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KNNOR-Reg: A python package for oversampling in imbalanced regression
IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Software Impacts
Pub Date : 2025-01-03 DOI: 10.1016/j.simpa.2024.100740
Samir Brahim Belhaouari , Ashhadul Islam , Khelil Kassoul , Ala Al-Fuqaha , Abdesselam Bouzerdoum
KNNOR-Reg is a Python package designed to address the challenge of imbalanced regression. While popular Python packages exist for tackling imbalanced classification, support for imbalanced regression remains limited. Imbalanced regression involves the underrepresentation of important ranges within a continuous target variable. KNNOR-Reg implements an oversampling technique that generates synthetic samples through interpolation between minority class samples and their nearest neighbors. The labels for synthetic samples are computed based on the inverse distance-weighted average of the nearest neighbors’ labels. KNNOR-Reg offers a user-friendly and extensible Python implementation for oversampling imbalanced regression data, aiming to reduce regressor bias and enhance model outcomes.
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引用次数: 0
pff-oc: A space–time phase-field fracture optimal control framework
IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Software Impacts
Pub Date : 2025-01-02 DOI: 10.1016/j.simpa.2024.100734
Denis Khimin, Marc Christian Steinbach, Thomas Wick
This codebase is developed to address optimal control problems in phase-field fracture, aiming to achieve a desired fracture pattern in brittle materials through the application of external forces. Built alongside our recent work (Khimin et al., 2022), this framework provides an efficient and precise approach for simulating space–time phase-field optimal control problems. In this setup, the fracture is controlled via Neumann boundary conditions, with the cost functional designed to minimize the difference between the actual and desired fracture states. The implementation relies on the open-source libraries DOpElib (Goll et al., 2017) and deal.II (Arndt et al. [1], [2])
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引用次数: 0
Synthetic dataset generation system for vehicle detection
IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Software Impacts
Pub Date : 2024-12-28 DOI: 10.1016/j.simpa.2024.100735
Mihaela Orić , Vlatko Galić , Filip Novoselnik
The success of machine learning models for object detection highly depends on the training data size and quality. Generating synthetic data speeds up the data acquisition process by removing the need for human annotation. Moreover, since annotation is done automatically, there is no room for human error. We present a pipeline that automatically generates and annotates aerial images of vehicles on roads. The pipeline is structured to allow easy adding of various new vehicles and is not limited to cars only. The resolution of the generated images and the level of detail can be modified by changing the output settings.
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引用次数: 0
DeepPack3D: A Python package for online 3D bin packing optimization by deep reinforcement learning and constructive heuristics
IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Software Impacts
Pub Date : 2024-12-27 DOI: 10.1016/j.simpa.2024.100732
Y.P. Tsang , D.Y. Mo , K.T. Chung , C.K.M. Lee
The rapid advancement of industrial robotic automation has increased the significance of online 3D bin packing optimization for applications, like palletization and container loading. Despite numerous learning-based methods emerging for informed decision-making in this process, the absence of a standardized benchmark makes it challenging to experience the process and validate new algorithms. To bridge this gap, we introduce DeepPack3D, a software package that integrates deep reinforcement learning and constructive heuristic approaches for online 3D bin packing optimization. DeepPack3D provides a foundation for benchmarking, allowing users to evaluate performance using customizable item lists and lookahead values, thereby facilitating consistent research advancements.
{"title":"DeepPack3D: A Python package for online 3D bin packing optimization by deep reinforcement learning and constructive heuristics","authors":"Y.P. Tsang ,&nbsp;D.Y. Mo ,&nbsp;K.T. Chung ,&nbsp;C.K.M. Lee","doi":"10.1016/j.simpa.2024.100732","DOIUrl":"10.1016/j.simpa.2024.100732","url":null,"abstract":"<div><div>The rapid advancement of industrial robotic automation has increased the significance of online 3D bin packing optimization for applications, like palletization and container loading. Despite numerous learning-based methods emerging for informed decision-making in this process, the absence of a standardized benchmark makes it challenging to experience the process and validate new algorithms. To bridge this gap, we introduce DeepPack3D, a software package that integrates deep reinforcement learning and constructive heuristic approaches for online 3D bin packing optimization. DeepPack3D provides a foundation for benchmarking, allowing users to evaluate performance using customizable item lists and lookahead values, thereby facilitating consistent research advancements.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100732"},"PeriodicalIF":1.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139882","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
A Web Application for exploratory data analysis and classification of Parkinson’s Disease patients using machine learning models on different datasets
IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Software Impacts
Pub Date : 2024-12-27 DOI: 10.1016/j.simpa.2024.100737
Daniel Hilário da Silva , Leandro Rodrigues da Silva Souza , Caio Tonus Ribeiro , Simone Hilário da Silva Brasileiro , José Renato Munari Nardo , Adriano Alves Pereira , Adriano de Oliveira Andrade
Automated biomedical data analysis tools are crucial in research and clinical practice; however, they are not always accessible to everyone. This paper introduces a web-based system that facilitates exploratory data analysis and machine learning, focusing on identifying audio and video data patterns. This system applies to various biomedical contexts, such as the study of Parkinson’s disease. Developed using Python and the Streamlit framework, it offers an intuitive interface for data analysis, visualization, and automated classification. Its flexibility makes it a valuable resource for researchers and healthcare professionals, enabling meaningful insights and fostering advancements in biomedical research.
