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Journal articles on the topic 'Opensource datasets'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

P, Dheeraj. "Landmark-based Dataset Generation using Mediapipe." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 90–92. https://doi.org/10.22214/ijraset.2025.68160.

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This paper focuses on creating structured landmarks-based feature extraction using MediPpipe. MediaPipe is an opensource framework for building pipelines to perform computer vision inference over arbitrary sensory data such as video or audio. Hand and facial expression recognition play a significant role in various domains like Human-computer interaction, assistive technology and emotion analysis. Traditional datasets primarily rely on raw images, which pose challenges in terms of computational complexity and privacy concerns. This paper represents a alternative approach for dataset creation b

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Kim, Kwang Hyeon, and Jae-Keun Cho. "Preliminary Study on Detecting Vocal Disorders Using Deep Learning in Laryngology." Journal of The Korean Society of Laryngology, Phoniatrics and Logopedics 36, no. 1 (2025): 5–11. https://doi.org/10.22469/jkslp.2025.36.1.5.

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Background and Objectives Voice disorders can significantly impact quality of life. This study evaluates the feasibility of using deep learning models to detect voice disorders using an opensource dataset.Materials and Method We utilized the Saarbrücken Voice Database, which contains 1231 voice recordings of various pathologies. Datasets were used for training (n=1036) and validation (n=195). Key vocal parameters, including fundamental frequency (F0), formants (F1, F2), harmonics-to-noise ratio, jitter, and shimmer, were analyzed. A convolutional neural network (CNN) was designed to classify v

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Gatenbee, Chandler Dean, Ann-Marie Baker, Sandhya Prabhakaran, Mark Robertson-Tessi, Trevor Graham, and Alexander R. Anderson. "Abstract 2078: VALIS: Virtual Alignment of pathoLogy Image Series for multi-gigapixel whole slide images." Cancer Research 83, no. 7_Supplement (2023): 2078. http://dx.doi.org/10.1158/1538-7445.am2023-2078.

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Abstract Interest in spatial omics is on the rise, but generation of highly multiplexed images used in many spatial analyses remains challenging, due to cost, expertise, methodical constraints, and/or access to technology. An alternative to performing highly multiplexed staining is to register collections of whole slide images (WSI), creating a collection of aligned images that can undergo spatial analyses. However, registration of WSI is two part problem, with the first being the alignment itself, and the second being the application of the transformations to huge multi-gigapixel images. To a

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Castorrini, A., A. Ortolani, E. Minisci, and M. S. Campobasso. "Opensource machine learning metamodels for assessing blade performance impairment due to general leading edge degradation." Journal of Physics: Conference Series 2767, no. 5 (2024): 052055. http://dx.doi.org/10.1088/1742-6596/2767/5/052055.

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Abstract Blades leading edge erosion can significantly reduce annual energy production of wind turbines. Accurate estimates of the resulting blade performance impairment are paramount to predict the resulting energy losses and enable cost-informed decisions on optimal maintenance and operational strategies, maximizing energy production and reducing maintenance costs. Computational Fluid Dynamics (CFD) is a robust approach for predicting the performance losses due to LEE. However, the impact of the damage on blade aerodynamics varies depending on damage pattern, extent and location. Therefore,

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Rodriguez-Navarro, C., A. Alcayde, V. Isanbaev, L. Castro-Santos, A. Filgueira-Vizoso, and F. G. Montoya. "DSUALMH- A new high-resolution dataset for NILM." Renewable Energy and Power Quality Journal 21, no. 1 (2023): 238–43. http://dx.doi.org/10.24084/repqj21.286.

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The optimisation of energy consumption requires a reasonably accurate measurement, so an appropriate and advanced monitoring system of the relevant electrical variables in the electrical installations is of paramount importance. In this context, interoperable and highly configurable devices play a crucial role. A clear example is the OpenZMeter (OZM) which is an open source, open hardware, multi-purpose precision smart meter that can measure a wide range of electrical variables at a high sampling rate and provide processed data on power quality. The aim of this work is to show the use and poss

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Chmielowski, Łukasz, Michał Kucharzak, and Robert Burduk. "APPLICATION OF EXPLAINABLE ARTIFICIAL INTELLIGENCE IN SOFTWARE BUG CLASSIFICATION." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 13, no. 1 (2023): 14–17. http://dx.doi.org/10.35784/iapgos.3396.

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Fault management is an expensive process and analyzing data manually requires a lot of resources. Modern software bug tracking systems may be armed with automated bug report assignment functionality that facilitates bug classification or bug assignment to proper development group. For supporting decision systems, it would be beneficial to introduce information related to explainability. The purpose of this work is to evaluate the use of explainable artificial intelligence (XAI) in processes related to software development and bug classification based on bug reports created by either software t

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Liu, Zhong, Dana Ostrenga, William Teng, and Steven Kempler. "Tropical Rainfall Measuring Mission (TRMM) Precipitation Data and Services for Research and Applications." Bulletin of the American Meteorological Society 93, no. 9 (2012): 1317–25. http://dx.doi.org/10.1175/bams-d-11-00152.1.

