PeerJ Computer Science最新文献

{"title":"Mam-Incept-Net: a novel inception model for precise interpretation of mammography images.","authors":"Amira Tandirovic Gursel, Yasin Kaya","doi":"10.7717/peerj-cs.3149","DOIUrl":"10.7717/peerj-cs.3149","url":null,"abstract":"<p><p>Early diagnosis of breast cancer through periodic screening is a vital ally in the fight for survival. Mammography, recognized as one of the most widely used and cost-effective tools for detecting early signs of asymmetry, calcification, masses, and architectural distortion in breast tissue, plays a significant role in nearly all screening scenarios. However, the interpretation and scoring of mammograms is a complex multi-parameter process that frequently leads to false-positive and false-negative results. This article introduces a new deep-learning-based model that classifies mammograms according to the Breast Imaging Reporting and Data System (BI-RADS) assessment categories. The model is trained on a private dataset, intentionally excluding no BI-RADS categories. A novel deep neural network architecture is employed to more accurately classify breasts, including their boundaries, as regions of interest (ROIs). The ConvNeXt architecture serves as a feature extractor for lower-level features, which are then combined with the layers of a randomly initialized naive inception module to capture higher-level features. Diagnosis is achieved through three experimental tests, yielding accuracy rates ranging from 82.08% to 86.27%. These promising accuracy levels, in comparison to previous studies, can be attributed to a more comprehensive approach to addressing BI-RADS scoring challenges. In addition to pursuing further enhancements in accuracy, future research should consider integrating prior radiology reports to create a more realistic end-to-end computer-aided detection system.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3149"},"PeriodicalIF":2.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132739","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}

{"title":"Next generation sequencing under attack: investigating insider threats and organizational behaviour.","authors":"Nasreen Anjum, Hani Alshahrani, Darakhshan Syed, Asadullah Shaikh, Mahreen Ul Hassan","doi":"10.7717/peerj-cs.3008","DOIUrl":"10.7717/peerj-cs.3008","url":null,"abstract":"<p><p>Next generation sequencing (NGS) has become a cornerstone of modern genomics, enabling high-throughput analysis of DNA and RNA with wide applications across medicine, research, and biotechnology. However, the growing adoption of NGS technologies has introduced significant cyber-biosecurity risks, particularly those arising from insider threats and organizational shortcomings. While technical vulnerabilities have received attention, the human and behavioral dimensions of cybersecurity in NGS environments remain underexplored. This study investigates the role of human factors and organizational behavior in shaping cyber-biosecurity risks in NGS workflows. A mixed-method approach was employed, combining survey data from 120 participants across four countries with statistical analyses including chi-square tests, cross-tabulations, and cluster analysis. The study assessed cybersecurity training availability, employee engagement, training effectiveness, and awareness of insider threats. Findings reveal substantial gaps in training frequency and participation, with 36% of respondents reporting no access to NGS-specific cybersecurity training. Only a minority of participants felt confident in detecting cyber threats, and 32.5% had never applied cybersecurity knowledge in practice. Chi-square results indicate significant associations between training frequency and threat recognition, training relevance, and knowledge application. Cluster analysis further categorized organizations into \"robust,\" \"moderate,\" and \"emergent\" cybersecurity maturity profiles. The study offers an evidence-based framework to enhance cyber-biosecurity in NGS settings by addressing human-centric risks. It recommends role-specific training, frequent policy updates, and improved organizational communication to mitigate insider threats. These insights support the development of targeted interventions and policies to strengthen the cybersecurity culture in genomics organizations.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3008"},"PeriodicalIF":2.5,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132733","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}

