PeerJ Computer Science最新文献_第4页

Mitigating inappropriate concepts in text-to-image generation with attention-guided Image editing. 使用注意力引导的图像编辑减少文本到图像生成中的不适当概念。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-09 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3170

Jiyeon Oh, Jae-Yeop Jeong, Yeong-Gi Hong, Jin-Woo Jeong

{"title":"Mitigating inappropriate concepts in text-to-image generation with attention-guided Image editing.","authors":"Jiyeon Oh, Jae-Yeop Jeong, Yeong-Gi Hong, Jin-Woo Jeong","doi":"10.7717/peerj-cs.3170","DOIUrl":"10.7717/peerj-cs.3170","url":null,"abstract":"Text-to-image generative models have recently garnered a significant surge due to their ability to produce diverse images based on given text prompts. However, concerns regarding the occasional generation of inappropriate, offensive, or explicit content have arisen. To address this, we propose a simple yet effective method that leverages attention map to selectively suppress inappropriate concepts during image generation. Unlike existing approaches that often sacrifice original image context or demand substantial computational overhead, our method preserves image integrity without requiring additional model training or extensive engineering effort. To evaluate our method, we conducted comprehensive quantitative assessments on inappropriateness reduction, text fidelity, image consistency, and computational cost, alongside an online human perceptual study involving 20 participants. The results from our statistical analysis demonstrated that our method effectively removes inappropriate content while preserving the integrity of the original images with high computational efficiency.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3170"},"PeriodicalIF":2.5,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132722","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

A novel deep learning based approach with hyperparameter selection using grey wolf optimization for leukemia classification and hematologic malignancy detection. 基于灰狼优化的超参数选择深度学习方法在白血病分类和血液恶性肿瘤检测中的应用。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-08 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3160

Shams Ur Rehman, Robertas Damaševicius, Hassan Al Sukhni, Abeer Aljohani, Ameer Hamza, Deema Mohammed Alsekait, Diaa Salama AbdElminaam

{"title":"A novel deep learning based approach with hyperparameter selection using grey wolf optimization for leukemia classification and hematologic malignancy detection.","authors":"Shams Ur Rehman, Robertas Damaševicius, Hassan Al Sukhni, Abeer Aljohani, Ameer Hamza, Deema Mohammed Alsekait, Diaa Salama AbdElminaam","doi":"10.7717/peerj-cs.3160","DOIUrl":"10.7717/peerj-cs.3160","url":null,"abstract":"Traditional diagnostic methods of leukemia, a blood cancer disease, are based on visual assessment of white cells in microscopic peripheral blood smears, and as a result, they are arbitrary, laborious, and susceptible to errors. This study proposes a new automated deep learning-based framework for accurately classifying leukemia cancer. A novel lightweight algorithm based on the hyperbolic sin function has been designed for contrast enhancement. In the next step, we proposed a customized convolutional neural network (CNN) model based on a parallel inverted dual self-attention network (PIDSAN4), and a tiny16 Vision Transformer (ViT) has been employed. The hyperparameters were tuned using the grey wolf optimization and then used to train the models. The experiment is carried out on a publicly available leukemia microscopic images dataset, and the proposed model achieved 0.913 accuracy, 0.892 sensitivity, 0.925 specificity, 0.883 precision, 0.894 F-measure, and 0.901 G-mean. The results were compared with state-of-the-art pre-trained models, showing that the proposed model improved accuracy.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3160"},"PeriodicalIF":2.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132618","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

HEMF: an adaptive hierarchical enhanced multi-attention feature fusion framework for cross-scale medical image classification. HEMF：一种用于跨尺度医学图像分类的自适应分层增强多关注特征融合框架。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-08 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3181

