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PubMedMetaTool: Automated metadata extraction from PubMed using Python for bibliometric analysis PubMedMetaTool：使用Python从PubMed自动提取元数据，用于文献计量分析
IF 1.3
Software Impacts Pub Date : 2025-06-01 Epub Date: 2025-04-29 DOI: 10.1016/j.simpa.2025.100766
Leandro Rodrigues da Silva Souza , Daniel Hilário da Silva , Caio Tonus Ribeiro , Daiane Alves da Silva , Slawomir J. Nasuto , Catherine M. Sweeney-Reed , Adriano de Oliveira Andrade , Adriano Alves Pereira
{"title":"PubMedMetaTool: Automated metadata extraction from PubMed using Python for bibliometric analysis","authors":"Leandro Rodrigues da Silva Souza ,&nbsp;Daniel Hilário da Silva ,&nbsp;Caio Tonus Ribeiro ,&nbsp;Daiane Alves da Silva ,&nbsp;Slawomir J. Nasuto ,&nbsp;Catherine M. Sweeney-Reed ,&nbsp;Adriano de Oliveira Andrade ,&nbsp;Adriano Alves Pereira","doi":"10.1016/j.simpa.2025.100766","DOIUrl":"10.1016/j.simpa.2025.100766","url":null,"abstract":"<div><div>Bibliometric analyses often depend on extracting metadata from large scientific databases, a process that is still largely manual, repetitive, and error prone. This paper presents PubMedMetaTool, an open-source Python-based solution that automates the retrieval and transformation of bibliographic metadata from PubMed, using either article titles or Digital Object Identifiers as input. The tool implements a modular pipeline that extracts metadata using NCBI’s Entrez programming utilities and transforms it into formats compatible with tools such as Bibliometrix, VOSviewer, and pyBibX. Designed to be transparent and configurable, the tool improves bibliometric workflow efficiency, accuracy, and interoperability workflows.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100766"},"PeriodicalIF":1.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941815","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
WECS: A wavelet energy correlation screening based method for unsupervised change detection using remote sensing image series 基于小波能量相关筛选的遥感图像序列无监督变化检测方法
IF 1.3
Software Impacts Pub Date : 2025-06-01 Epub Date: 2025-04-28 DOI: 10.1016/j.simpa.2025.100750
João Pedro M. Saraiva , Rodney V. Fonseca , Rogério G. Negri , Aluísio Pinheiro
{"title":"WECS: A wavelet energy correlation screening based method for unsupervised change detection using remote sensing image series","authors":"João Pedro M. Saraiva ,&nbsp;Rodney V. Fonseca ,&nbsp;Rogério G. Negri ,&nbsp;Aluísio Pinheiro","doi":"10.1016/j.simpa.2025.100750","DOIUrl":"10.1016/j.simpa.2025.100750","url":null,"abstract":"<div><div>This paper introduces an unsupervised method for detecting spatiotemporal changes in a series of remotely sensed images. Specifically, we employ a fully automatic, data-driven framework that incorporates wavelet approximation, wavelet energy apportionment, and high-dimensional correlation screening of wavelet coefficients. This approach processes sequences of images and produces a mapping of changed and non-changed locations over the analyzed period.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100750"},"PeriodicalIF":1.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881638","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
Symptom-based early detection and classification of plant diseases using AI-driven CNN+KNN Fusion Software (ACKFS) 基于ai驱动的CNN+KNN融合软件（ACKFS）基于症状的植物病害早期检测与分类
IF 1.3
Software Impacts Pub Date : 2025-06-01 Epub Date: 2025-04-26 DOI: 10.1016/j.simpa.2025.100755
Jayswal Hardik , Rishi Sanjaykumar Patel , Hetvi Desai , Hasti Vakani , Mithil Mistry , Nilesh Dubey
{"title":"Symptom-based early detection and classification of plant diseases using AI-driven CNN+KNN Fusion Software (ACKFS)","authors":"Jayswal Hardik ,&nbsp;Rishi Sanjaykumar Patel ,&nbsp;Hetvi Desai ,&nbsp;Hasti Vakani ,&nbsp;Mithil Mistry ,&nbsp;Nilesh Dubey","doi":"10.1016/j.simpa.2025.100755","DOIUrl":"10.1016/j.simpa.2025.100755","url":null,"abstract":"<div><div>This paper investigates and introduce an AI-driven CNN-KNN Fusion Software (ACKFS) for symptom-based early detection and classification of plant diseases. The approach integrates Convolutional Neural Networks and K-Nearest Neighbor’s to enhance classification accuracy. This research follows a structured four-phase process: pre-processing, segmentation, feature extraction, and classification. Using two datasets, ACKFS significantly improved accuracy to 94.56% and 87.52%, respectively. These results surpass the performance reported by previous researcher’s, demonstrating the effectiveness of CNN-KNN fusion for real-time plant disease detection on smart devices, contributing to precision agriculture and enhanced plant health monitoring.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100755"},"PeriodicalIF":1.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888039","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
MESCNN: Magnitude estimation system based on convolutional neural networks MESCNN：基于卷积神经网络的震级估计系统
IF 1.3
Software Impacts Pub Date : 2025-06-01 Epub Date: 2025-04-23 DOI: 10.1016/j.simpa.2025.100748
Ji’an Liao , Siran Yang , Yanwei Wang , Jianming Wang , Dengke Zhao , Zhaoyan Li , Zifa Wang
