PeerJ Computer Science最新文献_第5页

Hybrid deep layered network model based on multi-scale feature extraction and deep feature optimization for acute lymphoblastic leukemia anomaly detection. 基于多尺度特征提取和深度特征优化的急性淋巴细胞白血病异常检测混合深层网络模型。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-04 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3174

Gökalp Çınarer

{"title":"Hybrid deep layered network model based on multi-scale feature extraction and deep feature optimization for acute lymphoblastic leukemia anomaly detection.","authors":"Gökalp Çınarer","doi":"10.7717/peerj-cs.3174","DOIUrl":"10.7717/peerj-cs.3174","url":null,"abstract":"Acute lymphoblastic leukemia (ALL), one of the common diseases of our day, is one of the most common hematological malignant diseases in childhood. Early diagnosis of ALL, which plays a critical role in medical diagnosis processes, is of great importance especially for the effective management of the treatment process of cancer patients. Therefore, ALL cells must be detected and classified correctly. Traditional methods used today prolong the detection and classification processes of cells, cause hematologists to interpret them according to their expertise, and delay medical decision-making processes. In this study, the performance of the hybrid model developed with different deep learning models for ALL diagnosis was comparatively analyzed. In the proposed ALL detection architecture, blood cell images were processed using the center-based cropping strategy and irrelevant areas in the images were automatically removed. The dataset was divided into training, validation, and test sets, and then features were extracted with deep hyperparameters for convolution, pooling, and activation layers using a model based on Xception architecture. The obtained features were optimized to the advanced Extreme Gradient Boosting (XGBoost) classifier and model classification results were obtained. The results showed that the proposed model achieved 98.88% accuracy. This high accuracy rate was compared with different hybrid models and it was seen that the model was more successful in detecting ALL disease compared to existing studies.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3174"},"PeriodicalIF":2.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated lung cancer diagnosis from chest X-ray images using convolutional neural networks. 使用卷积神经网络从胸部x射线图像自动诊断肺癌。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-04 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3145

Aya Aboelghiet, Samaa M Shohieb, Amira Rezk, Ahmed Abou Elfetouh, Ahmed Sharaf, Islam Abdelmaksoud

{"title":"Automated lung cancer diagnosis from chest X-ray images using convolutional neural networks.","authors":"Aya Aboelghiet, Samaa M Shohieb, Amira Rezk, Ahmed Abou Elfetouh, Ahmed Sharaf, Islam Abdelmaksoud","doi":"10.7717/peerj-cs.3145","DOIUrl":"10.7717/peerj-cs.3145","url":null,"abstract":"Background/objectives: Lung cancer is the leading cause of cancer-related deaths worldwide. While computed tomography (CT) scans provide more comprehensive medical information than chest X-rays (CXR), the high cost and limited availability of CT technology in rural areas pose significant challenges. CXR images, however, could serve as a potential preliminary diagnostic tool in diagnosing lung cancer, especially when combined with a computer-aided diagnosis (CAD) system. This study aims to enhance the accuracy and accessibility of lung cancer detection using a custom-designed convolutional neural network (CNN) trained on CXR images.Methods: A custom-designed CNN was trained on an openly accessible CXR dataset from the Japanese Society for Radiological Technology (JSRT). Prior to training, the dataset underwent preprocessing, where each image was divided into overlapping patches. A t-test was applied to these patches to distinguish relevant from irrelevant ones. The relevant patches were retained for training the CNN model, while the irrelevant patches were excluded to enhance the model's performance.Results: The proposed model yielded a mean accuracy of 83.2 ± 2.91%, demonstrating its potential as a cost-effective and accessible preliminary diagnostic tool for lung cancer.Conclusions: This approach could significantly improve the accuracy and accessibility of lung cancer detection, making it a viable option in resource-limited settings.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3145"},"PeriodicalIF":2.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-stream transformer approach for pain assessment using visual-physiological data modeling. 使用视觉生理数据建模进行疼痛评估的双流变压器方法。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-03 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3158

Minh-Duc Nguyen, Hyung-Jeong Yang, Duy-Phuong Dao, Soo-Hyung Kim, Seung-Won Kim, Ji-Eun Shin, Ngoc Anh Thi Nguyen, Trong-Nghia Nguyen

