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  1. 5081

    ENHANCING WEIGHTED FUZZY TIME SERIES FORECASTING THROUGH PARTICLE SWARM OPTIMIZATION by Armando Jacquis Federal Zamelina, Suci Astutik, Rahma Fitriani, Adji Achmad Rinaldo Fernandes, Lucius Ramifidisoa

    Published 2024-10-01
    “…Furthermore, the length of the interval and the extent to which previous values (Order length) are utilized in predicting the subsequent value are pivotal factors in WFTS modelization and its forecasting accuracy. …”
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    Article
  2. 5082

    Novel transfer learning based bone fracture detection using radiographic images by Aneeza Alam, Ahmad Sami Al-Shamayleh, Nisrean Thalji, Ali Raza, Edgar Anibal Morales Barajas, Ernesto Bautista Thompson, Isabel de la Torre Diez, Imran Ashraf

    Published 2025-01-01
    “…In this study, we propose a novel transfer learning-based approach called MobLG-Net for feature engineering purposes. Initially, the spatial features are extracted from bone X-ray images using a transfer model, MobileNet, and then input into a tree-based light gradient boosting machine (LGBM) model for the generation of class probability features. …”
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  3. 5083

    Observation and Numerical Simulation of Cross-Mountain Airflow at the Hong Kong International Airport from Range Height Indicator Scans of Radar and LIDAR by Ying Wa Chan, Kai Wai Lo, Ping Cheung, Pak Wai Chan, Kai Kwong Lai

    Published 2024-11-01
    “…In order to study the feasibility of predicting such disturbed airflow, a mesoscale meteorological model and a computational fluid dynamics model with high spatial resolution are used in this paper. …”
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  4. 5084

    Deep Learning-Based MRI Brain Tumor Segmentation With EfficientNet-Enhanced UNet by Pradeep Kumar Tiwary, Prashant Johri, Alok Katiyar, Mayur Kumar Chhipa

    Published 2025-01-01
    “…Precisely delineating brain tumor areas from multimodal MRI scans is crucial for clinical diagnosis and predicting patient outcomes. However, challenges arise from similar intensity patterns, varying tumor shapes, and indistinct boundaries, which complicate brain tumor segmentation. …”
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  5. 5085

    Association between land use features and changes in walking patterns from pre-pandemic to post-pandemic: A case study of city of Sydney (2013–2023) by Fatemeh Nourmohammadi, Zahra Nourmohammadi, Tanapon Lilasathapornkit, Meead Saberi

    Published 2024-10-01
    “…The observed changes in pedestrian activities are, however, spatially heterogeneous. Modeling results reveal that areas with greater commercial land use, more points of interest (POIs), higher population density, and higher network connectivity experienced a significant negative change in the number of walking trips from the pre-pandemic to the pandemic period. …”
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  6. 5086

    La nouvelle insertion de l’agriculture urbaine dans la fabrication de la ville by Gustavo Nagib

    Published 2024-05-01
    “…This article addresses this theme following research conducted between 2018 and 2021 in Greater Paris, where UA has integrated as a model of edible landscaping, educational vegetable gardening or urban farming. …”
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    Article
  7. 5087

    Real-Time Fire Risk Classification Using Sensor Data and Digital-Twin-Enabled Deep Learning by In-Seop Na, Vani Rajasekar, Velliangiri Sarveshwaran

    Published 2025-01-01
    “…A key innovation is the use of digital twin technology, which dynamically integrates real-time data from IoT sensors and simulation models to predict fire disaster scenarios accurately. …”
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    Article
  8. 5088

    Estimating corn leaf chlorophyll content using airborne multispectral imagery and machine learning by Fengkai Tian, Jianfeng Zhou, Curtis J. Ransom, Noel Aloysius, Kenneth A. Sudduth

    Published 2025-03-01
    “…A UAV-based multispectral camera collected imagery at the same time as manual readings. Machine learning models developed based on image features derived from UAV images were used to predict leaf chlorophyll content. …”
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  9. 5089

    Infilling of missing rainfall radar data with a memory-assisted deep learning approach by J. Meuer, L. M. Bouwer, L. M. Bouwer, F. Kaspar, R. Lehmann, W. Karl, T. Ludwig, C. Kadow

