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Zeitschriftenartikel zum Thema „Dl-a (Architectural firm)“

Autor: Grafiati

Veröffentlicht am 28. Juli 2024

Zuletzt aktualisiert am 31. Juli 2025

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Ciprián-Sánchez, Jorge Francisco, Gilberto Ochoa-Ruiz, Lucile Rossi, and Frédéric Morandini. "Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation." Applied Sciences 11, no. 15 (2021): 7046. http://dx.doi.org/10.3390/app11157046.

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Wildfires stand as one of the most relevant natural disasters worldwide, particularly more so due to the effect of climate change and its impact on various societal and environmental levels. In this regard, a significant amount of research has been done in order to address this issue, deploying a wide variety of technologies and following a multi-disciplinary approach. Notably, computer vision has played a fundamental role in this regard. It can be used to extract and combine information from several imaging modalities in regard to fire detection, characterization and wildfire spread forecasti

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Hu, Xikun, Yifang Ban, and Andrea Nascetti. "Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning." Remote Sensing 13, no. 8 (2021): 1509. http://dx.doi.org/10.3390/rs13081509.

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Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2

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Grari, Mounir, Mimoun Yandouzi, Berrahal Mohammed, Mohammed Boukabous, and Idriss Idrissi. "Comparative study of teachable machine for forest fire and smoke detection by drone." Bulletin of Electrical Engineering and Informatics 13, no. 3 (2024): 1970–79. http://dx.doi.org/10.11591/eei.v13i3.6578.

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Forests play a vital role in maintaining ecological equilibrium and serving as vital habitats for wildlife. They regulate global climate, safeguard soil and water resources, and provide crucial ecosystem services such as air and water purification, essential for human well-being and sustainable development. Forest fires wreak havoc on ecosystems and wildlife, emitting harmful pollutants, disrupting communities, and increasing the risk of erosion and landslides. Detecting forest fires through satellite imaging, aerial reconnaissance, and ground-based sensors is pivotal for early detection and c

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Larson, Kyle B., and Aaron R. Tuor. "Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data." Remote Sensing 13, no. 7 (2021): 1246. http://dx.doi.org/10.3390/rs13071246.

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Cheatgrass (Bromus tectorum) invasion is driving an emerging cycle of increased fire frequency and irreversible loss of wildlife habitat in the western US. Yet, detailed spatial information about its occurrence is still lacking for much of its presumably invaded range. Deep learning (DL) has demonstrated success for remote sensing applications but is less tested on more challenging tasks like identifying biological invasions using sub-pixel phenomena. We compare two DL architectures and the more conventional Random Forest and Logistic Regression methods to improve upon a previous effort to map

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Muksimova, Shakhnoza, Sabina Umirzakova, Dilnoza Abduxalikovna Babaraximova, and Young Im Cho. "Lightweight Fire Detection in Tunnel Environments." Fire 8, no. 4 (2025): 134. https://doi.org/10.3390/fire8040134.

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Tunnel fires pose significant challenges to public safety due to their rapid development and the confined nature of tunnel environments. Traditional fire detection systems often struggle with delayed response times and high false alarm rates, particularly in complex scenarios. This study proposes a lightweight hybrid deep learning (DL) model that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal analysis, offering an efficient and robust solution for real-time tunnel fire detection. Leveraging transfer learnin

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Sunita, Kumari, Singh Saket, and Sharma Shaifali. "Lung Cancer Detection using CNN and Image Processing Techniques." Journal of Research in Artificial Neural Network Systems 1, no. 2 (2025): 45–53. https://doi.org/10.5281/zenodo.15404863.