{"title":"MESCNN: Magnitude estimation system based on convolutional neural networks","authors":"Ji’an Liao ,&nbsp;Siran Yang ,&nbsp;Yanwei Wang ,&nbsp;Jianming Wang ,&nbsp;Dengke Zhao ,&nbsp;Zhaoyan Li ,&nbsp;Zifa Wang","doi":"10.1016/j.simpa.2025.100748","DOIUrl":"10.1016/j.simpa.2025.100748","url":null,"abstract":"<div><div>Magnitude is a critical parameter in earthquake early warning systems, directly influencing alert issuance and warning levels. In this study, we introduce the Magnitude Estimation System based on Convolutional Neural Networks (MESCNN), a novel approach built upon the Python programming language and the TensorFlow deep learning framework. MESCNN automates the calculation of earthquake magnitudes using real-time seismic data, leveraging the capabilities of convolutional neural networks (CNN) to analyze seismic waveforms. The system is designed to enhance the accuracy and efficiency of magnitude estimation, thereby enabling more timely and reliable earthquake warnings to reduce the impact of seismic events.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100748"},"PeriodicalIF":1.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879267","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
Nomad Analytix: Text-rich visual reasoning using vision models for insights and recommendations Nomad Analytix：使用视觉模型进行文本丰富的视觉推理，以获得见解和建议
IF 1.3
Software Impacts Pub Date : 2025-06-01 Epub Date: 2025-04-29 DOI: 10.1016/j.simpa.2025.100765
Sai Jeevan Puchakayala , Allen Bijo T. , Aswathy Ravikumar , Harini Sriraman
{"title":"Nomad Analytix: Text-rich visual reasoning using vision models for insights and recommendations","authors":"Sai Jeevan Puchakayala ,&nbsp;Allen Bijo T. ,&nbsp;Aswathy Ravikumar ,&nbsp;Harini Sriraman","doi":"10.1016/j.simpa.2025.100765","DOIUrl":"10.1016/j.simpa.2025.100765","url":null,"abstract":"<div><div>Nomad Analytix is an innovative business intelligence tool that uses state-of-the-art vision models to transform data analysis. This software automates complex tasks traditionally handled by data analysts, empowering non-technical teams such as marketing and sales to access advanced data analysis easily. By using natural language prompts, users can interact with data intuitively and gain valuable insights without needing extensive technical expertise. A prototype of the software, built on the Streamlit platform, will showcase its ability to generate visualizations from various data sources, including CSV, JSON, SQLite, Excel, and databases, with potential extensions to data warehouses. The integration of Vision Language Models GPT 4 Omni and GPT 4 Turbo- with this framework provides a seamless interface for data querying, visualization creation, and recommendation generation. Nomad Analytix serves as an inclusive, intelligent, and intuitive solution, bridging the gap between data and decision-making across diverse industries.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100765"},"PeriodicalIF":1.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903526","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
An automated parameter optimizer for data transfer performance testing 用于数据传输性能测试的自动参数优化器
IF 1.3
Software Impacts Pub Date : 2025-06-01 Epub Date: 2025-04-29 DOI: 10.1016/j.simpa.2025.100764
Daqing Yun , Liudong Zuo , Yi Gu , Chase Wu
{"title":"An automated parameter optimizer for data transfer performance testing","authors":"Daqing Yun ,&nbsp;Liudong Zuo ,&nbsp;Yi Gu ,&nbsp;Chase Wu","doi":"10.1016/j.simpa.2025.100764","DOIUrl":"10.1016/j.simpa.2025.100764","url":null,"abstract":"<div><div>This work presents an automated tool for optimizing control parameters in performance testing of big data transfer over long-fat network connections. Supporting both TCP and UDT protocols, the tool identifies the optimal configurations to enhance the efficiency of large-scale data transfers. A stochastic approximation algorithm is employed for parameter optimization, streamlining the protocol and parameter selection. The tool has been evaluated in various network scenarios, including long-haul connections in real-world high-performance networks. Its modular design also enables straightforward integration of additional data transfer protocols and alternative optimization methods.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100764"},"PeriodicalIF":1.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903527","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
TextRegress: A Python package for advanced regression analysis on long-form text data texttregress：一个Python包，用于对长格式文本数据进行高级回归分析
IF 1.3
Software Impacts Pub Date : 2025-06-01 Epub Date: 2025-05-05 DOI: 10.1016/j.simpa.2025.100760
Jinhang Jiang , Ben Liu , Weiyao Peng , Karthik Srinivasan
