Pub Date : 2025-03-01DOI: 10.1016/j.simpa.2025.100741
Alma Karen Bañuelos-Mezquitan, Carlos Said Silva-Chacon, Fernando Castro-Galán, Arturo Guzmán-Vázquez, Israel Román-Godínez, Ricardo A. Salido-Ruiz, Sulema Torres-Ramos
SignAPROS is a cost-effective hardware–software system for signal acquisition, featuring modules for database management, protocol configuration, and machine learning-based analysis. It supports up to four Electromyography bi-polar channels and various sensors to measure heart rate, temperature, inclination, and galvanic skin response.
The system has already been used in the implementation of a protocol aimed at capturing electrical signals from facial and neck muscles to detect mispronunciation in a second language supporting a master’s project.
With a user-friendly interface, SignAPROS enables users to conduct bio-signal acquisition, analyze data, and visualize results efficiently, making it a versatile and accessible tool for scientific studies.
{"title":"SignAPROS: An integrated hardware and software system for acquisition, processing, and analysis of bio-signals","authors":"Alma Karen Bañuelos-Mezquitan, Carlos Said Silva-Chacon, Fernando Castro-Galán, Arturo Guzmán-Vázquez, Israel Román-Godínez, Ricardo A. Salido-Ruiz, Sulema Torres-Ramos","doi":"10.1016/j.simpa.2025.100741","DOIUrl":"10.1016/j.simpa.2025.100741","url":null,"abstract":"<div><div>SignAPROS is a cost-effective hardware–software system for signal acquisition, featuring modules for database management, protocol configuration, and machine learning-based analysis. It supports up to four Electromyography bi-polar channels and various sensors to measure heart rate, temperature, inclination, and galvanic skin response.</div><div>The system has already been used in the implementation of a protocol aimed at capturing electrical signals from facial and neck muscles to detect mispronunciation in a second language supporting a master’s project.</div><div>With a user-friendly interface, SignAPROS enables users to conduct bio-signal acquisition, analyze data, and visualize results efficiently, making it a versatile and accessible tool for scientific studies.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100741"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549883","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}
We present VCoFWMVIFCM, an open-source Python implementation of a multi-view fuzzy clustering algorithm based on Intuitionistic Fuzzy c-Means (IFCM). The method integrates adaptive view, feature, and sample weighting to account for varying importance and reduce outlier effects. Local neighborhood information enhances noise resistance, while a density-based initialization ensures stable centroid selection. These mechanisms collectively improve clustering robustness and accuracy for multi-view data. The modular implementation allows flexible execution and reproducibility, addressing real-world applications where multiple data perspectives exist. The code is publicly accessible on GitHub under the MIT license.
{"title":"VCoFWMVIFCM: An open-source code for viewpoint-based collaborative feature-weighted multi-view intuitionistic fuzzy clustering","authors":"Amin Golzari Oskouei , Negin Samadi , Asgarali Bouyer , Jafar Tanha","doi":"10.1016/j.simpa.2025.100743","DOIUrl":"10.1016/j.simpa.2025.100743","url":null,"abstract":"<div><div>We present VCoFWMVIFCM, an open-source Python implementation of a multi-view fuzzy clustering algorithm based on Intuitionistic Fuzzy c-Means (IFCM). The method integrates adaptive view, feature, and sample weighting to account for varying importance and reduce outlier effects. Local neighborhood information enhances noise resistance, while a density-based initialization ensures stable centroid selection. These mechanisms collectively improve clustering robustness and accuracy for multi-view data. The modular implementation allows flexible execution and reproducibility, addressing real-world applications where multiple data perspectives exist. The code is publicly accessible on GitHub under the MIT license.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100743"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580457","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}
This paper investigates a novel approach to plant disease classification, addressing cases where symptoms are not visually apparent. Traditional machine learning methods, reliant on observable symptoms, face challenges such as limited training data, high costs, and low interpretability. To overcome these limitations, a spectroscopy-based classification technique was developed. Experimental data, collected over 15 months at Anand Agriculture University, Gujarat, and Charotar University Space Research Centre, utilized spectral signatures (400–1000 nm) to detect mango diseases. The SSTAS Software, developed with a fine-tuned deep learning model, Deep-Spectro, demonstrated superior accuracy using an 80:20 training-to-testing ratio, surpassing existing models reported in prior research.
