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检索条件"任意字段=2nd International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2020"

共 2192 条记录，以下是11-20 订阅

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Design of a university English teaching system based on artificial intelligence technology 2

Design of a university English teaching system based on arti...

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2nd international conference on Big data, Computational Intelligence, and applications, BDCIA 2024

作者： Wang, Qi Shandong Vocational College of Science and Technology Shandong Province Weifang261053 China

ISBN: (纸本)9781510689053

The rapid development of artificial intelligence (AI) technology has introduced extensive potential applications in the field of education. Currently, university English teaching often faces challenges due to large class sizes, where teachers find it difficult to provide personalized guidance and feedback tailored to the specific needs of each student, thus hindering improvements in teaching quality and efficiency. To address this issue, this paper designs and develops a university English teaching system based on AI technology. The system employs advanced machine learning algorithms to intelligently assess student assignments, generate personalized learning suggestions, and automatically correct grammar and pronunciation. Its functional modules include learning task analysis, assignment evaluation and feedback, and voice learning assistance, aiming to comprehensively enhance students' learning experience and teachers' instructional efficiency. Experimental results indicate that the system demonstrates robust performance and high stability under high-concurrency user scenarios, with consistent operational behavior at different time points, effectively adapting to the practical teaching demands of higher education environments. This research provides theoretical support and practical reference for the application of AI technology in university English teaching, offering significant contributions to advancing educational informatization. © 2025 SPIE.

关键词： Adversarial machine learning

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Unsupervised Attack Detection on MIL-STD-1553 Bus for Avionic Platforms 2nd

Unsupervised Attack Detection on MIL-STD-1553 Bus for Avioni...

引用

2nd international conference on Information Technologies and Their applications

作者： Evcil, Mustafa Tok, Zaliha Yuce Sandikkaya, Mehmet Tahir ASELSAN Inc Ankara Turkiye Istanbul Tech Univ Dept Comp Engn Istanbul Turkiye

ISBN: (纸本)9783031734168;9783031734175

Avionic platforms are increasingly reliant on sophisticated onboard software and next-generation communication systems to carry out their operations. However, the expanding connectivity capabilities also introduce security vulnerabilities and the potential for attacks, posing significant challenges to the safety of these systems. In particular, the MIL-STD-1553 military standard, widely used as a serial communication protocol for mission-critical systems, lacks specific design considerations for security. The absence of authentication mechanisms in it allows for the potential impersonation of the Bus Controller (BC) and the injection of fake command words. Such attacks pose significant risks to the safety and integrity of mission-critical systems, highlighting the need for robust security measures. In this paper, we propose an unsupervised machine learning technique for anomaly detection on the MIL-STD-1553 communication bus using real platform data. We introduce a novel voting ensemble technique that combines Nearest Neighbors with thresholding algorithms such as Elliptical Boundary, Median Absolute Deviation, and Z-Score. The evaluation results demonstrate that our solution outperforms existing unsupervised machine learning techniques. The proposed method is very likely to be dependable and useful, as it provides a zero false positive rate.

关键词： MIL-STD-1553 anomaly detection machine learning aviation cybersecurity ensemble learning

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Construction and realization of modern Chinese vocabulary base driven by big data 2

Construction and realization of modern Chinese vocabulary ba...

引用

2nd international conference on Big data, Computational Intelligence, and applications, BDCIA 2024

作者： Luo, Zhong Faculty of Language and Literature Anhui Sanlian University Anhui Hefei230601 China

ISBN: (纸本)9781510689053

With the rapid advancement of big data technology, the construction of large-scale databases has become a critical focus in computer science. Existing research primarily explores the integration of big data with various computational methods, such as machine learning, data mining, and cloud computing, to enhance data processing efficiency and analytical capabilities. However, there is still a gap in the development of specialized databases that can effectively leverage big data for specific applications. This study aims to address this gap by proposing a novel approach to constructing a modern Chinese vocabulary database driven by big data techniques. The research focuses on the application of advanced computer methods, including data preprocessing, indexing, and retrieval algorithms, to optimize database performance. The ultimate goal is to develop a robust and scalable database system that can efficiently handle large volumes of data and provide accurate and timely information for various computational tasks. © 2025 SPIE.

关键词： data assimilation

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Design of a user electricity consumption reminder system and sensitivity analysis of credit ratings based on big data analytics 2

Design of a user electricity consumption reminder system and...