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Precipitation is a critical component of the Earth's hydrological cycle. Launched on 27 November 1997, TRMM is a joint U.S.–Japan satellite mission to provide the first detailed and comprehensive dataset of the four-dimensional distribution of rainfall and latent heating over vastly undersampled tropical and subtropical oceans and continents (40°S–40°N). Over the past 14 years, TRMM has been a major data source for meteorological, hydrological, and other research and application activities around the world. This short article describes how the NASA Goddard Earth Sciences Data and Information S

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Shakrani, Kalembo Vikalwe, Ngonidzashe Mathew Kanyangarara, Prince Tinashe Parowa, Vibhor Gupta, and Rajendra Kumar. "A DEEP LEARNING MODEL FOR FACE RECOGNITION IN PRESENCE OF MASK." Acta Informatica Malaysia 6, no. 2 (2022): 43–46. http://dx.doi.org/10.26480/aim.02.2022.43.46.

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Image classifications and object detection are common study topics in the rapidly expanding technological advancements to identify and detect real-time problems in major federal fields like public places, airports and army bases using webcams and surveillance cameras opensource platforms. The goal of this study is to suggest Open Source Computer Vision (OpenCV) and Convolutional Neural Network (CNN) techniques for identifying a person in presence of face mask from image datasets and real-time (live streaming video). For experimental purpose a parent directory consisting of three main directori

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Xu, Yongming, Benjamin Lewis, and Weihe Wendy Guan. "Developing the Chinese Academic Map Publishing Platform." ISPRS International Journal of Geo-Information 8, no. 12 (2019): 567. http://dx.doi.org/10.3390/ijgi8120567.

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The discipline of the humanities has long been inseparable from the exploration of space and time. With the rapid advancement of digitization, databases, and data science, humanities research is making greater use of quantitative spatiotemporal analysis and visualization. In response to this trend, our team developed the Chinese academic map publishing platform (AMAP) with the aim of supporting the digital humanities from a Chinese perspective. In compiling materials mined from China’s historical records, AMAP attempts to reconstruct the geographical distribution of entities including people,

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Nicholson, Calum, Mark Hanly, and David S. Celermajer. "An interactive geographic information system to inform optimal locations for healthcare services." PLOS Digital Health 2, no. 5 (2023): e0000253. http://dx.doi.org/10.1371/journal.pdig.0000253.

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Large health datasets can provide evidence for the equitable allocation of healthcare resources and access to care. Geographic information systems (GIS) can help to present this data in a useful way, aiding in health service delivery. An interactive GIS was developed for the adult congenital heart disease service (ACHD) in New South Wales, Australia to demonstrate its feasibility for health service planning. Datasets describing geographic boundaries, area-level demographics, hospital driving times, and the current ACHD patient population were collected, linked, and displayed in an interactive

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Sharma, Divya, and Shikha Lohchab. "Search based Software Engineering of Software Metrics, Code Smells and Refactoring Techniques Using Optimization Algorithms." NeuroQuantology 20, no. 5 (2022): 195–203. http://dx.doi.org/10.14704/nq.2022.20.5.nq22163.

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Software refactoring is a technique for reorganising and improving the efficiency of existing software code. By refining the non-functional aspects of software, numerous refactoring methods are currently being employed to build more intelligible and less composite codes. By applying multiple systems to the source code, refactoring can improve code maintainability even further, preserving the behaviour of the code. Refactoring allows for the eradication of bugs and the expansion of the program's capabilities. This paper provides a comprehensive assessment of source code with foul odours, the in

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Tahon, Nourel hoda, Nader Ashraf, Ahmed Moawad, et al. "DSAI-06 TECHNICAL AND ADMINISTRATIVE CHALLENGES FOR OPEN SCIENCE; REAL-LIFE EXPERIENCE FROM ASNR-MICCAI-BRATS-METS 2023 SEGMENTATION CHALLENGE DATASET." Neuro-Oncology Advances 6, Supplement_1 (2024): i12. http://dx.doi.org/10.1093/noajnl/vdae090.038.

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Abstract PURPOSE 1-Highlighting the Brain Tumor Segmentation (BraTS) challenge; the past, present and the future. 2- Emergence of ASNR-MICCAI BraTS-METS 2023 challenge with discussing our experience. 3-Discussing BraTS-METS 2023 challenge and how to overcome it. 4- Highlighting administrative and technical challenges for sharing data.4- Reviewing educational initiatives for the challenge. CLINICAL RELEVANCE/APPLICATION Brain Tumor Segmentation (BraTS) challenge: History of BraTS challenge and evolution of its opensource datasets, The challenge and its components. The challenge workflow. How we

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Marat, N., and A. Sarsembayev. "REVIEW OF MODERN METHODS OF SPEECH SYNTHESIS IN THE KAZAKH LANGUAGE." SCIENTIFIC-DISCUSSION, no. 77 (June 6, 2023): 30–34. https://doi.org/10.5281/zenodo.8009075.