Jingdong He, Qiang Shi, Jun Ma, Dacheng Shi, Tie Min

{"title":"HEMF: an adaptive hierarchical enhanced multi-attention feature fusion framework for cross-scale medical image classification.","authors":"Jingdong He, Qiang Shi, Jun Ma, Dacheng Shi, Tie Min","doi":"10.7717/peerj-cs.3181","DOIUrl":"10.7717/peerj-cs.3181","url":null,"abstract":"Medical image classification is essential for contemporary clinical diagnosis and decision support systems. However, medical images generally have similar inter-class features and complex structure patterns, making it a challenging task. While both local and global features are critical for noise reduction and discriminative pattern extraction in medical images, conventional approaches exhibit limitations. Specifically, convolutional neural networks (CNNs) focus on local features extraction but lack a comprehensive understanding of global semantic. Conversely, vision transformers (ViTs) can model long-range feature dependencies but may cause disruption to local features. To address these limitations, we propose Hierarchical Enhanced Multi-attention Feature (HEMF), an adaptive hierarchical enhanced multi-attention feature fusion framework to synergistically extract and fuse multi-scale local and global features. It comprises two core components: (1) the enhanced local and global feature extraction modules to extract multi-scale local and global features in parallel; (2) the hierarchical enhanced feature fusion module integrating a novel attention mechanism named Mixed Attention (MA) and a novel inverted residual block named Squeezed Inverted Residual Multi-Layer Perceptron (SIRMLP) to effectively fuse multi-scale features. Experimental results demonstrate that with nearly minimal model parameters compared to other advanced models, HEMF achieves the accuracy and F1-score of 87.34% and 78.89% on the ISIC2018 dataset, 87.03% and 87.02% on the Kvasir dataset, and 82.26% and 82.20% on the COVID-19 CT dataset, which are the state-of-the-art performance. Our code is open source and available from https://github.com/Esgjgd/HEMF.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3181"},"PeriodicalIF":2.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132659","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

Predicting academic performance for students' university: case study from Saint Cloud State University. 大学学生学业表现预测：来自圣克劳德州立大学的案例研究。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-08 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3087

Bilal I Al-Ahmad, Abdullah Alzaqebah, Rami Alkhawaldeh, Ala' M Al-Zoubi, Hsuehi Lo, Adel Ali

{"title":"Predicting academic performance for students' university: case study from Saint Cloud State University.","authors":"Bilal I Al-Ahmad, Abdullah Alzaqebah, Rami Alkhawaldeh, Ala' M Al-Zoubi, Hsuehi Lo, Adel Ali","doi":"10.7717/peerj-cs.3087","DOIUrl":"10.7717/peerj-cs.3087","url":null,"abstract":"Predicting students' performance is one of the essential educational data mining approaches aimed at observing learning outcomes. Predicting grade point average (GPA) helps to monitor academic performance and assists advisors in identifying students at risk of failure, major changes, or dropout. To enhance prediction performance, this study employs a long short-term memory (LSTM) model using a rich set of academic and demographic features. The dataset, drawn from 29,455 students at Saint Cloud State University (SCSU) over eight years (2016-2024), was carefully preprocessed by eliminating irrelevant and missing data, encoding categorical variables, and normalizing numerical features. Feature importance was determined using a permutation-based method to identify the most impactful variables on term GPA prediction. Furthermore, model hyperparameters, including the number of LSTM layers, units per layer, batch size, learning rate, and activation functions, were fine-tuned using experimental validation with the Adam optimizer and learning rate scheduling. Two experiments were conducted at both the college and department levels. The proposed model outperformed traditional machine learning models such as linear regression (LR), K-nearest neighbor (KNN), decision tree (DT), random forest (RF), and support vector regressor (SVR), and it surpasses two deep learning models, recurrent neural network (RNN) and convolutional neural network (CNN), achieving 9.54 mean absolute percentage error (MAPE), 0.0059 mean absolute error (MAE), 0.0001 root mean square error (RMSE), and an R² score of 99%.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3087"},"PeriodicalIF":2.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132678","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

SENSH: a blockchain-based searchable encrypted data sharing scheme in smart healthcare. SENSH：智能医疗中基于区块链的可搜索加密数据共享方案。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-08 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3166

