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Littérature scientifique sur le sujet « Deep Learning in CI/CD »

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/CD"

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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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Majtner, Tomáš, Jacob Broder Brodersen, Jürgen Herp, Jens Kjeldsen, Morten Lee Halling та Michael Dam Jensen. "A deep learning framework for autonomous detection and classification of Crohnʼs disease lesions in the small bowel and colon with capsule endoscopy". Endoscopy International Open 09, № 09 (2021): E1361—E1370. http://dx.doi.org/10.1055/a-1507-4980.

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Abstract Background and study aims Small bowel ulcerations are efficiently detected with deep learning techniques, whereas the ability to diagnose Crohnʼs disease (CD) in the colon with it is unknown. This study examined the ability of a deep learning framework to detect CD lesions with pan-enteric capsule endoscopy (CE) and classify lesions of different severity. Patients and methods CEs from patients with suspected or known CD were included in the analysis. Two experienced gastroenterologists classified anonymized images into normal mucosa, non-ulcerated inflammation, aphthous ulceration, ul

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Venkata Krishna Koganti. "Autonomous CI/CD Meshes: Self-healing deployment architectures with AI-ML Orchestration." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2731–45. https://doi.org/10.30574/wjaets.2025.15.2.0777.

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This article introduces a novel architecture for autonomous continuous integration and continuous deployment (CI/CD) systems capable of self-healing and self-optimization without human intervention. The article presents intelligent deployment meshes that integrate deep anomaly detection using LSTM networks with Bayesian change-point detection to identify deployment anomalies before they impact production environments. The proposed framework leverages causal CI/CD graphs to model complex interdependencies between microservices, enabling context-aware remediation strategies including automated r

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Gunda Brahma Sagara. "Hybrid Deep Learning Framework for Real-Time Source Code Vulnerability Detection." Communications on Applied Nonlinear Analysis 32, no. 7s (2025): 889–900. https://doi.org/10.52783/cana.v32.3493.

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Source code vulnerabilities threaten software security, making detection essential in modern development. Traditional methods like static and dynamic analysis often fail due to high false positives and limited scalability. This work introduces a hybrid deep learning framework using CNNs, LSTMs, and code embeddings to detect vulnerabilities in real time. Incorporating Abstract Syntax Trees (ASTs) and Graph Neural Networks (GNNs), the system ensures structural representation and program semantics analysis. Integrated into CI/CD pipelines, the approach improves precision, recall, and F1-score (up

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Tsounis, E., R. Forlano, T. Voulgaris, et al. "P0040 Measurement of collagen concentration throughout the intestinal layers using Deep Learning: implications in Inflammatory Bowel Disease (IBD)." Journal of Crohn's and Colitis 19, Supplement_1 (2025): i386—i387. https://doi.org/10.1093/ecco-jcc/jjae190.0214.

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Abstract Background IBD, particularly Crohn’s disease (CD), is marked by recurrent episodes of inflammation, leading to fibrosis and collagen deposition throughout the intestinal layers.1,2 In this study, we aim to quantify collagen levels across the distinct layers of the gut and examine their correlation with clinical characteristics and disease outcomes. Methods A total of 190 IBD patients were enrolled, including 98 with Ulcerative Colitis (UC) and 92 with CD (60% male; median age: 42 years, IQR: 29–48; follow-up: 110 months, IQR: 39-181), plus 73 controls. Biopsies were collected, stained

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Li, Y., and Y. Wang. "P205 Histologic image-based ensemble model to identify myenteric plexitis and predict endoscopic postoperative recurrence in Crohn’s disease: a multicentre, retrospective study." Journal of Crohn's and Colitis 18, Supplement_1 (2024): i526. http://dx.doi.org/10.1093/ecco-jcc/jjad212.0335.

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Abstract Background Myenteric plexitis is correlated with postoperative recurrence of Crohn’s disease (CD) when relying on traditional statistical methods. However, comprehensive assessment of the myenteric plexus remains challenging. This study aimed to develop and validate a deep learning system to predict postoperative recurrence through automatic screening and identification of features of the muscular layer and myenteric plexus. Methods In this study, we retrospectively reviewed 205 CD patients who underwent bowel resection surgery from 2 hospitals. Patients were divided into a training c

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Dhista Dwi Nur Ardiansyah and Handaru Jati. "Implementasi Continuous Integration dan Continuous Delivery (CI/CD) pada Model Deep Learning dengan Google Cloud Platform Studi Kasus Pembangkit Soal Otomatis." Journal of Information Technology and Education (JITED) 3, no. 1 (2025): 101–11. https://doi.org/10.21831/jited.v3i1.1053.

