Relevant bibliographies by topics / DEEP LEARNING MODEL

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'DEEP LEARNING MODEL'

Author: Grafiati
Published: 11 September 2023
Last updated: 25 July 2025

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Journal articles on the topic "DEEP LEARNING MODEL"

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Wang, Yipu, and Stuart Perrin. "Deep Chinese Teaching and Learning Model Based on Deep Learning." International Journal of Languages, Literature and Linguistics 10, no. 1 (2024): 32–35. http://dx.doi.org/10.18178/ijlll.2024.10.1.479.

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Deep learning is a more situational and reflective way of learning that integrates complex knowledge and skills into intuitive thinking. As a language that closely combines sound, form and meaning, Chinese teaching and learning from the perspective of deep learning can help break through the limitations of the current teaching model that only focuses on certain language knowledge or cultural behaviors. This paper combines deep learning with international Chinese education, creates deep Chinese teaching and learning model including “four stages and ten steps”, and carries out practical applicat
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Bhat Kannagi Rajkhowa, Puran. "Deep Learning Model to Revive Indian Manuscripts." International Journal of Science and Research (IJSR) 12, no. 4 (2023): 1365–68. http://dx.doi.org/10.21275/sr23422084622.

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Karthick Chaganty, Siva. "Database Failure Prediction Based on Deep Learning Model." International Journal of Science and Research (IJSR) 10, no. 4 (2021): 83–86. https://doi.org/10.21275/sr21329110526.

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Wang, Yating, Siu Wun Cheung, Eric T. Chung, Yalchin Efendiev, and Min Wang. "Deep multiscale model learning." Journal of Computational Physics 406 (April 2020): 109071. http://dx.doi.org/10.1016/j.jcp.2019.109071.

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Xu, Zongben, and Jian Sun. "Model-driven deep-learning." National Science Review 5, no. 1 (2017): 22–24. http://dx.doi.org/10.1093/nsr/nwx099.

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Shlezinger, Nir, and Yonina C. Eldar. "Model-Based Deep Learning." Foundations and Trends® in Signal Processing 17, no. 4 (2023): 291–416. http://dx.doi.org/10.1561/2000000113.

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Bakhtiari, Shahab. "Can Deep Learning Model Perceptual Learning?" Journal of Neuroscience 39, no. 2 (2019): 194–96. http://dx.doi.org/10.1523/jneurosci.2209-18.2018.

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Torkunova, J. V., V. Y. Ilichev, V. Drach, F. L. Chubarov, and A. N. Paсukevich. "UTILIZING DEEP LEARNING TECHNOLOGIES TO FORM PRICING MODELS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 6/3, no. 147 (2024): 262–69. http://dx.doi.org/10.36871/ek.up.p.r.2024.06.03.032.

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The work is devoted to evaluating pricing models for calculating the profitability of individual financial instruments, for example, such as stocks, using a multilayer generative-adversarial artificial neural network (GAN) and developing its own model based on the analysis. A huge amount of specially selected data is supplied to the inputs of the neural network, changing over time (dynamic). To improve the objectivity of the model, this work does not implement the arbitration capabilities of the markets. This is how one can analyze and explain variations and errors in pricing, as well as ident
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Parvez, Shaik. "Deep Learning Model for Image Classification Using Convolutional Neural Network." International Journal of Science and Research (IJSR) 11, no. 8 (2022): 132–37. http://dx.doi.org/10.21275/sr22731164616.

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Taraban, Roman, and Philip H. Marshall. "Deep Learning and Competition in Psycholinguistic Research." East European Journal of Psycholinguistics (2017) 4, no. 2 (2017): 67–74. https://doi.org/10.5281/zenodo.1147694.

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<strong>Abstract.</strong>&nbsp;MacWhinney, Bates, and colleagues developed the Competition Model in the 1980s as an alternate to Chomskyan models that encapsulate syntax as a special-purpose module. The Competition Model adopted the functional perspective that language serves communicative goals and functions. In contrast to the premise that knowledge of language is innate, the Competition model asserts that language is learned and processed through general cognitive mechanisms that identify and weight phonological, morphological, syntactic, and semantic cues in the language experiences of th
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Dissertations / Theses on the topic "DEEP LEARNING MODEL"

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Meng, Zhaoxin. "A deep learning model for scene recognition." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36491.

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Scene recognition is a hot research topic in the field of image recognition. It is necessary that we focus on the research on scene recognition, because it is helpful to the scene understanding topic, and can provide important contextual information for object recognition. The traditional approaches for scene recognition still have a lot of shortcomings. In these years, the deep learning method, which uses convolutional neural network, has got state-of-the-art results in this area. This thesis constructs a model based on multi-layer feature extraction of CNN and transfer learning for scene rec
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Zeledon, Lostalo Emilia Maria. "FMRI IMAGE REGISTRATION USING DEEP LEARNING." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/theses/2641.

