PeerJ Computer Science最新文献

{"title":"Enhancing semantic question similarity in Arabic using hybrid models and knowledge graph alignment.","authors":"Ammar Al-Edhari, Mohsen Kahani","doi":"10.7717/peerj-cs.3988","DOIUrl":"10.7717/peerj-cs.3988","url":null,"abstract":"<p><p>Semantic question similarity (SQS) plays a crucial role in enhancing question answering (QA) systems, particularly for low-resource languages, such as Arabic, which present challenges due to complex morphology, semantic ambiguity, and limited linguistic resources. Despite prior efforts, knowledge graphs (KGs), which are structured networks representing entities and relationships, remain underutilized in improving semantic understanding and reducing ambiguity. Existing semantic similarity approaches have inherent trade-offs: KGs capture deep meaning but lack adaptability; <i>corpus</i>-based methods, such as word embeddings, rely on statistical patterns without deep semantic understanding; and deep learning models efficiently perform but lack interpretability. In addressing these limitations, this study proposes a hybrid SQS model that integrates knowledge graph embedding (KGE) with AraBERT embeddings to enrich semantic representation. The model utilizes a bidirectional long short-term memory network with an attention mechanism to predict question similarity, leveraging the strengths of each approach while mitigating their individual shortcomings. Furthermore, we align LughaNet (a variant of Arabic WordNet) with Wikidata using the similarity flooding (SF) algorithm to enhance KG-based semantic representations, creating a comprehensive and large-scale Arabic knowledge graph. Experimental evaluation on an augmented Mawdoo3 dataset demonstrates that our approach achieves an F1-score of 93.88%, outperforming state-of-the-art benchmarks, including GPT-based models. These findings confirm the effectiveness of hybrid models in capturing structured and contextual semantic relationships, advancing Arabic natural language processing (NLP) research. Additionally, this study introduces a large enriched Arabic knowledge graph, providing a valuable linguistic resource that lays the foundation for future advancements in low-resource language processing, with direct applications in automated QA systems, intelligent search, and recommendation engines.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"12 ","pages":"e3988"},"PeriodicalIF":2.9,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13585214/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992511","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":"Dense121GAN: transfer learning-enhanced conditional generative adversarial network with DenseNet121 for reliable and efficient segmentation in medical and industrial imaging.","authors":"Muhammed Davud","doi":"10.7717/peerj-cs.4020","DOIUrl":"10.7717/peerj-cs.4020","url":null,"abstract":"<p><p>Accurate image segmentation in medical and industrial domains remains challenging due to small object sizes, complex textures, and diverse defect morphologies. To address these limitations, we propose Dense121GAN, a conditional generative adversarial network (cGAN) that integrates a pre-trained DenseNet121 as a frozen encoder. The proposed architecture promotes effective feature reuse and stable information flow through residual skip connections. By leveraging transfer learning, Dense121GAN captures rich hierarchical representations and improves training stability, particularly in data-constrained settings. Extensive experiments were conducted using five-fold cross-validation across 16 heterogeneous datasets. Dense121GAN consistently outperformed UNet and ResNet-based generative adversarial networks (GANs) across multiple evaluation metrics, including Intersection over Union (IoU), Dice coefficient (Dice), Matthews Correlation Coefficient (MCC), Cohen's Kappa, Structural Similarity Index Measure (SSIM), coefficient of determination (<i>R</i> ²), and Symmetric Mean Absolute Percentage Error (SMAPE). The model also demonstrated stable training behavior and mitigated common GAN failure modes such as mode collapse. In addition, Dense121GAN achieved faster convergence compared to the evaluated baseline architectures. These results highlight the effectiveness, robustness, and computational efficiency of Dense121GAN. The proposed framework provides a practical and versatile solution for both clinical diagnostic and industrial inspection applications.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"12 ","pages":"e4020"},"PeriodicalIF":2.9,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13679653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229236","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":"Systematic review of unveiling the potential of AI using machine learning and deep learning methods in neurodegenerative diseases.","authors":"S Mohanraj, Sujatha Radhakrishnan","doi":"10.7717/peerj-cs.3860","DOIUrl":"10.7717/peerj-cs.3860","url":null,"abstract":"<p><strong>Background: </strong>Neurodegenerative diseases (NDDs) are becoming a major worldwide issue, especially for the elderly because they are incurable and permanent. It is extremely difficult to provide any medication to people suffering from NDDs. Comprehending essential processes of NDDs are essential to establishing alternative strategies that can increase survival rates in patients. This review is an effort to provide insight into NDDs, innovative therapeutic methods along with their clinical significances and thus provide opportunities for improving therapies for NDDs in the near future.