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Littérature scientifique sur le sujet « Deep Learning in CI »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Deep Learning in CI"

Nagasawa, Toshihiko, Hitoshi Tabuchi, Hiroki Masumoto, et al. "Accuracy of deep learning, a machine learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes." PeerJ 6 (October 22, 2018): e5696. http://dx.doi.org/10.7717/peerj.5696.

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We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-field fundus images (Optos) with deep learning, which is a machine learning technology. The study included 910 Optos color images (715 normal images, 195 MH images). Of these 910 images, 637 were learning images (501 normal images, 136 MH images) and 273 were test images (214 normal images and 59 MH images). We conducted training with a deep convolutional neural network (CNN) using the images and constructed a deep-learning model. The CNN exhibited high sensitivity of 100% (95% confidence interval CI [93.5

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Marzouk, Mohamed, and Mohamed Zaher. "Artificial intelligence exploitation in facility management using deep learning." Construction Innovation 20, no. 4 (2020): 609–24. http://dx.doi.org/10.1108/ci-12-2019-0138.

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Purpose This paper aims to apply a methodology that is capable to classify and localize mechanical, electrical and plumbing (MEP) elements to assist facility managers. Furthermore, it assists in decreasing the technical complexity and sophistication of different systems to the facility management (FM) team. Design/methodology/approach This research exploits artificial intelligence (AI) in FM operations through proposing a new system that uses a deep learning pre-trained model for transfer learning. The model can identify new MEP elements through image classification with a deep convolutional n

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Lei, Ziyue, Xuewen Liao, Zhenzhen Gao, and Ang Li. "CI-NN: A Model-Driven Deep Learning-Based Constructive Interference Precoding Scheme." IEEE Communications Letters 25, no. 6 (2021): 1896–900. http://dx.doi.org/10.1109/lcomm.2021.3060065.

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DePaula Oliveira, Lia, Jiayun Lu, Eric Erak, et al. "Comparison of pathologist and deep learning–based prostate cancer grading for prediction of metastatic outcomes in primary prostate cancer." Journal of Clinical Oncology 42, no. 4_suppl (2024): 345. http://dx.doi.org/10.1200/jco.2024.42.4_suppl.345.

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345 Background: Gleason grading is the most potent prognostic variable in primary prostate cancer, however inter-observer variability remains a major issue, particularly where subspecialty-trained pathologists are not available. Artificial intelligence algorithms for prostate cancer grading may improve health care equity by ensuring widespread access to standardized, high quality grading, however most algorithms have not been tested for performance with respect to oncologic outcomes. Here, we compared deep learning-based and pathologist-based Gleason grading for prediction of metastatic outcom

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Visweswaran, Shyam, Jason B. Colditz, Patrick O’Halloran, et al. "Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study." Journal of Medical Internet Research 22, no. 8 (2020): e17478. http://dx.doi.org/10.2196/17478.

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Background Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available u

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Rezk, Eman, Mohamed Eltorki, and Wael El-Dakhakhni. "Improving Skin Color Diversity in Cancer Detection: Deep Learning Approach." JMIR Dermatology 5, no. 3 (2022): e39143. http://dx.doi.org/10.2196/39143.

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Background The lack of dark skin images in pathologic skin lesions in dermatology resources hinders the accurate diagnosis of skin lesions in people of color. Artificial intelligence applications have further disadvantaged people of color because those applications are mainly trained with light skin color images. Objective The aim of this study is to develop a deep learning approach that generates realistic images of darker skin colors to improve dermatology data diversity for various malignant and benign lesions. Methods We collected skin clinical images for common malignant and benign skin c

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R.Shankar and D. Sridhar Dr. "A Comprehensive Review on Test Case Prioritization in Continuous Integration Platforms." International Journal of Innovative Science and Research Technology 8, no. 4 (2023): 3223–29. https://doi.org/10.5281/zenodo.8282823.

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Continuous Integration (CI) platforms enable recurrent integration of software variations, creating software development rapidly and cost-effectively. In these platforms, integration, and regression testing play an essential role in Test Case Prioritization (TCP) to detect the test case order, which enhances specific objectives like early failure discovery. Currently, Artificial Intelligence (AI) models have emerged widely to solve complex software testing problems like integration and regression testing that create a huge quantity of data from iterative code commits and test executions. In CI

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Aliyev, Jamil. "A Conceptual Framework for Adaptive Ci/Cd Converyors Optimization Via Deep Reinforcement Learning." SCIENTIFIC RESEARCH 5, no. 5 (2025): 253–57. https://doi.org/10.36719/2789-6919/45/253-257.

