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检索条件"任意字段=IEEE Symposium on Computational Intelligence for Financial Engineering and Economics"

共 698 条记录，以下是81-90 订阅

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Designing Heuristic-Based Tuners for PID Controllers in Automatic Voltage Regulator Systems Using an Automated Hyper-Heuristic Approach

Designing Heuristic-Based Tuners for PID Controllers in Auto...

引用

2023 ieee symposium Series on computational intelligence, SSCI 2023

作者： Zambrano-Gutierrez, Daniel F. Molina-Porras, Alberto C. Avina-Cervantes, Juan G. Correa, Rodrigo Cruz-Duarte, Jorge M. Tecnologico de Monterrey Monterrey64849 Mexico University of Guanajuato Salamanca36885 Mexico Universidad Industrial de Santander Bucaramanga680002 Colombia

ISBN: (纸本)9781665430654

engineering processes often require optimizing model variables for satisfactory solutions. Reliable approaches exist in literature but are application-dependent. In that sense, metaheuristics have been proven to deliver outstanding results while imposing a low computing burden. However, choosing the most suitable one from the many available can overwhelm even experts. This study implements a methodology that automatically tailors a problem-based metaheuristic through a hyper-heuristic approach. We select the tuning problem of a Proportional Integral Derivative controller as a case study for achieving the best stable features in an Automatic Voltage Regulator system. The numerical results demonstrate the reliability and potential of the implemented methodology in solving control system tuning. Plus, we conduct an in-depth quantitative comparison with recent works in the literature that support those conclusions. © 2023 ieee.

关键词： Automatic Algorithm design Complex Systems Control engineering Metaheuristics Parameter Tuning

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A lightweight YOLOv8n snapper individual detection algorithm combining multiple convolutions 9

A lightweight YOLOv8n snapper individual detection algorithm...

引用

9th ieee International Conference on computational intelligence and Applications, ICCIA 2024

作者： Du, Zhangheng Wu, Yueting Su, Kangjie Huang, Zhong Liu, Huizhou Huang, Mengxing School of Information and Communication Engineering Hainan University Haikou China

ISBN: (纸本)9798350352214

In this paper, the problem of individual sorting of snapper in the fishing process is deeply discussed, and an improved detection method of snapper based on YOLOv8n algorithm is proposed. Through the introduction of distributed shift convolution (DSConv), space and channel reconstruction Convolution (ScConv) and evolutionary network (InceptionDW-Conv), the detection accuracy is improved while the model size is significantly reduced. The experimental results show that the average accuracy of the improved model on the verification set is increased to 89.4%, which is 1.1% higher than that of the original model. At the same time, compared with the original model, the calculation cost of the improved model is significantly reduced, from the original 8.2GFLOPs to 6.3GFLOPs, the training loss is also lower, the convergence speed is faster, and the verification set loss is lower. In addition, the introduction of WIOU loss function and SoftNMS further improves the accuracy of the detector. In summary, the improved model not only reduces the calculation cost, but also maintains the high-precision individual detection effect of fish, and provides a reliable technical support for the classification of snapper in the fishing process. © 2024 ieee.

关键词： Fisheries

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Fed-RD: Privacy-Preserving Federated Learning for financial Crime Detection

Fed-RD: Privacy-Preserving Federated Learning for Financial ...

引用

Proceedings of the ieee/IAFE computational intelligence for financial engineering (CIFEr)

作者： Md. Saikat Islam Khan Aparna Gupta Oshani Seneviratne Stacy Patterson Department of Computer Science Lally School of Management Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798350354836

ISBN: (纸本)9798350354843

We introduce Federated Learning for Relational Data (Fed-RD), a novel privacy-preserving federated learning algorithm specifically developed for financial transaction datasets partitioned vertically and horizontally across parties. Fed-RD strategically employs differential privacy and secure multiparty computation to guarantee the privacy of training data. We provide theoretical analysis of the end-to-end privacy of the training algorithm and present experimental results on realistic synthetic datasets. Our results demonstrate that Fed - Rdachieves high model accuracy with minimal degradation as privacy increases, while consistently surpassing benchmark results.

关键词： Training Data privacy Privacy Accuracy Federated learning Training data Real-time systems Partitioning algorithms Security General Data Protection Regulation

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Towards Enhanced Information Access in Finance: A Dataset for Table Structure Understanding in Annual Securities Reports

Towards Enhanced Information Access in Finance: A Dataset fo...

