Pub Date : 2025-10-01Epub Date: 2025-10-31DOI: 10.1016/j.simpa.2025.100798
N Annalakshmi , S Umarani
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 U2-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.
{"title":"SkinProNet: An attention-based deep learning system for skin disease classification and segmentation","authors":"N Annalakshmi , 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}
Pub Date : 2025-10-01Epub Date: 2025-11-28DOI: 10.1016/j.simpa.2025.100800
Manuel Domínguez-Dorado , David Cortés-Polo , Francisco J. Rodríguez-Pérez , Jesús Galeano-Brajones , Jesús Calle-Cancho
This article presents FLECO Studio 2.0, which enhances cybersecurity situational awareness training through realistic scenarios. Organizations face increasing cyber threats but struggle with a global shortage of cybersecurity professionals. To address this, they must upskill existing personnel across all functional areas. Situational awareness is crucial for identifying, understanding, and responding to threats dynamically. FLECO Studio 2.0 includes new features designed to improve this skill, enabling multidisciplinary teams to assess risks, anticipate attacks, and coordinate effective responses. These enhancements strengthen an organization’s cybersecurity posture, fostering a unified and proactive defense against evolving threats.
本文介绍了FLECO Studio 2.0，它通过现实场景增强了网络安全态势感知训练。企业面临着越来越多的网络威胁，但却面临着全球网络安全专业人员短缺的问题。为了解决这个问题，他们必须提高所有职能领域现有人员的技能。态势感知对于动态识别、理解和响应威胁至关重要。FLECO Studio 2.0包含了旨在提高此技能的新功能，使多学科团队能够评估风险、预测攻击并协调有效的响应。这些增强增强了组织的网络安全态势，促进了对不断变化的威胁的统一和主动防御。
{"title":"Version 2.0 — FLECO, enhancements for cyber situational awareness training and research","authors":"Manuel Domínguez-Dorado , David Cortés-Polo , Francisco J. Rodríguez-Pérez , Jesús Galeano-Brajones , Jesús Calle-Cancho","doi":"10.1016/j.simpa.2025.100800","DOIUrl":"10.1016/j.simpa.2025.100800","url":null,"abstract":"<div><div>This article presents FLECO Studio 2.0, which enhances cybersecurity situational awareness training through realistic scenarios. Organizations face increasing cyber threats but struggle with a global shortage of cybersecurity professionals. To address this, they must upskill existing personnel across all functional areas. Situational awareness is crucial for identifying, understanding, and responding to threats dynamically. FLECO Studio 2.0 includes new features designed to improve this skill, enabling multidisciplinary teams to assess risks, anticipate attacks, and coordinate effective responses. These enhancements strengthen an organization’s cybersecurity posture, fostering a unified and proactive defense against evolving threats.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100800"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623824","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}
Pub Date : 2025-10-01Epub Date: 2025-10-16DOI: 10.1016/j.simpa.2025.100792
Boris Prokhorov , Oleg Zolotov , Yulia Romanovskaya , Anton Tatarnikov , Yulia Shapovalova
The pyNusinov package implements Nusinov’s extreme ultraviolet (EUVT) and far ultraviolet (FUVT) solar radiation models. Jointly, these models cover the 5–242 [nm] solar irradiance range but with different wavelength steps and a small gap between 105–115 [nm]. To third-party users, EUVT and FUVT models were published as analytical formulas and tables of corresponding coefficients. The release of the pyNusinov package provides a robust way to use, disseminate, install, and update these models. It significantly improves the models’ usage workflow, benefits from Python3 infrastructure, and facilitates early career researchers’ engagement.
