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共 595 条记录，以下是1-10 订阅

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Proceedings - 2022 5th asia conference on machine learning and computing, acmlc 2022

Proceedings - 2022 5th Asia Conference on Machine Learning a...

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5th asia conference on machine learning and computing, acmlc 2022

ISBN: (纸本)9798350333923

the proceedings contain 22 papers. the topics discussed include: a data-driven pricing strategy for automobile insurance policies;a comparison of clustering method to determine depot location for a bike-sharing operation;mobile-based navigation assistant for visually impaired person with real-time obstacle detection using YOLO-based deep learning algorithm;PiXelNet: a DL-based method for diagnosing lung cancer using the histopathological images;rough set model and approximations in fuzzy formal contexts;an evolutionary strategy based training optimization of supervised machine learning algorithms (EStoTimeSMLAs);forecasting for wind farm energy output in South Australia: a comparative analysis of physical methods and deep learning methods;and natural language processing in advertising – a systematic literature review.

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A study of relationship between business performance and stock prices using machine learning techniques 24

A study of relationship between business performance and sto...

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6th asia conference on machine learning and computing, acmlc 2024

作者： Rukpanichsiri, Vorapat Soonthornphisaj, Nuanwan Department of Computer Science Faculty of Science Kasetsart University Bangkok Thailand

ISBN: (纸本)9798400710018

this research investigates the relationship between fundamental factors and stock prices. the study uses 50 stocks listed on the SET100 index in 2022. the fundamental factors of interest are earnings per share (EPS), price-to-book value (P/BV), net profit margin, dividend yield, return on assets (ROA), return on equity (ROE), and debt-to-equity ratio (D/E). the study uses quarterly data for 10 years, from 2012 to 2021. this results in 40 data points per stock. the relationship between fundamental factors and stock prices is investigated using machine learning methods, including Lasso, Regression Tree, Random Forest, AdaBoost, Xgboost, Gradient Boosting, and Neural Network. the relationship between fundamental factors and stock prices is evaluated using k-fold cross-validation. this method divides the data into training and testing sets. the performance of each model is measured using the R-squared value. the findings of the study suggest that some fundamental factors are significant predictors of stock prices. the strongest relationships are found in P/BV. the results also suggest that Neural Network methods can be used to identify the relationships between fundamental factors and stock prices. © 2024 Copyright held by the owner/author(s).

关键词： Forestry

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Application of machine learning in Enterprise Financial Risk Assessment: A Study About China's A-Share Listed Manufacturing Companies 5th

Application of Machine Learning in Enterprise Financial Risk...

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5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Yul, Kexin Yul, Zengyi Ma, Shuomin Xu, Pan Zhejiang Univ Technol Hangzhou 310023 Peoples R China SpringAI LLC Bellevue WA 98007 USA

ISBN: (纸本)9789819770038;9789819770045

Based on the financial data of domestic A-share listed firms from 2000 to 2022, this paper aims to explore the effectiveness of machine learning in identifying the financial risk of Chinese A-share listed manufacturing firms. To this end, a variety of machine learning models, including logistic regression, decision tree, random forest, XGBoost, SVM, and LSTM, are used to assess the financial risk of enterprises, and the key attributes of enterprise financial risk are extracted through the interpretability exploration and importance coefficient measurement. From this paper, the following conclusions are drawn: (i) XGBoost model performs the best on all attributes, showing its strong ability in dealing with complex financial datasets, and LSTM, which adds time-series factors, performs poorly, which is speculated that it may be related to the incompatibility of the characteristics of the financial data, the reliance on the time-series features, and the need for financial fine features. (ii) Audit opinion, Net Profit and ROA are key influencing factors.

关键词： Financial risk machine learning XGBoost LSTM

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A Survey on Deep learning-Based Medical Image Registration 5th

A Survey on Deep Learning-Based Medical Image Registration

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5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Xu, Ronghao Liu, Chongxin Liu, Shuaitong Huang, Weijie Zhang, Menghua Univ Jinan Sch Elect Engn Jinan 250000 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

In recent years, various methods have been proposed to address the fundamental task of medical image registration in medical image analysis. this paper systematically reviews the research progress in medical image registration using deep learning techniques. these methods utilize deep learning algorithms to achieve precise alignment of medical image data from different modalities, time points, or sources, thereby providing dependable help for clinical identification and care. this paper seeks to provide researchers and practitioners with the most current technological advances and potential applications in the realm of medical image registration.

关键词： Image registration Deep learning CNN Supervised learning

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Ensemble learning with Feature Fusion for Well-Overflow Detection 5th

Ensemble Learning with Feature Fusion for Well-Overflow Dete...