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Xu, Lei, Junling Gao, Quan Wang, et al. "Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis." European Thyroid Journal 9, no. 4 (2019): 186–93. http://dx.doi.org/10.1159/000504390.

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Background: Computer-aided diagnosis (CAD) systems are being applied to the ultrasonographic diagnosis of malignant thyroid nodules, but it remains controversial whether the systems add any accuracy for radiologists. Objective: To determine the accuracy of CAD systems in diagnosing malignant thyroid nodules. Methods: PubMed, EMBASE, and the Cochrane Library were searched for studies on the diagnostic performance of CAD systems. The diagnostic performance was assessed by pooled sensitivity and specificity, and their accuracy was compared with that of radiologists. The present systematic review

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Amruthalingam, Ludovic, Oliver Buerzle, Philippe Gottfrois, et al. "Quantification of Efflorescences in Pustular Psoriasis Using Deep Learning." Healthcare Informatics Research 28, no. 3 (2022): 222–30. http://dx.doi.org/10.4258/hir.2022.28.3.222.

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Objectives: Pustular psoriasis (PP) is one of the most severe and chronic skin conditions. Its treatment is difficult, and measurements of its severity are highly dependent on clinicians’ experience. Pustules and brown spots are the main efflorescences of the disease and directly correlate with its activity. We propose an automated deep learning model (DLM) to quantify lesions in terms of count and surface percentage from patient photographs. Methods: In this retrospective study, two dermatologists and a student labeled 151 photographs of PP patients for pustules and brown spots. The DLM was t

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Thèses sur le sujet "Deep Learning in CI"

Dufourq, Emmanuel. "Evolutionary deep learning." Doctoral thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/30357.

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The primary objective of this thesis is to investigate whether evolutionary concepts can improve the performance, speed and convenience of algorithms in various active areas of machine learning research. Deep neural networks are exhibiting an explosion in the number of parameters that need to be trained, as well as the number of permutations of possible network architectures and hyper-parameters. There is little guidance on how to choose these and brute-force experimentation is prohibitively time consuming. We show that evolutionary algorithms can help tame this explosion of freedom, by develo

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He, Fengxiang. "Theoretical Deep Learning." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25674.

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Deep learning has long been criticised as a black-box model for lacking sound theoretical explanation. During the PhD course, I explore and establish theoretical foundations for deep learning. In this thesis, I present my contributions positioned upon existing literature: (1) analysing the generalizability of the neural networks with residual connections via complexity and capacity-based hypothesis complexity measures; (2) modeling stochastic gradient descent (SGD) by stochastic differential equations (SDEs) and their dynamics, and further characterizing the generalizability of deep learning;

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FRACCAROLI, MICHELE. "Explainable Deep Learning." Doctoral thesis, Università degli studi di Ferrara, 2023. https://hdl.handle.net/11392/2503729.

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Il grande successo che il Deep Learning ha ottenuto in ambiti strategici per la nostra società quali l'industria, la difesa, la medicina etc., ha portanto sempre più realtà a investire ed esplorare l'utilizzo di questa tecnologia. Ormai si possono trovare algoritmi di Machine Learning e Deep Learning quasi in ogni ambito della nostra vita. Dai telefoni, agli elettrodomestici intelligenti fino ai veicoli che guidiamo. Quindi si può dire che questa tecnologia pervarsiva è ormai a contatto con le nostre vite e quindi dobbiamo confrontarci con essa. Da questo nasce l’eXplainable Artificial Intelli

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Halle, Alex, and Alexander Hasse. "Topologieoptimierung mittels Deep Learning." Technische Universität Chemnitz, 2019. https://monarch.qucosa.de/id/qucosa%3A34343.

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Die Topologieoptimierung ist die Suche einer optimalen Bauteilgeometrie in Abhängigkeit des Einsatzfalls. Für komplexe Probleme kann die Topologieoptimierung aufgrund eines hohen Detailgrades viel Zeit- und Rechenkapazität erfordern. Diese Nachteile der Topologieoptimierung sollen mittels Deep Learning reduziert werden, so dass eine Topologieoptimierung dem Konstrukteur als sekundenschnelle Hilfe dient. Das Deep Learning ist die Erweiterung künstlicher neuronaler Netzwerke, mit denen Muster oder Verhaltensregeln erlernt werden können. So soll die bislang numerisch berechnete Topologieoptimieru

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Goh, Hanlin. "Learning deep visual representations." Paris 6, 2013. http://www.theses.fr/2013PA066356.

