发布求助
文献互助 智能选刊 最新文献

Software Impacts最新文献

筛选
英文 中文
ICMP-Flood-SDN: A Python based machine learning application for ICMP flood DDoS attack detection in software defined networks ICMP- flood - sdn：一个基于Python的机器学习应用程序，用于在软件定义网络中检测ICMP flood DDoS攻击
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2026-02-12 DOI: 10.1016/j.simpa.2026.100819
Sudesh Kumar, Sunanda Gupta
{"title":"ICMP-Flood-SDN: A Python based machine learning application for ICMP flood DDoS attack detection in software defined networks","authors":"Sudesh Kumar,&nbsp;Sunanda Gupta","doi":"10.1016/j.simpa.2026.100819","DOIUrl":"10.1016/j.simpa.2026.100819","url":null,"abstract":"<div><div>ICMP-flood-SDN is an artificial intelligence based DDoS detection application that uses support vector machines (SVMs) as a machine learning model for the classification of ICMP flood DDoS traffic in software defined networks. The ICMP-flood-SDN was built using the ICMP-Flood DDoS dataset and Python-based machine learning libraries on Jupiter Notebook. The application utilizes the Mininet emulator, RYU controller, and hping3 tool to create a normal and ICMP flood traffic dataset in software defined network.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100819"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
ASReview Dory: Bringing new and exciting models to ASReview LAB ASReview多利：为ASReview LAB带来新的和令人兴奋的模型
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2026-01-06 DOI: 10.1016/j.simpa.2025.100809
Timo van der Kuil , Jelle Jasper Teijema , Jonathan de Bruin , Rens van de Schoot
{"title":"ASReview Dory: Bringing new and exciting models to ASReview LAB","authors":"Timo van der Kuil ,&nbsp;Jelle Jasper Teijema ,&nbsp;Jonathan de Bruin ,&nbsp;Rens van de Schoot","doi":"10.1016/j.simpa.2025.100809","DOIUrl":"10.1016/j.simpa.2025.100809","url":null,"abstract":"<div><div>Systematic reviewing is a time-consuming process which can be accelerated through screening prioritisation via active learning. ASReview Dory enables researchers to test, validate, and apply a wide range of embedders and classifiers in systematic literature screening. It extends ASReview LAB, an open source, lightweight, and user-friendly environment with proven default models and extensibility through Python entry points. ASReview Dory adds ready-to-use transformer-based embedders, neural classifiers, and a framework for integrating custom models. Once installed, these models are directly available in ASReview LAB without additional configuration and can be systematically evaluated using the API or ASReview Makita.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100809"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MatAbaAutoRel: A MATLAB–Abaqus framework for automated reliability analysis MatAbaAutoRel：用于自动化可靠性分析的MATLAB-Abaqus框架
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2026-01-07 DOI: 10.1016/j.simpa.2025.100808
S. Malekpour , S.M. Dehghan , M.A. Najafgholipour , S. Behravesh
{"title":"MatAbaAutoRel: A MATLAB–Abaqus framework for automated reliability analysis","authors":"S. Malekpour ,&nbsp;S.M. Dehghan ,&nbsp;M.A. Najafgholipour ,&nbsp;S. Behravesh","doi":"10.1016/j.simpa.2025.100808","DOIUrl":"10.1016/j.simpa.2025.100808","url":null,"abstract":"<div><div>Probabilistic and reliability analyses utilizing finite element software are frequently constrained by manual model creation and result extraction. This study presents an open-source, MATLAB-based framework integrated with Abaqus that automates randomized model generation through Monte Carlo simulation, performs analyses, and retrieves target results via a lightweight Python script in noGUI mode. The modular tool reduces user intervention and facilitates automated variations in geometry, material properties, and loading conditions. This framework enables rapid model generation and result extraction for hundreds of analyses in seconds, significantly reducing manual effort and potential human error.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100808"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
LeukoXAI-Lite: A reusable explainable AI toolkit for federated leukemia diagnosis with visual explanations and performance analysis LeukoXAI-Lite：一个可重复使用的可解释的AI工具包，用于联合白血病诊断，具有可视化解释和性能分析
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2026-01-28 DOI: 10.1016/j.simpa.2026.100813
Khadija Parwez , Syed Irfan Sohail , Ali Raza , Mohammad Abdullah Zia