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5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Cui, Ziliang Liu, Li Xiong, Yinzhou Liu, Yinguo Su, Yu Man, Zhimin Wang, Ye Chongqing Univ Posts & Telecommun Chongqing Peoples R China Chongqing Energy Big Data Ctr Co Ltd Chongqing Peoples R China Shandong Zibo Polytech Vocat Sch Chongqing Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Traditional methods for kick prediction rely on experience and basic physical models, but they struggle to maintain high accuracy under complex well conditions and varying geological environments. Moreover, conventional approaches often fail to fully leverage big data resources. therefore, this study aims to improve the accuracy of kick prediction through machine learning models, especially by adopting an innovative feature fusion strategy to optimize the prediction model. We evaluated the comprehensive performance of six different machine learning models: GBM, LightGBM, CatBoost, XGBoost, Random Forest, and AdaBoost. Based on these evaluations, we proposed a feature score weighted fusion method. this method weights the importance scores of features based on the accuracy of each model, selecting the features that have the most significant impact on the prediction results. Experimental results show that the model utilizing the feature fusion strategy performs better on the test set than any single base model. Specifically, the GBM model, optimized through feature fusion, achieved better values in accuracy, F1 score, and F2 score, proving the effectiveness of this feature fusion strategy in enhancing the accuracy of kick predictions during well control operations. Furthermore, this study compared the performance of different models, providing valuable insights for kick prediction in well control operations. this research not only demonstrates the potential of feature fusion strategies in improving the accuracy of kick prediction but also offers new insights for the future application of machine learning technologies in the field of oilfield data analysis.

关键词： machine learning feature fusion kill well overflow

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A Novel Elm Optimization and Its Application in IoT Intrusion Detection 5th

A Novel Elm Optimization and Its Application in IoT Intrusio...

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5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Maseno, Elijah M. Wang, Zenghui Univ South Africa Dept Comp Sci Florida South Africa Univ South Africa Dept Elect & Smart Syst Engn ZA-1709 Florida South Africa

ISBN: (纸本)9789819770038;9789819770045

Globally, the Internet of things (IoT) has grown exponentially in the last few years. the integration of numerous IoT devices enhances connectivity but also exposes them to various cyber-attacks. With the increase of complex and intelligent cyber-attacks, the traditional methods of cyber-attack detection, such as anomaly and misuse intrusion detection systems (IDS), have proven ineffective in combating emerging cyber-attacks. Scholars have suggested that the existing IDS's performance can be enhanced by integrating machine learning methods. this study explored the integration of ELM (Extreme learning machine) with NSGA-II (Non-Dominated Sorting Genetic Algorithm II). Our empirical analysis reveals that ELM outperforms other algorithms regarding learning rate, usability, and effectiveness of generalization. NSGA-II provides an equilibrium between exploration and exploitation, successfully managing the trade-offs present in multi-objective optimization problems to identify a range of excellent solutions along the Pareto front. Most of the existing works on ELM optimization focuses on improving one input parameter. this study explored the integration of ELM with NSGA-II for both input weights and hidden neurons. the model was trained and tested using two datasets namely IoT_ToN network and UNSWNB15 datasets. this research used TON_IoT Windows dataset to further test the capacity of the model to identify novel attacks. Using the UNSWNB15 and IoT_ToN network datasets, respectively, the model's accuracy was 0.68 and 0.65. In addition, the model recorded an accuracy 0.768 using TON_IoTWindows dataset, demonstrating the potential of the model in detection of new forms of attacks. this study acknowledges the ongoing evolution of challenges in IoT security and the need for continuous innovation to stay ahead of emerging threats in the future. the contributions of this study can be explored further to develop reliable intrusion detection solutions for the dynamic IoT landscape

关键词： Artificial Intelligence (AI) Internet of things (IoT) Cyber-Attack Intrusion Detection System (IDS) Evolutionary Algorithm Optimization machine learning

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A Byzantine Client Detection Method for Federated learning Based on Encrypted Repeated Median Linear Regression 24

A Byzantine Client Detection Method for Federated Learning B...

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5th International conference on Computer Science and Management Technology, ICCSMT 2024

作者： Guo, Jinlong Yang, Ming Li, Huijuan Wu, Xiaoming He, Yunpeng Wang, Xin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Shandong Jinan China Shandong Evayinfo Technology Co. Ltd. Shandong Jinan China

ISBN: (纸本)9798400709999

Federated learning is an emerging distributed machine learning technology that allows participants to jointly train machine learning models locally without the need for large-scale data transmission and sharing, thereby greatly improving data security. However, some Byzantine clients may intentionally tamper with local model parameter updates with the intention of manipulating the global model's update direction, which can lead to a decline in the overall performance of federated learning. Besides, existing Byzantine client detection research is conducted in plain text, which will lead to the leakage of private information. To solve the above issues, we propose a Byzantine client detection method for federated learning based on encrypted repeated median linear regression. Specifically, it combines the repeated median linear regression algorithm with homomorphic encryption, which can detect and eliminate clients with Byzantine behavior in the ciphertext state, ensuring the security and reliability of the parameters in the entire federated learning process. Additionally, we also designed an adaptive gradient clipping method based on historical iteration to clip the gradient to a reasonable range, which can not only reduce communication and computing overhead, but also improve the convergence speed and stability of the model. Experiments show that the proposed method can maintain high accuracy under the influence of malicious behavior of Byzantine clients. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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An Analysis on Balance Model of Exploration and Exploitation Under Decoupled-learning Pattern for Large-Scale Particle Swarm Optimizers 5th

An Analysis on Balance Model of Exploration and Exploitation...