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Speech synthesis, or text-to-speech (TTS), has made significant progress in recent years, with deep learning and neural network-based techniques yielding more natural and expressive synthesized speech. However, for lowresource languages like Kazakh, unique challenges arise due to limited availability of annotated speech data and complex linguistic features. This review provides a comprehensive overview of modern methods of speech synthesis in the Kazakh language, discussing the limitations of traditional methods and the potential of new approaches such as deep neural networks, generative model

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Treccani, D., and A. Adami. "SINGLE BUILDING POINT CLOUD SEGMENTATION: TOWARDS URBAN DATA MODELING AND MANAGEMENT." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W1-2023 (May 25, 2023): 511–16. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-511-2023.

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Abstract. To manage urban areas, a key step is the development of a geometric survey and its subsequent analysis and processing in order to provide useful information, and to become a good basis for urban modeling. Surveys of urban areas can be developed with various technologies, such as Aerial Laser Scanning, Unmanned Aerial Systems photogrammetry, and Mobile Mapping Systems. To make the resulting point clouds useful for subsequent steps, it is necessary to segment them into classes representing urban elements. On the other hand, there are 2D land representations that provide a variety of in

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Weng, Jiancong, Tian Chen, Yinlong Xie, et al. "IterCluster: a barcode clustering algorithm for long fragment read analysis." PeerJ 8 (March 24, 2020): e8431. http://dx.doi.org/10.7717/peerj.8431.

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Recent advances in long fragment read (LFR, also known as linked-read technologies or read-cloud) technologies, such as single tube long fragment reads (stLFR), 10X Genomics Chromium reads, and TruSeq synthetic long-reads, have enabled efficient haplotyping and genome assembly. However, in the case of stLFR and 10X Genomics Chromium reads, the long fragments of a genome are covered sparsely by reads in each barcode and most barcodes are contained in multiple long fragments from different regions, which results in inefficient assembly when using long-range information. Thus, methods to address

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Nash, Rebecca K., Samir Bhatt, Anne Cori, and Pierre Nouvellet. "Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool." PLOS Computational Biology 19, no. 8 (2023): e1011439. http://dx.doi.org/10.1371/journal.pcbi.1011439.

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The time-varying reproduction number (Rt) is an important measure of epidemic transmissibility that directly informs policy decisions and the optimisation of control measures. EpiEstim is a widely used opensource software tool that uses case incidence and the serial interval (SI, time between symptoms in a case and their infector) to estimate Rt in real-time. The incidence and the SI distribution must be provided at the same temporal resolution, which can limit the applicability of EpiEstim and other similar methods, e.g. for contexts where the time window of incidence reporting is longer than

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D'Urso, Maria Grazia, and Joel Aldrighettoni. "Geodatabase, metric reconstruction and a GIS platform of historical-archaeological sites in Aquino." Acta IMEKO 13, no. 3 (2024): 1–14. http://dx.doi.org/10.21014/actaimeko.v13i3.1779.

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This paper presents the contents of a geodatabase developed from the outcomes of survey operations carried out, in several stages since 2015, within the archaeological context of the Roman city of Aquinum, in southern Lazio. The proposed geodatabase integrates traditional topographic surveying techniques with total station, GPS and GNSS geodetic receivers with photogrammetric surveys and terrestrial laser-scanner (TLS) measurements, including the realization of HBIM (Historical Building Information Modeling) models, to investigate some specific historical-archaeological evidence useful to unde

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Hasheminasab, Seyyed Meghdad, Tian Zhou, and Ayman Habib. "GNSS/INS-Assisted Structure from Motion Strategies for UAV-Based Imagery over Mechanized Agricultural Fields." Remote Sensing 12, no. 3 (2020): 351. http://dx.doi.org/10.3390/rs12030351.

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Acquired imagery by unmanned aerial vehicles (UAVs) has been widely used for three-dimensional (3D) reconstruction/modeling in various digital agriculture applications, such as phenotyping, crop monitoring, and yield prediction. 3D reconstruction from well-textured UAV-based images has matured and the user community has access to several commercial and opensource tools that provide accurate products at a high level of automation. However, in some applications, such as digital agriculture, due to repetitive image patterns, these approaches are not always able to produce reliable/complete produc

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Strnad, Pavel, Lukáš Švarc, and Petr Berka. "Synthetic dataset generator for anomaly detection in a university environment." Intelligent Data Analysis 27, no. 2 (2023): 417–22. http://dx.doi.org/10.3233/ida-216511.