{"title":"pyNusinov: A Python3 software package for Solar Extreme and Far Ultraviolet radiation modeling","authors":"Boris Prokhorov , Oleg Zolotov , Yulia Romanovskaya , Anton Tatarnikov , Yulia Shapovalova","doi":"10.1016/j.simpa.2025.100792","DOIUrl":"10.1016/j.simpa.2025.100792","url":null,"abstract":"<div><div>The pyNusinov package implements Nusinov’s extreme ultraviolet (EUVT) and far ultraviolet (FUVT) solar radiation models. Jointly, these models cover the 5–242 [nm] solar irradiance range but with different wavelength steps and a small gap between 105–115 [nm]. To third-party users, EUVT and FUVT models were published as analytical formulas and tables of corresponding coefficients. The release of the pyNusinov package provides a robust way to use, disseminate, install, and update these models. It significantly improves the models’ usage workflow, benefits from Python3 infrastructure, and facilitates early career researchers’ engagement.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100792"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363255","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}
Pub Date : 2025-10-01Epub Date: 2025-09-29DOI: 10.1016/j.simpa.2025.100787
Zhanqi Cui, Yuanxin Qiao, Ruilin Xie, Li Li, Qifan He
Textual adversarial attacks generate adversarial examples that retain similar semantics to the original text and feed them into the target model to detect potential vulnerabilities by comparing output differences. This approach effectively addresses the scarcity of annotated test data during the testing phase. Existing methods often rely on greedy strategies for candidate word selection, which may result in contextually inappropriate or unnatural perturbations, thereby compromising the overall quality of the adversarial examples. To address this issue, we propose MOBTAG, a tool for generating textual adversarial examples based on multi-objective optimization. MOBTAG integrates principles from both multi-objective optimization and genetic algorithms. It improves the attack success rate while maintaining high semantic similarity and readability between adversarial examples and the original texts, thereby enabling the generation of high-quality adversarial examples
{"title":"A tool for textual adversarial attack via multi-objective optimization","authors":"Zhanqi Cui, Yuanxin Qiao, Ruilin Xie, Li Li, Qifan He","doi":"10.1016/j.simpa.2025.100787","DOIUrl":"10.1016/j.simpa.2025.100787","url":null,"abstract":"<div><div>Textual adversarial attacks generate adversarial examples that retain similar semantics to the original text and feed them into the target model to detect potential vulnerabilities by comparing output differences. This approach effectively addresses the scarcity of annotated test data during the testing phase. Existing methods often rely on greedy strategies for candidate word selection, which may result in contextually inappropriate or unnatural perturbations, thereby compromising the overall quality of the adversarial examples. To address this issue, we propose MOBTAG, a tool for generating textual adversarial examples based on multi-objective optimization. MOBTAG integrates principles from both multi-objective optimization and genetic algorithms. It improves the attack success rate while maintaining high semantic similarity and readability between adversarial examples and the original texts, thereby enabling the generation of high-quality adversarial examples</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100787"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268606","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}
Pub Date : 2025-10-01Epub Date: 2025-09-25DOI: 10.1016/j.simpa.2025.100786
Marta Moreno , Hugo Rocha , André Pilastri , Guilherme Moreira , Luís Miguel Matos , Paulo Cortez
Modern screwdriver systems generate real-time angle-torque data that form tightening curves that are valuable for quality inspection issues (e.g., detect faulty processes). This work describes the Screw Process Anomaly Visualization (SPAV) Python module, which provides several eXplainable AI (XAI) graphs for Machine Learning (ML) screw tightening results, namely global and local errors, with identification of most probable anomaly angle-torque locations. SPAV integrates seamlessly with the scientific Python ecosystem and is compatible with several ML implementations, including H2O and Keras deep AutoEncoders (AE).
{"title":"Screw Process Anomaly Visualization (SPAV): A Python module for local and global machine learning visualizations for screw tightening anomaly detection","authors":"Marta Moreno , Hugo Rocha , André Pilastri , Guilherme Moreira , Luís Miguel Matos , Paulo Cortez","doi":"10.1016/j.simpa.2025.100786","DOIUrl":"10.1016/j.simpa.2025.100786","url":null,"abstract":"<div><div>Modern screwdriver systems generate real-time angle-torque data that form tightening curves that are valuable for quality inspection issues (e.g., detect faulty processes). This work describes the Screw Process Anomaly Visualization (SPAV) Python module, which provides several eXplainable AI (XAI) graphs for Machine Learning (ML) screw tightening results, namely global and local errors, with identification of most probable anomaly angle-torque locations. SPAV integrates seamlessly with the scientific Python ecosystem and is compatible with several ML implementations, including H2O and Keras deep AutoEncoders (AE).</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100786"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221950","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}
Pub Date : 2025-10-01Epub Date: 2025-10-31DOI: 10.1016/j.simpa.2025.100795
Lejla Arapovic, Emir Sokic
This paper presents DefectDetect, a lightweight desktop tool for manual image annotation and automatic patch extraction, developed to assist in creating annotated datasets for machine learning problems. Although it is initially designed for annotating leather defects, the application supports broader use cases. Users can annotate freely, automatically extract smaller image patches and corresponding binary masks with adjustable stride, selected defect types and rating (0–2), and export data in PNG, JSON, YOLO, or Pascal VOC formats. The tool is fully GUI-based, requires no coding knowledge for usage, and supports session saving and batch image loading. The application has proven effective in academic contexts through its use in research activities. Future plans include the addition of shape annotation functionality and support for batch processing.