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5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Ni, Wenke Guo, Weian Li, Dongyang Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China Tongji Univ Sino German Coll Appl Sci Shanghai Peoples R China

ISBN: (纸本)9789819770007;9789819770014

To effectively tackle large-scale optimization problems, numerous efforts have been made by various swarm intelligence methodologies to strike a balance between two critical functions: exploration and exploitation. However, in the majority of existing particle swarm optimizers, these two functions often remain indistinguishable throughout the optimization process. To mitigate the mutual interference between exploration and exploitation, designers of optimizers have attempted to design these functions separately. Nonetheless, there has been limited research into analyzing the roles and the relationship between exploration and exploitation. Building upon our previous work, we have redesigned several state-of-the-art large-scale particle swarm optimizers using a decoupled learning pattern. through comprehensive experimental analysis on the synergistic effect of exploitation and exploration, we delve into the optimization performance and introduce a mathematical model to illustrate their interrelationship. Our investigation and analysis reveal that most existing algorithms exhibit sensitivity in the parameter settings for exploration and exploitation, an aspect that has not been adequately addressed before. Based on the proposed mathematical analysis of the parameters, we provide clear balance model for algorithm designers and users on how to balance these two factors when solving large-scale optimization problems.

关键词： Large scale optimization problems Exploration and exploitation Decoupled learning pattern Balance model

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Rapid Detection of Formaldehyde Emission from Wood-Based Panels Based on the IPOA-XGBoost 5th

Rapid Detection of Formaldehyde Emission from Wood-Based Pan...

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5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Wang, Yinuo Zheng, Huanqi Wu, Qiang Zhou, Yucheng Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Shandong Peoples R China Shandong Jianzhu Univ Sch Architecture & Urban Planning Jinan 250101 Shandong Peoples R China Natl Ctr Qual Inspect & Test Decorat Mat Jinan 250102 Peoples R China

ISBN: (纸本)9789819770038;9789819770045

Aiming at the complexity of the process of analyzing formaldehyde composition by physicochemical methods in the process of formaldehyde emission detection by the full-scale chamber method, and the problem of bias in the accuracy of sensor method, the IPOA-XGBoost formaldehyde emission modified deviation model for a class of panels is proposed for the fast and accurate detection of formaldehyde emission from wood-based panels. the model utilizes data obtained from the electrochemical sensor as input. the output is the formaldehyde emission measured by the full-scale chamber method. the model is fitted using the extreme gradient boosting (XGBoost). To address the weak optimization ability in the pelican optimization algorithm (POA), random opposition-based learning is employed to establish the initial population. Additionally, nonlinear weighting factors and a sparrow alert mechanism perturbation strategy are introduced to enhance the ability of global optimization and escape local optimal solution. the improved POA (IPOA) is used to optimize the key parameters of XGBoost, and then the modified deviation model of formaldehyde emission of the IPOA-XGBoost wood-based panel is constructed, and compared with four machine learning models. Experimental results indicate that the proposed model achieves a coefficient of determination of 0.9701 and a root mean square error of 2.7648e-03. Furthermore, it exhibits lower prediction errors compared to other models tested herein. this provides an effective and reliable solution for rapid detection of formaldehyde emission in wood-based panels.

关键词： the full-scale chamber Improved pelican optimization algorithm Extreme gradient boosting Formaldehyde emission detection

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A Predictive Maintenance Platform for a Conveyor Motor Sensor System Using Recurrent Neural Networks 5th

A Predictive Maintenance Platform for a Conveyor Motor Senso...

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5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Kiangala, Kahiomba Sonia Wang, Zenghui Univ South Africa UNISA Dept Elect & Smart Syst Engn ZA-1709 Johannesburg Gauteng South Africa

ISBN: (纸本)9789819770007;9789819770014

Since the effusion of Industry 4.0 (I40) and smart manufacturing, predictive maintenance (PdM) has become critical to prevent severe system breakdowns and costly production downtime in various industries. Several state-of-the-art Artificial Intelligence (AI) approaches, such as machine learning models (ML), empower the PdM design concept to produce more accurate outcomes. In this study, we propose the development of a PdM platform that effectively detects future possible warnings and failures in the vibration speed sensor of a conveyor motor. We build the platform backend using recurrent neural networks (RNN) regression ML models to predict vibration speed values and their trends. We train our RNN models using univariate time-series historical vibration sensor data recorded over the years. the experimental results demonstrate the robustness of our platform built on RNN models compared to other traditional regression machine learning models such as Extreme Gradient Boosting (XGboost). We compare the effectiveness of the PdM platform with two RNN models: a basic RNN model and a long short-term memory (LSTM) model. Unlike other regression PdM systems focusing on predicting devices Remaining Useful Life (RUL), our PdM platform forecasts and generates new vibration sensor data based on the desired future time range. It also pinpoints set warning and failure values and indicates when these faults are more likely to occur. Factories can utilize this feature for analytics purposes.

关键词： Conveyor Motor Sensor Predictive Maintenance (PdM) Recurrent Neural Networks (RNN) Regression Models time-series data

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