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This article introduces a recently developed synthetic dataset generator, which contains anonymised data from the Prague University of Economics and Business information system logs. The generator is opensource and is able to scale this data time-wise and also perform injection of the data with cyberattackers’ behaviour patterns. The anonymised data still contains user behaviour patterns; therefore, individual anomalous behaviour can be detected. Different types of real attack behaviour patterns in the university environment have been selected; they are used to demonstrate attackers’ behaviour

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Askari, Sajad, Devon Snyder, Chu Li, Michael Saugstad, Jon E. Froehlich, and Yochai Eisenberg. "Validating Pedestrian Infrastructure Data: How Well Do Street-View Imagery Audits Compare to Government Field Data?" Urban Science 9, no. 4 (2025): 130. https://doi.org/10.3390/urbansci9040130.

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Data on pedestrian infrastructure is essential for improving the mobility environment and for planning efficiency. Although governmental agencies are responsible for capturing data on pedestrian infrastructure mostly by field audits, most have not completed such audits. In recent years, virtual auditing based on street view imagery (SVI), specifically through geo-crowdsourcing platforms, offers a more inclusive approach to pedestrian movement planning, but concerns about the quality and reliability of opensource geospatial data pose barriers to use by governments. Limited research has compared

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Mogaraju, Jagadish Kumar. "Agglomerative and Divisive hierarchical cluster analysis of groundwater quality variables using opensource tools over YSR district, AP, India." Journal of Scientific Research 66, no. 04 (2022): 15–20. http://dx.doi.org/10.37398/jsr.2022.660403.

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Groundwater quality variables like F, Total Hardness (TH), Total Alkalinity (TA), Total Dissolved Solids (TDS), SO4, SAR, NA, EC, Cl, Ca, Mg, and pH were tested with Hierarchical clustering analysis (HCA) to identify the groupings or clusters that exist in the dataset. The dataset is subjected to Agglomerative and divisive hierarchical clustering. The observations were scaled to compare variables systematically. The clustering structure was determined using an agglomerative coefficient. Agglomerative approaches like complete, average, single, and ward are tested using agglomerative coefficient

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Iandelli, Niccolò, Massimo Coli, Tessa Donigaglia, and Anna Livia Ciuffreda. "An Unconventional Field Mapping Application: A Complete Opensource Workflow Solution Applied to Lithological Mapping of the Coatings of Cultural Heritage." ISPRS International Journal of Geo-Information 10, no. 6 (2021): 357. http://dx.doi.org/10.3390/ijgi10060357.

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The conservation of a primary importance historical building requires organization, management, continuous updating, comparison, and visualization of a large amount of data of different nature and origin. In relation to these aspects the use of a GIS brings various advantages including single and univocal management of the entire amount of existing data in a relational, dynamic, updatable and queryable way. The integration of a mobile solution permits the updating of the dataset and checking on site all information. The workflow presented uses opensource solutions, desktop and mobile, which al

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Sauddin, Adnan, and Risnawati Ibnas. "Klasifikasi Penderita Penyakit Anemia dengan Metode NBC menggunakan R Programming." Jurnal MSA ( Matematika dan Statistika serta Aplikasinya) 11, no. 2 (2024): 112–23. http://dx.doi.org/10.24252/msa.v11i2.36470.

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Era data digital menghasilkan data yang sangat besar. Menggali informasi yang termuat dalam data yang demikian sangat sulit jika dilakukan secara manual. Perkembangan aplikasi pengolahan data yang bersifat opensourcei sangat membantu setiap analis data dalam melakukan pekerjaannya. R Programming merupakan salah satu aplikasi yang dikembangkan dan penggunaannya sangat luas. Pada artikel ini akan membahas bagaimana memodelkan dataset penderita penyakit anemia yang dimaksudkan untuk memahami tingkat keakuratan diagnosis bahwa seseorang mengidap penyakit anemia atau tidak yang didasarkan pada ciri

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Dr., Sharada K.A, Fathima S. Md Samreen, Fathimunissa Sayeda, Raj S. Vishnu, and Surendran Sonu. "A Deep Learning Technique for Breast Cancer." Journal of Advancement in Software Engineering and Testing 5, no. 2 (2022): 1–13. https://doi.org/10.5281/zenodo.6951388.

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Breast Cancer location and order is exceptionally hard. As a matter of fact, growth or disease is a complicated cycle during which mammogram pictures goes through different changes. Likewise, various areas of picture which show variable and high appearance are portrayed by different tissues. Our principal grant of this cycle is picture characterization for disease expectation and ad lib its presentation. We prepared, tried the execution ofour work on an opensource dataset. This undertakingwill be created utilizing python3. The task will be conveyed on Jupyter IDE. By and large this venture cen

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K. A, Dr Sharada, Samreen Fathima S. Md, Sayeda Fathimunissa, Vishnu Raj S, and Sonu Surendran. "Breast Cancer Detection System Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 2391–94. http://dx.doi.org/10.22214/ijraset.2022.41149.