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fMRI imaging is considered key on the understanding of the brain and the mind, for this reason has been the subject of tremendous research connecting different disciplines. The intrinsic complexity of this 4-D type of data processing and analysis has been approached with every single computational perspective, lately increasing the trend to include artificial intelligence. One step critical on the fMRI pipeline is image registration. A model of Deep Networks based on Fully Convolutional Neural Networks, spatial transformation neural networks with a self-learning strategy was proposed for the i
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Giovanelli, Francesco. "Model Agnostic solution of CSPs with Deep Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18633/.

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Negli ultimi anni, le tecniche di Deep Learning sono state notevolmente migliorate, permettendo di affrontare con successo numerosi problemi. Il Deep Learning ha un approccio sub-simbolico ai problemi, perciò non si rende necessario descrivere esplicitamente informazioni sulla struttura del problema per fare sì che questo possa essere affrontato con successo; l'idea è quindi di utilizzare reti neurali di Deep Learning per affrontare problemi con vincoli (CSPs), senza dover fare affidamento su conoscenza esplicita riguardo ai vincoli dei problemi. Chiamiamo questo approccio Model Agnostic; esso
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Matsoukas, Christos. "Model Distillation for Deep-Learning-Based Gaze Estimation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261412.

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With the recent advances in deep learning, the gaze estimation models reached new levels, in terms of predictive accuracy, that could not be achieved with older techniques. Nevertheless, deep learning consists of computationally and memory expensive algorithms that do not allow their integration for embedded systems. This work aims to tackle this problem by boosting the predictive power of small networks using a model compression method called "distillation". Under the concept of distillation, we introduce an additional term to the compressed model’s total loss which is a bounding term between
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Lim, Steven. "Recommending TEE-based Functions Using a Deep Learning Model." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104999.

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Trusted execution environments (TEEs) are an emerging technology that provides a protected hardware environment for processing and storing sensitive information. By using TEEs, developers can bolster the security of software systems. However, incorporating TEE into existing software systems can be a costly and labor-intensive endeavor. Software maintenance—changing software after its initial release—is known to contribute the majority of the cost in the software development lifecycle. The first step of making use of a TEE requires that developers accurately identify which pieces of code would
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Del, Vecchio Matteo. "Improving Deep Question Answering: The ALBERT Model." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20414/.

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Natural Language Processing is a field of Artificial Intelligence referring to the ability of computers to understand human speech and language, often in a written form, mainly by using Machine Learning and Deep Learning methods to extract patterns. Languages are challenging by definition, because of their differences, their abstractions and their ambiguities; consequently, their processing is often very demanding, in terms of modelling the problem and resources. Retrieving all sentences in a given text is something that can be easily accomplished with just few lines of code, but what about c
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Wu, Xinheng. "A Deep Unsupervised Anomaly Detection Model for Automated Tumor Segmentation." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22502.

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Many researches have been investigated to provide the computer aided diagnosis (CAD) automated tumor segmentation in various medical images, e.g., magnetic resonance (MR), computed tomography (CT) and positron-emission tomography (PET). The recent advances in automated tumor segmentation have been achieved by supervised deep learning (DL) methods trained on large labelled data to cover tumor variations. However, there is a scarcity in such training data due to the cost of labeling process. Thus, with insufficient training data, supervised DL methods have difficulty in generating effective feat
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Kayesh, Humayun. "Deep Learning for Causal Discovery in Texts." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/415822.

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Causality detection in text data is a challenging natural language processing task. This is a trivial task for human beings as they acquire vast background knowledge throughout their lifetime. For example, a human knows from their experience that heavy rain may cause flood or plane accidents may cause death. However, it is challenging to automatically detect such causal relationships in texts due to the availability of limited contextual information and the unstructured nature of texts. The task is even more challenging for social media short texts such as Tweets as often they are informal, sh
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Зайяд, Абдаллах Мухаммед. "Ecrypted Network Classification With Deep Learning." Master's thesis, КПІ ім. Ігоря Сікорського, 2020. https://ela.kpi.ua/handle/123456789/34069.

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Дисертація складається з 84 сторінок, 59 Цифри та 29 джерел у довідковому списку. Проблема: Оскільки світ стає більш безпечним, для забезпечення належної передачі даних між сторонами, що спілкуються, було використано більше протоколів шифрування. Класифікація мережі стала більше клопоту з використанням деяких прийомів, оскільки перевірка зашифрованого трафіку в деяких країнах може бути незаконною. Це заважає інженерам мережі мати можливість класифікувати трафік, щоб відрізняти зашифрований від незашифрованого трафіку. Мета роботи: Ця стаття спрямована на проблему, спричинену попередні
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Zhao, Yajing. "Chaotic Model Prediction with Machine Learning." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8419.