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<em>Lung cancer remains one of the leading causes of cancer- related deaths worldwide. Beforehand </em><em>discovery is pivotal for perfecting patient survival rates. This paper presents a deep literacy- grounded approach for lung cancer discovery using Convolutional Neural Networks( CNN) and image processing ways. The proposed system leverages medical imaging data, similar as CT reviews andX-rays, to classify lung nodes as nasty or benign. The methodology involves preprocessing the images, rooting applicable features using CNNs, and training a robust bracket model. Experimental results demons

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Meimand, Hadi Mahmoudi, Jiaxin Chen, Daniel Kneeshaw, Mohammadreza Bakhtyari, and Changhui Peng. "Burned Area Detection in the Eastern Canadian Boreal Forest Using a Multi-Layer Perceptron and MODIS-Derived Features." Remote Sensing 17, no. 13 (2025): 2162. https://doi.org/10.3390/rs17132162.

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Wildfires play a critical role in boreal forest ecosystems, yet their increasing frequency poses significant challenges for carbon emissions, ecosystem stability, and fire management. Accurate burned area detection is essential for assessing post-fire landscape recovery and fire-induced carbon fluxes. This study develops, compares, and optimizes machine learning (ML)-based models for burned area classification in the eastern Canadian boreal forest from 2000 to 2023 using MODIS-derived features extracted from Google Earth Engine (GEE), and the feature extraction includes maximum, minimum, mean,

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Ullah, Naeem, Mehrez Marzougui, Ijaz Ahmad, and Samia Allaoua Chelloug. "DeepLungNet: An Effective DL-Based Approach for Lung Disease Classification Using CRIs." Electronics 12, no. 8 (2023): 1860. http://dx.doi.org/10.3390/electronics12081860.

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Infectious disease-related illness has always posed a concern on a global scale. Each year, pneumonia (viral and bacterial pneumonia), tuberculosis (TB), COVID-19, and lung opacity (LO) cause millions of deaths because they all affect the lungs. Early detection and diagnosis can help create chances for better care in all circumstances. Numerous tests, including molecular tests (RT-PCR), complete blood count (CBC) tests, Monteux tuberculin skin tests (TST), and ultrasounds, are used to detect and classify these diseases. However, these tests take a lot of time, have a 20% mistake rate, and are

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Yilmaz, Elif Ozlem, and Taskin Kavzoglu. "Burned Area Detection with Sentinel-2A Data: Using Deep Learning Techniques with eXplainable Artificial Intelligence." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-5-2024 (November 13, 2024): 251–57. http://dx.doi.org/10.5194/isprs-annals-x-5-2024-251-2024.

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Abstract. Annually, a considerable quantity of forest is burned on a global scale. Therefore, it is essential to obtain precise and fast information regarding the size of burned regions in order to effectively monitor the adverse consequences of wildfires. The objective of this investigation is to indicate the effectiveness and usefulness of a deep learning (DL) architecture, such as Convolutional Neural Networks (CNNs), in the mapping of areas affected by fire, employing an eXplainable artificial intelligence (XAI) algorithm known as SHapley Additive exPlanations (SHAP) with accuracy evaluati

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Yang, Jungyoon, and Prashant V. Nadkarni. "7235 Papillary Thyroid Carcinoma in a Hyperfunctioning Thyroid Nodule." Journal of the Endocrine Society 8, Supplement_1 (2024). http://dx.doi.org/10.1210/jendso/bvae163.2029.

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Abstract Disclosure: J. Yang: None. P.V. Nadkarni: None. Background: The prevalence of malignancy in a hyperfunctioning or hot thyroid nodule is considered to be low. According to the 2015 American Thyroid Association Guidelines, no cytologic evaluation is routinely recommended for hyperfunctioning nodules since they are rarely associated with malignancy (1). We present a case of a 71-year-old female with a hot nodule containing papillary thyroid cancer. Clinical Case: A 71-year-old female with Type 1 diabetes mellitus and coronary artery disease on dual antiplatelet therapy was found to have

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Honary, Ryan, Jeff Shelton, and Pirouz Kavehpour. "A Review of Technologies for the Early Detection of Wildfires." ASME Open Journal of Engineering 4 (January 1, 2025). https://doi.org/10.1115/1.4067645.

