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Relevant bibliographies by topics / Opensource datasets

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Academic literature on the topic 'Opensource datasets'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Opensource datasets"

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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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Book chapters on the topic "Opensource datasets"

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Bhattacharya, D., P. Pasquali, J. Komarkova, P. Sedlak, A. Saha, and P. Boccardo. "Interlinking Opensource Geo-Spatial Datasets for Optimal Utility in Ranking." In Lecture Notes in Geoinformation and Cartography. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07926-4_13.

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Sharma, Aman, and Rinkle Rani. "Machine Learning Perspective in Cancer Research." In Research Anthology on Bioinformatics, Genomics, and Computational Biology. IGI Global, 2023. http://dx.doi.org/10.4018/979-8-3693-3026-5.ch047.

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Advancement in genome sequencing technology has empowered researchers to think beyond their imagination. Researchers are trying their hard to fight against various genetic diseases like cancer. Artificial intelligence has empowered research in the healthcare sector. Moreover, the availability of opensource healthcare datasets has motivated the researchers to develop applications which can help in early diagnosis and prognosis of diseases. Further, next-generation sequencing (NGS) has helped to look into detailed intricacies of biological systems. It has provided an efficient and cost-effective

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Sharma, Aman, and Rinkle Rani. "Machine Learning Perspective in Cancer Research." In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2742-9.ch008.

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Advancement in genome sequencing technology has empowered researchers to think beyond their imagination. Researchers are trying their hard to fight against various genetic diseases like cancer. Artificial intelligence has empowered research in the healthcare sector. Moreover, the availability of opensource healthcare datasets has motivated the researchers to develop applications which can help in early diagnosis and prognosis of diseases. Further, next-generation sequencing (NGS) has helped to look into detailed intricacies of biological systems. It has provided an efficient and cost-effective

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Conference papers on the topic "Opensource datasets"

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Balakrishnan, P., D. Dumortier, P. Kenny, et al. "SKYSPECTRA: AN OPENSOURCE DATA PACKAGE OF WORLDWIDE SPECTRAL DAYLIGHT." In CIE 2023 Conference. International Commission on Illumination, CIE, 2023. http://dx.doi.org/10.25039/x50.2023.op026.

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This paper introduces SKYSPECTRA, an open-source data package comprising spectral daylight measurements collected from various sources worldwide. The dataset encompasses measurements from both long-term measurement sites and specific periods or experiments. The paper describes the data collection, selection, and organisation process within a standardised framework, ensuring consistency in naming conventions, formats, and units across all datasets. It also describes how the data is structured for operability in programming languages like R or Python for data analysis. SKYSPECTRA aims to facilit

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Chen, Tingwei, Jianpeng Chen, and Dawei Zhou. "3D-FuM: Benchmarking 3D Molecule Learning with Functional Groups." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/997.

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Molecular graph representation learning plays a crucial role in various domains, such as drug discovery and chemical reaction prediction, where molecular graphs are typically depicted as 2D topological structures. However, recent insights highlight the critical role of 3D geometric information and functional groups in accurately predicting molecular properties, aspects often neglected in existing molecular graph benchmark datasets. To bridge the research gap, we introduce a comprehensive molecular learning benchmark named 3D-FUM, which incorporates both 3D geometric information and functional

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Kim, Mingi, and Choong-Ki Chung. "Development of a GIS-Based System for Three-Dimensional Spatial Modeling of Offshore Site Investigation Information." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-19252.

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Abstract Offshore site investigation is time-consuming and relatively expensive compared to onshore site investigation, because it is affected by severe weather conditions and has low accessibility from the land. Due to these economic and spatial-temporal constraints, it is essential to integrate the available site investigation information for the planning and design of offshore infrastructures. In this study, a GIS-based system was developed to manage and utilize the offshore site investigation data using opensource software. The system mainly performs the function of modeling the geo-inform

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Benslimane, Salma, Josselin Kherroubi, Kamaljeet Singh, Jean-Luc Le Calvez, Thomas Berard, and Mikhail Lemarenko. "Automated Corrosion Analysis With Prior Domain Knowledge-Informed Neural Networks." In 2022 SPWLA 63rd Annual Symposium. Society of Petrophysicists and Well Log Analysts, 2022. http://dx.doi.org/10.30632/spwla-2022-0097.

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Well-integrity operations require knowledge of pipe conditions throughout the production life cycle of the wells. Corrosion defects in wellbores can have disastrous consequences on the safety and cost of operations and must therefore be closely monitored. However, data analysis over thousands of feet of a well for picking corrosion defects remains manual and time-consuming and still relies on a high level of expertise. To streamline and automate this data analysis process, we build on the availability of large public datasets and the recent progress of deep learning methods and propose a workf

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Badar, Muhammad Haseeb, Syed Sadaqat Ali, Yasser S. Ghamdi, and Muhammad Khan. "Seismic Interpretation Technologies Advancement and its Impact on Interactive and Iterative Interpretation Workflows." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-21920-ea.

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Abstract Seismic interpretation is a key task and foundation for hydrocarbons exploration and field development. Seismic data provides coverage from basin to reservoir scale workflows for identifying regional structures, delineate prospects and calculate rock properties. In this paper we discuss the evolution of seismic structural and stratigraphic interpretation through key technological milestones. This covers a broad spectrum, from conventional 2D interpretation methodologies to processes that help us see below the quarter wavelength resolution. We have captured the workflows that are redef

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Pereira, Gabriel, Luciano Barbosa, Johny Moreira, Tiago Melo, and Altigran Silva. "Enhancing Aspect-Based Sentiment Analysis for Portuguese Using Instruction Tuning." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2024. https://doi.org/10.5753/eniac.2024.245109.

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This study explores the application of instruction tuning in opensource small language models for Portuguese End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA), focusing on restaurant reviews. Utilizing a diverse dataset from sources such as Google Reviews, TripAdvisor, Instagram, and iFood, the research evaluates the performance of PTT5 Base, a T5 model pretrained on Portuguese data, in comparison to multilingual models, namely FLAN-T5 Base and mT0 Small. The results show that the PTT5 Base has superior capabilities in E2E-ABSA, achieving an F1 Score of 0.60, Precision of 0.61, and Recall

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Sharma, Shivesh N., and Navid Goudarzi. "Urban Airflow Analysis Using Reduced-Order Modeling." In ASME Power Applied R&D 2023. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/power2023-101973.

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Abstract The numerical and experimental techniques are widely used for urban air flow studies. As spatiotemporal scales increase, these models face an extended range of limitations, from the experimental setup sizing/equipment constraints to expensive physics-based computational modeling. Reduced order models (ROMs) are introduced as an alternative or a complementary tool to current practices for identifying urban airflow characteristics. This paper investigates the implementation of a group of ROMs on an opensource experimental dataset. These models follow two major steps dimensionality reduc

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Li, Guangjie, Yi Tang, Biyi Yi, Xiang Zhang, and Yan He. "A Deep Learning based Approach to Argument Recommendation." In 2nd International Conference on Machine Learning Techniques and NLP (MLNLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111408.

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Code completion is one of the most useful features provided by advanced IDEs and is widely used by software developers. However, as a kind of code completion, recommending arguments for method calls is less used. Most of existing argument recommendation approaches provide a long list of syntactically correct candidate arguments, which is difficult for software engineers to select the correct arguments from the long list. To this end, we propose a deep learning based approach to recommending arguments instantly when programmers type in method names they intend to invoke. First, we extract conte

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