{"title":"A Web Application for exploratory data analysis and classification of Parkinson’s Disease patients using machine learning models on different datasets","authors":"Daniel Hilário da Silva ,&nbsp;Leandro Rodrigues da Silva Souza ,&nbsp;Caio Tonus Ribeiro ,&nbsp;Simone Hilário da Silva Brasileiro ,&nbsp;José Renato Munari Nardo ,&nbsp;Adriano Alves Pereira ,&nbsp;Adriano de Oliveira Andrade","doi":"10.1016/j.simpa.2024.100737","DOIUrl":"10.1016/j.simpa.2024.100737","url":null,"abstract":"<div><div>Automated biomedical data analysis tools are crucial in research and clinical practice; however, they are not always accessible to everyone. This paper introduces a web-based system that facilitates exploratory data analysis and machine learning, focusing on identifying audio and video data patterns. This system applies to various biomedical contexts, such as the study of Parkinson’s disease. Developed using Python and the Streamlit framework, it offers an intuitive interface for data analysis, visualization, and automated classification. Its flexibility makes it a valuable resource for researchers and healthcare professionals, enabling meaningful insights and fostering advancements in biomedical research.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100737"},"PeriodicalIF":1.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139896","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
TeleCatch: An open-access software for visualizing, filtering and extracting Telegram messages data
IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Software Impacts
Pub Date : 2024-12-27 DOI: 10.1016/j.simpa.2024.100736
Giosuè Ruscica , Giulia Tucci , Bia Carneiro
Telegram’s growing role as a digital communication platform creates opportunities and challenges for analyzing public discourse. TeleCatch, an open-source tool, simplifies access to data from public Telegram groups and channels, requiring no programming skills. Built with FastAPI and Telethon, it enables collection management, rapid sampling, and retrieval of text and media, offering a privacy-focused, decentralized approach. TeleCatch has proven valuable in studies on human mobility and food security, supporting diverse research fields. Future updates will enhance search capabilities and visualization features, further expanding its applicability for digital communication and social media analysis.
{"title":"TeleCatch: An open-access software for visualizing, filtering and extracting Telegram messages data","authors":"Giosuè Ruscica ,&nbsp;Giulia Tucci ,&nbsp;Bia Carneiro","doi":"10.1016/j.simpa.2024.100736","DOIUrl":"10.1016/j.simpa.2024.100736","url":null,"abstract":"<div><div>Telegram’s growing role as a digital communication platform creates opportunities and challenges for analyzing public discourse. TeleCatch, an open-source tool, simplifies access to data from public Telegram groups and channels, requiring no programming skills. Built with FastAPI and Telethon, it enables collection management, rapid sampling, and retrieval of text and media, offering a privacy-focused, decentralized approach. TeleCatch has proven valuable in studies on human mobility and food security, supporting diverse research fields. Future updates will enhance search capabilities and visualization features, further expanding its applicability for digital communication and social media analysis.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100736"},"PeriodicalIF":1.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139897","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
SGML: A Python library for solution-guided machine learning
IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Software Impacts
Pub Date : 2024-12-27 DOI: 10.1016/j.simpa.2024.100739
Ruijin Wang , Yuchen Du , Chunchun Dai , Yang Deng , Jiantao Leng , Tienchong Chang
Researchers have long been concerned with the extrapolation capabilities of machine learning (ML) models, particularly when dealing with insufficient training data. The recently proposed solution-guided machine learning (SGML) method addresses this issue by integrating existing solutions as additional features to supplement limited training data. We have applied this method to solve the strong nonlinearity in nanoindentation and present an approximate solution to the tangential entropic force in an asymmetrical two dimensional bilayer. To make this method more accessible, we developed a user-friendly Python library called SGML, available on GitHub and PyPI. This paper introduces the architecture and functionality of the library, provides a usage example, and discusses its potential impact and applications.