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Abstract: Breast Cancer detection and classification is very hard. In fact, tumor or cancer is a complex process during which mammogram images undergoes various transformations. In addition, different areas of image which exhibit variable and high appearance are characterized by various tissues. Our main benefaction of this process is image classification for cancer prediction and improvise its performance. We have trained and tested the implementation of our work on an opensource dataset. This project will be developed using python3. The project will be deployed on Jupyter IDE. Overall this p

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Ai, M., M. Elhabiby, I. Asl Sabbaghian Hokmabadi, and N. El-Sheimy. "LIDAR-INERTIAL NAVIGATION BASED ON MAP AIDED DISTANCE CONSTRAINT AND FACTOR GRAPH OPTIMIZATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 875–80. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-875-2023.

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Abstract. The simultaneous localization and mapping (SLAM) is one of the well-developed positioning technology that provides high accuracy and reliability positioning for automatic vehicles and robotics applications. Integrating Light Detection and Ranging (LiDAR) with an Inertial Measurement Unit (IMU) has emerged as a promising technique for achieving stable navigation results in dense urban environments, outperforming vision-based or pure Inertial Navigation System (INS) solutions. However, conventional LiDAR-Inertial SLAM systems often suffer from limited perception of surrounding geometri

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Smith, Antony Douglas, Shengzhi Du, and Anish Kurien. "Vision Transformers for Anomaly Detection and Localisation in Leather Surface Defect Classification Based on Low-Resolution Images and a Small Dataset." Applied Sciences 13, no. 15 (2023): 8716. http://dx.doi.org/10.3390/app13158716.

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Genuine leather manufacturing is a multibillion-dollar industry that processes animal hides from varying types of animals such as sheep, alligator, goat, ostrich, crocodile, and cow. Due to the industry’s immense scale, there may be numerous unavoidable causes of damages, leading to surface defects that occur during both the manufacturing process and the bovine’s own lifespan. Owing to the heterogenous and manifold nature of leather surface characteristics, great difficulties can arise during the visual inspection of raw materials by human inspectors. To mitigate the industry’s challenges in t

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Han, Y., S. Wang, D. Gong, Y. Wang, Y. Wang, and X. Ma. "STATE OF THE ART IN DIGITAL SURFACE MODELLING FROM MULTI-VIEW HIGH-RESOLUTION SATELLITE IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 351–56. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-351-2020.

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Abstract. Data from the optical satellite imaging sensors running 24/7, is collecting in embarrassing abundance nowadays. Besides more suitable for large-scale mapping, multi-view high-resolution satellite images (HRSI) are cheaper when comparing to Light Detection And Ranging (LiDAR) data and aerial remotely sensed images, which are more accessible sources for digital surface modelling and updating. Digital Surface Model (DSM) generation is one of the most critical steps for mapping, 3D modelling, and semantic interpretation. Computing DSM from this dataset is relatively new, and several solu

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Mahajan, Shubham, Akshay Raina, Xiao-Zhi Gao, and Amit Kant Pandit. "Plant Recognition Using Morphological Feature Extraction and Transfer Learning over SVM and AdaBoost." Symmetry 13, no. 2 (2021): 356. http://dx.doi.org/10.3390/sym13020356.

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Plant species recognition from visual data has always been a challenging task for Artificial Intelligence (AI) researchers, due to a number of complications in the task, such as the enormous data to be processed due to vast number of floral species. There are many sources from a plant that can be used as feature aspects for an AI-based model, but features related to parts like leaves are considered as more significant for the task, primarily due to easy accessibility, than other parts like flowers, stems, etc. With this notion, we propose a plant species recognition model based on morphologica

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Ganesan, Anusha, Anand Paul, Ganesan Nagabushnam, and Malik Junaid Jami Gul. "Human-in-the-Loop Predictive Analytics Using Statistical Learning." Journal of Healthcare Engineering 2021 (July 29, 2021): 1–14. http://dx.doi.org/10.1155/2021/9955635.

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The human-in-the-loop cyber-physical system provides numerous solutions for the challenges faced by the doctors or medical practitioners. There is a linear trend of advancement and automation in the medical field for the early diagnosis of several diseases. One of the critical and challenging diseases in the medical field is coma. In the medical research field, currently, the prediction of these diseases is performed only using the data gathered from the devices only; however, the human’s input is much essential to accurately understand their health condition to take appropriate decision on ti

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Roberts, Leo, Daniel R. Little, Mervyn Jackson, and Matthew J. Spittal. "Test cricketers score quickly during the ‘nervous nineties’: Evidence from a regression discontinuity design." PLOS ONE 18, no. 6 (2023): e0287700. http://dx.doi.org/10.1371/journal.pone.0287700.