Song Luo, Lihuan Tan, Tan Hu, Maoshuang Hu

{"title":"SENSH: a blockchain-based searchable encrypted data sharing scheme in smart healthcare.","authors":"Song Luo, Lihuan Tan, Tan Hu, Maoshuang Hu","doi":"10.7717/peerj-cs.3166","DOIUrl":"10.7717/peerj-cs.3166","url":null,"abstract":"The rapid development of the Internet of Things technology has led to a boom in the adoption of intelligent healthcare management systems in the healthcare industry. However, it has also highlighted key issues such as security, privacy, and efficient query of medical data. Traditional methods for querying medical data suffer from severe data leakage risks, low query performance, and excessive storage space. This article proposes a comprehensive Secure ENcrypted Search for Health Scheme (SENSH) solution based on consortium blockchain and searchable encryption to address these challenges. SENSH enables efficient authorization management through Bloom filters, ensuring fast querying of large datasets by authorized users while saving storage space. It uses off-chain Advanced Encryption Standard (AES) and on-chain storage management for data protection, significantly reducing the likelihood of data exposure. The system is also enhanced with event triggering and logging mechanisms to support real-time monitoring and data tracing to meet audit compliance requirements. It provides version control and timestamping to accommodate dynamic data updates, employs an obfuscationfactor to prevent tag-based original data content leakage, and supports dynamic updating of tags to accommodate different access requirements. Experimental results show that SENSH excels in authorization management, privacy protection, defense against tampering, and anti-replay and Distributed Denial of Service (DDoS). Compared with existing schemes, SENSH has significant advantages in terms of gas consumption, computation cost, and execution time. It is particularly suited for the protection and efficient query of medical and health data.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3166"},"PeriodicalIF":2.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453776/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132701","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

Regularized multi-path XSENet ensembler for enhanced student performance prediction in higher education. 用于高等教育学生成绩预测的正则化多路径XSENet集成器。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-08 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3032

Eman Ali Aldhahri, Abdulwahab Ali Almazroi, Nasir Ayub

{"title":"Regularized multi-path XSENet ensembler for enhanced student performance prediction in higher education.","authors":"Eman Ali Aldhahri, Abdulwahab Ali Almazroi, Nasir Ayub","doi":"10.7717/peerj-cs.3032","DOIUrl":"10.7717/peerj-cs.3032","url":null,"abstract":"With the rapid expansion of educational data, institutions face increasing pressure to adopt advanced predictive models that can enhance academic planning, resource allocation, and student support. This study presents a novel educational data mining approach designed to forecast student performance levels categorized as low, medium, and high by analyzing historical and behavioral trends. This work proposes XSEJNet, an innovative hybrid model that integrates ResNeXt architecture with squeeze-and-excitation (SE) attention mechanisms, and employs the Jaya optimization algorithm to refine hyperparameters and boost predictive accuracy and computational efficiency. The model works with structured and unstructured academic data, effectively capturing complex, high-dimensional features to support accurate classification. Through extensive simulations and comparative evaluations, XSEJNet consistently outperforms conventional machine learning models and recent existing techniques such as reinforcement learning co-evolutionary hybrid intelligence (RLCHI), Enhanced AEO-XGBoost, convolution-based deep learning (Conv-DL), and dual graph neural network (DualGNN). The model achieves a high prediction accuracy of 97.98% while also demonstrating faster convergence and reduced computational overhead, making it a scalable and practical solution for real-world educational settings. The findings underscore XSEJNet's ability to support early intervention, strengthen e-learning platforms, and inform institutional decision-making. By advancing predictive capabilities in education, this work makes a meaningful contribution to developing inclusive, data-driven, and sustainable academic systems.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3032"},"PeriodicalIF":2.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453854/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132713","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

Quality of experience-aware application deployment in fog computing environments using machine learning. 使用机器学习的雾计算环境中体验感知应用程序部署的质量。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-05 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3143

