Gotowe bibliografie tematyczne / Ensemble learning model

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Gotowa bibliografia na temat „Ensemble learning model”

Autor: Grafiati
Data publikacji: 2 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Ensemble learning model"

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Wang, Wenjia, and Yi-Hui Zhou. "A Double Penalty Model for Ensemble Learning." Mathematics 10, no. 23 (2022): 4532. http://dx.doi.org/10.3390/math10234532.

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Modern statistical learning techniques often include learning ensembles, for which the combination of multiple separate prediction procedures (ensemble components) can improve prediction accuracy. Although ensemble approaches are widely used, work remains to improve our understanding of the theoretical underpinnings of aspects such as identifiability and relative convergence rates of the ensemble components. By considering ensemble learning for two learning ensemble components as a double penalty model, we provide a framework to better understand the relative convergence and identifiability of
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Deshmukh, Pratiksha, and Harshali Patil. "Depression Prediction Model based on Ensemble Learning Classifier." Indian Journal Of Science And Technology 17, no. 39 (2024): 4084–93. http://dx.doi.org/10.17485/ijst/v17i39.159.

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Objective: The main objective of this research was to develop a suitable prediction model to classify the symptoms of depression experienced by people. Methodology: This research incorporates the dataset of the “Centres for Disease Control and Prevention National Health and Nutrition Examination Survey,” which was available on GitHub. After that, pre-processing of the dataset was done using the infinite latent feature selection (ILFS) algorithm to extract the appropriate features from the dataset. After that, the dataset was split into 70:30 ratios. About 70% of the data is employed for traini
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Theresa, S. Josephine. "Weighted Model Fusion for Imbalanced Learning." Indian Journal Of Science And Technology 18, no. 23 (2025): 1818–24. https://doi.org/10.17485/ijst/v18i23.904.

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Objectives: To develop a Weighted Multi-model Ensemble (WME) to improve binary and multiclass data predictions, particularly in handling imbalanced datasets. The model aims to achieve high performance metrics, such as precision and recall, while minimizing false positive rates. Additionally, the study seeks to explore better handling mechanisms to enhance prediction accuracy further. Methods: The methodology involves a two-phase approach for the Weighted Multi-Model Ensemble (WME). The first phase includes data preprocessing, segregating training and test data, and training models like Decisio
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Patel, Mamta, and Mehul Shah. "Deep Ensemble Learning Model for Diagnosis of Lung Diseases from Chest X -Ray Images." Indian Journal Of Science And Technology 17, no. 8 (2024): 702–12. http://dx.doi.org/10.17485/ijst/v17i8.3151.

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Objectives: This study aims to develop a robust medical recognition system using deep learning for the identification of various lung diseases, including COVID-19, pneumonia, lung opacity, and normal states, from chest X-ray images. The focus is on implementing ensemble fixed features learning methods to enhance diagnostic capabilities, contributing to the development of a cost-effective and reliable diagnostic tool for combating the global epidemic of lung disorders. Methods: The study utilizes a Kaggle dataset containing COVID-19 chest radiography images. Raw X-ray images undergo preprocessi
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j, j., Jin Gwang Koh, and Sung Keun Lee. "Harvest Forecasting Improvement Using Federated Learning and Ensemble Model." Korean Institute of Smart Media 12, no. 10 (2023): 9–18. http://dx.doi.org/10.30693/smj.2023.12.10.9.

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Harvest forecasting is the great demand of multiple aspects like temperature, rain, environment, and their relations. The existing study investigates the climate conditions and aids the cultivators to know the harvest yields before planting in farms. The proposed study uses federated learning. In addition, the additional widespread techniques such as bagging classifier, extra tees classifier, linear discriminant analysis classifier, quadratic discriminant analysis classifier, stochastic gradient boosting classifier, blending models, random forest regressor, and AdaBoost are utilized together.
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Chang-You Zhang, Chang-You Zhang, Jing-Jing Wang Chang-You Zhang, Li-Xia Wan Jing-Jing Wang, and Ruo-Xue Yu Li-Xia Wan. "An Emotional Analysis Method Based on Multi Model Ensemble Learning." 電腦學刊 34, no. 1 (2023): 001–11. http://dx.doi.org/10.53106/199115992023023401001.

