGAME STUDY DATABASE IV is a definitive collection of 76,132
Bleicher, Eiko, Haworth, Guy McCrossan, van der Heijden, Harold M J F   +2 more
core   +1 more source

Immersive cultural heritage digital documentation and information service for historical figure metaverse: a case of Zhu Xi, Song Dynasty, China

Heritage Science, 2022
Cultural heritage is closely linked with individual historical figures, who become a key focus for cultural tourism. Confucianism laid the foundation for much of Chinese civilization, and Confucius and Mencius have been studied extensively and have been ...
Zhanling Fan, Chongcheng Chen, Hongyu Huang   +2 more
doaj   +1 more source

Learning Data Mining [PDF]

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), 2018
In the last decade the usage and study of data mining and machine learning algorithms have received an increasing attention from several and heterogeneous fields of research. Learning how and why a certain algorithm returns a particular result, and understanding which are the main problems connected to its execution is a hot topic in the education of ...
Guidotti R., Monreale A., Rinzivillo S.
openaire   +4 more sources

Semi-Supervised Learning Based on Generative Adversarial Network and Its Applied to Lithology Recognition

IEEE Access, 2019
Lithology recognition is an essential part of reservoir parameter prediction. Compared to conventional algorithms, deep learning that needs a large amount of training data as support can extract features automatically.
Guohe Li, Yinghan Qiao, Yifeng Zheng, Ying Li, Weijiang Wu   +4 more
doaj   +1 more source

Crowd counting via Multi-Scale Adversarial Convolutional Neural Networks

Journal of Intelligent Systems, 2020
The purpose of crowd counting is to estimate the number of pedestrians in crowd images. Crowd counting or density estimation is an extremely challenging task in computer vision, due to large scale variations and dense scene.
Zhu Liping, Zhang Hong, Ali Sikandar, Yang Baoli, Li Chengyang   +4 more
doaj   +1 more source