{"title":"Plant diseases classification with Spectral Signature Taxonomy & Analysis Software (SSTAS)","authors":"Hardik Jayswal, Hetvi Desai, Hasti Vakani, Mithil Mistry, Nilesh Dubey","doi":"10.1016/j.simpa.2025.100744","DOIUrl":"10.1016/j.simpa.2025.100744","url":null,"abstract":"<div><div>This paper investigates a novel approach to plant disease classification, addressing cases where symptoms are not visually apparent. Traditional machine learning methods, reliant on observable symptoms, face challenges such as limited training data, high costs, and low interpretability. To overcome these limitations, a spectroscopy-based classification technique was developed. Experimental data, collected over 15 months at Anand Agriculture University, Gujarat, and Charotar University Space Research Centre, utilized spectral signatures (400–1000 nm) to detect mango diseases. The SSTAS Software, developed with a fine-tuned deep learning model, Deep-Spectro, demonstrated superior accuracy using an 80:20 training-to-testing ratio, surpassing existing models reported in prior research.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100744"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563740","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}
Pub Date : 2025-03-01DOI: 10.1016/j.simpa.2025.100745
Aleix Seguí , Arantza Ugalde , Juan José Egozcue
hvarma is a Python software for estimating the horizontal-to-vertical (H/V) spectral ratio through seismic ambient vibration measurements. It employs a parametric approach to model the H/V transfer function using an AutoRegressive Moving Average (ARMA) filter. Compared to traditional methods, this technique enhances accuracy and reliability in spectral estimates, determining the ground fundamental resonance frequency with high spectral resolution, which is important for engineering geology projects. The program inverts to find optimal filter coefficients and computes coherence between horizontal and vertical components, generating H/V transfer function visualizations across both negative and positive frequencies. Results are saved as image and text files.
{"title":"hvarma: Autoregressive moving average model of microtremor H/V spectral ratio","authors":"Aleix Seguí , Arantza Ugalde , Juan José Egozcue","doi":"10.1016/j.simpa.2025.100745","DOIUrl":"10.1016/j.simpa.2025.100745","url":null,"abstract":"<div><div>hvarma is a Python software for estimating the horizontal-to-vertical (<em>H</em>/<em>V</em>) spectral ratio through seismic ambient vibration measurements. It employs a parametric approach to model the <em>H</em>/<em>V</em> transfer function using an AutoRegressive Moving Average (ARMA) filter. Compared to traditional methods, this technique enhances accuracy and reliability in spectral estimates, determining the ground fundamental resonance frequency with high spectral resolution, which is important for engineering geology projects. The program inverts to find optimal filter coefficients and computes coherence between horizontal and vertical components, generating <em>H</em>/<em>V</em> transfer function visualizations across both negative and positive frequencies. Results are saved as image and text files.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100745"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580377","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}
Pub Date : 2025-01-03DOI: 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.
{"title":"KNNOR-Reg: A python package for oversampling in imbalanced regression","authors":"Samir Brahim Belhaouari , Ashhadul Islam , Khelil Kassoul , Ala Al-Fuqaha , Abdesselam Bouzerdoum","doi":"10.1016/j.simpa.2024.100740","DOIUrl":"10.1016/j.simpa.2024.100740","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100740"},"PeriodicalIF":1.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139899","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}
Pub Date : 2025-01-02DOI: 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])
这个代码库是为了解决相场断裂的最优控制问题而开发的，旨在通过施加外力来实现脆性材料的理想断裂模式。该框架与我们最近的工作（Khimin et al., 2022）一起构建，为模拟时空相场最优控制问题提供了一种有效而精确的方法。在这种设置中，裂缝是通过Neumann边界条件控制的，成本函数的设计是为了最小化实际和期望的裂缝状态之间的差异。实现依赖于开源库DOpElib （Goll et al., 2017）和deal。II （Arndt et al. [1], [2]）
{"title":"pff-oc: A space–time phase-field fracture optimal control framework","authors":"Denis Khimin, Marc Christian Steinbach, Thomas Wick","doi":"10.1016/j.simpa.2024.100734","DOIUrl":"10.1016/j.simpa.2024.100734","url":null,"abstract":"<div><div>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. <span><span>[1]</span></span>, <span><span>[2]</span></span>)</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100734"},"PeriodicalIF":1.3,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139898","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}
Pub Date : 2024-12-28DOI: 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.
{"title":"Synthetic dataset generation system for vehicle detection","authors":"Mihaela Orić , Vlatko Galić , Filip Novoselnik","doi":"10.1016/j.simpa.2024.100735","DOIUrl":"10.1016/j.simpa.2024.100735","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100735"},"PeriodicalIF":1.3,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143140076","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}
Pub Date : 2024-12-27DOI: 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 , D.Y. Mo , K.T. Chung , 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}
Pub Date : 2024-12-27DOI: 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 , Leandro Rodrigues da Silva Souza , Caio Tonus Ribeiro , Simone Hilário da Silva Brasileiro , José Renato Munari Nardo , Adriano Alves Pereira , 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}
Pub Date : 2024-12-27DOI: 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 , Giulia Tucci , 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}
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