{"title":"Artificial intelligence-driven insights into Arab media's sustainable development goals coverage.","authors":"Mohammed Alsuhaibani, Kamel Gaanoun, Ali Mustafa Qamar","doi":"10.7717/peerj-cs.3071","DOIUrl":"10.7717/peerj-cs.3071","url":null,"abstract":"<p><p>This study examines how Arab media have engaged with the United Nations Sustainable Development Goals (SDGs) over the past decade and evaluates the alignment between media coverage and official government priorities. The research addresses the lack of large-scale, Arabic-focused analyses in SDG discourse, which is often dominated by English-language studies. We collected and processed a unique dataset of over 1.2 million Arabic news articles from ten countries between 2010 and 2024. Using a combination of data augmentation, deep learning (specifically, Transformer-based models), and large language models (LLMs), we trained classifiers to detect references to the SDGs and categorize articles by specific SDGs. The results reveal regional patterns in SDG coverage, with North African countries focusing more on governance-related goals, while Gulf countries emphasize economic and environmental themes. Our findings reveal a general alignment between media discourse and official SDG priorities, with notable exceptions. This study is the first to combine artificial intelligence (AI) methods and Arabic media at this scale for SDG analysis, offering new tools and insights for policymakers, media professionals, and development stakeholders.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3071"},"PeriodicalIF":2.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132477","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}

{"title":"MLPruner: pruning convolutional neural networks with automatic mask learning.","authors":"Sihan Chen, Ying Zhao","doi":"10.7717/peerj-cs.3132","DOIUrl":"10.7717/peerj-cs.3132","url":null,"abstract":"<p><p>In recent years, filter pruning has been recognized as an indispensable technique for mitigating the significant computational complexity and parameter burden associated with deep convolutional neural networks (CNNs). To date, existing methods are based on heuristically designed pruning metrics or implementing weight regulations to penalize filter parameters during the training process. Nevertheless, human-crafted pruning criteria tend not to identify the most critical filters, and the introduction of weight constraints can inadvertently interfere with weight training. To rectify these obstacles, this article introduces a novel mask learning method for autonomous filter pruning, negating requirements for weight penalties. Specifically, we attribute a learnable mask to each filter. During forward propagation, the mask is transformed to a binary value of 1 or 0, serving as indicators for the necessity of corresponding filter pruning. In contrast, throughout backward propagation, we use straight-through estimator (STE) to estimate the gradient of masks, accommodating the non-differentiable characteristic of the rounding function. We verify that these learned masks aptly reflect the significance of corresponding filters. Concurrently, throughout the mask learning process, the training of neural network parameters remains uninfluenced, therefore protecting the normal training process of weights. The efficacy of our proposed filter pruning method based on mask learning, termed MLPruner, is substantiated through its application to prevalent CNNs across numerous representative benchmarks.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3132"},"PeriodicalIF":2.5,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453823/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132736","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}

{"title":"Plagiarism detection across languages: a comprehensive study of Arabic and English-to-Arabic long documents.","authors":"Ahmad Abdelaal, Abdallah Elsaadany, Abdelrhman Ahmed Medhat, Aysha Al Shamsi, Noha Gamal ElDin Saad Ali","doi":"10.7717/peerj-cs.3128","DOIUrl":"https://doi.org/10.7717/peerj-cs.3128","url":null,"abstract":"<p><p>Plagiarism detection in Arabic texts remains a significant challenge due to the complex morphological structure, rich linguistic diversity, and scarcity of high-quality labeled datasets. This study proposes a robust framework for Arabic plagiarism detection by integrating Siamese neural networks (SNN) with state-of-the-art transformer architectures, specifically AraT5 and Longformer. The system employs a hybrid workflow, combining transformer-based encoders and a classification objective to implicitly learn textual similarity. To address the inherent imbalance in Arabic plagiarism datasets, both weighted cross-entropy loss and Dice loss functions were utilized to optimize model training. Extensive experiments were conducted using the ExAraCorpusPAN2015 dataset, demonstrating the effectiveness of the proposed architecture. Results indicate that AraT5 with weighted cross-entropy loss outperformed other configurations, achieving an F1-score of 0.9058. Additionally, comparative analysis with existing methodologies highlights the superiority of our approach in handling nuanced semantic and structural variations within Arabic texts. This study underscores the importance of transformer-based architectures and class-specific loss functions in enhancing plagiarism detection accuracy in under-resourced languages like Arabic.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3128"},"PeriodicalIF":2.5,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132585","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}

