Procedia Computer Science最新文献_第9页

Construction of Question Answering System Based on English Pre-Trained Language Model Enhanced by Knowledge Graph 基于知识图增强的英语预训练语言模型的问答系统构建

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.256

Pei Song

{"title":"Construction of Question Answering System Based on English Pre-Trained Language Model Enhanced by Knowledge Graph","authors":"Pei Song","doi":"10.1016/j.procs.2025.04.256","DOIUrl":"10.1016/j.procs.2025.04.256","url":null,"abstract":"<div><div>With the continuous development of technology, question answering systems based on pre trained language models have become an important component of intelligent applications. However, traditional question-answering systems often face challenges in terms of understanding accuracy and insufficient knowledge coverage when dealing with open-domain questions. To this end, this paper uses a method for building a question-answering system based on a knowledge graph-enhanced BERT pre-trained language model. First, this paper trains the basic model based on a large-scale BERT pre-trained language model. Then, this paper adopts knowledge graph technology to introduce structured knowledge information into the model by integrating domain-specific knowledge bases. Finally, in order to effectively integrate knowledge graph information, this paper uses graph neural network (GNN) to model graph data, and combines the self-attention mechanism in the BERT model to optimize the weighted fusion process of knowledge graph information. Experimental results show that the BERT model enhanced with knowledge graph performs well in multiple question-answering tasks. On the simple question of the first experiment, the average accuracy of the enhanced model increased from 84.6% of the standard BERT model to 89.8%, and the F1 score increased from 0.86 to 0.91. In complex reasoning tasks, the BERT+knowledge graph model demonstrates stronger reasoning ability and higher knowledge coverage. In the experimental conclusion, the introduction of the knowledge graph significantly improves the model’s reasoning ability and knowledge coverage, especially in professional field problems and multi-step reasoning tasks, the enhanced model shows stronger capabilities. This research provides a new construction method for question-answering systems, demonstrates the great potential of knowledge graphs in natural language processing, and has broad application prospects.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"261 ","pages":"Pages 647-655"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Test Scenario Design and Optimization of Automated Driving Lane Keeping System Based On PCA and Intelligent Algorithm 基于PCA和智能算法的自动驾驶车道保持系统测试场景设计与优化

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.194

Linfeng Hao

{"title":"Test Scenario Design and Optimization of Automated Driving Lane Keeping System Based On PCA and Intelligent Algorithm","authors":"Linfeng Hao","doi":"10.1016/j.procs.2025.04.194","DOIUrl":"10.1016/j.procs.2025.04.194","url":null,"abstract":"<div><div>With the rapid development of autonomous driving technology, lane keeping system (LKS) has become an important function to improve vehicle driving safety. It effectively reduces the risk of accidents caused by lane departure by ensuring stable running of vehicles in the lane. Therefore, before large-scale application, it is particularly important to ensure its safety and reliability in complex environments. Aiming at the shortcomings of existing test methods in efficiency and scene quality, this paper proposes a test scene design and optimization method based on principal component analysis (PCA) and intelligent optimization algorithm to improve the comprehensiveness and scientificity of the test. Through the analysis of LKS system function characteristics, automatic classification and operation domain, the test requirements are defined, four types of typical test scenarios are designed, and hierarchical mathematical models are established. Based on real accident data, the key variables of discrete scene elements were extracted by PCA method to enhance scene authenticity. Aiming at the elements of the continuous scene, an intelligent optimization model based on safety boundary was constructed, which was solved by combining genetic algorithm (GA) and particle swarm optimization algorithm (PSO), and finally formed a key test scenario set covering 14 lane keeping, 34 front vehicle stationary, 34 front vehicle braking and 17 neighboring vehicles entering the scene. Through the hardware-in-the-loop test platform to verify the effectiveness of the scenario, the experimental results show that the proposed method significantly improves the test efficiency and scene quality, and provides a strong support for the full-scale LKS test and large-scale application of autonomous driving technology.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"261 ","pages":"Pages 237-246"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of an Intelligent Navigation System Integrating Reinforcement Learning and Computer Vision Algorithms 集成强化学习和计算机视觉算法的智能导航系统设计