{"title":"Dual-stream transformer approach for pain assessment using visual-physiological data modeling.","authors":"Minh-Duc Nguyen, Hyung-Jeong Yang, Duy-Phuong Dao, Soo-Hyung Kim, Seung-Won Kim, Ji-Eun Shin, Ngoc Anh Thi Nguyen, Trong-Nghia Nguyen","doi":"10.7717/peerj-cs.3158","DOIUrl":"10.7717/peerj-cs.3158","url":null,"abstract":"Automatic pain assessment involves accurately recognizing and quantifying pain, dependent on the data modality that may originate from various sources such as video and physiological signals. Traditional pain assessment methods rely on subjective self-reporting, which limits their objectivity, consistency, and overall effectiveness in clinical settings. While machine learning offers a promising alternative, many existing approaches rely on a single data modality, which may not adequately capture the multifaceted nature of pain-related responses. In contrast, multimodal approaches can provide a more comprehensive understanding by integrating diverse sources of information. To address this, we propose a dual-stream framework for classifying physiological and behavioral correlates of pain that leverages multimodal data to enhance robustness and adaptability across diverse clinical scenarios. Our framework begins with masked autoencoder pre-training for each modality: facial video and multivariate bio-psychological signals, to compress the raw temporal input into meaningful representations, enhancing their ability to capture complex patterns in high-dimensional data. In the second stage, the complete classifier consists of a dual hybrid positional encoding embedding and cross-attention fusion. The pain assessment evaluations reveal our model's superior performance on the AI4Pain and BioVid datasets for electrode-based and heat-induced settings.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3158"},"PeriodicalIF":2.5,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent educational systems based on adaptive learning algorithms and multimodal behavior modeling. 基于自适应学习算法和多模态行为建模的智能教育系统。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-03 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3157

Yuwei Li, Botao Lu

{"title":"Intelligent educational systems based on adaptive learning algorithms and multimodal behavior modeling.","authors":"Yuwei Li, Botao Lu","doi":"10.7717/peerj-cs.3157","DOIUrl":"10.7717/peerj-cs.3157","url":null,"abstract":"With the rapid advancement of artificial intelligence, the demand for personalized and adaptive learning has driven the development of intelligent educational systems. This article proposes a novel adaptive learning-driven architecture that combines multimodal behavioral modeling and personalized educational resource recommendation. Specifically, we introduce a multimodal fusion (MMF) algorithm to extract and integrate heterogeneous learning behavior data-including text, images, and interaction logs-via stacked denoising autoencoders and Restricted Boltzmann Machines. We further design an adaptive learning (AL) module that constructs a student-resource interaction graph and dynamically recommends learning materials using a graph-enhanced contrastive learning strategy and a dual-MLP-based enhancement mechanism. Extensive experiments on the Students' Academic Performance Dataset demonstrate that our method significantly reduces prediction error (mean absolute error (MAE) = 0.01, mean squared error (MSE) = 0.0053) and achieves high precision (95.3%) and recall (96.7%). Ablation studies and benchmark comparisons validate the effectiveness and generalization ability of both MMF and AL. The system exhibits strong scalability, real-time responsiveness, and high user satisfaction, offering a robust technical foundation for next-generation AI-powered educational platforms.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3157"},"PeriodicalIF":2.5,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453766/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of unsafe workplace behaviors: Sec-YOLO model with FEHA attention. 不安全工作场所行为的检测：FEHA关注的Sec-YOLO模型。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-03 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3151

Yang Liu, Shuaixian Liu, Jie Gao, Tao Song, Wenyu Dong

{"title":"Detection of unsafe workplace behaviors: Sec-YOLO model with FEHA attention.","authors":"Yang Liu, Shuaixian Liu, Jie Gao, Tao Song, Wenyu Dong","doi":"10.7717/peerj-cs.3151","DOIUrl":"10.7717/peerj-cs.3151","url":null,"abstract":"Detecting unsafe human behaviors is crucial for enhancing safety in industrial production environments. Current models face limitations in multi-scale target detection within such settings. This study introduces a novel model, Sec-YOLO, which is specifically designed for detecting unsafe behaviors. Firstly, the model incorporates a receptive-field attention convolution (RFAConv) module to better focus on the key features of unsafe behaviors. Secondly, a deformable convolution network v2 (DCNv2) is integrated into the C2f module to enhance the model's adaptability to the continually changing feature structures of unsafe behaviors. Additionally, inspired by the multi-branch auxiliary feature pyramid network (MAFPN) structure, the neck architecture of the model has been restructured. Importantly, to improve feature extraction and fusion, feature-enhanced hybrid attention (FEHA) is introduced and integrated with DCNv2 and MAFPN. Experimental results demonstrate that Sec-YOLO achieves a mean average precision (mAP) at 0.5 of 92.6% and mAP at 0.5:0.95 of 63.6% on a custom dataset comprising four common unsafe behaviors: falling, sleeping at the post, using mobile phones, and not wearing safety helmets. These results represent a 2.0% and 2.5% improvement over the YOLOv8n model. Sec-YOLO exhibits excellent performance in practical applications, focusing more precisely on feature handling and detection.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3151"},"PeriodicalIF":2.5,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453777/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Introducing the municipal digital offering index for evaluating online services and addressing the digital divide. 引入市政数字服务指数，以评估在线服务，解决数字鸿沟。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-03 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3049