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Les avancées récentes en apprentissage profond et en traitement d'image présentent l'opportunité d'unifier ces deux champs de recherche complémentaires pour une meilleure résolution du problème de classification d'images dans des catégories sémantiques. L'apprentissage profond apporte au traitement d'image le pouvoir de représentation nécessaire à l'amélioration des performances des méthodes de classification d'images. Cette thèse propose de nouvelles méthodes d'apprentissage de représentations visuelles profondes pour la résolution de cette tache. L'apprentissage profond a été abordé sous deu

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Geirsson, Gunnlaugur. "Deep learning exotic derivatives." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-430410.

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Monte Carlo methods in derivative pricing are computationally expensive, in particular for evaluating models partial derivatives with regard to inputs. This research proposes the use of deep learning to approximate such valuation models for highly exotic derivatives, using automatic differentiation to evaluate input sensitivities. Deep learning models are trained to approximate Phoenix Autocall valuation using a proprietary model used by Svenska Handelsbanken AB. Models are trained on large datasets of low-accuracy (10^4 simulations) Monte Carlo data, successfully learning the true model with

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Wülfing, Jan [Verfasser], and Martin [Akademischer Betreuer] Riedmiller. "Stable deep reinforcement learning." Freiburg : Universität, 2019. http://d-nb.info/1204826188/34.

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White, Martin. "Deep Learning Software Repositories." W&M ScholarWorks, 2017. https://scholarworks.wm.edu/etd/1516639667.

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Bridging the abstraction gap between artifacts and concepts is the essence of software engineering (SE) research problems. SE researchers regularly use machine learning to bridge this gap, but there are three fundamental issues with traditional applications of machine learning in SE research. Traditional applications are too reliant on labeled data. They are too reliant on human intuition, and they are not capable of learning expressive yet efficient internal representations. Ultimately, SE research needs approaches that can automatically learn representations of massive, heterogeneous, datase

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Sun, Haozhe. "Modularity in deep learning." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG090.

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L'objectif de cette thèse est de rendre l'apprentissage profond plus efficace en termes de ressources en appliquant le principe de modularité. La thèse comporte plusieurs contributions principales : une étude de la littérature sur la modularité dans l'apprentissage profond; la conception d'OmniPrint et de Meta-Album, des outils qui facilitent l'étude de la modularité des données; des études de cas examinant les effets de l'apprentissage épisodique, un exemple de modularité des données; un mécanisme d'évaluation modulaire appelé LTU pour évaluer les risques en matière de protection de la vie pr

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Arnold, Ludovic. "Learning Deep Representations : Toward a better new understanding of the deep learning paradigm." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00842447.

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Since 2006, deep learning algorithms which rely on deep architectures with several layers of increasingly complex representations have been able to outperform state-of-the-art methods in several settings. Deep architectures can be very efficient in terms of the number of parameters required to represent complex operations which makes them very appealing to achieve good generalization with small amounts of data. Although training deep architectures has traditionally been considered a difficult problem, a successful approach has been to employ an unsupervised layer-wise pre-training step to init

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Livres sur le sujet "Deep Learning in CI"

Saefken, Benjamin, Alexander Silbersdorff, and Christoph Weisser, eds. Learning deep. Göttingen University Press, 2020. http://dx.doi.org/10.17875/gup2020-1338.

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Bishop, Christopher M., and Hugh Bishop. Deep Learning. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-45468-4.

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Kruse, René-Marcel, Benjamin Säfken, Alexander Silbersdorff, and Christoph Weisser, eds. Learning Deep Textwork. Göttingen University Press, 2021. http://dx.doi.org/10.17875/gup2021-1608.

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

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Fergus, Paul, and Carl Chalmers. Applied Deep Learning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04420-5.

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Calin, Ovidiu. Deep Learning Architectures. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36721-3.

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El-Amir, Hisham, and Mahmoud Hamdy. Deep Learning Pipeline. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5349-6.

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Matsushita, Kayo, ed. Deep Active Learning. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5660-4.

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Michelucci, Umberto. Applied Deep Learning. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3790-8.

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Moons, Bert, Daniel Bankman, and Marian Verhelst. Embedded Deep Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-99223-5.

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Chapitres de livres sur le sujet "Deep Learning in CI"

Sharif, Muddsair, Charitha Buddhika Heendeniya, and Gero Lückemeyer. "ARaaS: Context-Aware Optimal Charging Distribution Using Deep Reinforcement Learning." In iCity. Transformative Research for the Livable, Intelligent, and Sustainable City. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92096-8_12.