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The ‘nervous nineties’ is a well-known cricket colloquialism that implies that batting within reach of 100 runs is mentally demanding. Despite common acceptance of this phenomenon, no study has used a historical test cricket dataset to examine how batting behaviour and performance changes on approach to a century. Accordingly, we explored opensource ball-by-ball data from 712 test cricket matches played between 2004 and 2022 to model the regression discontinuity of batting performance metrics either side of 100 runs. Models were fit using multi-level regression, adjusted for the clustering of

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Stepanova, A. I., A. I. Khalyasmaa, and P. V. Matrenin. "Short-term forecasting of consumption of the oil and gas enterprises using technological factors and Shapley additive explanations." Power engineering: research, equipment, technology 26, no. 4 (2024): 75–88. http://dx.doi.org/10.30724/1998-9903-2024-26-4-75-88.

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RELEVANCE of the study lies in the development of system for the short-term forecasting of power consumption by the enterprise of the oil and gas industry with consideration of technological factors and interpretation of their influence on the result of the forecast.THE PURPOSE. To consider the problems of short-term forecasting. To test the applicability of the multi-agent approach to determine the features used to build a machine learning model of short-term forecasting of power consumption. To build machine learning models. To study the influence of technological factors on the accuracy of

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Achouri, Emna, Sebastien Felt, Matthew Hackbart, Nicole Rivera-Espinal, and Carolina B. Lopez. "VODKA2: A fast and accurate method to detect non-standard viral genomes from large RNA-seq datasets." RNA, October 27, 2023, rna.079747.123. http://dx.doi.org/10.1261/rna.079747.123.

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During viral replication, viruses carrying an RNA genome produce non-standard viral genomes (nsVGs), including copy-back viral genomes (cbVGs) and deletion viral genomes (delVGs), that play a crucial role in regulating viral replication and pathogenesis. Because of their critical roles in determining the outcome of RNA virus infections, the study of nsVGs has flourished in recent years exposing a need for bioinformatic tools that can accurately identify them within Next-Generation Sequencing data obtained from infected samples. Here, we present our data analysis pipeline, Viral Opensource DVG

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Noyce, Richard, and Dan Thayer. "Programmatically encrypting data linkage fields at a project level within the Secure Anonymised Information Linkage (SAIL) databank." International Journal of Population Data Science 1, no. 1 (2017). http://dx.doi.org/10.23889/ijpds.v1i1.170.

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ABSTRACT Introduction/BackgroundThe ability to link datasets within the Secure Anonymised Information Linkage (SAIL) databank provides researchers with a powerful tool to analyse multiple datasets. The ability to combine several datasets also has the adverse effect of potential identification of an individual. Further encrypting linkage fields at a project level limits the links to datasets specific to the project only. This presentation discusses the opensource web based administration tool that programmatically applies project encryption in a consistent and timely manner, logging admin

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Wibowo, Sandi, Jatniko Nur Mutaqin, Ari Apriansyah, Muhamad Komiyatu, and Gusti Ayu Putri Saptawati Soekidjo. "Development of Lung Cancer Risk Screening Tool with Causal Discovery Model Evaluation Approach." Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, May 8, 2025. https://doi.org/10.22219/kinetik.v10i2.2188.

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Causal graph discovery approaches in healthcare for detecting high-risk diseases have been more widely applied in the last decade. The main challenge in causal graph discovery in healthcare data is the complexity of big data, which requires appropriate algorithms to reveal causal relationships between variables. This study focuses on evaluating the performance of seven causal discovery models—Peter-Clark (PC), Greedy Equivalent Search (GES), Direct LiNGAM, Directed Acyclic Graph-Graph Neural Network (DAG-GNN), Greedy Sparsest Permutation (GraSP), and Recursive Causal Discovery (RCD)—on opensou

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Jia, Yinglu, Pengchong Ma, and Qiuming Yao. "CellMarkerPipe: cell marker identification and evaluation pipeline in single cell transcriptomes." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-024-63492-z.

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AbstractAssessing marker genes from all cell clusters can be time-consuming and lack systematic strategy. Streamlining this process through a unified computational platform that automates identification and benchmarking will greatly enhance efficiency and ensure a fair evaluation. We therefore developed a novel computational platform, cellMarkerPipe (https://github.com/yao-laboratory/cellMarkerPipe), for automated cell-type specific marker gene identification from scRNA-seq data, coupled with comprehensive evaluation schema. CellMarkerPipe adaptively wraps around a collection of commonly used

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Su, Hanqi, and Jay Lee. "Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Industrial Open Source Data." International Journal of Prognostics and Health Management 15, no. 2 (2024). http://dx.doi.org/10.36001/ijphm.2024.v15i2.3993.