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Proceedings of the ieee/IAFE computational intelligence for financial engineering (CIFEr)

作者： Kazuma Kadowaki Yasutomo Kimura Hokuto Ototake The Japan Research Institute Limited Tokyo Japan Otaru University of Commerce Hokkaido Japan Fukuoka University Fukuoka Japan

ISBN: (数字)9798350354836

ISBN: (纸本)9798350354843

Despite advancements in information access and natural language processing technologies, research on information retrieval for non-textual information in real-world documents remains limited. Tables, in particular, serve as crucial sources of various kinds of information, which makes structuring tabular data an important issue. In this work, we focus on understanding the structures of tables in Japanese annual securities reports, a specific type of real-world document, and undertake a cell-type classification task. We constructed a new dataset by manually annotating over 111,000 cells from more than 4,000 tables. In addition to implementing a baseline program, we organized a shared task using this new dataset. The results revealed that the best-performing system completed only 75% of the tables, thus indicating the ongoing challenge of understanding table structures. Our dataset and baseline program will be made available on GitHub for researchers in this field.

关键词： economics Relational databases Information retrieval Natural language processing Security computational intelligence Software development management

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Evidential Reasoning in the Calculation of Individual Injury Claims Reserve

Evidential Reasoning in the Calculation of Individual Injury...

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Proceedings of the ieee/IAFE computational intelligence for financial engineering (CIFEr)

作者： Karim Derrick Xi Liu Alliance Manchester Business School University of Manchester Manchester United Kingdom Kennedys IQ London United Kingdom

ISBN: (数字)9798350354836

ISBN: (纸本)9798350354843

Effective claims reserving, putting aside enough money to meet expected claim liabilities, is required by insurer regulators to ensure insurer solvency. Traditional techniques for reserving have tended to focus on aggregated techniques such as chain-ladder or left-to-expert estimation from claim attor-neys/solicitors. Individual claim approaches to reserving have emerged using contemporary statistical techniques to consider individual claim attributes. Evidential Reasoning is a technique for decision-making under uncertainty that has gained popularity in industrial and engineering contexts but has not been explored in the context of professional services. Since a claim reserve is a professional judgement based on uncertainty, incomplete data and a mixture of qualitative and quantitative data, Evidential Reasoning presents a potential alternative approach to traditional claims reserving. The contribution of this paper is to demonstrate that Evidential Reasoning is an effective approach to individual claims reserving demonstrating higher efficacy than expert esti-mates or traditional aggregated claims techniques in the context of UK injury claims. The results of the study also suggest that the new proposed reserving strategy can be applied beyond injury across all claim categories.

关键词： economics Uncertainty Regulators Decision making Estimation Cognition Injuries computational intelligence

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Large Language Model in financial Regulatory Interpretation

Large Language Model in Financial Regulatory Interpretation

引用

Proceedings of the ieee/IAFE computational intelligence for financial engineering (CIFEr)

作者： Zhiyu Cao Zachary Feinstein School of Business Stevens Institute of Technology Hoboken New Jersey USA

ISBN: (数字)9798350354836

ISBN: (纸本)9798350354843

This study explores the innovative use of Large Language Models (LLMs) as analytical tools for interpreting complex financial regulations. The primary objective is to design effective prompts that guide LLMs in distilling verbose and intricate regulatory texts, such as the Basel III capital requirement regulations, into a concise mathematical framework that can be subsequently translated into actionable code. This novel approach aims to streamline the implementation of regulatory mandates within the financial reporting and risk management systems of global banking institutions. A case study was conducted to assess the performance of various LLMs, demonstrating that GPT-4 outperforms other models in processing and collecting necessary information, as well as executing mathematical calculations. The case study utilized numerical simulations with asset holdings - including fixed income, equities, currency pairs, and commodities - to demonstrate how LLMs can effectively implement the Basel III capital adequacy requirements.

关键词： Text analysis Systematics Large language models Numerical simulation Regulation Robustness Risk management Stress Testing Synthetic data

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Time Series Generation with GANs for Momentum Effect Simulation on Moscow Stock Exchange

Time Series Generation with GANs for Momentum Effect Simulat...

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Proceedings of the ieee/IAFE computational intelligence for financial engineering (CIFEr)

作者： Maksim Kazadaev Vitaliy Pozdnyakov Ilya Makarov Faculty of Computer Science National Research University Higher School of Economics Moscow Russia AIRI Moscow Russia

ISBN: (数字)9798350354836

ISBN: (纸本)9798350354843

The ability to accurately simulate financial markets is crucial, as it allows researchers and practitioners to rigorously test and refine trading strategies without the high risks associ-ated with real-world experimentation. By leveraging Generative Adversarial Networks (GANs), this research aims to enhance the robustness and effectiveness of trading strategies by providing a controlled environment to assess potential outcomes and strategy resilience under varied market conditions. In this work, we propose the application of GAN s for simu-1ating multidimensional time series in the context of developing and testing trading strategies. We conduct an experimental study to jointly simulate the log-returns of several stocks on Moscow Exchange for Momentum effect evaluation. Compared to traditional methods such as bootstrapping, GANs can better model and interpolate the non-parametric complex nature of the data, providing an increased diverse sample size. This methodology could be beneficial for investors seeking new opportunities to test and tune hyperparameters of other trading strategies.

