Online public opinion prediction based on a novel conformable fractional discrete grey model DOI Creative Commons
Feng Feng, Xiaoxiao Ge, Stefania Tomasiello

et al.

Kybernetes, Journal Year: 2024, Volume and Issue: 53(13), P. 72 - 100

Published: Nov. 27, 2024

Purpose As social networks have developed to be a ubiquitous platform of public opinion spreading, it becomes more and crucial for maintaining security stability by accurately predicting various trends dissemination in networks. Considering the fact that online is dynamic process full uncertainty complexity, this study establishes novel conformable fractional discrete grey model with linear time-varying parameters, namely CFTDGM(1,1) model, accurate prediction trends. Design/methodology/approach First, accumulation difference operators are employed build enhancing traditional integer-order parameters. Then, improve forecasting accuracy, base value correction term introduced optimize iterative model. Next, differential evolution algorithm selected determine optimal order proposed through comparison whale optimization particle swarm algorithm. The least squares method utilized estimate parameter values In addition, effectiveness tested event about “IG team winning championship”. Finally, we conduct empirical analysis on two hot events regarding “Chengdu toddler mauled Rottweiler” “Mayday band suspected lip-syncing,” further assess ability applicability seven other existing models. Findings test case recent reveal outperforms most models terms performance. Therefore, chosen forecast development these events. results indicate attention both will decline slowly over next three days. Originality/value A help has higher accuracy feasibility trend prediction.

Language: Английский

An adaptive financial trading strategy based on proximal policy optimization and financial signal representation DOI
Lin Wang, Xuerui Wang

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 138, P. 109365 - 109365

Published: Sept. 26, 2024

Language: Английский

Citations

0
Multi-view Deep Embedded Clustering: Exploring a new dimension of air pollution DOI
Hassan Kassem, Sally El Hajjar, Fahed Abdallah

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 139, P. 109509 - 109509

Published: Oct. 28, 2024

Language: Английский

Citations

0
Prediction of CO 2 emissions to achieve net-zero objectives in the iron and steel sectors of North America DOI

Ángel Francisco Galaviz Román,

Golam Kabir, Mohammad I. Azim

et al.

International Journal of Management Science and Engineering Management, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 14

Published: Nov. 14, 2024

Predictive models are widely used to create effective plans for reducing CO2 emissions in manufacturing. The Iron and Steel (I&S) industries play a crucial role meeting international commitments achieve Net-zero by 2050. objective of this study is forecast carbon dioxide from the I&S North America through utilization Multi-Objective Mathematical model. proposed data-driven approach integrated with various machine learning algorithms capable accurately predicting future values small dataset. Additionally, sensitivity analyses under different scenarios conducted evaluate impact implementing solutions research community. Results show significant improvement accuracy employment Whale Optimization Algorithm (WOA). Forecasts reveal sustained increment 0.7 MtCO2 every year spanning between 2022 This provides valuable information stakeholders policymakers as it allows more precise evaluation integrate new technologies abate forthcoming emissions.

Language: Английский

Citations

0
Integrating agent-based models and clustering methods for improving image segmentation DOI Creative Commons
Erik Cuevas,

Sonia García-De-Lira,

Cesar Ascencio-Piña

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 11(1), P. e40698 - e40698

Published: Nov. 29, 2024

Language: Английский

Citations

0
Online public opinion prediction based on a novel conformable fractional discrete grey model DOI Creative Commons
Feng Feng, Xiaoxiao Ge, Stefania Tomasiello

et al.

Kybernetes, Journal Year: 2024, Volume and Issue: 53(13), P. 72 - 100

Published: Nov. 27, 2024

Purpose As social networks have developed to be a ubiquitous platform of public opinion spreading, it becomes more and crucial for maintaining security stability by accurately predicting various trends dissemination in networks. Considering the fact that online is dynamic process full uncertainty complexity, this study establishes novel conformable fractional discrete grey model with linear time-varying parameters, namely CFTDGM(1,1) model, accurate prediction trends. Design/methodology/approach First, accumulation difference operators are employed build enhancing traditional integer-order parameters. Then, improve forecasting accuracy, base value correction term introduced optimize iterative model. Next, differential evolution algorithm selected determine optimal order proposed through comparison whale optimization particle swarm algorithm. The least squares method utilized estimate parameter values In addition, effectiveness tested event about “IG team winning championship”. Finally, we conduct empirical analysis on two hot events regarding “Chengdu toddler mauled Rottweiler” “Mayday band suspected lip-syncing,” further assess ability applicability seven other existing models. Findings test case recent reveal outperforms most models terms performance. Therefore, chosen forecast development these events. results indicate attention both will decline slowly over next three days. Originality/value A help has higher accuracy feasibility trend prediction.

Language: Английский

Citations

0