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<p>Traditional machine learning models generally use weak supervision model, which is difficult to adapt to the scene of multi classification for emotional text. Therefore, a multi model ensemble learning algorithm for emotional text classification is proposed. The algorithm takes the labeled emotional text data as the training sample, uses the improved TF-IDF algorithm to train the word vector space model, selects three weakly supervised machine learning algorithms, linear SVC, xgboost and logistic regression, to construct the base classifier, and uses the random forest algorithm to con
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d, d., d. d, d. d, and d. d. "Optimized Deep Learning Models Using Ensemble Learning for COVID-19 Detection on CT Scan Images." Korean Data Analysis Society 25, no. 6 (2023): 2027–39. http://dx.doi.org/10.37727/jkdas.2023.25.6.2027.

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Early identification of COVID-19 can facilitate the establishment of a swift medical response plan, thereby slowing the rapid dissemination of this deadly disease. Recent advancements in medical imaging technology, coupled with the successful application of deep learning to visual tasks, have driven numerous studies investigating early disease diagnosis through medical imaging. In particular, deep learning has been employed for COVID-19 diagnosis from CT scan images. This paper proposes an ensemble COVID detection model that integrates four models including GoogleNet, EfficientNet, Hybrid Effi
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Siswoyo, Bambang, Zuraida Abal Abas, Ahmad Naim Che Pee, Rita Komalasari, and Nano Suryana. "Ensemble machine learning algorithm optimization of bankruptcy prediction of bank." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 679. http://dx.doi.org/10.11591/ijai.v11.i2.pp679-686.

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The ensemble consists of a single set of individually trained models, the predictions of which are combined when classifying new cases, in building a good classification model requires the diversity of a single model. The algorithm, logistic regression, support vector machine, random forest, and neural network are single models as alternative sources of diversity information. Previous research has shown that ensembles are more accurate than single models. Single model and modified ensemble bagging model are some of the techniques we will study in this paper. We experimented with the banking in
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Bambang, Siswoyo, Abal Abas Zuraida, Naim Che Pee Ahmad, Komalasari Rita, and Suyatna Nano. "Ensemble machine learning algorithm optimization of bankruptcy prediction of bank." International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 679–86. https://doi.org/10.11591/ijai.v11.i2.pp679-686.

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The ensemble consists of a single set of individually trained models, the predictions of which are combined when classifying new cases, in building a good classification model requires the diversity of a single model. The algorithm, logistic regression, support vector machine, random forest, and neural network are single models as alternative sources of diversity information. Previous research has shown that ensembles are more accurate than single models. Single model and modified ensemble bagging model are some of the techniques we will study in this paper. We experimented with the banking in
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Pahno, Steve, Jidong J. Yang, and S. Sonny Kim. "Use of Machine Learning Algorithms to Predict Subgrade Resilient Modulus." Infrastructures 6, no. 6 (2021): 78. http://dx.doi.org/10.3390/infrastructures6060078.

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Modern machine learning methods, such as tree ensembles, have recently become extremely popular due to their versatility and scalability in handling heterogeneous data and have been successfully applied across a wide range of domains. In this study, two widely applied tree ensemble methods, i.e., random forest (parallel ensemble) and gradient boosting (sequential ensemble), were investigated to predict resilient modulus, using routinely collected soil properties. Laboratory test data on sandy soils from nine borrow pits in Georgia were used for model training and testing. For comparison purpos
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Rozprawy doktorskie na temat "Ensemble learning model"

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Ngo, Khai Thoi. "Stacking Ensemble for auto_ml." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83547.

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Machine learning has been a subject undergoing intense study across many different industries and academic research areas. Companies and researchers have taken full advantages of various machine learning approaches to solve their problems; however, vast understanding and study of the field is required for developers to fully harvest the potential of different machine learning models and to achieve efficient results. Therefore, this thesis begins by comparing auto ml with other hyper-parameter optimization techniques. auto ml is a fully autonomous framework that lessens the knowledge prerequisi
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Kim, Jinhan. "J-model : an open and social ensemble learning architecture for classification." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/7672.