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In the field of Prognostics and Health Management (PHM), recent years have witnessed a significant surge in the application of machine learning (ML). Despite this growth, the field grapples with a lack of unified guidelines and systematic approaches for effectively implementing these ML techniques and comprehensive analysis regarding industrial open-source data across varied scenarios. To address these gaps, this paper provides a comprehensive review of ML approaches for diagnostics and prognostics of industrial systems using opensource datasets from PHM Data Challenge Competitions held betwee

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Veglia, Alex J., Ramón E. Rivera‐Vicéns, Carsten G. B. Grupstra, Lauren I. Howe‐Kerr, and Adrienne M. S. Correa. "vAMPirus: A versatile amplicon processing and analysis program for studying viruses." Molecular Ecology Resources, May 22, 2024. http://dx.doi.org/10.1111/1755-0998.13978.

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AbstractAmplicon sequencing is an effective and increasingly applied method for studying viral communities in the environment. Here, we present vAMPirus, a user‐friendly, comprehensive, and versatile DNA and RNA virus amplicon sequence analysis program, designed to support investigators in exploring virus amplicon sequencing data and running informed, reproducible analyses. vAMPirus intakes raw virus amplicon libraries and, by default, performs nucleotide‐ and amino acid‐based analyses to produce results such as sequence abundance information, taxonomic classifications, phylogenies and communi

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Hardy-Werbin, Max, José Maria Maiques, Marcos Busto, et al. "MultiCOVID: a multi modal deep learning approach for COVID-19 diagnosis." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-46126-8.

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AbstractThe rapid spread of the severe acute respiratory syndrome coronavirus 2 led to a global overextension of healthcare. Both Chest X-rays (CXR) and blood test have been demonstrated to have predictive value on Coronavirus Disease 2019 (COVID-19) diagnosis on different prevalence scenarios. With the objective of improving and accelerating the diagnosis of COVID-19, a multi modal prediction algorithm (MultiCOVID) based on CXR and blood test was developed, to discriminate between COVID-19, Heart Failure and Non-COVID Pneumonia and healthy (Control) patients. This retrospective single-center

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Luaphol, Bancha, Jantima Polpinij, and Manasawee Kaenampornpan. "Mining Bug Report Repositories to Identify Significant Information for Software Bug Fixing." Applied Science and Engineering Progress, March 17, 2021. http://dx.doi.org/10.14416/j.asep.2021.03.005.

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Most studies relating to bug reports aims to automatically identify necessary information from bug reports for software bug fixing. Unfortunately, the study of bug reports focuses only on one issue, but more complete and comprehensive software bug fixing would be facilitated by assessing multiple issues concurrently. This becomes a challenge in this study, where it aims to present a method of identifying bug reports at severe level from a bug report repository, together with assembling their related bug reports to visualize the overall picture of a software problem domain. The proposed method

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Lu, Miaoshan, Shaowei An, Ruimin Wang, Jinyin Wang, and Changbin Yu. "Aird: a computation-oriented mass spectrometry data format enables a higher compression ratio and less decoding time." BMC Bioinformatics 23, no. 1 (2022). http://dx.doi.org/10.1186/s12859-021-04490-0.

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Abstract Background With the precision of the mass spectrometry (MS) going higher, the MS file size increases rapidly. Beyond the widely-used open format mzML, near-lossless or lossless compression algorithms and formats emerged in scenarios with different precision requirements. The data precision is often related to the instrument and subsequent processing algorithms. Unlike storage-oriented formats, which focus more on lossless compression rate, computation-oriented formats concentrate as much on decoding speed as the compression rate. Results Here we introduce “Aird”, an opensource and com

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Swaileh A. Alzaidi, Muhammad, Alya Alshammari, Manar Almanea, et al. "A Text-Inception-Based Natural Language Processing Model for Sentiment Analysis of Drug Experiences." ACM Transactions on Asian and Low-Resource Language Information Processing, August 12, 2024. http://dx.doi.org/10.1145/3678470.

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The study of sentiment in Natural Language Processing (NLP) is among the most successful research areas because of the availability of millions of user opinions online since the turn of the century. The economic, political, and medical fields are just some of the many that have benefited from studies of sentiment research. While numerous studies have examined more mainstream topics like consumer electronics, movies, and restaurants, relatively few have examined health and medical concerns. Considerable insight into where to direct efforts to improve public health might be gained by a study of

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Iandelli, Niccolò, Massimo Coli, Tessa Donigaglia, and Anna Livia Ciuffreda. "An Unconventional Field Mapping Application: A Complete Opensource Workflow Solution Applied to Lithological Mapping of the Coatings of Cultural Heritage." May 24, 2021. https://doi.org/10.3390/ijgi10060357.