</p><p><strong>Objective: </strong>This work aims at a thorough, methodical and critical evaluation of Machine Learning (ML)/Deep Learning (DL) efforts in diagnostics of NDDs, as well as their potential for use in pharmacological, clinical and research settings. An additional objective is to discuss problems and limitations of models, with an eye on furthering research directions.</p><p><strong>Methods: </strong>The databases of Elsevier, Sage, Wiley, Springer Link, Emerald Insights and Research Gate were searched for useful assessments of NDDs using ML/DL approaches.</p><p><strong>Results: </strong>Numerous potential diagnostic ML/DL models have been suggested and effectively applied to detect NDDs. Biological, neuroimaging and key clinical characteristics in patients have been used to evaluate prognostic models. Additionally, these models provide options for patient categorization, facilitate earlier detections, enhanced diagnosis and better disease outcome predictions for personalized therapies.</p><p><strong>Conclusion: </strong>This work points out the rising burden of NDDs on the global elderly while highlighting the significances of ML/DL technological advancements with their limitations for NDDs in diagnostics, identification of effective disease bio-markers and management.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"12 ","pages":"e3860"},"PeriodicalIF":2.9,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13536175/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148882450","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":"Explainable AI for malware analysis: a systematic review of benchmark datasets, traffic-oriented detection, and interpretability methods.","authors":"Bowen Yu, Wanting Zhang, Yishen Liu, Awais Ahmad, Waeal J Obidallah, Yousef Ahmed Alduraywish, Farhan Ullah, Kenji Yoshigoe, Yue Zhao","doi":"10.7717/peerj-cs.3902","DOIUrl":"https://doi.org/10.7717/peerj-cs.3902","url":null,"abstract":"<p><p>In recent years, malicious software (malware) has remained a persistent and evolving threat, affecting both personal and organizational devices across diverse computing environments. This article presents a systematic review of recent progress in malware analysis over the past 5 years, with particular emphasis on benchmark datasets, traffic-oriented detection settings, deep learning methods, and explainable artificial intelligence (XAI) approaches. Representative benchmark resources discussed in the reviewed literature include CIC IoMT 2024, CIC-MalMem-2022, and CICMalDroid 2020, which reflect different malware-analysis contexts such as IoMT/network traffic, memory-based Windows malware behavior, and Android malware analysis. The review examines how prior studies characterize malicious behaviors, evaluate detection paradigms, and employ interpretability techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) to improve model transparency and analyst understanding. In addition to summarizing the practical strengths of these approaches, the article discusses current limitations related to interpretability overhead, dataset dependence, scalability, and deployment constraints. The review concludes by identifying major research gaps and outlining future directions for building more scalable, explainable, and trustworthy malware-analysis systems.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"12 ","pages":"e3902"},"PeriodicalIF":2.9,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13716350/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149302320","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":"Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.","authors":"Sabari Vasan S, Jayalakshmi P","doi":"10.7717/peerj-cs.3387","DOIUrl":"10.7717/peerj-cs.3387","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is a neurodegenerative disorder that affects a wide range of individuals worldwide. It is of utmost importance to detect AD at an earlier stage and diagnose it to manage the disease effectively. Detecting AD using traditional methodologies is not cost-effective and time-consuming because of the clinical tests and neuroimaging methods involved. Over the last few years, quantum computing and deep learning (DL) have become practical approaches for detecting and diagnosing AD. Unlike conventional methods, quantum computing allows for faster solving complex and entangled computable problems. DL models have a high potential for automatically learning and extracting pertinent features even from larger datasets. Hence, a new approach combining multiple concepts such as deep neural network (DNN), quantum computing, simulated annealing (SA) optimisation, and Haralick feature extraction has been proposed in this work for detecting AD. A quantum deep neural network (QDNN) is introduced in this article to take over the extraordinary computational capability of quantum systems. Haralick feature extraction is implemented in this study to extract the texture features from the medical images, resulting in a rich feature set for the model. The dataset used in this study, The Best Alzheimer's MRI Dataset contains 11,519 axial MRI images in .jpg format with a resolution of 128 × 128 pixels, categorised into four balanced classes-no impairment, very mild impairment, mild impairment, and moderate impairment-each comprising 2,560 images. To optimise the Haralick features from medical images and to enhance the model's learning process with optimised parameters, a new feature-specific simulated annealing method (FSSA) has been introduced in this article. The experimental results proved that our model achieved an accuracy of 98%, a precision of 99%, a sensitivity of 97%, and a specificity of 98%. The results achieved in this study are better than the traditional model's performance, and thus better in all performance metrics. The results indicated that the proposed QDNN model is a good framework for AD detection.