Weißbuch Citizen Science Strategie 2030 für Deutschland

Published: Aug. 7, 2021

This is the final version of White Paper Citizen Science Strategy 2030 for Germany, launched on 29/4/2022. English see http://zenodo.org/record/7117771

Language: Английский

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Autonomous Data Association and Intelligent Information Discovery Based on Multimodal Fusion Technology

Symmetry, Journal Year: 2024, Volume and Issue: 16(1), P. 81 - 81

Published: Jan. 8, 2024

The effective association of multimodal data is the basis massive multi-source heterogeneous sharing in era big data. How to realize autonomous between databases and automatic intelligent screening valuable information from associated data, so as provide a reliable source for artificial intelligence (AI), an urgent problem be solved. In this paper, method based on organizational structure cells proposed, including transaction abstraction nucleuses, symmetric asymmetric strategies pipes, generation To screen meaningful associations, information-driven discovery task-driven are proposed. former screens associations by training reward punishment model simulate manual scoring associations. latter task-oriented ranking related task requests. Through above work, effectively realized fusion technology, which provides novel mining approach using discovery.

Language: Английский

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Modelling heterogeneity in the classification process in multi‐species distribution models can improve predictive performance

Ecology and Evolution, Journal Year: 2024, Volume and Issue: 14(3)

Published: March 1, 2024

Abstract Species distribution models and maps from large‐scale biodiversity data are necessary for conservation management. One current issue is that prone to taxonomic misclassifications. Methods account these misclassifications in multi‐species have assumed the classification probabilities constant throughout study. In reality, likely vary with several covariates. Failure such heterogeneity can lead biased prediction of species distributions. Here, we present a general model accounts process. The proposed assumes multinomial generalised linear confusion matrix. We compare performance heterogeneous homogeneous by assessing how well they estimate parameters their predictive on hold‐out samples. applied gull Norway, Denmark Finland, obtained Global Biodiversity Information Facility. Our simulation study showed accounting process increased precision true species' identity predictions 30% accuracy recall 6%. Since all this accounted misclassification some sort, there was no significant effect inference about ecological Applying framework dataset did not improve between (with parametric distributions) due smaller misclassified sample sizes. However, when machine learning scores were used as weights inform process, 70%. recommend multiple regression be variation contains relatively larger Machine should

Language: Английский

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Evaluating OSM Building Footprint Data Quality in Québec Province, Canada from 2018 to 2023: A Comparative Study

Geomatics, Journal Year: 2023, Volume and Issue: 3(4), P. 541 - 562

Published: Dec. 9, 2023

OpenStreetMap (OSM) is among the most prominent Volunteered Geographic Information (VGI) initiatives, aiming to create a freely accessible world map. Despite its success, data quality of OSM remains variable. This study begins by identifying metrics proposed earlier research assess building footprints. It then evaluates from 2018 and 2023 for five cities within Québec, Canada. The analysis reveals significant improvement over time. In 2018, completeness footprints in examined averaged around 5%, while 2023, it had increased approximately 35%. However, this was not evenly distributed. For example, Shawinigan saw surge 2% 99%. also finds that contributors were more likely digitize larger buildings before smaller ones. Positional accuracy enhancement, with average error shrinking 3.7 m 2.3 2023. distance measure suggests modest increase shape same period. Overall, has indeed improved, shows extent varied significantly across different cities. experienced substantial compared counterparts.

Language: Английский

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Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Sensors, Journal Year: 2023, Volume and Issue: 24(1), P. 18 - 18

Published: Dec. 19, 2023

Wireless sensor networks (WSNs) have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. This study introduces an innovative approach to WSN data collection tailored for detection through signal processing healthcare scenarios. The proposed strategy leverages the DANA (data aggregation using neighborhood analysis) algorithm semi-supervised clustering-based model enhance precision effectiveness of WSNs. optimizes energy consumption prolongs node lifetimes by dynamically adjusting communication routes based on network’s real-time conditions. Additionally, clustering utilizes both labeled unlabeled create more robust adaptable technique. Through extensive simulations practical deployments, our experimental assessments demonstrate remarkable efficacy method model. We conducted comparative analysis efficiency, utilization, accuracy against conventional techniques, revealing significant improvements quality, rapid diagnosis. combined offers WSNs compelling solution responsiveness reliability diagnosis processing. research contributes advancement systems offering avenue improved care, ultimately transforming landscape enhanced capabilities.

Language: Английский

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