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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Explainability in machine learning & AI Models for Complex Data Structures on Scorecards Development in Retail Banking 4

Explainability in Machine Learning & AI Models for Complex D...

引用

4th international Multidisciplinary Information Technology and Engineering conference, IMITEC 2024

作者： Cheteni, Eddwin Vambe, William Tichaona Belgium Campus ITversity It Faculty Pretoria South Africa National Institute for Theoretical and Computational Sciences and Walter Sisulu University Mathematical Sciences and Computing Mthatha South Africa

ISBN: (纸本)9798350387988

As machine learning (ML) and artificial intelligence (AI) models continue to revolutionize retail banking, their application in tasks such as predicting customer behaviour, assessing credit risk, and detecting fraudulent activities has become increasingly complex. While these advanced models offer significant predictive power, they often operate as 'black boxes,' making their decision-making processes opaque and challenging to interpret. this lack of transparency poses critical issues in retail banking, where trust and clarity are paramount. through a comprehensive review of the literature on machine learning explainability techniques, this work found that traditional scorecards, which rely on straightforward models like logistic regression, have been surpassed by sophisticated AI systems capable of handling high-dimensional, heterogeneous, and temporally interdependent data. However, these systems' complexity undermines stakeholder confidence and understanding. Current explainability approaches, including model-agnostic techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), as well as model-specific techniques such as decision trees and Layer-wise Relevance Propagation (LRP), provide valuable insights but fall short in managing the intricate data structures and interactions characteristic of retail banking. these methods are often limited by their computational demands, instability, and difficulty scaling to more complex models. therefore, to address these gaps, this study recommends and proposes the development of hybrid models that blend interpretable components with complex AI systems, interactive visualization tools that facilitate dynamic exploration and understanding of model predictions, and the application of natural language processing for automated, accessible explanation generation. By enhancing the explainability of AI models, these approaches aim to improve transparency, regulatory compliance

关键词： Decision trees

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How board characteristics influence corporate innovation: evidence from machine learning 4

How board characteristics influence corporate innovation: ev...

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4th international conference on computer Science and Management Technology, ICCSMT 2023

作者： Li, Xianlin Dalian University of Technology Liaoning Dalian116081 China

ISBN: (纸本)9798400709517

With the rapid growth of society and economy, the competition among companies becomes more and more intense, and innovation is an important indicator of the competitiveness of companies. As an important part of corporate governance, the board of directors plays a pivotal role in corporate innovation. this paper collects data from Chinese A-share listed companies, establishes a model of board characteristics variables, and uses machine learning methods to explore the predictive ability of board characteristics on corporate innovation. the results of the study show that the relationship between board characteristics and corporate innovation of Chinese enterprises is a complex, non-linear relationship, and the use of machine learning methods can excellently complete the processing of the relationship between variables and improve the accuracy of the prediction of corporate innovation. At the same time, this paper seeks out the relationship between some of the more important board characteristics variables and corporate innovation. the findings of the study provide an important reference value for board governance in corporate governance. © 2023 ACM.

关键词： machine learning

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Predictability of Post-Earnings Announcement Drift with Textual and Contextual Factors of Earnings Calls 23

Predictability of Post-Earnings Announcement Drift with Text...

引用

4th ACM international conference on AI in Finance (ICAIF)

作者： Chung, Andy Tanaka-Ishii, Kumiko Univ Tokyo Grad Sch Engn Dept Adv Interdisciplinary Studies Tokyo Japan Waseda Univ Sch Fundamental Sci & Engn Dept Comp Sci & Engn Tokyo Japan

ISBN: (纸本)9798400702402

Post-Earnings Announcement Drift (PEAD), a well-known anomaly in financial markets, describes the tendency of cumulative stock returns to drift in the direction of an earnings surprise for a prolonged period following an earnings announcement. Numerous studies have used a supervised learning approach to predict PEAD, using earnings, fundamental and technical factors. However, there is a lack of study on how the context of the earnings call can be used for the PEAD prediction task. this paper uses computational linguistics techniques and large language models to examine the effectiveness of incorporating textual and contextual features from earnings calls for the PEAD prediction task. Our proposed supervised model includes four categories of features: 1) textual features, 2) contextual features, 3) earnings features, and 4) fundamental and technical features. We study the proposed model using earnings from 2010/01/01 to 2022/12/31 of all point-in-time S&P500 constituents in the US stock market. Our results show that contextual features provide information unexplained by earnings, fundamental and technical features, improving the average returns per trade of a hypothetical long-short portfolio against baseline solution in out-of-sample across all four different abnormal return calculations, ranging from 53 to 354 basis points and 16.9% to 108.5% improvement from baseline model, which uses only earnings, fundamental and technical features.