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AbstractElectromobility has profound economic and ecological impacts on human society. Much of the mobility sector’s transformation is catalyzed by digitalization, enabling many stakeholders, such as vehicle users and infrastructure owners, to interact with each other in real time. This article presents a new concept based on deep reinforcement learning to optimize agent interactions and decision-making in a smart mobility ecosystem. The algorithm performs context-aware, constrained optimization that fulfills on-demand requests from each agent. The algorithm can learn from the surrounding envi

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Khouani, Amin, and Ihsane Mekki. "A Device-Agnostic Deep Learning Approach for Predicting Ci-DME Onset Using UWF-CFP Images." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86651-7_3.

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Rudy, Kathryn M. "Chapter 3." In Touching Parchment: How Medieval Users Rubbed, Handled, and Kissed Their Manuscripts. Open Book Publishers, 2024. http://dx.doi.org/10.11647/obp.0379.03.

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Chapter 3 explores the pedagogical use of manuscripts in the Late Middle Ages, particularly how medieval individuals learned to interact with books and adopt reading behaviors. It begins by examining a miniature in a private prayer book from the1440s showing Christ as a teacher and brandishing a book. The image emphasizes how books served as educational tools linking teachers and students in a shared learning experience. This chapter shifts the discussion from production to reception, considering how medieval learners mimicked behaviors through performative demonstrations with books. The chapt

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Modi, Ritesh. "CI/CD with Terraform." In Deep-Dive Terraform on Azure. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7328-9_7.

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Brouwer, Jasperina, and Carlos A. de Matos Fernandes. "Using Stochastic Actor-Oriented Models to Explain Collaboration Intentionality as a Prerequisite for Peer Feedback and Learning in Networks." In The Power of Peer Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29411-2_5.

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AbstractPeer feedback and collaboration intentionality (CI) are key prerequisites to advance in higher education. For learning, it is crucial that peers do not merely interact, but that students are willing to function as scaffolds by sharing their knowledge from different perspectives and asking each other for academic support. Peer feedback can only take place within a collaborative learning approach and when students are willing to initiate feedback relationships with their peers. Therefore, we analyze peer feedback networks (in terms of academic help and advice-seeking) and CI as an indivi

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Kim, Kwangjo, Muhamad Erza Aminanto, and Harry Chandra Tanuwidjaja. "Deep Learning." In SpringerBriefs on Cyber Security Systems and Networks. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1444-5_4.

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Shaules, Joseph. "Deep Learning." In Language, Culture, and the Embodied Mind. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0587-4_5.

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Du, Ke-Lin, and M. N. S. Swamy. "Deep Learning." In Neural Networks and Statistical Learning. Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-7452-3_24.

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Taulli, Tom. "Deep Learning." In Artificial Intelligence Basics. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5028-0_4.

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Quinto, Butch. "Deep Learning." In Next-Generation Machine Learning with Spark. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5669-5_7.

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Actes de conférences sur le sujet "Deep Learning in CI"

Phosit, Salisa, Sawarod Kongsamlit, and Kitsuchart Pasupa. "Detecting Cyberbullying in Thai Memes: A Multimodal Approach Using Deep Learning." In 2025 IEEE Symposium on Computational Intelligence in Natural Language Processing and Social Media (CI-NLPSoMe). IEEE, 2025. https://doi.org/10.1109/ci-nlpsome64976.2025.10970667.

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Niveditha, J., S. Supreeth, and Kirankumari Patil. "Renal Cell Carcinoma Classification: Deep Learning with MLflow, DVC, and AWS CI/CD Deployment." In 2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2024. https://doi.org/10.1109/iceca63461.2024.10800992.

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Dhanumjaya, Mora Venkata, Akhilesh Kocherla, Jeripiti Rama Krishna, Mylavarapu Sethu, Tripty Singh, and Adhirath Mandal. "Comparative Analysis by Machine Learning of Waste Biodiesels in CI Engine." In 2024 IEEE Recent Advances in Intelligent Computational Systems (RAICS). IEEE, 2024. http://dx.doi.org/10.1109/raics61201.2024.10689952.

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Barba-Seara, Oscar, Carolina Cano-Cardona, Martín Molina-Álvarez, and David Díaz-Rodríguez. "Applying Machine Learning to Detect Periodicity in Transactional Banking Data." In 2025 IEEE Symposium on Computational Intelligence in Natural Language Processing and Social Media (CI-NLPSoMe Companion). IEEE, 2025. https://doi.org/10.1109/ci-nlpsomecompanion65206.2025.10977864.

