Procedia Computer Science最新文献_第8页

A systematic review of active learning approaches in the selection of medical images 主动学习方法在医学图像选择中的系统回顾

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.02.186

Maria Santos , Goreti Marreiros

{"title":"A systematic review of active learning approaches in the selection of medical images","authors":"Maria Santos , Goreti Marreiros","doi":"10.1016/j.procs.2025.02.186","DOIUrl":"10.1016/j.procs.2025.02.186","url":null,"abstract":"<div><div>Background: Active Learning has been proven to be an effective way to maximize the model’s learning capacity, using fewer amounts of labeled data. In the field of medical imaging data, data and annotations can be scarce and very expensive to obtain, so techniques like Active Learning can be a useful solution. Methods: For this systematic review, the data sources were obtained through IEEE Explore, PubMed, and ACM Digital Library, between the period of 2018 and 2023. Only studies that belonged to the field of healthcare (using medical images as a dataset) and machine learning, written in English and that were not a book, or a survey were used. Covidence was used as a tool to synthesize the results. Results: From 336 records, 51 were included in this review. Interpretation: Most studies showed that Active Learning can have a positive impact on the construction of models, however, it is important to not consider only the informativeness/uncertainty of the sample, but also the distribution of the data, reducing the probability of selecting samples that are not representative enough of the dataset or outliers. Active Learning is usually an iterative process until a stop criterion is met, for example, the model’s performance. To evaluate an Active Learning solution, the proposed method is usually compared with random sampling, or other Active Learning queries.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"256 ","pages":"Pages 843-851"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

K-operator as a predictor for Alzheimer-Perusini’s disease k算子作为阿尔茨海默病的预测因子

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.02.173

Maria Mannone , Norbert Marwan , Peppino Fazio , Patrizia Ribino

引用次数: 0

Neural Network Ensemble for Detecting Parasite Eggs in Microscopic Images 显微图像中寄生虫卵检测的神经网络集成

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.02.174

Matheus L.L. Bessa , Geraldo Braz Junior , João Dallyson Souza de Almeida

引用次数: 0

Optimization Application of Dynamic Programming Algorithm in Computer Security Management 动态规划算法在计算机安全管理中的优化应用

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.238

Ying Li

{"title":"Optimization Application of Dynamic Programming Algorithm in Computer Security Management","authors":"Ying Li","doi":"10.1016/j.procs.2025.04.238","DOIUrl":"10.1016/j.procs.2025.04.238","url":null,"abstract":"<div><div>With the continuous development of information technology and the increasing complexity of network attacks, computer security management faces increasingly severe challenges, especially in how to optimize resource allocation and improve security protection efficiency. In order to cope with these problems, this paper introduces an optimization method based on dynamic programming algorithm to improve the effectiveness of computer security management. Firstly, a multi-level security management model is constructed. Then, the dynamic programming algorithm is applied to optimize the response sequence and resource allocation strategy of security incidents. Finally, the optimal strategies under different attack modes are designed, and the simulated annealing algorithm is used to further improve the quality of the solution. Experimental results show that this method significantly outperforms traditional strategies on multiple indicators. In terms of security incident response time, the optimization strategy shortens the response time by an average of 30%. In terms of resource utilization, the average resource utilization of the optimization strategy reaches 87.5%, which is significantly higher than the traditional strategy. In addition, in terms of security cost and risk control, the optimization strategy reduces costs by 4.7% and risks by 4.4%. The experiment verifies the effectiveness of the optimization strategy based on dynamic programming in improving response efficiency, optimizing resource allocation and reducing security risks, providing new ideas for computer security management.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"261 ","pages":"Pages 494-503"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124573","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

Construction of Intelligent Electronic Fence System Based on Computer Vision Algorithm 基于计算机视觉算法的智能电子围栏系统构建

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.239

Yaokuan Wen, Qingyu Zhi, Kan Zhang, Yong Li, Yichen Cui, Haiyang Du

{"title":"Construction of Intelligent Electronic Fence System Based on Computer Vision Algorithm","authors":"Yaokuan Wen, Qingyu Zhi, Kan Zhang, Yong Li, Yichen Cui, Haiyang Du","doi":"10.1016/j.procs.2025.04.239","DOIUrl":"10.1016/j.procs.2025.04.239","url":null,"abstract":"<div><div>With the continuous development of technology, electronic fences face more and more security issues and challenges. This paper used convolutional neural network (CNN) technology to establish an intrusion detection system to achieve high-precision recognition and real-time response to intrusion behavior. The system used image preprocessing technology to improve image quality and reduce environmental interference, and used multi-sensor information fusion to improve system robustness. In order to improve real-time response capabilities, the system uses multi-threaded design and model optimization to achieve rapid and accurate identification of safety hazards in complex environments. At the same time, the system also integrates functions such as behavior recognition and remote control to achieve automated intrusion defense and rapid response. The results show that the intelligent electronic fence system is superior to the traditional system in terms of response time, with an average response time of 109.1 milliseconds. The false alarm rate and missed alarm rate are significantly lower than those of the traditional system. The false alarm rate and missed alarm rate for flame detection are 0.7% and 0.1% respectively, and the detection range is superior to other systems under different conditions. The intelligent electronic fence system has significant advantages in improving security and protection capabilities, and provides a new technical solution for modern security protection.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"261 ","pages":"Pages 504-511"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124574","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