关键词： Post-earnings announcement drift earnings call computational linguistics large language models machine learning

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application of Stock Trading-Related Emotion Recognition from EEG Signals using Deep learning EEGNet 4

Application of Stock Trading-Related Emotion Recognition fro...

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4th international conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Zuo, Mingliang Wang, Fei University of Shanghai for Science and Technology School of Health Science and Engineering Shanghai200093 China University of Shanghai for Science and Technology Sino-German College Shanghai200093 China

ISBN: (纸本)9798400708831

this paper applies deep learning EEGNet to stock emotion recognition using EEG signals, achieving significantly higher accuracy than prior machine learning methods by utilizing comprehensive feature extraction and selection techniques. In the domain of stock emotion recognition, previous studies have predominantly relied on machine learning classification methods, rooted in the Valence/Arousal model and electroencephalogram (EEG) signals. this paper distinguishes itself by placing a primary focus on the application of deep learning techniques, specifically highlighting EEGNet, a well-recognized deep learning method in EEG-related research. the principal objective of this research is to address the issue of low classification accuracy within the stock emotion dataset. the article offers a comprehensive explanation of the workflow and methodologies employed within the EEG emotion recognition system, and provides detailed descriptions and analyses of the dataset. the dataset includes five frequency bands and various features, including DE, DASM, and RASM. Feature selection utilizes mutual information-based filtering, chi-square statistics, and embedded algorithms in deep learning classifiers. the EEGNet method achieves high accuracy rates, specifically 95.18% for Arousal and 97.9% for Valence. this stands in contrast to the prior researchers' machine learning ANN accuracy rates, which were 70% for Arousal and 71% for Valence in the context of stock emotion datasets. these results underscore its exceptional performance. © 2023 ACM.

关键词： Emotion Recognition

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Developing a YOLO based Object Detection application using OpenCV 7

Developing a YOLO based Object Detection Application using O...

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7th international conference on Computing Methodologies and Communication, ICCMC 2023

作者： Ponika, Mannem Jahnavi, Kopalli Sridhar, P.S.V.S. Veena, Kavuri Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation A.P. Vaddeswaram522502 India

ISBN: (纸本)9781665464086

computer Vision is a technology in which computers are trained using machine learning and deep learning algorithms to recognize and process objects from video/images in the same way that humans do. Since 2012, Convolutional Neural Network and its variations have experienced exceptional improvement, making object detection a crucial application of image processing. the main issue with CNN is that this consumes more time to instruct/train as it has to analyze two thousand regions per picture so for this task in real time it needs approximately 47 sec per picture. this article has employed the YOLO real-time object identification approach to train our machine learning model. YOLO is one of the best CNN representatives that defies new way for object recognition, which is simple and highly efficient. Unlike existing algorithms, YOLO looks at the picture entirely by predicting the bounding boxes and class probabilities for these boxes using neural networks. this results in a faster detection of the image than other algorithms. © 2023 IEEE.

关键词： Object detection

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machine learning Basics and Potential applications in Power Systems 4

Machine Learning Basics and Potential Applications in Power ...

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4th international conference on Electrical, Communication and computer Engineering, ICECCE 2023

作者： Xue, Tao Karaagac, Ulas Kocar, Ilhan Vavdareh, Masoud Babaei Ghafouri, Mohsen Department of Electrical Engineering Hong Kong Polytechnic University Hong Kong Hong Kong Concordia University Montreal Canada

ISBN: (纸本)9798350369694

Power system studies have relied on physical model-driven methods for decades. However, uncertainties arising from integrating renewable energies, nonlinearities introduced by power electronic devices, increased dependence on cyber-physical systems, and the need for fast and accurate big data analysis challenge traditional power system methodologies. In recent years, machine learning (ML) has revolutionized scientific research, making it possible to address constantly changing and nonlinear questions without the need for pre-determined models. this paper first introduces the basics of ML and typical algorithms to new researchers and readers. then typical examples of applying ML to power systems are proposed but not limited to electricity customer clustering, load and electricity price forecasting, power system dynamics prediction, impedance model identification, power system security, optimal load flow, load management control and inverter-based resources (IBR) control. In future studies, it is encouraged to embrace this emerging technology and utilize a combination of data-driven and model-driven methods. © 2023 IEEE.