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2nd international conference on Big data, Computational Intelligence, and applications, BDCIA 2024

作者： Liao, Fengran Han, Tao Yang, Yang Jia, Legang Li, Tianhui State Grid Shandong Electric Power Company Yantai Power Supply Company Shandong264000 China

ISBN: (纸本)9781510689053

With the continuous growth of electricity demand, the efficient management of energy consumption has become a critical issue. However, many users lack awareness of their electricity usage patterns, leading to inefficient consumption behaviors and financial burdens. This paper proposes a novel user electricity consumption reminder system integrated with big data analytics to address these challenges. The proposed system analyzes user electricity consumption patterns using clustering algorithms and machine learning models, providing personalized consumption reminders. Additionally, a sensitivity analysis of credit ratings is conducted to explore the impact of electricity payment behavior on user credit. Experimental results demonstrate the system's effectiveness in enhancing user awareness, promoting sustainable energy practices, and maintaining financial stability. The study highlights the advantages of integrating big data techniques into user-centered energy management systems and suggests directions for future research. © 2025 SPIE.

关键词： Power consumption Analytics machine learning Lithium Sustainability Analytical research data modeling Monte Carlo methods Systems modeling data integration

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Analysis of aeolian vibration failure mechanisms in transmission line fittings using machine learning algorithms 2

Analysis of aeolian vibration failure mechanisms in transmis...

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2nd international conference on Big data, Computational Intelligence, and applications, BDCIA 2024

作者： Jia, Ran Zhou, Chao Liu, Hui Liu, Chuanbin Liu, Rong Zhou, Qiang State Grid Shandong Electric Power Research Institute Shandong Jinan250002 China College of Electrical and Electronic Engineering Shandong University of Technology Shandong Zibo255000 China

ISBN: (纸本)9781510689053

The integration of advanced machine learning algorithms in structural engineering has become a transformative approach for addressing complex failure mechanisms in critical infrastructure. This study leverages machine learning to develop a hybrid predictive framework for analyzing failure mechanisms in transmission line fittings subjected to aeolian vibration. The proposed framework combines physics-informed neural networks (PINNs), which embed domain-specific physical principles for theoretical consistency and interpretability, with gradient boosting machines (GBMs), which excel at identifying nonlinear interactions in high-dimensional datasets. These machine learning algorithms work in tandem to predict critical failure points and material-specific fatigue behaviors with unprecedented accuracy. A comprehensive dataset, including vibration frequencies, stress amplitudes, and environmental conditions, was collected through laboratory and field experiments to train and validate the hybrid machine learning model. The framework achieved superior performance metrics, with a mean squared error (MSE) of 0.008 and a prediction accuracy of 96.5%, significantly outperforming traditional empirical methods and finite element analysis (FEA). Key machine learning-driven insights include the accurate identification of critical vibration frequencies (10 Hz to 30 Hz) and stress accumulation patterns, which align closely with experimental observations. The use of SHapley Additive exPlanations (SHAP) further enhances the interpretability of the machine learning model, providing actionable insights into the contributions of vibration frequency, stress amplitude, and environmental factors to failure risks. © 2025 SPIE.

关键词： Vibration analysis

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Dynamic credit rating evaluation algorithm for customers using Reinforcement learning and multi-feature weight analysis 2

Dynamic credit rating evaluation algorithm for customers usi...

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2nd international conference on Big data, Computational Intelligence, and applications, BDCIA 2024

作者： Liao, Fengran State Grid Shandong Electric Power Company Yantai Power Supply Company Shandong264000 China

ISBN: (纸本)9781510689053

Effective credit rating evaluation is critical for financial institutions to manage risks and ensure sustainable operations. This study proposes a dynamic credit rating evaluation algorithm based on Reinforcement learning (RL), integrating multi-feature weight analysis to adaptively assess customer creditworthiness. The RL framework models the credit evaluation process as a sequential decision-making problem, where actions correspond to dynamic adjustments in customer credit ratings. A multi-feature weight analysis mechanism is incorporated to capture the relative importance of key factors, such as income stability, transaction history, and debt-to-income ratio, ensuring robust credit assessments. Experimental validation using real-world financial datasets demonstrates the algorithm's ability to improve classification accuracy by 12% compared to conventional machine learning models, while effectively adapting to changing customer behaviors. The results highlight the potential of Reinforcement learning in enhancing the accuracy and adaptability of credit rating systems. © 2025 SPIE.

关键词： Sales

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Research on stock prediction of the automobile manufacturing industry and agricultural product processing industry based on the Decision Tree Optimization Algorithm 2

Research on stock prediction of the automobile manufacturing...