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.411

Lili Wang

{"title":"Design of an Intelligent Navigation System Integrating Reinforcement Learning and Computer Vision Algorithms","authors":"Lili Wang","doi":"10.1016/j.procs.2025.04.411","DOIUrl":"10.1016/j.procs.2025.04.411","url":null,"abstract":"<div><div>Traditional static guidance systems have problems such as poor interactivity and low path planning efficiency. This paper designs an intelligent guidance system to achieve efficient, accurate and personalized navigation services. This paper constructs an intelligent guidance system that integrates reinforcement learning and computer vision algorithms, and adopts a multi-level architecture: the perception layer collects environmental data, the data processing layer uses YOLO and semantic segmentation to extract features, the decision layer uses deep Q network (DQN) to plan and optimize the path, and the interaction layer provides intuitive navigation and user feedback mechanism. The system effectively solves the limitations of traditional guidance systems in complex environments and improves navigation efficiency and user experience. In terms of path planning efficiency, the average path planning time of the intelligent guidance system is shorter than that of the traditional system; in terms of path navigation accuracy, the average accuracy of the intelligent guidance system reaches 99.1%, which is much higher than the 95.2% of the traditional system. These data fully prove the effectiveness of the intelligent guidance system proposed in this paper in improving the quality of navigation services and user experience.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"261 ","pages":"Pages 829-837"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Path Planning and Control of Building Robots Based on Reinforcement Learning Algorithm in Intelligent Construction 智能建筑中基于强化学习算法的建筑机器人路径规划与控制

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.255

Rendong Jin

{"title":"Path Planning and Control of Building Robots Based on Reinforcement Learning Algorithm in Intelligent Construction","authors":"Rendong Jin","doi":"10.1016/j.procs.2025.04.255","DOIUrl":"10.1016/j.procs.2025.04.255","url":null,"abstract":"<div><div>At present, the construction industry is actively promoting new construction methods such as intelligent and object-intensive construction. Among them, mobile operation robots, as one of the important solutions in the intelligent construction environment, involve key technologies such as obstacle avoidance, path planning, positioning, navigation, sensing and communication, and motion control and path planning problems are considered to be its most complex tasks. This paper studies the algorithm principle and optimization method based on reinforcement learning for the path planning and control problem of mobile operation robots in the intelligent construction environment. Reinforcement learning realizes strategy iteration through a reward and punishment mechanism, and can adaptively find the best course of action in an unfamiliar setting. Q-learning, as a classic algorithm, seeks to maximize long-term rewards by updating the value function. Nevertheless, the conventional Q-learning algorithm has issues with sparse rewards, sluggish convergence, and a propensity to enter local optimality when Q values are initialized. To this end, this paper introduces the artificial potential field method, uses gravitational and repulsive potential fields to optimize path planning, and improves the Q value function by integrating the reward mechanism of potential fields. The gravitational potential field attracts the robot to approach the target point, while the repulsive potential field avoids collision with obstacles. Experiments show that this method effectively solves the issue of path planning in intricate settings and offers a fresh concept for intelligent navigation of construction robots.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"261 ","pages":"Pages 637-646"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Social Media Data Mining and Online Consumer Behavior Analysis 社交媒体数据挖掘与在线消费者行为分析

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.220

Hongxin Li

{"title":"Social Media Data Mining and Online Consumer Behavior Analysis","authors":"Hongxin Li","doi":"10.1016/j.procs.2025.04.220","DOIUrl":"10.1016/j.procs.2025.04.220","url":null,"abstract":"<div><div>This study explores the impact of social media marketing activities on consumer behavior, emotional attitudes, and purchasing decisions, aiming to optimize brand marketing strategies. As social media becomes increasingly important in consumer decision-making, how to improve marketing effectiveness through sentiment analysis and behavioral analysis has become a pressing issue to be addressed. Research methods include dynamic sentiment tracking, user behavior cluster analysis, and purchase decision path modeling. Firstly, the study uses sentiment analysis to track consumers’ sentiment fluctuations on social media in real time and analyze the impact of sentiment changes on purchase intention and brand awareness. Secondly, cluster analysis is used to segment users and identify the behavioral characteristics and purchase preferences of different groups to support personalized marketing strategies. Finally, a consumer purchase decision model is constructed through path analysis to explore the role of factors such as emotional tendencies, interactive behaviors and social influences in the decision-making process. The experimental results show that brand activities significantly increase users’ interaction frequency, sharing frequency and purchase frequency. After the activity, the user’s interaction index increased by an average of 47.6, the average number of shares increased by 6.3 times, and the average purchase frequency increased by 5.5 times. In particular, users with high interaction and positive emotional tendencies showed stronger purchase intentions and brand loyalty. Research has found that social media activities not only increase brand exposure but also promote consumers’ emotional identification and brand loyalty. Therefore, brands should flexibly adjust marketing strategies based on the portraits of different user groups, and use sentiment analysis and social influence to optimize the purchase decision path, thereby improving marketing effectiveness.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"261 ","pages":"Pages 406-413"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application Research of Deep Learning in Financial Time Series Prediction 深度学习在金融时间序列预测中的应用研究

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.172

Lin Zou

引用次数: 0

Application of Computer Image Recognition Algorithm in Agricultural Product Quality Inspection 计算机图像识别算法在农产品质量检测中的应用

