Intelligent Asset Allocation Portfolio Division and Recommendation DOI Open Access

Liang Cai,

Zhixin Wu

Journal of Organizational and End User Computing, Journal Year: 2024, Volume and Issue: 36(1), P. 1 - 23

Published: Sept. 16, 2024

With the continuous development of financial markets, intelligent asset allocation has become a topic great concern in investment field. However, traditional methods often face difficulties grasping relationship between diversity, risk and return, which limits its application complex market environments. To solve this problem, study introduces deep learning knowledge graphs proposes an model. Our model makes full use advantages Knowledge Graph Embedding Model (KGE), LSTM, Genetic Algorithm (GA) to build multi-level multi-dimensional KGE helps capture relationships different assets, LSTM is used learn key patterns historical portfolio performance, GA finds optimal combination by simulating natural selection genetic mechanisms. Experimental findings indicate that our demonstrated substantial improvements across various performance metrics outperforms conventional approaches.

Language: Английский

A high-precision crown control strategy for hot-rolled electric steel using theoretical model-guided BO-CNN-BiLSTM framework DOI
Chunning Song, Jianguo Cao,

Qiufang Zhao

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 152, P. 111203 - 111203

Published: Jan. 5, 2024

Language: Английский

Citations

8
Comparative analysis of advanced deep learning models for predicting evapotranspiration based on meteorological data in bangladesh DOI

Sourov Paul,

Syeda Zehan Farzana, Saikat Das

et al.

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 4, 2024

Language: Английский

Citations

3
Physics-informed machine learning for HSV performance degradation prediction in water hydraulic manipulator DOI
Ruidong Hong, Songlin Nie,

Hui Ji

et al.

Reliability Engineering & System Safety, Journal Year: 2025, Volume and Issue: unknown, P. 111106 - 111106

Published: April 1, 2025

Language: Английский

Citations

0
Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network DOI Creative Commons

Wuyi Chen,

Huafeng Cai, Sun Qiu

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(8), P. 4430 - 4430

Published: April 17, 2025

In order to combat the difficulty of fault feature extraction and recognition in field bearing diagnosis, a diagnosis method based on improved variational mode decomposition (VMD) parallel hybrid neural network is proposed, which combines reweighted kurtosis (RK) with variable uses as evaluation index select times decomposition, while removing part interference signal retaining its impact characteristics. Afterwards, processed data set brought into model global average pooling layer (GAP) for extraction, fusion, classification. The can extract features more comprehensively improve accuracy speed up training testing. Experiments Xian Jiao tong University (XJTU) Case Western Reserve (CWRU) public sets show that reaches 99.72% 99.73%, respectively, indicating has good better performance compared other models.

Language: Английский

Citations

0
Advancements in Healthcare Medical Imaging through SHO optimized CNN DOI Open Access
Umang Kumar Agrawal, Nibedan Panda, Prithviraj Mohanty

et al.

Procedia Computer Science, Journal Year: 2025, Volume and Issue: 258, P. 4128 - 4135

Published: Jan. 1, 2025

Language: Английский

Citations

0
Application of a grey wolf optimization-enhanced convolutional neural network and bidirectional gated recurrent unit model for credit scoring prediction DOI Creative Commons
Yetong Fang

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(5), P. e0322225 - e0322225

Published: May 27, 2025

With the digital transformation of financial industry, credit score prediction, as a key component risk management, faces increasingly complex challenges. Traditional scoring methods often have difficulty in fully capturing characteristics large-scale, high-dimensional data, resulting limited prediction performance. To address these issues, this paper proposes model that combines CNNs and BiGRUs, uses GWO algorithm for hyperparameter tuning. CNN performs well feature extraction can effectively capture patterns customer historical behaviors, while BiGRU is good at handling time dependencies, which further improves accuracy model. The introduced to improve overall performance by optimizing parameters. Experimental results show CNN-BiGRU-GWO proposed on multiple public datasets, significantly improving efficiency prediction. On LendingClub loan dataset, MAE 15.63, MAPE 4.65%, RMSE 3.34, MSE 12.01, are 64.5%, 68.0%, 21.4%, 52.5% lower than traditional method plawiak 44.07, 14.51%, 4.25, 25.29, respectively. In addition, compared with methods, also shows stronger advantages adaptability generalization ability. By integrating advanced technologies, not only provides an innovative technical solution but valuable insights into application deep learning field, making up shortcomings existing demonstrating its potential wide management.

Language: Английский

Citations

0
Evaluating smart grid investment drivers and creating effective policies via a fuzzy multi-criteria approach DOI
Hasan Dınçer, R. Krishankumar, Serhat Yüksel

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 208, P. 115052 - 115052

Published: Oct. 31, 2024

Language: Английский

Citations

2
Deep learning approaches for short-crop reference evapotranspiration estimation: a case study in Southeastern Australia DOI

Uaktho Baishnab,

Md. Sahadat Hossen Sajib,

Ashraful Islam

et al.

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Dec. 4, 2024

Language: Английский

Citations

2
Enterprise financial sharing and risk identification model combining recurrent neural networks with transformer model supported by blockchain DOI Creative Commons
Yang Wu

Heliyon, Journal Year: 2024, Volume and Issue: 10(12), P. e32639 - e32639

Published: June 1, 2024

The objective of this study is to investigate methodologies concerning enterprise financial sharing and risk identification mitigate concerns associated with the safeguarding data. Initially, analysis examines security vulnerabilities inherent in conventional information practices. Subsequently, blockchain technology introduced transition various entity nodes within centralized networks into a decentralized framework, culminating formulation blockchain-based model for data sharing. Concurrently, integrates Bi-directional Long Short-Term Memory (BiLSTM) algorithm transformer model, presenting an referred as BiLSTM-fused model. This amalgamates multimodal sequence modeling comprehensive understanding both textual visual It stratifies values levels 1 5, where level signifies most favorable condition, followed by relatively good (level 2), average 3), high 4), severe 5). Subsequent construction, experimental conducted, revealing that, comparison Byzantine Fault Tolerance (BFT) mechanism, proposed achieves throughput exceeding 80 node count 146. Both message leakage packet loss rates remain below 10 %. Moreover, when juxtaposed recurrent neural (RNNs) algorithm, demonstrates accuracy surpassing 94 %, AUC value 0.95, reduction time required approximately s. Consequently, facilitates more precise efficient potential risks, thereby furnishing crucial support management strategic decision-making endeavors.

Language: Английский

Citations

0
Intelligent Asset Allocation Portfolio Division and Recommendation DOI Open Access

Liang Cai,

Zhixin Wu

Journal of Organizational and End User Computing, Journal Year: 2024, Volume and Issue: 36(1), P. 1 - 23

Published: Sept. 16, 2024

With the continuous development of financial markets, intelligent asset allocation has become a topic great concern in investment field. However, traditional methods often face difficulties grasping relationship between diversity, risk and return, which limits its application complex market environments. To solve this problem, study introduces deep learning knowledge graphs proposes an model. Our model makes full use advantages Knowledge Graph Embedding Model (KGE), LSTM, Genetic Algorithm (GA) to build multi-level multi-dimensional KGE helps capture relationships different assets, LSTM is used learn key patterns historical portfolio performance, GA finds optimal combination by simulating natural selection genetic mechanisms. Experimental findings indicate that our demonstrated substantial improvements across various performance metrics outperforms conventional approaches.

Language: Английский

Citations

0