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Chaos theory is a branch of modern mathematics concerning the non-linear dynamic systems that are highly sensitive to their initial states. It has extensive real-world applications, such as weather forecasting and stock market prediction. The Lorenz system, defined by three ordinary differential equations (ODEs), is one of the simplest and most popular chaotic models. Historically research has focused on understanding the Lorenz system's mathematical characteristics and dynamical evolution including the inherent chaotic features it possesses. In this thesis, we take a data-driven approach and
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Books on the topic "DEEP LEARNING MODEL"

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Cianci, Davio. A Deep-Learning-Based Muon Neutrino CCQE Selection for Searches Beyond the Standard Model with MicroBooNE. [publisher not identified], 2021.

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Gamba, Jonah. Deep Learning Models. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9672-8.

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Poonkuntran, S., Balamurugan Balusamy, and Rajesh Kumar Dhanraj. Object Detection with Deep Learning Models. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003206736.

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Bisong, Ekaba. Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8.

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Paper, David. State-of-the-Art Deep Learning Models in TensorFlow. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7341-8.

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Dulhare, Uma N., and Essam Halim Houssein, eds. Deep Learning and Computer Vision: Models and Biomedical Applications. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1285-7.

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Kumar, Ajay, Deepak Dembla, Seema Tinker, and Surbhi Bhatia Khan. Handbook of Deep Learning Models for Healthcare Data Processing. CRC Press, 2025. https://doi.org/10.1201/9781003467281.

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Dulhare, Uma N., and Essam Halim Houssein, eds. Deep Learning and Computer Vision: Models and Biomedical Applications. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3648-8.

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Shlezinger, Nir. Model-Based Deep Learning. Now Publishers, 2023.

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El-Amir, Hisham, and Mahmoud Hamdy. Deep Learning Pipeline: Building a Deep Learning Model with TensorFlow. Apress, 2019.

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Book chapters on the topic "DEEP LEARNING MODEL"

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Rodriguez, Andres. "Training a Model." In Deep Learning Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-01769-8_4.

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Kumar, R. Santhosh, and M. Kalaiselvi Geetha. "Deep Learning Model." In Data Science. CRC Press, 2019. http://dx.doi.org/10.1201/9780429263798-14.

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Rodriguez, Andres. "Reducing the Model Size." In Deep Learning Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-01769-8_6.

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Singaram, Jayakumar, S. S. Iyengar, and Azad M. Madni. "Model of Deep Learning Networks." In Deep Learning Networks. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39244-3_6.

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Ren, Jianfeng, and Dong Xia. "Deep Learning Model Optimization." In Autonomous driving algorithms and Its IC Design. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2897-2_8.

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Ghayoumi, Mehdi. "Finding the Best Model." In Deep Learning in Practice. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003025818-8.

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Sanghi, Nimish. "Model-Based Approaches." In Deep Reinforcement Learning with Python. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0273-7_3.

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Sanghi, Nimish. "Model-Free Approaches." In Deep Reinforcement Learning with Python. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0273-7_4.

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Sanghi, Nimish. "Model-Free Approaches." In Deep Reinforcement Learning with Python. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6809-4_4.

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Sanghi, Nimish. "Model-Based Algorithms." In Deep Reinforcement Learning with Python. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6809-4_3.

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Conference papers on the topic "DEEP LEARNING MODEL"

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Napiorkowska, Milena, Hamid Omidvar, Johannes Hansen, et al. "KombinatorNet: A Multisensor Deep Learning Model." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642509.

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Reddy, Gaddam Jaswanth, Srujan Bendili, Vinodha D, Jenefa J, Rakoth Kandan Sambandam, and Divya Vetriveeran. "Deep Learning Based Age Estimation Model." In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT). IEEE, 2024. http://dx.doi.org/10.1109/tqcebt59414.2024.10545269.

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Dixit, Anuja, Sachin Jain, Ahmad Kazmi, Aakanksha, and Km Manorama. "Gesture Based Model using Deep Learning." In 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET). IEEE, 2024. http://dx.doi.org/10.1109/acet61898.2024.10729981.

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Vimala, M., S. Ramasamy, M. Nandhini, G. Asvini, S. Durgadevi, and K. Jeyapriya. "Aquaculture Classification Using Deep Learning Model." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894620.

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Singh, Shubhanshi, and Neha Tyagi. "Deep Learning Model for Weather Prediction." In 2024 International Conference on Computing, Sciences and Communications (ICCSC). IEEE, 2024. https://doi.org/10.1109/iccsc62048.2024.10830369.

