Analysis of Human Resource Intelligent Recommendation Method Based on Improved Decision Tree Algorithm DOI

Xiaochen Chen

Published: June 28, 2024

Language: Английский

A novel hybrid deep-learning framework for medium-term container throughput forecasting: an application to China’s Guangzhou, Qingdao and Shanghai hub ports DOI
Di Zhang,

Xinyuan Li,

Chengpeng Wan

et al.

Maritime Economics & Logistics, Journal Year: 2024, Volume and Issue: 26(1), P. 44 - 73

Published: Feb. 16, 2024

Language: Английский

Citations

9
Variational autoencoder based on knowledge sharing and correlation weighting for process-quality concurrent fault detection DOI
Ziyuan Wang, Chengzhu Wang, Yonggang Li

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108051 - 108051

Published: Feb. 13, 2024

Language: Английский

Citations

5
TCLN: A Transformer-based Conv-LSTM network for multivariate time series forecasting DOI

Shusen Ma,

Tianhao Zhang, Yun‐Bo Zhao

et al.

Applied Intelligence, Journal Year: 2023, Volume and Issue: 53(23), P. 28401 - 28417

Published: Oct. 2, 2023

Language: Английский

Citations

12
Vacant Parking Space Prediction Based on Auto-Correlation Mechanism DOI
Chao Zeng, Yufan Zhai, Xiaoting Huang

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
A review of research on urban parking prediction DOI Creative Commons
Changxi Ma, Xiaoting Huang, Jiangchen Li

et al.

Journal of Traffic and Transportation Engineering (English Edition), Journal Year: 2024, Volume and Issue: 11(4), P. 700 - 720

Published: July 30, 2024

The rapid growth of urban traffic has intensified daily congestion, affecting both flow and parking. Accurate parking prediction plays a vital role in effectively managing limited resources is essential for the successful implementation advanced intelligent systems. In an effort to comprehensively assess latest developments prediction, we curated dataset 639 articles spanning from 2010 present, using Scopus database. Initially, performed bibliometric analysis utilizing VOSviewer software. These findings not only illuminate emerging trends within field but also provide strategic guidance its progression. Subsequently, categorized advancements three focal areas: behavior demand space prediction. A comprehensive overview present research status future directions was then provided. underscore substantial progress achieved current models, through diverse avenues like multi-source data integration, multi-variable feature extraction, nonlinear relationship modeling, deep learning techniques application, ensemble model utilization. innovative endeavors have pushed theoretical boundaries significantly heightened precision applicability predictive models practical scenarios. Prospective should explore such as processing unstructured datasets, developing small-scale data, mitigating noise interference harnessing potent platform fusion techniques. This study's significance transcends guiding catalyzing advancement academic domains; it holds paramount relevance across research, technological innovation, decision-making support, business applications, policy formulation.

Language: Английский

Citations

3
Traffic Flow Prediction Framework That Can Appropriately Process the Noise, Volatility, and Nonlinearity in Traffic Flow Data DOI Creative Commons

Yingping Tang,

Qiang Shang,

Longjiao Yin

et al.

International Journal of Intelligent Systems, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Accurate traffic flow prediction is crucial for improving transportation efficiency. To improve the accuracy of prediction, we developed a framework—namely, multicomponent network—that appropriately processes noise, volatility, and nonlinearity in data. This framework comprises three components: factor selection component, decomposition component. The component considers dynamic effects weather‐related, environmental, spatiotemporal factors on flow; it then extracts analyzes exhibiting strong correlations with flow. optimizes parameters variational mode basis envelope entropy by using sparrow search algorithm; transforms into multiple intrinsic functions to enable accurate prediction. Finally, constructs feature matrices bidirectional gated recurrent unit model identify relationships within Moreover, uses an attention mechanism assign different weights features importance these thereby enabling efficient processing large volume performance proposed was examined experiments conducted volumes data time granularities. results indicated that achieved high stability various granularities, samples, dataset sizes, noise conditions. generally outperformed existing models under all experimental

Language: Английский

Citations

0
Exploring Urban Parking Solutions: A Literature Review of Predictive Occupancy Models DOI

Sai Sneha Channamallu,

Sharareh Kermanshachi, Jay M. Rosenberger

et al.

International Journal of Intelligent Transportation Systems Research, Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

Language: Английский

Citations

0
Enhanced prediction of parking occupancy through fusion of adaptive neuro-fuzzy inference system and deep learning models DOI
Akram Elomiya, Jiří Křupka, Stefan Jovčić

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 129, P. 107670 - 107670

Published: Dec. 12, 2023

Language: Английский

Citations

9
Spatial and temporal attention-based and residual-driven long short-term memory networks with implicit features DOI

Yameng Zhang,

Yan Song, Guoliang Wei

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 134, P. 108549 - 108549

Published: May 29, 2024

Language: Английский

Citations

3
Enhancing Urban Parking Efficiency Through Machine Learning Model Integration DOI Creative Commons

Sai Sneha Channamallu,

Sharareh Kermanshachi, Jay M. Rosenberger

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 81338 - 81347

Published: Jan. 1, 2024

Language: Английский

Citations

3