    Published 2025-08-01
    “…This novel approach represents a step forward in hydrological applications, potentially improving the way we predict and manage water-related events by increasing the accuracy and reliability of precipitation data analysis.…”
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  10. 5090

    Incorporating long-term numerical weather forecasts to quantify dynamic vulnerability of irrigation supply system: A case study of Shihmen Reservoir in Taiwan by Chia-Chuan Hsu, Yu-Pin Lin

    Published 2024-12-01
    “…The simulation of historical conditions shows successful predictions of 6 out of 7 historical drought-induced irrigation stoppages during 2000–2021. …”
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  11. 5091

    Basin-wide and coastal modes of north tropical Atlantic variability have distinct impacts on hurricanes by Yi Liu, Michael J. McPhaden, Wenju Cai, Yu Zhang, Jiuwei Zhao, Hyacinth C. Nnamchi, Xiaopei Lin, Ziguang Li, Jun-Chao Yang

    Published 2025-07-01
    “…Here we use observations and model outputs over the past several decades to determine whether there exists inherent diversity in north tropical Atlantic surface temperature spatial structures and impacts. …”
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  12. 5092

    Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images by Soohyun Wang, Byoungkug Kim, Doo-Seop Eom

    Published 2025-05-01
    “…Furthermore, the proposed model more accurately captures the relative size and spatial alignment of the OD and OC and produces smooth and consistent boundary predictions in clinically significant regions such as the region of interest (ROI). …”
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  13. 5093

    A method to quantify jump dispersal of invasive species from occurrence data: the case of the spotted lanternfly, Lycorma delicatula by Nadège Belouard, Sebastiano De Bona, Matthew R. Helmus, Isabella G. Smith, Jocelyn E. Behm

    Published 2025-05-01
    “…The accuracy of predicting the spread of biological invasions is improved if models explicitly incorporate the two main dispersal mechanisms: diffusive spread and jump dispersal. …”
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  14. 5094

    Role of Cairns–Gurevich ion distribution on nonlinear wave propagation in negatively charged dusty plasma by N.S. Alharthi

    Published 2024-12-01
    “…The results demonstrated that soliton waves, explosive waves, and kink waves exhibit distinct behaviors depending on these parameters and the spatial context of Earth's magnetosphere. These findings provide new insights compared to previous studies into the conditions under which these waveforms emerge and evolve, especially in magnetized environments where earlier models were less effective at accurately characterizing or predicting wave behavior. …”
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  15. 5095

    Micro, Small or Medium, New or Old—Are There Differences? Testing Business-Specific Difficulties by Mihaela Brîndușa Tudose, Savin Dorin Ionesi, Ionuț Dulgheriu, Liliana Buhu, Valentina Diana Rusu

    Published 2024-12-01
    “…To formulate general conclusions or predictions, in economic research, large databases are often used, related to more or less homogeneous samples, without taking into account the spatial or structural differences of the analysed processes or phenomena. …”
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  16. 5096
  17. 5097

    Land Use and Climate Change Accelerate the Loss of Habitat and Ecological Corridor to Reeves's Pheasant (Syrmaticus reevesii) in China by Qingqing He, Shan Tian, Junqin Hua, Zhengxiao Liu, Yating Liu, Ting Jin, Jiliang Xu

    Published 2024-11-01
    “…However, determining the causal factors of their extinction and carrying out protection measures appear to be challenging owing to a lack of long‐term data with high spatial and temporal resolutions. Here, based on a national field survey, we used habitat suitability models and integrated data on geographical environment, road development, land use, and climate change to predict the potential changes in the distribution and connectivity of the habitat of Reeves's pheasant from 1995 to 2050. …”
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  18. 5098

    Unsupervised semantic label generation in agricultural fields by Gianmarco Roggiolani, Julius Rückin, Marija Popović, Jens Behley, Cyrill Stachniss, Cyrill Stachniss

    Published 2025-02-01
    “…Using our generated labels to train deep learning models boosts our prediction performance on previously unseen fields with respect to unseen crop species, growth stages, or different lighting conditions. …”
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  19. 5099
  20. 5100