Research on Crack Segmentation and Detection of Red Brick Wall Structure based on Deep Learning 基于深度学习的红砖墙体结构裂缝分割与检测研究

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.240

Wenjuan Peng , Wei Zhao , Qiusheng Zhang , Zhuoya Bai , Ying Zeng , Mingyang Qi , Jinshun Nan

{"title":"Research on Crack Segmentation and Detection of Red Brick Wall Structure based on Deep Learning","authors":"Wenjuan Peng , Wei Zhao , Qiusheng Zhang , Zhuoya Bai , Ying Zeng , Mingyang Qi , Jinshun Nan","doi":"10.1016/j.procs.2025.04.240","DOIUrl":"10.1016/j.procs.2025.04.240","url":null,"abstract":"<div><div>The present paper discusses a technique for crack segmentation and detection in red brick walls that is based on deep learning. This technology is designed to improve the efficiency and accuracy of assessing building safety. With the development of the construction industry, the detection of cracks in red brick walls has become particularly important. Traditional detection methods are labor-intensive and error-prone, while deep learning models provide an efficient and reliable solution. In this paper, we study a variety of deep learning models, including PSPNet, DeepLabV3+, ERFNet, ANN, CCNet, and SegFormer, and compare their performance in the wall crack detection and segmentation task through experiments that use a real scene dataset to validate the model’s accuracy and generalization ability in the presence of interfering factors. Experimental results show that the SegFormer model performs best in IoU, F1, ACC and Recall, reaching 65.99%, 77.37%, 99.87%, and 80.79%, respectively, and with the addition of the attention mechanism to the SegFormer model for optimization, the model’s IoU and F1 are improved by 1.16% and 1.13%, respectively. The performance was significantly improved. The results provide technical support for detecting and repairing cracks in red brick walls, which helps to detect and repair potential safety hazards in a timely manner.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"261 ","pages":"Pages 512-519"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124575","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

Emotion Recognition and Intervention Technology for Autistic Children Based on the Fusion of Neural Networks and Biological Signals 基于神经网络与生物信号融合的自闭症儿童情绪识别与干预技术

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.243

Yifei Wang

{"title":"Emotion Recognition and Intervention Technology for Autistic Children Based on the Fusion of Neural Networks and Biological Signals","authors":"Yifei Wang","doi":"10.1016/j.procs.2025.04.243","DOIUrl":"10.1016/j.procs.2025.04.243","url":null,"abstract":"<div><div>Given the significant difficulties that children with autism face in emotion recognition and intervention, there is an urgent need to develop accurate and efficient technical means to improve their social interaction and emotional understanding abilities. This study discusses a biological signal emotion recognition and intervention technology that integrates Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). First, this paper collects a variety of biological signal data of autistic children in different emotional states, including heart rate, galvanic skin response (GSR) and electroencephalogram (EEG), and preprocesses and extracts features of the data. Next, this paper builds and trains a deep learning model that integrates CNN and LSTM, classifies and analyzes the extracted features into emotional states, and achieves high-precision emotion recognition. Finally, this paper designs personalized intervention strategies based on the recognition results, and provides emotional guidance and intervention to children through a real-time feedback system. In the experimental conclusion, the accuracy of emotion recognition of the proposed fusion model in the training set and the verification set is 97.5% and 94.2% respectively, which is significantly better than the single mode signal processing method. In addition, the personalized intervention strategy based on this model achieved improvements of 45%, 3.8 points, and 4.2 points in reducing the amplitude of emotional fluctuations, enhancing emotional regulation ability, and improving social behavior, respectively, demonstrating the significant advantages and application potential of multimodal biosignal fusion in improving emotion recognition and intervention effects in children with autism.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"261 ","pages":"Pages 538-547"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124578","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

Innovation of Multimodal Learning Paths Based on Learning Behavior and Sentiment Analysis in AI Digital Intelligence Platform AI数字智能平台中基于学习行为和情感分析的多模态学习路径创新