{"title":"DefectDetect: A lightweight application for manual image annotation and patch extraction","authors":"Lejla Arapovic, Emir Sokic","doi":"10.1016/j.simpa.2025.100795","DOIUrl":"10.1016/j.simpa.2025.100795","url":null,"abstract":"<div><div>This paper presents DefectDetect, a lightweight desktop tool for manual image annotation and automatic patch extraction, developed to assist in creating annotated datasets for machine learning problems. Although it is initially designed for annotating leather defects, the application supports broader use cases. Users can annotate freely, automatically extract smaller image patches and corresponding binary masks with adjustable stride, selected defect types and rating (0–2), and export data in PNG, JSON, YOLO, or Pascal VOC formats. The tool is fully GUI-based, requires no coding knowledge for usage, and supports session saving and batch image loading. The application has proven effective in academic contexts through its use in research activities. Future plans include the addition of shape annotation functionality and support for batch processing.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100795"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466177","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}
Pub Date : 2025-10-01Epub Date: 2025-11-07DOI: 10.1016/j.simpa.2025.100794
Azeem Ahmad
Test flakiness, characterized by inconsistent test outcomes without changes to the codebase, is a prevalent issue in continuous integration (CI) environments. This paper introduces a practical tool named MDFlaker that implements a multi-factor approach for detecting flaky tests, grounded in empirical research. The tool analyzes four key factors—traceback coverage, test smells, flakiness frequency, and test size—and applies a K-Nearest Neighbors (KNN) model for classification. Depending on the test case’s execution history, the tool switches between traceback-prioritized or multi-factor classification strategy. Integrated with platforms like GitHub and Travis CI, it processes real-time data from commits, builds, and test executions to support both research experimentation and industrial deployment. By improving the accuracy and interpretability of flaky test detection, the tool helps developers regain trust in CI pipelines, reduces debugging overhead, and fosters high-quality, rapid software delivery. This work not only enhances software reliability in practice but also opens new avenues for research in automated software quality assurance.
{"title":"MDFlaker: A tool for multi-factor detection and root cause analysis of flaky tests","authors":"Azeem Ahmad","doi":"10.1016/j.simpa.2025.100794","DOIUrl":"10.1016/j.simpa.2025.100794","url":null,"abstract":"<div><div>Test flakiness, characterized by inconsistent test outcomes without changes to the codebase, is a prevalent issue in continuous integration (CI) environments. This paper introduces a practical tool named <em>MDFlaker</em> that implements a multi-factor approach for detecting flaky tests, grounded in empirical research. The tool analyzes four key factors—traceback coverage, test smells, flakiness frequency, and test size—and applies a K-Nearest Neighbors (KNN) model for classification. Depending on the test case’s execution history, the tool switches between traceback-prioritized or multi-factor classification strategy. Integrated with platforms like GitHub and Travis CI, it processes real-time data from commits, builds, and test executions to support both research experimentation and industrial deployment. By improving the accuracy and interpretability of flaky test detection, the tool helps developers regain trust in CI pipelines, reduces debugging overhead, and fosters high-quality, rapid software delivery. This work not only enhances software reliability in practice but also opens new avenues for research in automated software quality assurance.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100794"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466181","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}
Pub Date : 2025-10-01Epub Date: 2025-10-31DOI: 10.1016/j.simpa.2025.100797
Gihan S. Edirisinghe , Charles L. Munson
This paper introduces a MATLAB-based framework for shelf-space optimization in retail, integrating AMPL via an API for solving linear and nonlinear programs. It supports two strategies: (1) Guided Random Rearrangement, creating constraint-aware layouts without prior data, and (2) Data-Driven Rearrangement, using association rule mining and mixed-integer programming to boost impulse buys. Core data structures are developed in MATLAB, while CPLEX and BARON solvers are employed through AMPL when required. The system adapts to varied retail environments, enhancing profitability and customer experience. New experiments with the Microsoft FoodMart dataset show that the data-driven method consistently outperforms random strategies.