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Ensemble learning is a promising direction of research in machine learning, in which an ensemble classifier gives better predictive and more robust performance for classification problems by combining other learners. Meanwhile agent-based systems provide frameworks to share knowledge from multiple agents in an open context. This thesis combines multi-agent knowledge sharing with ensemble methods to produce a new style of learning system for open environments. We now are surrounded by many smart objects such as wireless sensors, ambient communication devices, mobile medical devices and even inf
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Iyer, Vasanth. "Ensemble Stream Model for Data-Cleaning in Sensor Networks." FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/973.

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Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the
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Ali, Rozniza. "Ensemble classification and signal image processing for genus Gyrodactylus (Monogenea)." Thesis, University of Stirling, 2014. http://hdl.handle.net/1893/21734.

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This thesis presents an investigation into Gyrodactylus species recognition, making use of machine learning classification and feature selection techniques, and explores image feature extraction to demonstrate proof of concept for an envisaged rapid, consistent and secure initial identification of pathogens by field workers and non-expert users. The design of the proposed cognitively inspired framework is able to provide confident discrimination recognition from its non-pathogenic congeners, which is sought in order to assist diagnostics during periods of a suspected outbreak. Accurate identif
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Darwiche, Aiman A. "Machine Learning Methods for Septic Shock Prediction." Diss., NSUWorks, 2018. https://nsuworks.nova.edu/gscis_etd/1051.

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Sepsis is an organ dysfunction life-threatening disease that is caused by a dysregulated body response to infection. Sepsis is difficult to detect at an early stage, and when not detected early, is difficult to treat and results in high mortality rates. Developing improved methods for identifying patients in high risk of suffering septic shock has been the focus of much research in recent years. Building on this body of literature, this dissertation develops an improved method for septic shock prediction. Using the data from the MMIC-III database, an ensemble classifier is trained to identify
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Li, Jianeng. "Research on a Heart Disease Prediction Model Based on the Stacking Principle." Thesis, Högskolan Dalarna, Informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:du-34591.

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In this study, the prediction model based on the Stacking principle is called the Stacking fusion model. Little evidence demonstrates that the Stacking fusion model possesses better prediction performance in the field of heart disease diagnosis than other classification models. Since this model belongs to the family of ensemble learning models, which has a bad interpretability, it should be used with caution in medical diagnoses. The purpose of this study is to verify whether the Stacking fusion model has better prediction performance than stand-alone machine learning models and other ensemble
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Boulegane, Dihia. "Machine learning algorithms for dynamic Internet of Things." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT048.

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La croissance rapide de l’Internet des Objets (IdO) ainsi que la prolifération des capteurs ont donné lieu à diverses sources de données qui génèrent continuellement de grandes quantités de données et à une grande vitesse sous la forme de flux. Ces flux sont essentiels dans le processus de prise de décision dans différents secteurs d’activité et ce grâce aux techniques d’intelligence artificielle et d’apprentissage automatique afin d’extraire des connaissances précieuses et les transformer en actions pertinentes. Par ailleurs, les données sont souvent associées à un indicateur temporel, appelé
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Henriksson, Aron. "Ensembles of Semantic Spaces : On Combining Models of Distributional Semantics with Applications in Healthcare." Doctoral thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-122465.

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Distributional semantics allows models of linguistic meaning to be derived from observations of language use in large amounts of text. By modeling the meaning of words in semantic (vector) space on the basis of co-occurrence information, distributional semantics permits a quantitative interpretation of (relative) word meaning in an unsupervised setting, i.e., human annotations are not required. The ability to obtain inexpensive word representations in this manner helps to alleviate the bottleneck of fully supervised approaches to natural language processing, especially since models of distribu
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Whiting, Jeffrey S. "Cognitive and Behavioral Model Ensembles for Autonomous Virtual Characters." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1873.pdf.

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He, Wenbin. "Exploration and Analysis of Ensemble Datasets with Statistical and Deep Learning Models." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574695259847734.