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2nd international conference on Big data, Computational Intelligence, and applications, BDCIA 2024

作者： Jiang, Yuxiao Yang, Lei Du, Shipei Sun, Yu Liang, Yuhao Guangdong University of Science and Technology Dongguan China Dongguan Key Laboratory of Industrial Software for Intelligent Manufacturing Dongguan China

ISBN: (纸本)9781510689053

machine learning algorithms provide new ways to solve the problem of stock prediction. Among them, the decision tree algorithm has been widely used in financial market prediction because of its strong interpretability, no need for complex data preprocessing, and the ability to handle various types of data. However, the decision tree algorithm is prone to overfitting during the training process, resulting in poor generalization ability of the model on unseen data and affecting prediction accuracy. In this research, the decision tree pruning technique is used to conduct predictive research on the closing prices of stocks in two representative industries. Through experimental analysis, it is verified that the pruned decision tree improves both the accuracy and stability of its prediction, providing more effective methods and bases for financial market prediction. © 2025 SPIE.

关键词： Prediction models

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K-means for Small Earthquakes. Alternative Disaggregation Analyses by Considering Wave Components and Soil Types 2nd

K-means for Small Earthquakes. Alternative Disaggregation An...

引用

2nd international conference on Information Technologies and Their applications

作者： Zacchei, Enrico Brasil, Reyolando Itecons Coimbra Portugal Univ Coimbra CERIS Coimbra Portugal Univ Sao Paulo Polytech Sch Sao Paulo 380 Prof Luciano Gualberto Sao Paulo SP Brazil Fed Univ ABC UFABC Ctr Engn Modeling & Appl Social Sci 3 Rua Arcturus Sao Bernardo Do Campo SP Brazil

ISBN: (纸本)9783031734199;9783031734205

Prediction of ground motion by earthquakes is still the main issue for seismologists, geo-technicians, engineers, and researchers. In this paper k-means algorithm has been used to disaggregate seismic parameters to evaluate their inter-correlations. A goal is to quantify the weights and effects of each parameter with respect other ones. From database, about 4900.0 data, divided in 22 categories, have been collected. The main divisions regard the wave components in horizontal and vertical axis, and the soil characteristics. The studied seismic zone is the "Norpirenaica oriental", placed at Pyrenees area between Spain and France, classified as a very high seismic hazard. Numerical and analytical analyses have been carried out to implement the algorithm. These analyses could quantify the role of the sand horizontal stratigraphy, the non-linear response, and the elasticity of the soil. Results are plotted in stochastic distributions and elastic spectra accelerations. Rigorously, results are valid only for the studied seismogenic zone under predefined constrictions and range.

关键词： AI machine learning k-means Pyrenees Area Seismic Analyses

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Computer network information security monitoring system 2

Computer network information security monitoring system

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2nd international conference on Big data, Computational Intelligence, and applications, BDCIA 2024

作者： Lin, Hongkai Wuhan Business University Hubei Wuhan430056 China

ISBN: (纸本)9781510689053

Although traditional protection methods can improve the security of the network to a certain extent, its effect is often limited in the face of complex network environment and various attacks. Therefore, it is an important problem to be solved in the field of communication network security to find a more efficient and intelligent communication network link data security protection method. This paper proposes a computer network information security monitoring system combined with knowledge mapping technology, which aims to improve the data security protection effect of computer communication network. this paper designs and implements an information security situation awareness system, which can find and deal with network security threats in time by monitoring and analyzing network data traffic and security event information in real time, and improves the efficiency and level of information security protection. The results show that the information security situational awareness system based on artificial intelligence technology has high detection accuracy and strong real-time response capability, and can effectively deal with various network security threats. It has a good application prospect in data security protection of communication network link. © 2025 SPIE.

关键词： Network security data communications Computer security Defense and security Information security data modeling machine learning Computer networks Evolutionary algorithms Education and training

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Research on false news detection based on BERT model and attention mechanism 2

Research on false news detection based on BERT model and att...

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2nd international conference on Big data, Computational Intelligence, and applications, BDCIA 2024

作者： Fang, Jiamin Xi'an Eurasia University Xi'an710000 China

ISBN: (纸本)9781510689053

Artificial intelligence and intelligent algorithms have made remarkable progress in information processing. However, driven by the rapid development of the Internet, fake news interferes with people's access to accurate information. Aiming at the problem of false news detection, a multimodal false news detection model is proposed on the basis of the attention mechanism optimization bidirectional converter model. The designed model has an average accuracy of 98.61% in identifying news and an average response time of 1.74 seconds. It has a high recognition accuracy on various types of news data, especially on plain text news, with an accuracy of 99.82% and a response time of 1.15s. This shows that the new model can accurately and quickly determine the authenticity of news. This study provides an effective fake news identification tool that helps reduce the false information spread and improve the authenticity of network information. © 2025 SPIE.

关键词： Performance modeling data modeling Mathematical optimization Image processing Feature extraction Image enhancement Image fusion Internet machine learning Semantics

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