{"title":"LeukoXAI-Lite: A reusable explainable AI toolkit for federated leukemia diagnosis with visual explanations and performance analysis","authors":"Khadija Parwez ,&nbsp;Syed Irfan Sohail ,&nbsp;Ali Raza ,&nbsp;Mohammad Abdullah Zia","doi":"10.1016/j.simpa.2026.100813","DOIUrl":"10.1016/j.simpa.2026.100813","url":null,"abstract":"<div><div>LeukoXAI-Lite: A flexible and modular software framework for the interpretable diagnosis of Acute Lymphoblastic Leukemia by deep learning algorithms and visual explanation tools. The system adopts EfficientNetB3-based convolutional neural network, which is embedded in a hierarchical federated learning framework. This approach facilitates distributed model training with simulated health participants cooperating yet guarantee the private of sensitive patient information. In addition to disease categorization, our framework is equipped with a profound explainable artificial intelligence module, based upon 18 distinct visualization methods that includes saliency maps, guided backpropagation, gradient_based methods, SmoothGrad, VarGrad, SquareGrad, Grad-CAM, Grad-CAM++, HiResCAM, Respond-CAM, Score-CAM, Faster Score-CAM, oclusion sensitivity, LIME, SHAP, sobol attribution, and a fusion approach. These approaches produce visual heatmaps which highlight diagnostically important regions in microscopic images of blood cells, making the model more interpretable for clinical deployment. LeukoXAI-Lite also comes with instruments for systematic evaluation of the performance of the predictive model and explanation methods. We support common classification on based metrics (accuracy, precision, recall, F1 score, Kappa score and MCC) as well the explanation specific ones like Deletion, Insertion, Fidelity and Stability. It is implemented with open-source python libraries to be lightweight, adaptable and compatible with real-world use in medical imaging. LeukoXAI-Lite facilitates such kind of trustworthy and interpretable artificial intelligence solutions for the clinical diagnostics by means promoting transparency, reproducibility and privacy friendly learning.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100813"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
RAGCacheSim: A discrete-event simulator for evaluating caching strategies in Retrieval-Augmented Generation systems RAGCacheSim：用于评估检索增强生成系统中的缓存策略的离散事件模拟器
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-09-01 DOI: 10.1016/j.simpa.2025.100783
Hardik Ruparel, Tatsat Patel
{"title":"RAGCacheSim: A discrete-event simulator for evaluating caching strategies in Retrieval-Augmented Generation systems","authors":"Hardik Ruparel,&nbsp;Tatsat Patel","doi":"10.1016/j.simpa.2025.100783","DOIUrl":"10.1016/j.simpa.2025.100783","url":null,"abstract":"<div><div>Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) with external knowledge retrieval but incur significant compute and latency costs. In distributed RAG deployments, semantically similar queries routed to different nodes — each with its own cache — can lead to redundant processing. We present <em>RAGCacheSim</em>, a discrete-event simulator for evaluating caching strategies such as Centralized Exact-match Cache (CEC), Independent Semantic Caches (IC), and Distributed Semantic Cache Coordination (DSC). It reports metrics like cache hit rate, average query latency, and coordination overhead. Built using <span>SimPy</span>, <span>FastEmbed</span>, and <span>pybloom_live</span>, it helps researchers optimize distributed RAG architectures.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100783"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
STFATool: A Sparse Time–Frequency Analysis Toolkit for non-stationary signals 用于非平稳信号的稀疏时频分析工具包
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-09-16 DOI: 10.1016/j.simpa.2025.100784
Baijian Wu, Gang Yu
{"title":"STFATool: A Sparse Time–Frequency Analysis Toolkit for non-stationary signals","authors":"Baijian Wu,&nbsp;Gang Yu","doi":"10.1016/j.simpa.2025.100784","DOIUrl":"10.1016/j.simpa.2025.100784","url":null,"abstract":"<div><div>STFATool is a professional signal-processing application implemented in Python. It integrates several state-of-the-art sparse time–frequency analysis algorithms, including Synchroextracting Transform, Transient-Extracting Transform, Multisynchrosqueezing Transform, and Time-Reassigned Multisynchrosqueezing Transform. It provides a user-friendly interface, users can import signals for detailed time–frequency feature visualization and processing, enabling efficient extraction of critical signal characteristics.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100784"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
‘Forensic-DataFusion-Tool’: A Python-based application for exploratory forensic data analysis using merged datasets from analytical sensors “法医数据融合工具”：一个基于python的应用程序，用于探索性法医数据分析，使用来自分析传感器的合并数据集
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-11-03 DOI: 10.1016/j.simpa.2025.100799
Giorgio Felizzato , Michele Verdi , Angelo Michele Gargantini , Nico Pellegrinelli , Francesco Saverio Romolo
{"title":"‘Forensic-DataFusion-Tool’: A Python-based application for exploratory forensic data analysis using merged datasets from analytical sensors","authors":"Giorgio Felizzato ,&nbsp;Michele Verdi ,&nbsp;Angelo Michele Gargantini ,&nbsp;Nico Pellegrinelli ,&nbsp;Francesco Saverio Romolo","doi":"10.1016/j.simpa.2025.100799","DOIUrl":"10.1016/j.simpa.2025.100799","url":null,"abstract":"<div><div>Portable sensors for on-site forensic analysis have advanced significantly, enabling reliable methods for crime scene investigation. Non-destructive analytical instruments are especially useful for providing chemical information from the same specimen. Combining data from these instruments through data fusion enhances analytical responses. Data fusion merges data from different sources to improve exploratory and predictive models. No current application supports multi-dataset fusion on a single platform. To address this, we developed a Python-based ‘Forensic-DataFusion-Tool’ to merge raw and preprocessed data from multiple sensors, speeding up data fusion and enabling future machine learning updates, including classification algorithms.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100799"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MDM: An R package for causal multivariate time series tasks MDM：用于因果关系多变量时间序列任务的R包