关键词： Training Correlation Time series analysis Neural networks Generative adversarial networks Data models Robustness Convolutional neural networks Long short term memory Testing

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financial Semantic Textual Similarity: A New Dataset and Model

Financial Semantic Textual Similarity: A New Dataset and Mod...

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Proceedings of the ieee/IAFE computational intelligence for financial engineering (CIFEr)

作者： Shanshan Yang Steve Yang Feng Mai School of Business Stevens Institute of Technology Hoboken NJ USA Department of Business Analytics University of Iowa Iowa City IA USA

ISBN: (数字)9798350354836

ISBN: (纸本)9798350354843

We introduce FinSTS, a novel dataset for financial semantic textual similarity (STS), comprising 4,000 sentence pairs from earnings calls and SEC filings. To improve models for the financial STS task, we propose an active learning (AL) algorithm that efficiently selects informative sentence pairs for annotation by GPT-4 and creates high-quality training data. Using this approach, we train FinSentenceBERT, a model that generates semantic embeddings specifically for financial text. FinSentenceBERT establishes a new performance benchmark on FinSTS, outperforming models that use basic pooling strategies or are fine-tuned on general datasets. Surprisingly, a general SBERT model trained using our AL approach surpasses even models based on FinBERT, a language model pre-trained on financial text. Our research contributes a specialized dataset, model, and methodology that advance semantic understanding in the financial domain, with potential applications to other specialized domains.

关键词： Analytical models Adaptation models Biological system modeling Semantics Supervised learning Training data Benchmark testing Vectors Unsupervised learning Text processing

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$\alpha$-dominance two-objective Optimization Genetic Programming for Algorithmic Trading under a Directional Changes Environment

$\alpha$-dominance two-objective Optimization Genetic Progra...

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Proceedings of the ieee/IAFE computational intelligence for financial engineering (CIFEr)

作者： Xinpeng Long Michael Kampouridis School of Computer Science and Electronic Engineering University of Essex Wivenhoe Park United Kingdom

ISBN: (数字)9798350354836

ISBN: (纸本)9798350354843

We present a novel genetic programming (GP) algorithm that combines physical time and event-based time indicators to trade on the stock market. Rather than only using data in fixed intervals (e.g. daily closing prices), we use directional changes to transform physical time into events and allow the GP to make trading decisions based on when significant price movements have occurred. We use a two-objective fitness function, which simultaneously optimizes return and risk. To overcome challenges with the convergence ability of the multi-objective GP, we apply an a-dominance strategy, which is able to relax the strict Pareto dominance criteria. We run experiments on 110 stocks from 10 international markets and compare results against a single-objective GP, as well as strategies based on technical analysis indicators and buy-and-hold. Results show that the proposed GP algorithm offers statistically significant improvements when compared to the above benchmarks.

关键词： Event detection Genetic programming Transforms Benchmark testing Stock markets Optimization computational intelligence Investment Convergence

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Optimization of CNN for Diagnosis on Lung Disease by Lung Segmentation and Rib Suppression 15

Optimization of CNN for Diagnosis on Lung Disease by Lung Se...

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15th International symposium on computational intelligence and Design, ISCID 2022

作者： Yang, Xinye Liu, Yuhang Lin, Zhiwei Zhong, Lingyu Toe, Teoh Teik University of Science and Technology Beijing School of Intelligence Science and Technology Beijing China Central University of Finance and Economics School of Economics Beijing China Beihang Univerisity School of Electronic and Information Engineering Beijing China Central University of Finance and Economics China Academy of Public Finance and Public Policy Beijing China Nanyang Technological University NTU Bussiness AILUB Singapore

ISBN: (纸本)9781665456166

The discrimination of lung diseases by chest X- ray images is a clinically important tool. How to use artificial intelligence to accurately and quickly help doctors to diagnose different lung diseases is very important in the context of the current COVID-19 global pandemic. In this paper, we propose a model structure, including two U-Net, which implement lung segmentation and rib suppression for chest X-ray images respectively, image enhancement techniques such as histogram equalization, which enhances images contrast, and a Xception- based CNN, which classifies the processed images finally. The model can effectively avoid the interference of regions outside the lung to CNN for feature recognition and the influence of environmental factors such as X-ray machines on the quality of X-ray images and thus on the classification. The experimental results show that the classification accuracy of the model is higher than that of the direct use of the Xception model for classification. © 2022 ieee.

关键词： Biological organs

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