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ArSLR-ML: A Python-based machine learning application for arabic sign language recognition
IF 1.3
Software Impacts Pub 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":"2025-03-27","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
UsmleGPT: An AI application for developing MCQs via multi-agent system
IF 1.3
Software Impacts Pub Date : 2025-03-01 DOI: 10.1016/j.simpa.2025.100742
Zhehan Jiang , Shicong Feng
{"title":"UsmleGPT: An AI application for developing MCQs via multi-agent system","authors":"Zhehan Jiang ,&nbsp;Shicong Feng","doi":"10.1016/j.simpa.2025.100742","DOIUrl":"10.1016/j.simpa.2025.100742","url":null,"abstract":"<div><div>Driven by the trending multi-agent system (MAS) that harnesses collective intelligence from numerous large language models (LLMs), UsmleGPT is a Python-based application designed to enhance LLM-generated content tailored for the USMLE Step 1 scenario. The MAS aligns with the NBME’s framework, incorporating advanced prompt strategies to guide each LLM through their respective tasks and specialties. This enables UsmleGPT to surpass conventional practices in automating item generation. Beyond the original script, a freely accessible, user-friendly website complements the tool, facilitating its adoption. UsmleGPT represents a breakthrough in medical education, transforming medical exam preparation and setting new standards for high-stakes medical assessments.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100742"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549578","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
SignAPROS: An integrated hardware and software system for acquisition, processing, and analysis of bio-signals
IF 1.3
Software Impacts Pub Date : 2025-03-01 DOI: 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
{"title":"SignAPROS: An integrated hardware and software system for acquisition, processing, and analysis of bio-signals","authors":"Alma Karen Bañuelos-Mezquitan,&nbsp;Carlos Said Silva-Chacon,&nbsp;Fernando Castro-Galán,&nbsp;Arturo Guzmán-Vázquez,&nbsp;Israel Román-Godínez,&nbsp;Ricardo A. Salido-Ruiz,&nbsp;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}
引用次数: 0
VCoFWMVIFCM: An open-source code for viewpoint-based collaborative feature-weighted multi-view intuitionistic fuzzy clustering
IF 1.3
Software Impacts Pub Date : 2025-03-01 DOI: 10.1016/j.simpa.2025.100743
Amin Golzari Oskouei , Negin Samadi , Asgarali Bouyer , Jafar Tanha
{"title":"VCoFWMVIFCM: An open-source code for viewpoint-based collaborative feature-weighted multi-view intuitionistic fuzzy clustering","authors":"Amin Golzari Oskouei ,&nbsp;Negin Samadi ,&nbsp;Asgarali Bouyer ,&nbsp;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}
引用次数: 0
Plant diseases classification with Spectral Signature Taxonomy & Analysis Software (SSTAS)
IF 1.3
Software Impacts Pub Date : 2025-03-01 DOI: 10.1016/j.simpa.2025.100744
Hardik Jayswal, Hetvi Desai, Hasti Vakani, Mithil Mistry, Nilesh Dubey
{"title":"Plant diseases classification with Spectral Signature Taxonomy & Analysis Software (SSTAS)","authors":"Hardik Jayswal,&nbsp;Hetvi Desai,&nbsp;Hasti Vakani,&nbsp;Mithil Mistry,&nbsp;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}
引用次数: 0
hvarma: Autoregressive moving average model of microtremor H/V spectral ratio
IF 1.3
Software Impacts Pub Date : 2025-03-01 DOI: 10.1016/j.simpa.2025.100745
Aleix Seguí , Arantza Ugalde , Juan José Egozcue
{"title":"hvarma: Autoregressive moving average model of microtremor H/V spectral ratio","authors":"Aleix Seguí ,&nbsp;Arantza Ugalde ,&nbsp;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}
引用次数: 0
KNNOR-Reg: A python package for oversampling in imbalanced regression
IF 1.3
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
{"title":"KNNOR-Reg: A python package for oversampling in imbalanced regression","authors":"Samir Brahim Belhaouari ,&nbsp;Ashhadul Islam ,&nbsp;Khelil Kassoul ,&nbsp;Ala Al-Fuqaha ,&nbsp;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}
引用次数: 0
pff-oc: A space–time phase-field fracture optimal control framework
IF 1.3
Software Impacts Pub Date : 2025-01-02 DOI: 10.1016/j.simpa.2024.100734
Denis Khimin, Marc Christian Steinbach, Thomas Wick
{"title":"pff-oc: A space–time phase-field fracture optimal control framework","authors":"Denis Khimin,&nbsp;Marc Christian Steinbach,&nbsp;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}
引用次数: 0
Synthetic dataset generation system for vehicle detection
IF 1.3
Software Impacts Pub Date : 2024-12-28 DOI: 10.1016/j.simpa.2024.100735
Mihaela Orić , Vlatko Galić , Filip Novoselnik
{"title":"Synthetic dataset generation system for vehicle detection","authors":"Mihaela Orić ,&nbsp;Vlatko Galić ,&nbsp;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}
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
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
{"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
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