Carolina Busco, Felipe González, Paula Farina, Jonathan Vivas, Fernanda Saavedra, Lizbeth Avalos

引用次数: 0

A cluster-assisted differential evolution-based hybrid oversampling method for imbalanced datasets. 一种基于聚类辅助差分进化的不平衡数据混合过采样方法。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-02 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3177

Muhammed Abdulhamid Karabiyik, Bahaeddin Turkoglu, Tunc Asuroglu

{"title":"A cluster-assisted differential evolution-based hybrid oversampling method for imbalanced datasets.","authors":"Muhammed Abdulhamid Karabiyik, Bahaeddin Turkoglu, Tunc Asuroglu","doi":"10.7717/peerj-cs.3177","DOIUrl":"10.7717/peerj-cs.3177","url":null,"abstract":"Class imbalance remains a significant challenge in machine learning, leading to biased models that favor the majority class while failing to accurately classify minority instances. Traditional oversampling methods, such as Synthetic Minority Over-sampling Technique (SMOTE) and its variants, often struggle with class overlap, poor decision boundary representation, and noise accumulation. To address these limitations, this study introduces ClusterDEBO, a novel hybrid oversampling method that integrates K-Means clustering with differential evolution (DE) to generate synthetic samples in a more structured and adaptive manner. The proposed method first partitions the minority class into clusters using the silhouette score to determine the optimal number of clusters. Within each cluster, DE-based mutation and crossover operations are applied to generate diverse and well-distributed synthetic samples while preserving the underlying data distribution. Additionally, a selective sampling and noise reduction mechanism is employed to filter out low-impact synthetic samples based on their contribution to classification performance. The effectiveness of ClusterDEBO is evaluated on 44 benchmark datasets using k-Nearest Neighbors (kNN), decision tree (DT), and support vector machines (SVM) as classifiers. The results demonstrate that ClusterDEBO consistently outperforms existing oversampling techniques, leading to improved class separability and enhanced classifier robustness. Moreover, statistical validation using the Friedman test confirms the significance of the improvements, ensuring that the observed gains are not due to random variations. The findings highlight the potential of cluster-assisted differential evolution as a powerful strategy for handling imbalanced datasets.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3177"},"PeriodicalIF":2.5,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing credit card fraud detection with a stacking-based hybrid machine learning approach. 基于堆叠的混合机器学习方法增强信用卡欺诈检测。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-02 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3007

Eyad Abdel Latif Marazqah Btoush, Xujuan Zhou, Raj Gururajan, Ka Ching Chan, Omar Alsodi