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Abstract Wildfires have become a persistent and growing global risk, causing increasing financial, human, and environmental damage. By all accounts and predictions, they will continue to rise in frequency and intensity throughout the 21st century. This paper begins by analyzing the physics of fire and outlines why detecting wildfires in their incipient stages is the most effective way to manage them. We review the various architectures and approaches adopted for wildfire detection, including spaceborne, airborne, fixed cameras, and sensor networks. The paper further analyzes the pros and cons

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Lin, Yu-Chun, Meng-Chi Chen, Cheng-Hsueh Chen, Mu-Hsiung Chen, Kang-Yi Liu, and Cheng-Chun Chang. "Fully automated film mounting in dental radiography: a deep learning model." BMC Medical Imaging 23, no. 1 (2023). http://dx.doi.org/10.1186/s12880-023-01064-9.

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Abstract Background Dental film mounting is an essential but time-consuming task in dental radiography, with manual methods often prone to errors. This study aims to develop a deep learning (DL) model for accurate automated classification and mounting of both intraoral and extraoral dental radiography. Method The present study employed a total of 22,334 intraoral images and 1,035 extraoral images to train the model. The performance of the model was tested on an independent internal dataset and two external datasets from different institutes. Images were categorized into 32 tooth areas. The VGG

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Oliveira Cruz, Thais Nascimento de Andrade, Juneo Freitas Silva, Fabiana Lessa Silva, and Renata Santiago Alberto Carlos. "Renal Dysplasia in a Maltese dog." Acta Scientiae Veterinariae 47 (June 23, 2019). http://dx.doi.org/10.22456/1679-9216.93264.

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Background: Renal dysplasia (RD) is a common cause of renal failure in young dogs. It is defined as a disorganization in renal parenchymal development, with abnormal differentiation. In all domestic animal species, RD may be hereditary or acquired. The affected animals show clinical signs of early chronic kidney disease, usually between 3 months to 3 years of age. The alterations include persistent metanephric ducts surrounded by primitive mesenchyme, glomeruli and fetal tubules, and abnormal interstitial fibrous tissue. We aimed to report the case of a 1-year-old canine with renal dysplasia.C

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Abdelhafiz, Dina, Jinbo Bi, Reda Ammar, Clifford Yang, and Sheida Nabavi. "Convolutional neural network for automated mass segmentation in mammography." BMC Bioinformatics 21, S1 (2020). http://dx.doi.org/10.1186/s12859-020-3521-y.

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Abstract Background Automatic segmentation and localization of lesions in mammogram (MG) images are challenging even with employing advanced methods such as deep learning (DL) methods. We developed a new model based on the architecture of the semantic segmentation U-Net model to precisely segment mass lesions in MG images. The proposed end-to-end convolutional neural network (CNN) based model extracts contextual information by combining low-level and high-level features. We trained the proposed model using huge publicly available databases, (CBIS-DDSM, BCDR-01, and INbreast), and a private dat

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Ullah, Naeem, Javed Ali Khan, Sultan Almakdi, et al. "An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model." Frontiers in Plant Science 14 (October 11, 2023). http://dx.doi.org/10.3389/fpls.2023.1212747.

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IntroductionRecently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant

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Morley, Sarah. "The Garden Palace: Building an Early Sydney Icon." M/C Journal 20, no. 2 (2017). http://dx.doi.org/10.5204/mcj.1223.

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IntroductionSydney’s Garden Palace was a magnificent building with a grandeur that dominated the skyline, stretching from the site of the current State Library of New South Wales to the building that now houses the Sydney Conservatorium of Music. The Palace captivated society from its opening in 1879. This article outlines the building of one of Sydney’s early structural icons and how, despite being destroyed by fire after three short years in 1882, it had an enormous impact on the burgeoning colonial community of New South Wales, thus building a physical structure, pride and a suite of memori

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