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"12 ","pages":"e3387"},"PeriodicalIF":2.9,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13067259/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147678202","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 new era in identification of tick genera; artificial intelligence for precision and speed.","authors":"Ibrahim A Ame, Abdullahi Ibrahim Umar, Cenk S Ozverel, Erdal Şanlıdağ, Ayse Seyer, Fadi Al-Turjman, Tamer Sanlidag","doi":"10.7717/peerj-cs.3291","DOIUrl":"10.7717/peerj-cs.3291","url":null,"abstract":"<p><strong>Background: </strong>The occurrence of pandemics in the last 20 years highlighted the unpreparedness of healthcare systems. There is a worldwide increased trend in the vector borne diseases. Ticks are one of the most common organisms that play a vital role in global ecosystem as well as being vectors of diseases affecting human and livestock. They are able to carry infectious agents that might cause illnesses including paralysis and to some certain extend death. Therefore, it is crucial to identify different genera of ticks to track infectious agents. Conventionally, tick classification is done by acarologists who are experts in the field. For this reason, the identification process is carried out in a difficult and time-consuming manner.</p><p><strong>Method: </strong>The aim of the study was to develop a web-based application by using artificial intelligence-based algorithms to easily identify Hyalomma and Rhipicephalus ticks, which are the most abundant genera in Northern Cyprus, with high sensitivity and accuracy. The experimental procedure is structured based on five phases. Phase 1 revolves around data collection in which pictures of 35 identified ticks are taken by experienced acarologists and the curation of non-tick images (spiders, beetles, mites, mosquitos and scorpions). Phase 2 revolves around pre-processing steps and data split. Phase 3 involves training and testing custom Convolutional Neural Network (CNN), Visual Geometry Group 16 (VGG16), Residual Network 50 (ResNet-50) using 6,972 images (3,486 images for each class) for discrimination between ticks and non-ticks and 9,556 images (4,778 images for each class) for the discrimination between Hyalomma and Rhipicephalus. Phase 4 revolves around performance evaluation. Phase 5 is characterized by development of a web-based application (I-TickNet), created to enable a widespread use of the tick classifier.</p><p><strong>Results: </strong>The performance evaluation and comparison of the model performance has shown that ResNet50 achieved the best result for binary classification of tick and non-tick (experiment A) with accuracy of 100% and Area Under the Curve (AUC) score of 100%. Moreover, VGG16 achieved the best result for binary classification of ticks (experiment B) with an accuracy of 96.97% and AUC score of 99.55% respectively. All the three models were employed for the development of artificial intelligence/Internet of Things (AI/IoT) framework known as I-TickNet for real-time and on-spot classification of tick images. In conclusion, this study provided a web-based application that can identify two distinct tick genera with high accuracy and sensitivity. The application developed enabled a user-friendly interface to identify genera without requiring any expertise.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"12 ","pages":"e3291"},"PeriodicalIF":2.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12851202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146088006","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":"MS-YieldStackNet: multi-source data fusion for wheat yield estimation using a stacked ensemble neural network.","authors":"Waqas Ali, Zeeshan Ramzan, Muhammad Shahbaz, Qamar Ul Zaman Bhutta, Muhammad Talha, Mohammed J AlGhamdi","doi":"10.7717/peerj-cs.3434","DOIUrl":"10.7717/peerj-cs.3434","url":null,"abstract":"<p><p>Accurate crop yield prediction is vital for ensuring food security and informing agricultural policy, particularly in wheat-dependent regions like Pakistan where manual estimation methods are labor-intensive and imprecise. This study introduces a novel algorithmic framework, MS-YieldStackNet, to predict wheat yield with high spatial resolution by integrating multispectral satellite imagery, <i>in-situ</i> soil analytics, and meteorological variables. A unified feature space is constructed using Normalized Difference Vegetation Index (NDVI) and Difference Vegetation Index (DVI), soil physicochemical attributes, and temporal climate patterns, processed through a stacked ensemble neural architecture (MS-YieldStackNet) combining three parallel feed-forward neural networks (FFNNs) and a Random Forest meta-learner. The model achieved robust performance with an R-squared of 0.81, Mean Squared Error (MSE) of 6,114.30 kg/ha, root mean squared error (RMSE) of 78.19 kg/ha, mean absolute error (MAE) of 59.07 kg/ha, and mean absolute percentage error (MAPE) of 3.55%, demonstrating its potential for precise and scalable crop yield forecasting.