{"title":"TextRegress: A Python package for advanced regression analysis on long-form text data","authors":"Jinhang Jiang ,&nbsp;Ben Liu ,&nbsp;Weiyao Peng ,&nbsp;Karthik Srinivasan","doi":"10.1016/j.simpa.2025.100760","DOIUrl":"10.1016/j.simpa.2025.100760","url":null,"abstract":"<div><div>TextRegress is an open-source Python package that leverages state-of-the-art deep learning techniques to perform regression analysis on long-form text data. Departing from conventional text mining tools that are confined to classification, sentiment, or readability metrics, TextRegress provides a unified framework for conducting predictive modeling of continuous outcomes. By integrating advanced encoding methods – including transformer-based embeddings, TF-IDF, and pre-trained Hugging Face models – with a robust PyTorch Lightning backend, TextRegress efficiently processes long texts through automatic chunking and dynamic feature integration. Its flexible architecture and customizable training paradigms empower researchers and practitioners across diverse domains to deploy sophisticated regression models, fostering reproducibility and accelerating innovation in text analytics.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100760"},"PeriodicalIF":1.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935810","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
MRTASim: An agent-based multi-robot task allocation simulation MRTASim：基于agent的多机器人任务分配仿真
IF 1.3
Software Impacts Pub Date : 2025-06-01 Epub Date: 2025-04-22 DOI: 10.1016/j.simpa.2025.100754
Savaş Öztürk
{"title":"MRTASim: An agent-based multi-robot task allocation simulation","authors":"Savaş Öztürk","doi":"10.1016/j.simpa.2025.100754","DOIUrl":"10.1016/j.simpa.2025.100754","url":null,"abstract":"<div><div>In hazardous scenarios with life-threatening risks or in costly or time-consuming situations, autonomous systems must first be tested in a computer environment. This paper introduces MRTASim software developed for multi-robot task allocation experiments. MRTASim uses JADE (Java Agent-Based Development Environment) and allows comparing variations of functions such as path planning, bid valuation, and task scheduling. It displays the results on maps prepared in the environment editor, and automated experiments ensure the accuracy of the results. Academic studies produced with the software, which is constantly updated with new methods and parameters, have been presented at conferences and published in journals.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100754"},"PeriodicalIF":1.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917551","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
Infinite Type Centroid java library: An implementation of parameterized coordinates for an enhanced centroid calculation during K-means classification 无限型质心java库：参数化坐标的实现，用于在K-means分类期间增强质心计算
IF 1.3
Software Impacts Pub Date : 2025-06-01 Epub Date: 2025-04-28 DOI: 10.1016/j.simpa.2025.100751
Victor Hugo Silva-Blancas , José Manuel Álvarez-Alvarado , Hugo Jiménez-Hernández , Ana Marcela Herrera-Navarro , Diana Margarita Córdova-Esparza , Juvenal Rodríguez-Reséndiz
{"title":"Infinite Type Centroid java library: An implementation of parameterized coordinates for an enhanced centroid calculation during K-means classification","authors":"Victor Hugo Silva-Blancas ,&nbsp;José Manuel Álvarez-Alvarado ,&nbsp;Hugo Jiménez-Hernández ,&nbsp;Ana Marcela Herrera-Navarro ,&nbsp;Diana Margarita Córdova-Esparza ,&nbsp;Juvenal Rodríguez-Reséndiz","doi":"10.1016/j.simpa.2025.100751","DOIUrl":"10.1016/j.simpa.2025.100751","url":null,"abstract":"<div><div>InfiniteTypeCentroid presents a theorem design to enhance centroid calculation on the K-means algorithm by integrating a parameters list that produces hidden information and offers improved results choose in the course of research. It shows a capacity for universalization and adaptability to address any mathematical analysis and it can be used in any Java compiler a parent class. Data analysis on use cases results in 18.22% improved accuracy and data behavior for a specific dataverse produces seven tendency charts with enriched significance. Improves data structures by debugging non-significant values and offering an improved methodology for hypothesis definition.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100751"},"PeriodicalIF":1.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895941","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
ArSLR-ML: A Python-based machine learning application for arabic sign language recognition 基于python的机器学习应用程序，用于阿拉伯手语识别
IF 1.3
Software Impacts Pub Date : 2025-06-01 Epub Date: 2025-03-27 DOI: 10.1016/j.simpa.2025.100746
Lamis Ali Hussein , Ziad Saeed Mohammed
{"title":"ArSLR-ML: A Python-based machine learning application for arabic sign language recognition","authors":"Lamis Ali Hussein ,&nbsp;Ziad Saeed Mohammed","doi":"10.1016/j.simpa.2025.100746","DOIUrl":"10.1016/j.simpa.2025.100746","url":null,"abstract":"<div><div>The ArSLR-ML is a real-time interactive application that uses multi-class Support Vector Machines (SVM) machine learning applied in the classification procedure and MediaPipe in the feature extraction procedure to recognize static Arabic sign language gestures, focusing on numbers and letters and translating them into text and Arabic audio output. The ArSLR-ML was built within the PyCharm IDE using Python with a graphical user interface (GUI), thereby allowing for effective recognition of gestures. The application utilizes the laptop camera and GUI to capture hand gestures to create dataset for machine learning models and implement them in real time.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100746"},"PeriodicalIF":1.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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