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Książki na temat "Ensemble learning model"

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Kyriakides, George, and Konstantinos G. Margaritis. Hands-On Ensemble Learning with Python: Build Highly Optimized Ensemble Machine Learning Models Using Scikit-Learn and Keras. Packt Publishing, Limited, 2019.

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Head, Paul D. The Choral Experience. Edited by Frank Abrahams and Paul D. Head. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199373369.013.3.

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Much has changed in the choral rehearsal room over the past two generations, particularly in regard to the role the choral conductor assumes—or commands—in the rehearsal process. This chapter discusses the ever-evolving stereotypical roles of the conductor, while examining alternatives to traditional leadership models with particular emphasis on the encouragement of student engagement and peer-based learning. In addition to the facilitation of collaborative learning exercises, the chapter outlines a specific process of written interaction with the choral ensemble. This section is inspired by t
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Benatan, Matt, Jochem Gietema, and Marian Schneider. Bayesian Deep Learning: Work with Bayesian Neural Networks BNN and BDL to Employ an Ensemble of Deep Learning Models. Packt Publishing, Limited, 2023.

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Summerson, Samantha R., and Caleb Kemere. Multi-electrode Recording of Neural Activity in Awake Behaving Animals. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199939800.003.0004.

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Systems neuroscience is being revolutionized by the ability to record the activity of large numbers of neurons simultaneously. Chronic recording with multi- electrode arrays in animal models is a critical tool for studies of learning and memory, sensory processing, motor control, emotion, and decision-making. The experimental process for gathering large amounts of neural ensemble data can be very time consuming, however, the resulting data can be incredibly rich. We present a detailed overview of the process of acquiring multichannel neural data, with a particular focus on chronic tetrode reco
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Wheelahan, Leesa. Rethinking Skills Development. Edited by John Buchanan, David Finegold, Ken Mayhew, and Chris Warhurst. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199655366.013.30.

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This article critiques models of competency-based training in vocational education and training in Anglophone countries and contrasts it to ‘kompetenz’ in Germanic countries. It identifies six key problems with Competency-Based Training (CBT): first, CBT is tied to specific ensembles of workplace roles and requirements; second, the outcomes of learning are tied to descriptions of work as it currently exists; third, CBT does not provide adequate access to underpinning knowledge; fourth, CBT is based on the simplistic and behaviourist notion that processes of learning are identical with the skil
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Rodrigues, Valerian. Ambedkar's Political Philosophy. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/9780198925422.001.0001.

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Abstract This study is organized around a set of key concepts that Ambedkar, the Indian thinker and leader of the socially marginalized, proposed to reconstruct public life, factoring in oppression and degradation. This framework conceived human beings as endowed with a distinct set of attributes entitling them to consideration as moral equals despite other differences among them. It also accorded a procedural priority to consciousness in human understanding. Ambedkar deployed this framework to contend against social institutions of caste, untouchability, and other forms of marginalities and t
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Części książek na temat "Ensemble learning model"

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Hennicker, Rolf, Alexander Knapp, and Martin Wirsing. "Epistemic Ensembles." In Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19759-8_8.

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AbstractAn ensemble consists of a set of computing entities which collaborate to reach common goals. We introduce epistemic ensembles that use shared knowledge for collaboration between agents. Collaboration is achieved by different kinds of knowledge announcements. For specifying epistemic ensemble behaviours we use formulas of dynamic logic with compound ensemble actions. Our semantics relies on an epistemic notion of ensemble transition systems as behavioural models. These transition systems describe control flow over epistemic states for expressing knowledge-based collaboration of agents.
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Juniper, Matthew P. "Machine Learning for Thermoacoustics." In Lecture Notes in Energy. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16248-0_11.

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AbstractThis chapter demonstrates three promising ways to combine machine learning with physics-based modelling in order to model, forecast, and avoid thermoacoustic instability. The first method assimilates experimental data into candidate physics-based models and is demonstrated on a Rijke tube. This uses Bayesian inference to select the most likely model. This turns qualitatively-accurate models into quantitatively-accurate models that can extrapolate, which can be combined powerfully with automated design. The second method assimilates experimental data into level set numerical simulations
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Patidar, Sanjay, Madhvan Sharma, and Himesh Mahabi. "Software Change Prediction Model Using Ensemble Learning." In Proceedings of Data Analytics and Management. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6550-2_2.