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Sritharan, Braveenan, Uthayasanker Thayasivam, and Supun Jayaminda Bandara. "SUPERB-EP: Evaluating Encoder Pooling Techniques in Self-Supervised Learning Models for Speech Classification." In 2025 IEEE Symposium on Computational Intelligence in Natural Language Processing and Social Media (CI-NLPSoMe). IEEE, 2025. https://doi.org/10.1109/ci-nlpsome64976.2025.10970770.

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Alba, Charles. "ConText Mining: Complementing Topic Models with Few-Shot In-Context Learning to Generate Interpretable Topics." In 2025 IEEE Symposium on Computational Intelligence in Natural Language Processing and Social Media (CI-NLPSoMe Companion). IEEE, 2025. https://doi.org/10.1109/ci-nlpsomecompanion65206.2025.10977890.

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Vaidya, Nishtha N., Thomas A. Runkler, Thomas Hubauer, Veronika Haderlein-Hoegberg, and Maja Milicic Brandt. "Conceptual In-Context Learning and Chain of Concepts: Solving Complex Conceptual Problems Using Large Language Models." In 2025 IEEE Symposium on Computational Intelligence in Natural Language Processing and Social Media (CI-NLPSoMe). IEEE, 2025. https://doi.org/10.1109/ci-nlpsome64976.2025.10970773.

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Lee, Chang-Shing, Mei-Hui Wang, Chih-Yu Chen, et al. "Transformer-Based Semantic SBERT Robot with CI Mechanism for Students and Machine Co-Learning." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10611786.

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Yao, Jenq-Foung, Yu-Hsiang John Huang, Cheng-Ying Yang, and Min-Shiang Hwang. "Deep Learning Applications." In 2024 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE, 2024. https://doi.org/10.1109/ispacs62486.2024.10869071.

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Chen, Larry, Nihal Obeyesekere, Aline Kina, and Lisa Greaney. "Development of a Combined Corrosion and Scale Inhibitor for Subsea Multiphase Oil Field in Brazil." In CONFERENCE 2023. AMPP, 2023. https://doi.org/10.5006/c2023-18810.

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Abstract Estimated about ~3 million bbls/day, the Brazil oil field plays a significant part in the South American unconventional thriving, especially in current global oil supply constrains due to Ukraine crise. The oil field located in subsea Latin America, produces a large amount of heavy oil in the range of 16 to 24° API. The water composition is characterized with high chloride and high total dissolved solids (TDS), posing integrity and flow-assurance challenges to the operating asset. To mitigate the corrosion and scale reparations in deep-water environment through limited injection point

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Rapports d'organisations sur le sujet "Deep Learning in CI"

Catanach, Thomas, and Jed Duersch. Efficient Generalizable Deep Learning. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1760400.

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Dell, Melissa. Deep Learning for Economists. National Bureau of Economic Research, 2024. http://dx.doi.org/10.3386/w32768.

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Groh, Micah. NOvA Reconstruction using Deep Learning. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1462092.

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Geiss, Andrew, Joseph Hardin, Sam Silva, William Jr., Adam Varble, and Jiwen Fan. Deep Learning for Ensemble Forecasting. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769692.

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Harris, James, Shannon Kinkead, Dylan Fox, and Yang Ho. Continual Learning for Pattern Recognizers using Neurogenesis Deep Learning. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1855019.

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Draelos, Timothy John, Nadine E. Miner, Christopher C. Lamb, et al. Neurogenesis Deep Learning: Extending deep networks to accommodate new classes. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1505351.

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Balaji, Praveen. Detecting Stellar Streams through Deep Learning. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1637622.

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Li, Li. Deep Learning for Hydro-Biogeochemistry Processes. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769693.

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Eydenberg, Michael, Lisa Batsch-Smith, Charles Bice, et al. Resilience Enhancements through Deep Learning Yields. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1890044.

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Oskolkov, Nikolay. Deep Learning for the Life Sciences. Instats Inc., 2024. https://doi.org/10.61700/zjxxse1x3u05y1846.

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This intensive workshop provides a comprehensive exploration of deep learning applications in life sciences, focusing on practical techniques for analyzing complex biological datasets. Participants will gain theoretical and hands-on experience with deep learning tools such as TensorFlow and Keras, learning to construct neural networks and apply them to areas like genomics and personalized medicine.

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