{"title":"Enhancing credit card fraud detection with a stacking-based hybrid machine learning approach.","authors":"Eyad Abdel Latif Marazqah Btoush, Xujuan Zhou, Raj Gururajan, Ka Ching Chan, Omar Alsodi","doi":"10.7717/peerj-cs.3007","DOIUrl":"10.7717/peerj-cs.3007","url":null,"abstract":"The swift progression of technology has increased the complexity of cyber fraud, posing an escalating challenge for the banking sector to reliably and efficiently identify fraudulent credit card transactions. Conventional detection approaches fail to adapt to the advancing strategies of fraudsters, resulting in heightened false positives and inefficiency within fraud detection systems. This study overcomes these restrictions by creating an innovative stacking hybrid machine learning (ML) approach that combines decision trees (DT), random forests (RF), support vector machines (SVM), XGBoost, CatBoost, and logistic regression (LR) within a stacking ensemble framework. This method uses stacking to integrate diverse ML models, enhancing predictive performance, with a meta-model consolidating base model predictions, resulting in superior detection accuracy compared to any single model. The methodology utilizes sophisticated data preprocessing techniques, such as correlation-based feature selection and principal component analysis (PCA), to enhance computing efficiency while preserving essential information. Experimental assessments of a credit card transaction dataset reveal that the stacking ensemble model exhibits higher performance, achieving an F1-score of 88.14%, thereby efficiently balancing precision and recall. This outcome highlights the significance of ensemble methods such as stacking in attaining strong and dependable cyber fraud detection, emphasizing its capacity to markedly enhance the security of financial transactions.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3007"},"PeriodicalIF":2.5,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453863/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing a 3D convolutional neural network to detect Alzheimer's disease based on MRI. 优化基于MRI的三维卷积神经网络检测阿尔茨海默病。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-08-29 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3129

Maitha Alarjani, Abdulmajeed Almuaibed

{"title":"Optimizing a 3D convolutional neural network to detect Alzheimer's disease based on MRI.","authors":"Maitha Alarjani, Abdulmajeed Almuaibed","doi":"10.7717/peerj-cs.3129","DOIUrl":"10.7717/peerj-cs.3129","url":null,"abstract":"Alzheimer's disease (AD) is a progressive neurological disorder that affects millions worldwide, leading to cognitive decline and memory impairment. Structural changes in the brain gradually impair cognitive functions, and by the time symptoms become evident, significant and often irreversible neuronal damage has already occurred. This makes early diagnosis critical, as timely intervention can help slow disease progression and improve patients' quality of life. Recent advancements in machine learning and neuroimaging have enabled early detection of AD using imaging data and computer-aided diagnostic systems. Deep learning, particularly with magnetic resonance imaging (MRI), has gained widespread recognition for its ability to extract high-level features by leveraging localized connections, weight sharing, and three-dimensional invariance. In this study, we present a 3d convolutional neural network (3D-CNN) designed to enhance classification accuracy using data from the latest version of the OASIS database (OASIS-3). Unlike traditional 2D approaches, our model processes full 3D MRI scans to preserve spatial information and prevent information loss during dimensionality reduction. Additionally, we applied advanced preprocessing techniques, including intensity normalization and noise reduction, to enhance image quality and improve classification performance. Our proposed 3D-CNN achieved an impressive classification accuracy of 91%, outperforming several existing models. These results highlight the potential of deep learning in developing more reliable and efficient diagnostic tools for early Alzheimer's detection, paving the way for improved clinical decision-making and patient outcomes.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3129"},"PeriodicalIF":2.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An End-to-End autonomous driving model based on visual perception for temporary roads. 基于视觉感知的临时道路端到端自动驾驶模型。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-08-29 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3152

Qinghua Su, Min Xie, Liyong Wang, Yue Song, Ao Cui, Zhihao Xie

{"title":"An End-to-End autonomous driving model based on visual perception for temporary roads.","authors":"Qinghua Su, Min Xie, Liyong Wang, Yue Song, Ao Cui, Zhihao Xie","doi":"10.7717/peerj-cs.3152","DOIUrl":"https://doi.org/10.7717/peerj-cs.3152","url":null,"abstract":"Background: The research on autonomous driving using deep learning has made significant progress on structured roads, but there has been limited research on temporary roads. The End-to-End autonomous driving model is highly integrated, allowing for the direct translation of input data into desired driving actions. This method eliminates inter-module coupling, thereby enhancing the safety and stability of autonomous vehicles.Methods: Therefore, we propose a novel End-to-End model for autonomous driving on temporary roads specifically designed for mobile robots. The model takes three road images as input, extracts image features using the Global Context Vision Transformer (GCViT) network, plans local paths through a Transformer network and a gated recurrent unit (GRU) network, and finally outputs the steering angle through a control model to manage the automatic tracking of unmanned ground vehicles. To verify the model performance, both simulation tests and field tests were conducted.Results: The experimental results demonstrate that our End-to-End model accurately identifies temporary roads. The trajectory planning time for a single frame is approximately 100 ms, while the average trajectory deviation is 0.689 m. This performance meets the real-time processing requirements for low-speed vehicles, enabling unmanned vehicles to execute tracking tasks in temporary road environments.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3152"},"PeriodicalIF":2.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0