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.246

Lei Wang , Nan Peng , Lu Liu , Sheng Wei

{"title":"Innovation of Multimodal Learning Paths Based on Learning Behavior and Sentiment Analysis in AI Digital Intelligence Platform","authors":"Lei Wang , Nan Peng , Lu Liu , Sheng Wei","doi":"10.1016/j.procs.2025.04.246","DOIUrl":"10.1016/j.procs.2025.04.246","url":null,"abstract":"<div><div>This study develops an intelligent multimodal learning path recommendation system to address the problems of lack of personalized learning paths and insufficient attention to students’ emotional impact in traditional digital education models. By integrating learning behavior analysis and sentiment analysis, the Random Forest (RF) model is used to deeply mine students’ learning behavior data, such as analyzing learning time, resource access frequency, etc., to precisely understand students’ learning patterns. At the same time, with the help of sentiment analysis technology based on BERT (Bidirectional Encoder Representations from Transformers), students’ emotional states during the learning process are monitored in real-time, including positive, negative, neutral, and other emotions. The experimental results show that by dynamically adjusting the learning path and monitoring students’ emotional states, students’ average scores increase by 6.0%, and homework completion rate increases by 4.4%. This study not only improves the overall quality of education, but also provides educators with more scientific decision-making support tools.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"261 ","pages":"Pages 566-573"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124581","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 of Voice Recognition Technology in Diary Applications 语音识别技术在日记应用中的应用

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.250

Mi Zhou

{"title":"Application of Voice Recognition Technology in Diary Applications","authors":"Mi Zhou","doi":"10.1016/j.procs.2025.04.250","DOIUrl":"10.1016/j.procs.2025.04.250","url":null,"abstract":"<div><div>Traditional diary applications mainly rely on keyboard input, which makes it difficult for users to quickly record their thoughts and feelings when their emotions fluctuate violently. This paper uses voice recognition technology to innovate the recording method of diary applications and optimize the user experience. This paper uses multiple voice data sets for training to ensure the accuracy and generalization ability of the model; a voice recognition method is constructed based on a one-dimensional convolutional neural network (1D CNN), which can accurately extract features from continuous voices and achieve high-quality voice transcription. The AM and NLP technology are introduced to further process the recognized text and improve the accuracy of its grammar, logic and emotional expression. Experimental results show that the method based on 1D CNN has an accuracy rate, word missing rate and vocabulary coverage of 94.61%, 3.17% and 93.11% respectively. Regarding time efficiency, the average input time of 1D CNN is 6.46 seconds. Voice recognition technology has great potential in diary applications. It can significantly improve recording efficiency and user experience, making diary content more authentic, fluent and personalized.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"261 ","pages":"Pages 598-604"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125106","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

Accurate Detection and Classification of Surface Defects in Electric Porcelain Insulators Based on Deep Learning Intelligent Algorithms 基于深度学习智能算法的电瓷绝缘子表面缺陷精确检测与分类

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.04.248

Liwei Tan, Yuhan Hu

{"title":"Accurate Detection and Classification of Surface Defects in Electric Porcelain Insulators Based on Deep Learning Intelligent Algorithms","authors":"Liwei Tan, Yuhan Hu","doi":"10.1016/j.procs.2025.04.248","DOIUrl":"10.1016/j.procs.2025.04.248","url":null,"abstract":"<div><div>The traditional surface defect detection method of porcelain insulators has obvious shortcomings in accuracy. This paper introduces an intelligent algorithm based on deep learning to achieve accurate detection and classification of surface defects of porcelain insulators. First, a high-resolution image acquisition system is used to comprehensively scan the surface of porcelain insulators to obtain high-quality original image data. Then, the original data is processed based on data enhancement technology to generate diversified training samples to improve the generalization ability of the model. Then, a deep learning model that integrates YOLOv5 (You Only Look Once Version 5) and ResNet50 (Residual Networks 50) is designed. The pre-training weights are optimized through transfer learning, which improves the recognition effect of the model on complex defect types. Finally, in order to further improve the detection accuracy, multi-scale detection and feature fusion technology are used to solve the problems in small-size defects and large-scale image data processing. The deep learning model proposed in this study has an accuracy of 91.50%, a recall of 89.00%, a precision of 90.20%, and an F1-score of 89.60% in the detection and classification of defects in porcelain insulators without using data enhancement. Finally, the triple data enhancement combination of rotation, cropping, and brightness adjustment further improves the performance of the model, with an accuracy of 94.30%, a recall of 92.50%, and a precision of 93.20%, respectively, and an F1-score of 92.80%. This method not only has high accuracy and robustness but also can achieve efficient and automated defect detection in actual industrial applications, providing a strong guarantee for the safe operation of the power system.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"261 ","pages":"Pages 582-588"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125103","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