{"title":"SGML: A Python library for solution-guided machine learning","authors":"Ruijin Wang ,&nbsp;Yuchen Du ,&nbsp;Chunchun Dai ,&nbsp;Yang Deng ,&nbsp;Jiantao Leng ,&nbsp;Tienchong Chang","doi":"10.1016/j.simpa.2024.100739","DOIUrl":"10.1016/j.simpa.2024.100739","url":null,"abstract":"<div><div>Researchers have long been concerned with the extrapolation capabilities of machine learning (ML) models, particularly when dealing with insufficient training data. The recently proposed solution-guided machine learning (SGML) method addresses this issue by integrating existing solutions as additional features to supplement limited training data. We have applied this method to solve the strong nonlinearity in nanoindentation and present an approximate solution to the tangential entropic force in an asymmetrical two dimensional bilayer. To make this method more accessible, we developed a user-friendly Python library called SGML, available on GitHub and PyPI. This paper introduces the architecture and functionality of the library, provides a usage example, and discusses its potential impact and applications.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100739"},"PeriodicalIF":1.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139884","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
Multi-browser VE: Enhancing internet browsing experience through virtual reality
IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Software Impacts
Pub Date : 2024-12-27 DOI: 10.1016/j.simpa.2024.100733
Mochammad Hannats Hanafi Ichsan , Cecilia Sik-Lanyi , Tibor Guzsvinecz
This paper presents the development of a Multi-Browser Virtual Environment (VE) aimed at improving the user experience of internet browsing through Desktop Virtual Reality (VR) technology. By integrating multiple web browsers within the Virtual Environment (VE), users can engage in more intuitive and interactive browsing experiences. This study explores the development of Multi-Browser VE in the early stage of development, an evaluation model to assess this system by measuring usability and user feedback compared to the traditional browsing experience. Initial studies suggest that the Multi-Browser VE offers good usability and a more excellent browsing experience than traditional desktop-based interfaces.
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引用次数: 0
AbNumPro: A comprehensive offline toolkit for antibody numbering and antigen-binding region prediction
IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Software Impacts
Pub Date : 2024-12-27 DOI: 10.1016/j.simpa.2024.100738
Wenzhen Li , Hongyan Lin , Lvxin Peng , Qianhu Jiang , Yushu Gou , Lu Xie , Jian Huang
Identifying complementary-determining regions (CDRs) and antigen-binding regions (ABRs) requires accurate antibody numbering, which is essential for therapeutic antibody development. AbNumPro is a comprehensive offline toolkit developed for antibody numbering and ABRs prediction, addressing the limitations of existing tools, which often lack comprehensiveness and rely solely on online services. By integrating five established numbering schemes—Kabat, Chothia, IMGT, Aho, and Martin—AbNumPro provides precise delineation of CDRs and ABRs, offering both compatibility with diverse research applications and the assurance of data security.
{"title":"AbNumPro: A comprehensive offline toolkit for antibody numbering and antigen-binding region prediction","authors":"Wenzhen Li ,&nbsp;Hongyan Lin ,&nbsp;Lvxin Peng ,&nbsp;Qianhu Jiang ,&nbsp;Yushu Gou ,&nbsp;Lu Xie ,&nbsp;Jian Huang","doi":"10.1016/j.simpa.2024.100738","DOIUrl":"10.1016/j.simpa.2024.100738","url":null,"abstract":"<div><div>Identifying complementary-determining regions (CDRs) and antigen-binding regions (ABRs) requires accurate antibody numbering, which is essential for therapeutic antibody development. AbNumPro is a comprehensive offline toolkit developed for antibody numbering and ABRs prediction, addressing the limitations of existing tools, which often lack comprehensiveness and rely solely on online services. By integrating five established numbering schemes—Kabat, Chothia, IMGT, Aho, and Martin—AbNumPro provides precise delineation of CDRs and ABRs, offering both compatibility with diverse research applications and the assurance of data security.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100738"},"PeriodicalIF":1.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139900","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
A multi-agent system simulation framework with optimized spatial neighborhood search
IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Software Impacts
Pub Date : 2024-12-18 DOI: 10.1016/j.simpa.2024.100725
Candelaria E. Sansores , Joel A. Trejo-Sánchez , Mirbella Gallareta Negrón
BioMASS is an innovative multi-agent spatial model designed to enhance computational efficiency in simulations involving complex sensory and locomotion functions. Traditional agent-based modeling (ABM) platforms suffer from performance degradation as the number of agents and their perception ranges increase, resulting in a quadratic growth in computational cost. BioMASS addresses this issue employing a quadruply linked list structure, which allows constant-time neighborhood search and movement. This feature allows BioMASS to simulate large populations in dynamic environments efficiently. The model has been successfully applied to marine ecosystem simulations, demonstrating its ability to track species interactions across multiple trophic levels in real-time, outperforming existing platforms.
{"title":"A multi-agent system simulation framework with optimized spatial neighborhood search","authors":"Candelaria E. Sansores ,&nbsp;Joel A. Trejo-Sánchez ,&nbsp;Mirbella Gallareta Negrón","doi":"10.1016/j.simpa.2024.100725","DOIUrl":"10.1016/j.simpa.2024.100725","url":null,"abstract":"<div><div>BioMASS is an innovative multi-agent spatial model designed to enhance computational efficiency in simulations involving complex sensory and locomotion functions. Traditional agent-based modeling (ABM) platforms suffer from performance degradation as the number of agents and their perception ranges increase, resulting in a quadratic growth in computational cost. BioMASS addresses this issue employing a quadruply linked list structure, which allows constant-time neighborhood search and movement. This feature allows BioMASS to simulate large populations in dynamic environments efficiently. The model has been successfully applied to marine ecosystem simulations, demonstrating its ability to track species interactions across multiple trophic levels in real-time, outperforming existing platforms.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100725"},"PeriodicalIF":1.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139885","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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