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Abdennour, Ghada Ben, Karim Gasmi, and Ridha Ejbali. "Ensemble Learning Model for Medical Text Classification." In Web Information Systems Engineering – WISE 2023. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7254-8_1.

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Singh, Sapna, and Sonali Gupta. "Prediction of Diabetes Using Ensemble Learning Model." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9516-5_4.

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Wakade, Aman, Amay Wakde, Roshni Maywade, Atharv Pandey, Jatinder Kumar, and Ashutosh Kumar Singh. "An Ensemble Learning Based Career Prediction Model." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60935-0_45.

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Bikku, Thulasi, K. P. N. V. Satyasree, T. Penchalaiah, and Jarugula Jyothi. "Breast Cancer Detection Using Ensemble Learning Model." In Advanced Technologies and Societal Change. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-99-2832-3_92.

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Li, Weishan. "Developed Ensemble Model Based on Multiple Machine Learning Models." In Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023). Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-300-9_69.

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El-rashidy, Nora, Amir El-Ghamry, and Nesma E. ElSayed. "Machine Learning for Blood Donors Classification Model Using Ensemble Learning." In Green Sustainability: Towards Innovative Digital Transformation. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4764-5_11.

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Chakraborty, Tanushree, Arya Bose, Akash Samanta, Aditya Ghosh, Archisman Samanta, and Kartick Chandra Mondal. "Ensemble Machine Learning Model for Better Crop Production." In Innovations in Sustainable Technologies and Computing. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3485-6_10.

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Streszczenia konferencji na temat "Ensemble learning model"

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Zhang, Tong, and Weiqiang Wu. "Incremental Learning Model Based on Ensemble Learning." In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2024. https://doi.org/10.1109/icicml63543.2024.10958030.

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Boughareb, Djalila, Said Bouteldja, Hazem Bensalah, Rima Boughareb, and Hamid Seridi. "Optimizing Diabetes Prediction Using Hybrid Ensemble Learning Model." In 2025 4th International Conference on Computing and Information Technology (ICCIT). IEEE, 2025. https://doi.org/10.1109/iccit63348.2025.10989474.

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Nagasoudhamani, N., Addala Revathi, Dadinaboina A. K. Rao, Gudapati Dianakamal, Tammineni Rama Tulasi, and S. Rajasekhar Reddy. "Machine Learning Ensemble Model for Heart Disease Prediction." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10985246.

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Wu, Meihan, Li Li, Tao Chang, et al. "FedEKT: Ensemble Knowledge Transfer for Model-Heterogeneous Federated Learning." In 2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS). IEEE, 2024. http://dx.doi.org/10.1109/iwqos61813.2024.10682872.

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Anand, Yash Sharma, Vansh Jain, and Sandhya Tarwani. "Ensemble Machine Learning Model for Predicting Postpartum Depression Disorder." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752305.

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Kang, Yuliang, Yongke Li, and Gongxuan Zhang. "Forecasting Cloud Workload through Reinforcement Learning-Based Ensemble Model." In 2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC). IEEE, 2024. https://doi.org/10.1109/eiecc64539.2024.10929137.

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Chisty, Tanjir Alam, and Md Mahbubur Rahman Rahman. "Ransomware Detection Utilizing Ensemble Based Interpretable Deep Learning Model." In 2024 IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON). IEEE, 2024. https://doi.org/10.1109/peeiacon63629.2024.10800005.

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Ali, Md Mamun, Kawsar Ahmed, Francis M. Bui, and Fang-Xiang Wu. "DrugEL: Ensemble Learning Model for Identification of Druggable Proteins." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821823.

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Akber, Syed, Sadia Kazmi, Ali Muqtadir, and Syed Akber. "ForecastBoost: An Ensemble Learning Model for Road Traffic Forecasting." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013155100003890.