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The conservation of a primary importance historical building requires organization, management, continuous updating, comparison, and visualization of a large amount of data of different nature and origin. In relation to these aspects the use of a GIS brings various advantages including single and univocal management of the entire amount of existing data in a relational, dynamic, updatable and queryable way. The integration of a mobile solution permits the updating of the dataset and checking on site all information. The workflow presented uses opensource solutions, desktop and mobile, which al

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Carnagie, Jeremy Onesimus, Aditya Rio Prabowo, Iwan Istanto, et al. "Technical review of supervised machine learning studies and potential implementation to identify herbal plant dataset." Open Engineering 13, no. 1 (2023). http://dx.doi.org/10.1515/eng-2022-0385.

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Abstract The use of technology in everyday life is unavoidable, considering that technological advancement occurs very quickly. The current era is also known as industry 4.0. In the industry 4.0 era, there is a convergence between the industrial world and information technology. The use of modern machines in the industry makes it possible for business actors to digitize their production facilities and open up new business opportunities. One of the developments in information technology that is being widely used in its implementation is machine learning (ML) technology and its branches such as

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Jamadar, Sharna D., Shenjun Zhong, Alexandra Carey, et al. "Task-evoked simultaneous FDG-PET and fMRI data for measurement of neural metabolism in the human visual cortex." Scientific Data 8, no. 1 (2021). http://dx.doi.org/10.1038/s41597-021-01042-2.

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AbstractUnderstanding how the living human brain functions requires sophisticated in vivo neuroimaging technologies to characterise the complexity of neuroanatomy, neural function, and brain metabolism. Fluorodeoxyglucose positron emission tomography (FDG-PET) studies of human brain function have historically been limited in their capacity to measure dynamic neural activity. Simultaneous [18 F]-FDG-PET and functional magnetic resonance imaging (fMRI) with FDG infusion protocols enable examination of dynamic changes in cerebral glucose metabolism simultaneously with dynamic changes in blood oxy

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Saraj, Singh Manes, and Baysal Olga. "How Often and What StackOverflow Posts Do Developers Reference in Their GitHub Projects?" MSR 2019, March 22, 2019. https://doi.org/10.5281/zenodo.2604130.

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Kitlasten, Wesley, Catherine R. Moore, and Brioch Hemmings. "Model structure and ensemble size: Implications for predictions of groundwater age." Frontiers in Earth Science 10 (December 19, 2022). http://dx.doi.org/10.3389/feart.2022.972305.

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This paper examines the influence of simplified vertical discretization using 50- to four- layer models and ensemble size on history matching and predictions of groundwater age for a national scale model of New Zealand (approximately 265,000 km2). A reproducible workflow using a combination of opensource tools and custom python scripts is used to generate three models that use the same model domain and underlying data with only the vertical discretization changing between the models. The iterative ensemble smoother approach is used for history matching each model to the same synthetic dataset.

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Kaan, Eroltu. "Comparing different Convolutional Neural Networks for the classification of Alzheimer's Disease." July 31, 2023. https://doi.org/10.5281/zenodo.8210597.

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Alzheimer's disease (AD) is a degenerative, incurable neurological disorder that progressively damages cognitive abilities. AD affects millions of people worldwide. The biopsy method is the most reliable method to identify AD, but it has the chance to cause irreparable harm. There are many non-invasive alternatives to biopsies that can be used to diagnose AD without undue risk. One of these alternatives comprises computer-assisted diagnostic systems, which enable the identification of brain-impairing illnesses/diseases. This paper used Magnetic Resonance Imaging scans of brains with four d

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Verma, Manoj Kumar, and Mayank Yuvaraj. "AI-Based Literature Reviews: A Topic Modeling Approach." SRELS Journal of Information Management, May 13, 2023, 97–104. http://dx.doi.org/10.17821/srels/2023/v60i2/170967.

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The purpose of this paper is to highlight the importance of topic modelling in conducting literature reviews using the opensource LDAShiny package in the R environment, with green libraries literature as a case study. To conduct the analysis, a title and abstract dataset were prepared using the Scopus database and imported into the LDAShiny package for further analysis. It was found that the green libraries' literature ranged from 1989-2023, with a sharp increase in research topics since 2003. The study also identified key themes and documents associated with green libraries research, revealin

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Dugac, Gaspar, and Tilmann Altwicker. "Classifying legal interpretations using large language models." Artificial Intelligence and Law, April 15, 2025. https://doi.org/10.1007/s10506-025-09447-9.

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Abstract In the civil law tradition, legal arguments are used to justify the outcomes of judicial decision-making. These arguments are formed relying on a canon of interpretation techniques (e.g. textual or teleological interpretation). We study the identifiability of interpretation techniques as they are employed by the European Court of Human Rights (ECtHR) from a computational law perspective using a unique dataset. We show how Large Language Models (LLMs) can be utilized to classify legal interpretations, and we compare their performance. We evaluate proprietary and opensource models using

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