{"title":"An advanced error state Kalman filter (ESKF)-based terrain contour matching (TERCOM) method for tracking an aerial vehicle using a low-cost digital elevation map.","authors":"Muhammad Bilal Kadri, Sofia Yousuf","doi":"10.7717/peerj-cs.3118","DOIUrl":"10.7717/peerj-cs.3118","url":null,"abstract":"<p><p>Terrain Aided Navigation (TAN) systems hold significant potential for delivering accurate navigation for Uncrewed Aerial Vehicles (UAVs). However, a major limitation of conventional TAN systems lies in the time-consuming correlation technique used to search the <i>a priori</i> map, specifically the Digital Elevation Maps (DEM). This article presents a fuzzy heuristic method for the mean absolute deviation (MAD) correlation scheme (FH-MAD), aimed at reducing the computational complexity and execution time of the TAN algorithm. The fuzzy logic system uses heading and roll angle data from onboard sensors to determine the aircraft's matching area. The output membership functions are designed based on parameters that depend on terrain features. Additionally, the proposed method incorporates an error state Kalman Filter (ESKF) as the navigation algorithm to estimate the UAV's position under various maneuvering conditions. To evaluate the effectiveness of the proposed system, tests were conducted using two distinct DEMs with varying topographical characteristics and dimensions. The results demonstrate improved position accuracy and a significant reduction in computation time compared to traditional TAN methods, making the approach suitable for real-time UAV navigation applications.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3118"},"PeriodicalIF":2.5,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132538","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}

{"title":"Forecasting temperature and rainfall using deep learning for the challenging climates of Northern India.","authors":"Syed Nisar Hussain Bukhari, Kingsley A Ogudo","doi":"10.7717/peerj-cs.3012","DOIUrl":"10.7717/peerj-cs.3012","url":null,"abstract":"<p><p>Accurate temperature and rainfall (T&R) forecasting is vital for the climate-sensitive regions of Northern India, particularly Jammu, Kashmir, and Ladakh, where volatile weather patterns significantly affect livelihoods, socio-economic development, and disaster management efforts. Despite their importance, traditional forecasting methods often fall short due to their high computational demands and inability to provide localized, real-time predictions, leaving a critical research gap in addressing these challenges. This study addresses the need for precise and efficient T&R forecasting using deep learning-based framework tailored to the unique climatic conditions of these regions. The major research focus is to develop and evaluate a model capable of capturing complex temporal dependencies in localized time-series weather data. Utilizing data from the Indian Meteorological Department (IMD) for Jammu, Srinagar, and Ladakh stations covering the period from January 1, 2000, to December 31, 2023, the proposed framework employs recurrent neural networks (RNN) and long short-term memory (LSTM) architectures, both optimized for time-series forecasting. Key findings reveal that while both RNN and LSTM models exhibit robust performance in single input single output (SISO) setups, RNN model consistently outperforms the LSTM in capturing intricate temporal relationships. The RNN model in MIMO configuration achieved significantly lower mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) for Jammu, Srinagar, and Ladakh, with respective values of [0.0636, 0.1011, 0.0401] for Jammu, [0.1048, 0.1555, 0.0455] for Srinagar, and [0.0854, 0.1344, 0.0411] for Ladakh. These results underscore the RNN model's precision, making it a practical tool for real-time weather forecasting. By enhancing the accuracy of T&R predictions in regions with challenging meteorological conditions, this study contributes to improved climate adaptation strategies, disaster preparedness, and sustainable development. Its findings hold broader implications for advancing localized forecasting technologies in other regions with similar climatic complexities.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3012"},"PeriodicalIF":2.5,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132597","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}