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"12 ","pages":"e3434"},"PeriodicalIF":2.5,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12818367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146020675","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 hybrid algorithmic model for enhancing security in intelligent reflecting surface-assisted wireless communication.","authors":"Sivasankar S, Markkandan S","doi":"10.7717/peerj-cs.3285","DOIUrl":"10.7717/peerj-cs.3285","url":null,"abstract":"<p><p>This article introduces Synergistic Gradient Projection with Dynamic Adaptive Risk Expansion (SGP-DARE), a hybrid optimization framework designed to enhance physical-layer security in wireless networks supported by intelligent reflecting surfaces (IRSs). The proposed framework integrates Synergistic Gradient Projection (SGP) for low-complexity joint optimization of base station beamforming and IRS phase shifts, with Dynamic Adaptive Risk Expansion (DARE) ensuring robustness against channel state information (CSI) uncertainties and user mobility. SGP-DARE operates effectively under hardware limitations, including phase quantization, while targeting key objectives such as minimizing secrecy outage probability and improving energy efficiency. Simulation results demonstrate that SGP-DARE significantly outperforms baseline methods in critical metrics of security and efficiency.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3285"},"PeriodicalIF":2.9,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12915676/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229787","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":"Robust coffee plant disease classification using deep learning and advanced feature engineering techniques.","authors":"Hanin Ardah, Maher Alrahhal, Walaa M Abd-Elhafiez, Doaa Trabay","doi":"10.7717/peerj-cs.3386","DOIUrl":"10.7717/peerj-cs.3386","url":null,"abstract":"<p><p>Coffee, the world's most traded tropical crop, is vital to the economies of many producing countries. However, coffee leaf diseases pose a serious threat to coffee quality and sustainable production. Deep learning has shown strong performance in plant disease identification through automatic image classification. Nevertheless, reliance on a single convolutional neural networks (CNNs) architecture restricts feature variability and real-world generalization. Moreover, limited work has systematically combined feature selection/reduction with CNNs, which constrains the advancement of hybrid models capable of capturing complementary features while ensuring computational efficiency without accuracy loss. This article presents an enhanced deep learning-based framework for coffee disease classification incorporating a hybrid strategy that integrates CNNs and advanced feature selection algorithms. GoogLeNet and ResNet18 are paired for complementary feature extraction, Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) are employed for dimensionality reduction, and ANOVA and Chi-square are applied to select the most informative features. An Adam optimizer (learning rate = 0.001, batch size = 20, epochs = 50) with early stopping is used for training. Experiments on the BRACOL dataset achieved 99.78% accuracy, with precision, recall, and F1-score all exceeding 99% across classes. To the best of our knowledge, this study systematically integrates GoogLeNet and ResNet18 with PCA/SVD dimensionality reduction and analysis of variance (ANOVA)/Chi-square feature selection, for coffee disease classification, thereby addressing a key gap in prior research.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3386"},"PeriodicalIF":2.5,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12695173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145745697","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":"DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.","authors":"Itsuki Shimizu, Takuya Ida, Yuhei Ozawa, Satoshi Fukuchi, Hiroto Anbo","doi":"10.7717/peerj-cs.3343","DOIUrl":"10.7717/peerj-cs.3343","url":null,"abstract":"<p><strong>Background: </strong>Intrinsically disordered proteins (IDPs) are proteins that contain intrinsically disordered regions (IDRs), which lack stable three-dimensional structures under physiological conditions. These regions are known to play crucial roles in many biological processes. While IDRs can be predicted from their amino acid sequences, and several accurate IDR prediction programs have been developed, such programs often require substantial computational resources, including long execution times, large databases for homology searches, and advanced computer architectures. Since DNA sequence data continues to grow rapidly, particularly at a genomic scale, there is an increasing need for fast and accurate IDR prediction programs that demand fewer computational resources.</p><p><strong>Methods: </strong>In this study, we developed DARUMA (Disorder order clAssifier by Rapid and User-friendly MAchine), an IDR prediction program designed for speed and ease of use. DARUMA uses a one-dimensional convolutional neural network (1D-CNN) that processes the physicochemical properties of amino acid residues instead of relying on sequence profiles. DARUMA employs a simple neural network that predicts IDRs using the output of 1D-CNN as input features. To ensure easy installation on users' systems, DARUMA was written entirely in Python using standard and NumPy libraries.</p><p><strong>Results: </strong>DARUMA achieves fast performance by avoiding iterative homology searches while delivering accuracy comparable to the latest predictors that use sequence profiles. In addition to the advantage of execution time, DARUMA requires no additional homology search programs and operates using standard Python libraries, making it easy to install and run on users' own environments without the need for specialized computational resources. DARUMA is available at https://antepontem.org/daruma/, which also provides the stand-alone distribution.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3343"},"PeriodicalIF":2.9,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13293392/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148334224","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}