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Gong, Jianfeng, Yi Qi, Wentao Xu, et al. "Deep-learning-based demand response optimization and prediction model." In International Conference on Cloud Computing, Performance Computing, and Deep Learning, edited by Wanyang Dai and Xiangjie Kong. SPIE, 2024. http://dx.doi.org/10.1117/12.3051186.

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Garcia-Salgado, Beatriz P., Nasser Kehtarnavaz, Volodymyr I. Ponomaryov, Rogelio Reyes-Reyes, and Jose A. Almaraz-Damian. "Efficient stroke lesion segmentation in MRI using a modified deep learning model." In Real-Time Image Processing and Deep Learning 2025, edited by Nasser Kehtarnavaz and Mukul V. Shirvaikar. SPIE, 2025. https://doi.org/10.1117/12.3053832.

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Chaturvedi, Rajnish Kumar, and Nitin Arvind Shelke. "Music generation using hybrid deep neural model." In 2024 Intelligent Systems and Machine Learning Conference (ISML). IEEE, 2024. https://doi.org/10.1109/isml60050.2024.11007294.

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Halford, Gavin, Arthur C. Depoian, and Colleen P. Bailey. "A computationally efficient deep learning model for real-time image dehazing on edge devices." In Real-Time Image Processing and Deep Learning 2025, edited by Nasser Kehtarnavaz and Mukul V. Shirvaikar. SPIE, 2025. https://doi.org/10.1117/12.3053948.

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Jiang, Ruizhe, Paul Salama, and Lauren Christopher. "Towards the generative model-based synthetization and latent space level analysis of 3D MRA dataset." In Real-Time Image Processing and Deep Learning 2025, edited by Nasser Kehtarnavaz and Mukul V. Shirvaikar. SPIE, 2025. https://doi.org/10.1117/12.3052848.

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Reports on the topic "DEEP LEARNING MODEL"

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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p
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Zheng, Jian. Relational Patterns Discovery in Climate with Deep Learning Model. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2021. http://dx.doi.org/10.7546/crabs.2021.01.05.

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Huang, Lei, Meng Song, Hui Shen, et al. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48221.

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One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or nonmonotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advan
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Fernández-Villaverde, Jesús, Galo Nuño, and Jesse Perla. Taming the curse of dimensionality: quantitative economics with deep learning. Banco de España, 2024. http://dx.doi.org/10.53479/38233.

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We argue that deep learning provides a promising approach to addressing the curse of dimensionality in quantitative economics. We begin by exploring the unique challenges involved in solving dynamic equilibrium models, particularly the feedback loop between individual agents’ decisions and the aggregate consistency conditions required to achieve equilibrium. We then introduce deep neural networks and demonstrate their application by solving the stochastic neoclassical growth model. Next, we compare deep neural networks with traditional solution methods in quantitative economics. We conclude wi
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Mosalam, Khalid, Issac Pang, and Selim Gunay. Towards Deep Learning-Based Structural Response Prediction and Ground Motion Reconstruction. Pacific Earthquake Engineering Research Center, 2025. https://doi.org/10.55461/ipos1888.

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This research presents a novel methodology that uses Temporal Convolutional Networks (TCNs), a state-of-the-art deep learning architecture, for predicting the time history of structural responses to seismic events. By leveraging accelerometer data from instrumented buildings, the proposed approach complements traditional structural analysis models, offering a computationally efficient alternative to nonlinear time history analysis. The methodology is validated across a broad spectrum of structural scenarios, including buildings with pronounced higher-mode effects and those exhibiting both line
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Pasupuleti, Murali Krishna. Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv525.

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Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-
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Aihara, Shimpei, Takara Saki, Tyusei Shibata, et al. Deep Learning Model for Integrated Estimation of Wheelchair and Human Poses Using Camera Images. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317545.

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Albert, Sarah, Daniel Bowman, Douglas Seastrand, and Melissa Wright. Using Deep Learning to Develop a High Resolution Planetary Boundary Layer Model for Infrasound Propagation. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2432153.

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Panta, Manisha, Md Tamjidul Hoque, Kendall Niles, Joe Tom, Mahdi Abdelguerfi, and Maik Flanagin. Deep learning approach for accurate segmentation of sand boils in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49460.

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Sand boils can contribute to the liquefaction of a portion of the levee, leading to levee failure. Accurately detecting and segmenting sand boils is crucial for effectively monitoring and maintaining levee systems. This paper presents SandBoilNet, a fully convolutional neural network with skip connections designed for accurate pixel-level classification or semantic segmentation of sand boils from images in levee systems. In this study, we explore the use of transfer learning for fast training and detecting sand boils through semantic segmentation. By utilizing a pretrained CNN model with ResNe
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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject
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