{"title":"A social information sensitive model for conversational recommender systems.","authors":"Abdulaziz Mohammed, Mingwei Zhang, Gehad Abdullah Amran, Husam M Alawadh, Ruizhe Wang, Amerah Alabrah, Ali A Al-Bakhrani","doi":"10.7717/peerj-cs.3067","DOIUrl":"10.7717/peerj-cs.3067","url":null,"abstract":"<p><p>Conversational recommender systems (CRS) facilitate natural language interactions for more effective item suggestions. While these systems show promise, they face challenges in effectively utilizing and integrating informative data with conversation history through semantic fusion. In this study we present an innovative framework for extracting social information from conversational datasets by inferring ratings and constructing user-item interaction and user-user relationship graphs. We introduce a social information sensitive semantic fusion (SISSF) method that employs contrastive learning (CL) to bridge the semantic gap between generated social information and conversation history. We evaluated the framework on two public datasets (ReDial and INSPIRED) using both automatic and human evaluation metrics. Our SISSF framework demonstrated significant improvements over baseline models across all metrics. For the ReDial dataset, SISSF achieved superior performance in recommendation tasks (R@1: 0.062, R@50: 0.437) and conversational quality metrics (Distinct-2: 4.223, Distinct-3: 5.595, Distinct-4: 6.155). Human evaluation showed marked improvement in both fluency (1.81) and informativeness (1.63). We observed similar performance gains on the INSPIRED dataset, with notable improvements in recommendation accuracy (R@1: 0.046, R@10: 0.129, R@50: 0.269) and response diversity (Distinct-2: 2.061, Distinct-3: 4.293, Distinct-4: 6.242). The experimental results consistently validate the effectiveness of our approach in both recommendation and conversational tasks. These findings suggest that incorporating social context through CL can significantly improve the personalization and relevance of recommendations in conversational systems.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3067"},"PeriodicalIF":2.5,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132396","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}

{"title":"A lightweight fabric defect detection with parallel dilated convolution and dual attention mechanism.","authors":"Zheqing Zhang, Kezhong Lu, Gaoming Yang","doi":"10.7717/peerj-cs.3136","DOIUrl":"10.7717/peerj-cs.3136","url":null,"abstract":"<p><p>Detecting defects in fabrics is essential to quality control in the manufacturing process of textile productions. To increase detection efficiency, a variety of automatic fabric defect detections have been developed. However, most of these methods rely on complex model with heavy parameters, leading to high computational costs that hinder their adaptation to real-time detection environments. To overcome these obstacles, we proposed a lightweight fabric defect detection (Light-FDD), building upon the You Only Look Once v8 Nano (YOLOv8n) framework with further optimizations. Specifically, the backbone employed an improved FasterNet architecture for feature extraction. In order to capture multi-scale contextual information, we designed a parallel dilated convolution downsampling (PDCD) block to replace the conventional downsampling block in the backbone. In addition, a novel dual attention mechanism, called the global context and receptive-filed (GCRF) attention, was presented to help the model focus on key regions. Furthermore, a lightweight cross-stage partial (CSP) layer was deployed by dual convolution for feature fusion, reducing redundant parameters to further lighten the model. Results from extensive experiments on public fabric defect datasets showed that Light-FDD outperforms existing state-of-the-art lightweight models in terms of detection accuracy while requiring low computational cost. The present study suggests that the performance and effectiveness of detection models can be balanced through the adoption of reasonable strategies.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3136"},"PeriodicalIF":2.5,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132645","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}

{"title":"GaitTriViT and GaitVViT: Transformer-based methods emphasizing spatial or temporal aspects in gait recognition.","authors":"Hongyun Sheng","doi":"10.7717/peerj-cs.3061","DOIUrl":"10.7717/peerj-cs.3061","url":null,"abstract":"<p><p>In image recognition tasks, subjects with long distances and low resolution remain a challenge, whereas gait recognition, identifying subjects by walking patterns, is considered one of the most promising biometric technologies due to its stability and efficiency. Previous gait recognition methods mostly focused on constructing a sophisticated model structure for better model performance during evaluation. Moreover, these methods are primarily based on traditional convolutional neural networks (CNNs) due to the dominance of CNNs in computer vision. However, since the alternative form of Transformer, named Vision Transformers (ViTs), has been introduced into the computer vision field, the ViTs have gained strong attention for its outstanding performance in various tasks. Thus, unlike previous methods, this project introduces two Transformer-based methods: a completely ViTs-based method GaitTriViT, and a Video Vision Transformer (Video ViT) based method GaitVViT. The GaitTriViT leverages the ViTs to gain more fine-grained spatial features, while GaitVViT enhances the capacity of temporal extraction. This work evaluates their performances and the results show the still-existing gaps and several encouraging outperforms compared with current state-of-the-art (SOTA), demonstrating the difficulties and challenges these Transformer-based methods will encounter continuously. However, the future of Vision Transformers in gait recognition is still promising.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3061"},"PeriodicalIF":2.5,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453820/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132575","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}