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Zhao, Kui, ZhiJia Wang, and ShuBo Guo. "Application of Ensemble Learning Model in Predicting Gestational diabetes." In 2024 13th International Conference of Information and Communication Technology (ICTech). IEEE, 2024. https://doi.org/10.1109/ictech63197.2024.00021.

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Raporty organizacyjne na temat "Ensemble learning model"

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de Luis, Mercedes, Emilio Rodríguez, and Diego Torres. Machine learning applied to active fixed-income portfolio management: a Lasso logit approach. Banco de España, 2023. http://dx.doi.org/10.53479/33560.

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The use of quantitative methods constitutes a standard component of the institutional investors’ portfolio management toolkit. In the last decade, several empirical studies have employed probabilistic or classification models to predict stock market excess returns, model bond ratings and default probabilities, as well as to forecast yield curves. To the authors’ knowledge, little research exists into their application to active fixed-income management. This paper contributes to filling this gap by comparing a machine learning algorithm, the Lasso logit regression, with a passive (buy-and-hold)
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Pedersen, Gjertrud. Symphonies Reframed. Norges Musikkhøgskole, 2018. http://dx.doi.org/10.22501/nmh-ar.481294.

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Symphonies Reframed recreates symphonies as chamber music. The project aims to capture the features that are unique for chamber music, at the juncture between the “soloistic small” and the “orchestral large”. A new ensemble model, the “triharmonic ensemble” with 7-9 musicians, has been created to serve this purpose. By choosing this size range, we are looking to facilitate group interplay without the need of a conductor. We also want to facilitate a richness of sound colours by involving piano, strings and winds. The exact combination of instruments is chosen in accordance with the features of
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Zhang, Caiyun, David Brodylo, Mizanur Rahman, Md Atiqur Rahman, Thomas Douglas, and Xavier Comas. Using an object-based machine learning ensemble approach to upscale evapotranspiration measured from eddy covariance towers in a subtropical wetland. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48056.

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Accurate prediction of evapotranspiration (ET) in wetlands is critical for understanding the coupling effects of water, carbon, and energy cycles in terrestrial ecosystems. Multiple years of eddy covariance (EC) tower ET measurements at five representative wetland ecosystems in the subtropical Big Cypress National Preserve (BCNP), Florida (USA) provide a unique opportunity to assess the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) ET operational product MOD16A2 and upscale tower measured ET to generate local/regional wetland ET maps. We developed an object-based mac
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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 sam
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Lasko, Kristofer, and Elena Sava. Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42402.

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Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environmental mapping applications. The currently available products are produced only for a single season or a specific year. Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period. In order to enable on-demand generation of timely and accurate land cover type products, we developed a sensor-agnostic framework leveraging pre-trained machine learning models. We also generated land cover models for Sentinel-2 (
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Douglas, Thomas, and Caiyun Zhang. Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41222.

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The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. V
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Pettit, Chris, and D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41034.

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We describe what we believe is the first effort to develop a physics-informed neural network (PINN) to predict sound propagation through the atmospheric boundary layer. PINN is a recent innovation in the application of deep learning to simulate physics. The motivation is to combine the strengths of data-driven models and physics models, thereby producing a regularized surrogate model using less data than a purely data-driven model. In a PINN, the data-driven loss function is augmented with penalty terms for deviations from the underlying physics, e.g., a governing equation or a boundary condit
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Perdigão, Rui A. P., and Julia Hall. Empowering Next-Generation Synergies among Models and Data with Information Physical Quantum Technological Intelligence. Synergistic Manifolds, 2024. https://doi.org/10.46337/241209.

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We present and deploy our methodological and technological framework of Information Physical Quantum Technological Intelligence (IPQuTI), to empower next-generation mathematically robust, physically consistent, computationally efficient and operationally scalable synergies among models and data across multisectoral theoretical and applied workflows. Going beyond digital computing platforms, IPQuTI encompasses a richer basis alphabet of fundamental quantum states (information building blocks) and a high-order set of superposition and entanglement functionals (grammar) beyond the state of the ar
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Maher, Nicola, Pedro DiNezio, Antonietta Capotondi, and Jennifer Kay. Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769719.

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