关键词： Uncertainty analysis

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FRPoint: learning spatial correlations in the frequency domain for point cloud classification 4

FRPoint: Learning spatial correlations in the frequency doma...

引用

4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Jiao, Yang School of Information Science and Engineering Yunnan University Kunming650500 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

the use of neural networks for identifying irregular 3D point cloud data is gaining increasing attention from researchers. Similar to CNN, in order to extract local features of point clouds, point cloud models typically construct neighborhoods, extract features from each neighborhood point, and finally use symmetric aggregation functions to capture local information. However, how to accurately utilizing spatial correlation in 3D space remains a challenging problem. To address this issue, we propose FRPoint, which transforms neighborhoods into frequency domain space and capture long and short distance dependencies in the frequency domain using a learnable weight matrices. Benefit to the frequency domain's ability to well describe the spatial relationships, our method efficiently extracts accurate spatial correlations. More importantly, our method only requires the introduction of a simple learnable matrix, without the need for any complex Attention operations or stacking of intricate feature modules. the effectiveness of our method has been demonstrated through experiments on the classification datasets ScanObjectNN and ModelNet40. © 2025 SPIE.

关键词： Deep learning

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QNet: A Quantum-Native Sequence Encoder Architecture 4

QNet: A Quantum-Native Sequence Encoder Architecture

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4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Day, Wei Chen, Hao-Sheng Sun, Min-Te National Central University Department of Computer Science Taoyuan City Taiwan

ISBN: (纸本)9798350343236

this work proposes QNet, a novel sequence encoder model that entirely inferences on the quantum computer using a minimum number of qubits. Let n and d represent the length of the sequence and the embedding size, respectively. the dot-product attention mechanism requires a time complexity of O(n2 ·d), while QNet has merely O(n+d) quantum circuit depth. In addition, we introduce ResQNet, a quantum-classical hybrid model composed of several QNet blocks linked by residual connections, as an isomorph Transformer Encoder. We evaluated our work on various natural language processing tasks, including text classification, rating score prediction, and named entity recognition. Our models exhibit compelling performance over classical state-of-the-art models with a thousand times fewer parameters. In summary, this work investigates the advantage of machine learning on near-term quantum computers in sequential data by experimenting with natural language processing tasks. © 2023 IEEE.

关键词： Deep learning

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Multiple Kernel Extreme learning machine with Kernel Alignment Regularization 11

Multiple Kernel Extreme Learning Machine with Kernel Alignme...

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11th international conference on Information Systems and Computing Technology, ISCTech 2023

作者： Wang, Chuanli Li, Tianyu Xin, Dongjun Central South University of Forestry and Technology School of Computer and Information Engineering Chang Sha China University of Washington College of Art and Science Seattle United States

ISBN: (纸本)9798350342406

Multiple kernel extreme learning machine (MKELM) is a research hotspot in the field of kernel learning. But we found that MKELM ignores the label information of samples when it optimizes the base kernel combination coefficient. In order to address this issue, the paper proposes multiple kernel extreme learning machine based on kernel alignment regularization, and the corresponding optimization method is given. the specific idea is that considering the sample label matrix as the ideal kernel in the kernel alignment criterion, and adding the criterion as a regularization term to the optimization goal of the multiple kernel extreme learning machine. then, the corresponding optimization method is designed. the experimental results show that the proposed multiple kernel extreme learning machine based on kernel alignment regularization has good classification performance. © 2023 IEEE.

关键词： Knowledge acquisition

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Trademark Detection Algorithm Based on Artificial Intelligence 4

Trademark Detection Algorithm Based on Artificial Intelligen...

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4th international conference for Emerging Technology, INCET 2023

作者： Chen, Hong Tongji University Shanghai Institute of International Intellectual Property Shanghai200092 China

ISBN: (纸本)9798350335750

In the field of computer vision, the performance of deep learning in target detection task has exceeded the past and reached the highest level in history. the trademark detection algorithm is based on artificial intelligence. Artificial intelligence based algorithms are used to detect trademarks of specific products. Its working principle is to compare the images of products with those of other products with similar images in the database. Different from the target detection task in the open field, the trademark detection task has the characteristics of large target scale span, complex scene change, serious target rotation deformation and a large number of small targets. At present, the mainstream target recognition algorithm has low accuracy in the recognition task in the trademark field. the trademark detection algorithm compares the images and finds the similarity between them. If there is any similarity, it will be marked as a trademark item. the algorithm is developed using artificial intelligence and machine learning technology to detect trademarks in images or video frames captured from different angles. © 2023 IEEE.

关键词： Signal detection

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