P Jenifer, J Angela Jennifa Sujana

{"title":"Quality of experience-aware application deployment in fog computing environments using machine learning.","authors":"P Jenifer, J Angela Jennifa Sujana","doi":"10.7717/peerj-cs.3143","DOIUrl":"10.7717/peerj-cs.3143","url":null,"abstract":"Edge intelligence is fast becoming indispensable as billions of sensors demand real-time inference without saturating backbone links or exposing sensitive data in remote data centres and emerging artificial intelligence (AI)-edge boards such as NVIDIA CPUs, 16 GB RAM, and microcontrollers with chip neural processing unit (NPU) (<1 W). This article introduces the Energy-Smart Component Placement (ESCP) algorithm of fog devices like fog cluster manager nodes (FCMNs) and fog nodes (FNs), allocates modules to fog devices, and saves energy by deactivating inactive devices framework transparently distributes compressed neural workloads across serverless. To optimize the deployment of AI workloads on fog edge devices as a service (FEdaaS), this project aims to provide a reliable and dynamic architecture that guarantees quality of service (QoS) and quality of experience (QoE). The cloud, fog, and extreme edge layers while upholding application-level QoS and QoE. Two machine learning (ML) methods that fuse eXtreme Gradient Boosting (XGB)-based instantaneous QoS scoring and long short term memory (LSTM) forecasting of node congestion, and a meta-heuristic scheduler that uses XGB for instantaneous QoS scoring and LSTM for short-horizon load forecasting. Compared with a cloud-only baseline, ESCP improved bandwidth utilization by 5.2%, scalability (requests per second) by 3.2%, energy consumption by 3.8% and response time by 2.1% while maintaining prediction accuracy within +0.4%. The results confirm that low-resource AI-edge devices, when orchestrated through our adaptive framework, can meet QoE targets such as 250 ms latency and 24 h of battery life. Future work will explore federated on-device learning to enhance data privacy, extend the scheduler to neuromorphic processors, and validate the architecture in real-time intensive care and smart city deployments.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3143"},"PeriodicalIF":2.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132724","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

Periodontitis bone loss detection in panoramic radiographs using modified YOLOv7. 改良YOLOv7在全景x线片牙周炎骨质流失检测中的应用。

IF 2.5 4区计算机科学

PeerJ Computer Science Pub Date : 2025-09-05 eCollection Date: 2025-01-01 DOI: 10.7717/peerj-cs.3102

Mohammed Gamal Ragab, Said Jadid Abdulkadir, Nadhem Qaid, Taimoor Muzaffar Gondal, Alawi Alqushaibi, Rizwan Qureshi, Furqan Shaukat

{"title":"Periodontitis bone loss detection in panoramic radiographs using modified YOLOv7.","authors":"Mohammed Gamal Ragab, Said Jadid Abdulkadir, Nadhem Qaid, Taimoor Muzaffar Gondal, Alawi Alqushaibi, Rizwan Qureshi, Furqan Shaukat","doi":"10.7717/peerj-cs.3102","DOIUrl":"10.7717/peerj-cs.3102","url":null,"abstract":"Periodontitis is a common dental disease that results in tooth loss, if not diagnosed and treated in time. However, diagnosing bone loss due to periodontitis from panoramic radiographs is a time-consuming and error-prone process, requiring extensive training and expertise. This work addresses the research gap in automated periodontitis bone loss diagnosis using deep learning techniques. We have proposed a modified version of You Only Look Once (YOLO)v2, called YOLOv7-M, that includes a focus module and a feature fusion module for rapid inference and improved feature extraction ability. The proposed YOLOv7-M model was evaluated on a tooth detection dataset and demonstrated superior performance, achieving an F1-score, precision, recall, and mean average precision (mAP) of 92.5, 91.7, 87.1, and 91.0, respectively. Experimental results indicate that YOLOv7-M outperformed other state-of-the-art object detectors, including YOLOv5 and YOLOv7, in terms of both accuracy and speed. In addition, our comprehensive performance tests show that YOLOv7-M outperforms robust object detectors in terms of various statistical evaluation measures. The proposed method has potential applications in automated periodontitis diagnosis and can assist in the detection and treatment of the disease, eventually enhancing patient outcomes.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3102"},"PeriodicalIF":2.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453760/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132663","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