{"title":"MATLAB-AMPL integration with heuristics and association mining: An optimization-driven framework for retail shelf space allocation","authors":"Gihan S. Edirisinghe , Charles L. Munson","doi":"10.1016/j.simpa.2025.100797","DOIUrl":"10.1016/j.simpa.2025.100797","url":null,"abstract":"<div><div>This paper introduces a MATLAB-based framework for shelf-space optimization in retail, integrating AMPL via an API for solving linear and nonlinear programs. It supports two strategies: (1) Guided Random Rearrangement, creating constraint-aware layouts without prior data, and (2) Data-Driven Rearrangement, using association rule mining and mixed-integer programming to boost impulse buys. Core data structures are developed in MATLAB, while CPLEX and BARON solvers are employed through AMPL when required. The system adapts to varied retail environments, enhancing profitability and customer experience. New experiments with the Microsoft FoodMart dataset show that the data-driven method consistently outperforms random strategies.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100797"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525789","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}
Pub Date : 2025-10-01DOI: 10.1016/j.simpa.2025.100791
Adam McArthur , Stephanie Wichuk , Stephen Burnside , Andrew Kirby , Alexander Scammon , Damian Sol , Abhilash Hareendranathan , Jacob L. Jaremko
Developmental dysplasia of the hip (DDH) poses significant diagnostic challenges, hindering timely intervention. Current screening methodologies lack standardization, and AI-driven studies suffer from reproducibility issues due to limited data and code availability. To address these limitations, we introduce Retuve, an open-source framework for multi-modality DDH analysis, encompassing both ultrasound (US) and X-ray imaging. Retuve provides a complete and reproducible workflow, offering open datasets comprising expert-annotated US and X-ray images, pre-trained models with training code and weights, and a user-friendly Python Application Programming Interface (API). The framework integrates segmentation and landmark detection models, enabling automated measurement of key diagnostic parameters such as the alpha angle and acetabular index. By adhering to open-source principles, Retuve promotes transparency, collaboration, and accessibility in DDH research. This framework can democratize DDH screening, facilitate early diagnosis, and improve patient outcomes by enabling widespread screening and early intervention. The GitHub repository/code can be found here: https://github.com/radoss-org/retuve
{"title":"Retuve: Automated multi-modality analysis of hip dysplasia with open source AI","authors":"Adam McArthur , Stephanie Wichuk , Stephen Burnside , Andrew Kirby , Alexander Scammon , Damian Sol , Abhilash Hareendranathan , Jacob L. Jaremko","doi":"10.1016/j.simpa.2025.100791","DOIUrl":"10.1016/j.simpa.2025.100791","url":null,"abstract":"<div><div>Developmental dysplasia of the hip (<strong>DDH</strong>) poses significant diagnostic challenges, hindering timely intervention. Current screening methodologies lack standardization, and AI-driven studies suffer from reproducibility issues due to limited data and code availability. To address these limitations, we introduce Retuve, an open-source framework for multi-modality <strong>DDH</strong> analysis, encompassing both ultrasound (<strong>US</strong>) and X-ray imaging. Retuve provides a complete and reproducible workflow, offering open datasets comprising expert-annotated <strong>US</strong> and X-ray images, pre-trained models with training code and weights, and a user-friendly Python Application Programming Interface (<strong>API</strong>). The framework integrates segmentation and landmark detection models, enabling automated measurement of key diagnostic parameters such as the alpha angle and acetabular index. By adhering to open-source principles, Retuve promotes transparency, collaboration, and accessibility in <strong>DDH</strong> research. This framework can democratize <strong>DDH</strong> screening, facilitate early diagnosis, and improve patient outcomes by enabling widespread screening and early intervention. The GitHub repository/code can be found here: <span><span>https://github.com/radoss-org/retuve</span><svg><path></path></svg></span></div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100791"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221951","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}
Pub Date : 2025-10-01Epub Date: 2025-09-25DOI: 10.1016/j.simpa.2025.100788
Sergio R. Geninatti , Manuel Ortiz-Lopez , José Luis Ávila-Jiménez , José M. Flores , Francisco J. Rodriguez-Lozano
The task of evaluating hives is arduous and time-consuming for beekeepers. One of the tasks involves evaluating the honey in the combs to determine the available surface area, as this is directly related to the health of the hive. Currently, there are very few software tools specifically designed for beekeeping that help alleviate the work of beekeepers. Therefore, this paper presents HoneySeg, a Python-based application for calculating honey area and segmenting honey zones in images of honeycombs. It is an open-source tool designed specifically for beekeeping that does not require prior training for use by the beekeeper.
{"title":"HoneySeg: A segmentation tool for detecting honey areas in honeycombs","authors":"Sergio R. Geninatti , Manuel Ortiz-Lopez , José Luis Ávila-Jiménez , José M. Flores , Francisco J. Rodriguez-Lozano","doi":"10.1016/j.simpa.2025.100788","DOIUrl":"10.1016/j.simpa.2025.100788","url":null,"abstract":"<div><div>The task of evaluating hives is arduous and time-consuming for beekeepers. One of the tasks involves evaluating the honey in the combs to determine the available surface area, as this is directly related to the health of the hive. Currently, there are very few software tools specifically designed for beekeeping that help alleviate the work of beekeepers. Therefore, this paper presents <em>HoneySeg</em>, a Python-based application for calculating honey area and segmenting honey zones in images of honeycombs. It is an open-source tool designed specifically for beekeeping that does not require prior training for use by the beekeeper.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100788"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159127","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号