Credit Risk Prediction Model for Listed Companies Based on CNN-LSTM and Attention Mechanism

Electronics, Journal Year: 2023, Volume and Issue: 12(7), P. 1643 - 1643

Published: March 30, 2023

The financial market has been developing rapidly in recent years, and the issue of credit risk concerning listed companies become increasingly prominent. Therefore, predicting is an urgent concern for banks, regulators investors. commonly used models are Z-score, Logit (logistic regression model), kernel-based virtual machine (KVM) neural network models. However, results achieved could be more satisfactory. This paper proposes a credit-risk-prediction model based on CNN-LSTM attention mechanism, Our approach benefits long short-term memory (LSTM) long-term time-series prediction combined with convolutional (CNN) model. Furthermore, advantages being integrated into include reducing complexity data, improving calculation speed training solving possible lack historical data sequence LSTM model, resulting accuracy. To reduce problems, we introduced mechanism to assign weights independently optimize show that our distinct compared other CNNs, LSTMs, CNN-LSTMs research credit-risk listing formula significant meaning.

Language: Английский

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Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

Sensors, Journal Year: 2023, Volume and Issue: 23(8), P. 3852 - 3852

Published: April 10, 2023

Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by cameras. With the advancement of technology in recent years, it has received a lot attention researchers applications such as intelligent transportation, public safety self-driving driving technology. As result, large number excellent research results have emerged field MOMCT. To facilitate rapid development need to keep abreast latest current challenges related field. Therefore, this paper provide comprehensive review multi-object multi-camera tracking based on deep learning for transportation. Specifically, we first introduce main object detectors MOMCT detail. Secondly, give an in-depth analysis evaluate advanced methods through visualisation. Thirdly, summarize popular benchmark data sets metrics quantitative comparisons. Finally, point out faced transportation present practical suggestions future direction.

Language: Английский

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Automatic Lip-reading Classification Using Deep Learning Approaches and Optimized Quaternion Meixner Moments by GWO algorithm

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 304, P. 112430 - 112430

Published: Sept. 5, 2024

Language: Английский

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RL-GCN: Traffic flow prediction based on graph convolution and reinforcement learning for smart cities

Displays, Journal Year: 2023, Volume and Issue: 80, P. 102513 - 102513

Published: Sept. 4, 2023

Language: Английский

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An incremental learning approach to dynamic parallel machine scheduling with sequence-dependent setups and machine eligibility restrictions

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 164, P. 112002 - 112002

Published: July 15, 2024

Language: Английский

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Causal embedding of user interest and conformity for long-tail session-based recommendations

Information Sciences, Journal Year: 2023, Volume and Issue: 644, P. 119167 - 119167

Published: May 18, 2023

Language: Английский

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MS3Net: Multiscale stratified-split symmetric network with quadra-view attention for hyperspectral image classification

Signal Processing, Journal Year: 2023, Volume and Issue: 212, P. 109153 - 109153

Published: June 15, 2023

Language: Английский

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Intelligent large-scale flue-cured tobacco grading based on deep densely convolutional network

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: July 10, 2023

Abstract Flue-cured tobacco grading plays a crucial role in leaf purchase and the formulation of groups. However, traditional flue-cured mode is usually manual, which time-consuming, laborious, subjective. Hence, it essential to research more efficient intelligent methods. Most existing methods suffer from classes less accuracy problem. Meanwhile, limited by different industry applications, datasets are hard be obtained publicly. The employ relatively small lower resolution data that apply practice. Therefore, aiming at insufficiency feature extraction ability inadaptability multiple grades, we collected largest highest dataset proposed an method based on deep densely convolutional network (DenseNet). Diverging other approaches, our has unique connectivity pattern neural concatenates preceding data. This connects all previous layers subsequent layer directly for transmission. idea can better extract depth image information features transmit each layer’s data, thereby reducing loss encouraging reuse. Then, designed whole pre-processing process experimented with learning algorithms verify usability. experimental results showed DenseNet could easily adapted changing output fully connected layers. With 0.997, significantly higher than methods, came best model solving

Language: Английский

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A review of image features extraction techniques and their applications in image forensic

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(40), P. 87801 - 87902

Published: March 20, 2024

Language: Английский

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Path analysis of energy economic management standardization in the context of carbon neutralization and carbon peak

Frontiers in Ecology and Evolution, Journal Year: 2023, Volume and Issue: 11

Published: March 13, 2023

Carbon neutrality and carbon peak are two important measures to control climate change. They have a huge impact on many companies in the fields of energy, industry, construction, transportation, etc. can change development pattern related industries increase new investment opportunities. This paper proposes path analysis standardization energy economic management under background peak, aiming study forecast low-carbon conditions. The algorithm proposed this is an consumption based IPAT model, which be combined with model analyze process data. In addition, by analyzing evaluating contribution various factors, people better understand environment formulate corresponding solutions. experimental results show that, from 2013 2017, baseline scenario, emissions increased year year, 9.25 billion tons 10.48 tons. Under neutral its 9.22 tons, 9.24 9.19 9.21 respectively. Obviously, controlled through strategies. Through these prediction results, it proved that peaking excellent effects promoting management. At same time, also provides valuable reference information for further research peaks.

Language: Английский

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