A measurement framework to assess software maturity models. 评估软件成熟度模型的度量框架。

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.3183

Reem Alshareef, Mohammad Alshayeb, Mahmood Niazi, Sajjad Mahmood

{"title":"A measurement framework to assess software maturity models.","authors":"Reem Alshareef, Mohammad Alshayeb, Mahmood Niazi, Sajjad Mahmood","doi":"10.7717/peerj-cs.3183","DOIUrl":"10.7717/peerj-cs.3183","url":null,"abstract":"Software maturity models can be utilized by organizations to evaluate and enhance their development processes. Established and recognized models such as the Capability Maturity Model Integrated (CMMI) and ISO/IEC 15504 (Software Process Improvement and Capability Determination (SPICE)) have proven their value. However, many new software maturity models exist, and their quality and potential value remain questionable until they are properly assessed before adoption. Without such an assessment, organizations can implement poor or ineffective models, resulting in wasted resources and failed improvement initiatives. Our research aims to address this challenge by developing a measurement framework based on ISO/IEC 15504-3 standards to assess the quality of developed software maturity models. We derived our quality assessment criteria through literature analysis, analyzing four main categories: basic model information, structural design, assessment methods, and implementation support. After developing this framework, we validated it with expert reviews to assess its design and usability and through a series of case studies. Feedback from academics and industry practitioners confirmed the framework's utility, especially recognizing its clear structure and comprehensiveness of evaluation criteria. Case studies also revealed the framework's effectiveness in identifying strengths and areas of improvement, finding that evaluated models had quality scores ranging from 83.3% to 93.2%. Our study enhances software maturity models' practical utility and adoption across different software contexts, providing professionals and academics with a structured way to evaluate and enhance maturity models.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3183"},"PeriodicalIF":2.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132648","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

Ensemble techniques for detecting profile cloning attacks in online social networks. 在线社交网络中配置文件克隆攻击检测的集成技术。

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.3182

Irfan Mohiuddin, Ahmad Almogren

{"title":"Ensemble techniques for detecting profile cloning attacks in online social networks.","authors":"Irfan Mohiuddin, Ahmad Almogren","doi":"10.7717/peerj-cs.3182","DOIUrl":"10.7717/peerj-cs.3182","url":null,"abstract":"Detecting cloned and impersonated profiles on online social networks (OSNs) has become an increasingly critical challenge, particularly with the proliferation of AI-generated content that closely emulates human communication patterns. Traditional identity deception detection methods are proving inadequate against adversaries who exploit large language models (LLMs) to craft syntactically accurate and semantically plausible fake profiles. This article focuses on the detection of profile cloning on LinkedIn by introducing a multi-stage, content-based detection framework that classifies profiles into four distinct categories: legitimate profiles, human-cloned profiles, LLM-generated legitimate profiles, and LLM-generated cloned profiles. The proposed framework integrates multiple analytical layers, including semantic representation learning through attention-based section embedding aggregation, linguistic style modeling using stylometric-perplexity features, anomaly scoring via cluster-based outlier detection, and ensemble classification through out-of-fold stacking. Experiments conducted on a publicly available dataset comprising 3,600 profiles demonstrate that the proposed meta-ensemble model consistently outperforms competitive baselines, achieving macro-averaged accuracy, precision, recall, and F1-scores above 96%. These results highlight the effectiveness of leveraging a combination of semantic, stylistic, and probabilistic signals to detect both human-crafted and artificial intelligence (AI)-generated impersonation attempts. Overall, this work presents a robust and scalable content-driven methodology for identity deception detection in contemporary OSNs.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3182"},"PeriodicalIF":2.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132424","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