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.237

Xiangyu Lu

{"title":"Application of Computer Image Recognition Algorithm in Agricultural Product Quality Inspection","authors":"Xiangyu Lu","doi":"10.1016/j.procs.2025.04.237","DOIUrl":"10.1016/j.procs.2025.04.237","url":null,"abstract":"<div><div>In agricultural product quality inspection, subtle defects of agricultural products are easily disturbed by complex background and lighting changes, making them difficult to identify. To solve this problem, this paper uses Vision Transformer (ViT) to increase the precision of identifying minute flaws on agricultural items’ surfaces. The training data set is enlarged using data augmentation technology, which rotates, crops, and modifies brightness to enhance the model’s capacity to adjust to various changes. Using a self-attention mechanism, the ViT model detects minor surface flaws globally and captures long-range dependencies in the image. Combining transfer learning and fine-tuning strategies, the ViT model pre-trained on a large-scale image dataset was optimized on a specific agricultural product dataset, further improving recognition accuracy and robustness. Experimental results indicate that the ViT model in this work has F1-score of 0.89 and a precision of 92.5% when compared to other models. This shows that ViT has high accuracy and robustness in agricultural product quality detection. The results of this paper have made a great contribution to the development of future agricultural automation detection.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"261 ","pages":"Pages 485-493"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of Machine Learning Algorithms for Heart Disease Prediction 心脏疾病预测的机器学习算法比较

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.172

Ujjwal Daharwal , Indrasen Singh , Ganesh Khekare

{"title":"Comparison of Machine Learning Algorithms for Heart Disease Prediction","authors":"Ujjwal Daharwal , Indrasen Singh , Ganesh Khekare","doi":"10.1016/j.procs.2025.03.172","DOIUrl":"10.1016/j.procs.2025.03.172","url":null,"abstract":"<div><div>Heart disease remains a significant health concern globally, prompting the exploration of advanced methodologies for its early prediction and intervention. In this research, we use a comprehensive method to predict heart disease by using the capability of various machine learning algorithms. A dataset comprising diverse patient attributes and medical indicators is utilized for training and testing the models. Our study explores the effectiveness of various prominent machine learning algorithms such as Random Forest, and K-Nearest Neighbors (KNN) in predicting heart disease risk. Through meticulous feature selection and engineering, we enhance the algorithm’s ability to discern patterns and relationships within the data. To improve model interpretability and generalizability, feature selection and optimization procedures are examined. Furthermore, a comparison between contrast the strengths and limitations of these algorithms to identify the most suitable model for heart disease prediction has been presented. The findings of this research contribute to the ongoing efforts to develop accurate and reliable predictive tools for early detection of cardiovascular diseases, thereby facilitating timely intervention and improving patient outcomes. Integration of machine learning into cardiovascular risk assessment holds great promise for advancing personalized medicine and preventive healthcare strategies.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"260 ","pages":"Pages 12-21"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Safeguarding the Landscape of Mental Wellness: Analyzing Cyber Threats and Mitigation Strategies in Digital Healthcare 保护心理健康：分析数字医疗保健中的网络威胁和缓解策略

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.173

Cyrus Mehra , Arvind K. Sharma

{"title":"Safeguarding the Landscape of Mental Wellness: Analyzing Cyber Threats and Mitigation Strategies in Digital Healthcare","authors":"Cyrus Mehra , Arvind K. Sharma","doi":"10.1016/j.procs.2025.03.173","DOIUrl":"10.1016/j.procs.2025.03.173","url":null,"abstract":"<div><div>With the pervasiveness of digital technologies across various sectors, mental health care has not been left out, where the use of IoT has enhanced therapeutic practice through better monitoring of patients, collection of data, and proper alignment of treatment measures. However, the data collected for mental health is synonymous with high sensitivity; the information collected via IoT devices is prone to cybersecurity challenges which may compromise patient privacy or interfere with the treatment process. This review paper seeks to synthesize the two sides of IoT application in mental health, which are; advantages and cybersecurity threats. This paper aims at conducting a deep review of sources to ascertain the level of cybersecurity threats surrounding IoT in mental health, the performance of the current security measures, and possible future measures. It aims to identify the best measures to curb insecurity tendencies in the use of IoT in mental health. The review will contribute to the existing sources in the quest to secure health data in the rapidly evolving digital health field hence helping to create trust in secure information sharing.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"260 ","pages":"Pages 22-31"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Framework for Customer Segmentation to Improve Marketing Strategies Using Machine Learning 使用机器学习改进营销策略的客户细分框架

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.240

Aya Ashraf , Christina Albert Rayed , Nancy Awadallah Awad , Heba M. Sabry

引用次数: 0