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-11-05 DOI: 10.1016/j.simpa.2025.100796
Lilia Costa , Arthur Azevedo , Michel Miler Rocha dos Santos , Diego Carvalho Nascimento
{"title":"MDM: An R package for causal multivariate time series tasks","authors":"Lilia Costa ,&nbsp;Arthur Azevedo ,&nbsp;Michel Miler Rocha dos Santos ,&nbsp;Diego Carvalho Nascimento","doi":"10.1016/j.simpa.2025.100796","DOIUrl":"10.1016/j.simpa.2025.100796","url":null,"abstract":"<div><div>The Multiregression Dynamic Model (MDM) is a framework that combines graph theory with dynamic linear models, allowing a non-Gaussian multivariate structure to emerge in the context of causal time series. Since an optimal DAG structure is an NP-hard task, this package overcomes the all-combinations search (Integer Programming Algorithm) using heuristic algorithms (like Hill Climbing). Written using R S4 Object programming, it performs learning functions (estimating network structure and its dynamic arcs), as well as includes DAG (causal) visualization, time-varying coefficients visualization, and graphical performance checks. The MDM R package is distributed under the GPL license and is accessible from GitHub.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100796"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SkinProNet: An attention-based deep learning system for skin disease classification and segmentation SkinProNet：一个基于注意力的深度学习系统，用于皮肤病分类和分割
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-10-31 DOI: 10.1016/j.simpa.2025.100798
N Annalakshmi , S Umarani
{"title":"SkinProNet: An attention-based deep learning system for skin disease classification and segmentation","authors":"N Annalakshmi ,&nbsp;S Umarani","doi":"10.1016/j.simpa.2025.100798","DOIUrl":"10.1016/j.simpa.2025.100798","url":null,"abstract":"<div><div>SkinProNet is an AI-powered software tool designed for the classification and segmentation of skin lesions, including potentially life-threatening conditions like melanoma. It employs a novel hybrid deep learning architecture that combines advanced preprocessing methods with state-of-the-art models: EfficientNetV2Small for feature extraction, an optimized ACRNN for accurate classification, and U<sup>2</sup>-Net++ for precise lesion segmentation. This integrated approach enhances early detection and diagnosis of skin diseases. The model classifies six types of skin diseases with a high accuracy of 94.04% using both benchmark datasets and real-world clinical images. The results highlight the model’s potential as a reliable computer-aided diagnostic tool in dermatology. By leveraging attention-based mechanisms and efficient neural architectures, the software supports healthcare practitioners in diagnosing skin conditions quickly, accurately, and non-invasively.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100798"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
IRJT-Secure: Open-source image steganography with Quadristego embedding and Huffman compression IRJT-Secure：开源图像隐写与Quadristego嵌入和霍夫曼压缩
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-11-14 DOI: 10.1016/j.simpa.2025.100801
Irsyad Fikriansyah Ramadhan , Ntivuguruzwa Jean De La Croix , Tohari Ahmad
{"title":"IRJT-Secure: Open-source image steganography with Quadristego embedding and Huffman compression","authors":"Irsyad Fikriansyah Ramadhan ,&nbsp;Ntivuguruzwa Jean De La Croix ,&nbsp;Tohari Ahmad","doi":"10.1016/j.simpa.2025.100801","DOIUrl":"10.1016/j.simpa.2025.100801","url":null,"abstract":"<div><div>In information security, image steganography remains a crucial technique for covert data transmission. However, achieving an optimal balance between payload capacity, imperceptibility, and robustness against steganalysis attacks remains a significant challenge. This paper presents IRJT-Secure, an open-source software implementation based on the Quadristego Logic paradigm and Huffman coding for data compression. The proposed technique creates four stego images from a single cover image, in contrast to conventional dual-image steganography. This maintains the original image’s visual integrity while enabling more effective and uniform data embedding. IRJT-Secure provides a valuable resource for advancing research and development in spatial domain steganography, supporting the creation of more secure, robust, and efficient data hiding techniques for digital security</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100801"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145576323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信
小红书