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Proses deployment yang tidak efektif dapat berdampak buruk pada perilisan aplikasi kepada pengguna dan stabilitas sistem, yang pada gilirannya akan mempengaruhi pengalaman pengguna dalam menggunakan aplikasi. Tujuan penelitian ini adalah mengimplementasikan penggunaan Docker dan Kubernetes pipeline melalui pendekatan DevOps MLOps untuk meningkatkan efektivitas proses deployment, dan menganalisis kinerja penggunaan Docker dan Kubernetes sebagai alternatif environment dalam melakukan deployment web server AQG. Metode yang digunakan adalah Research and Development dengan prosedur pengembangan Dev

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Garg, Shally. "GRAPH NEURAL NETWORKS FOR DEPENDENCY MAPPING AND IMPACT ESTIMATION IN CI/CD WORKFLOWS." International Journal of Core Engineering & Management 6, no. 11 (2021): 439–49. https://doi.org/10.5281/zenodo.15552165.

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Baladari, Venkata. "AI-Powered Debugging: Exploring Machine Learning Techniques for Identifying and Resolving Software Errors." International Journal of Science and Research (IJSR) 12, no. 3 (2023): 1864–69. https://doi.org/10.21275/SR230314114650.

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Software development is being revolutionized by AI-powered debugging, which uses machine learning and deep learning methods to automate the discovery, identification, and correction of errors. Traditional debugging techniques are labour-intensive and time-consuming, whereas AI-assisted solutions can inspect extensive code archives, identify recurring patterns, and propose on-the-fly corrections, ultimately enhancing software stability and shortening the debugging process. Error detection is improved by supervised and unsupervised learning models, and code repair is automated through reinforcem

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Amisetty, Venkata Amarnath Rayudu. "Test Automation in HR Solutions: A Technical Deep Dive." European Journal of Computer Science and Information Technology 13, no. 32 (2025): 128–44. https://doi.org/10.37745/ejcsit.2013/vol13n32128144.

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Test automation has emerged as a cornerstone capability in modern human resource technology, enabling organizations to deliver reliable, efficient, and user-friendly systems across recruitment, learning, and broader HR domains. This technical deep dive examines how HR solution providers leverage frameworks like Cypress, WebDriver, and Appium alongside CI/CD pipelines to address complex testing challenges unique to HR systems. The integration of artificial intelligence enhances testing effectiveness through visual validation, smart element identification, and predictive failure analysis, while

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Plus de sources

Livres sur le sujet "Deep Learning in CI/CD"

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Learning GitHub Actions: Automation and Integration of CI/CD with GitHub. O'Reilly Media, Incorporated, 2023.

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Learning Continuous Integration with Jenkins: An End-To-end Guide to Creating Operational, Secure, Resilient, and Cost-effective CI/CD Processes. de Gruyter GmbH, Walter, 2024.

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Learning Continuous Integration with Jenkins: An End-To-end Guide to Creating Operational, Secure, Resilient, and Cost-effective CI/CD Processes. Packt Publishing, Limited, 2024.

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

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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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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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Parihar, Ashish Singh, Umesh Gupta, Utkarsh Srivastava, Vishal Yadav, and Vaibhav Kumar Trivedi. "Automated Machine Learning Deployment Using Open-Source CI/CD Tool." In Proceedings of Data Analytics and Management. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7615-5_19.

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Giorgio, Lazzarinetti, Massarenti Nicola, Sgrò Fabio, and Salafia Andrea. "Continuous Defect Prediction in CI/CD Pipelines: A Machine Learning-Based Framework." In AIxIA 2021 – Advances in Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08421-8_41.

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Stein, Peter, Jibinraj Antony, Simon Bergweiler, and Christian Schorr. "Generalized Authoring Tool for Computer Vision Machine Learning Application Deployments." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86489-6_10.

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Abstract Automated authoring enables simplified deployment of applications and services for complex use cases, especially in the field of machine learning. This paper presents the development and implementation of a specialized authoring tool that can be used for computer vision applications, enabling automated creation of machine learning services. The proposed authoring tool realizes a microservices architecture to facilitate the conversion and deployment of machine learning inference services, especially in image classification and object detection use cases. The authoring process addresses

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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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Yu, Kunfeng, Tao Xu, Bo Hou, Yuan Shen, Yan Li, and Wenshuo Li. "UJN-CD: A Large Scale Dateset for Change Detection with Comprehensive Analysis Based on Deep Learning." In Proceedings of International Conference on Image, Vision and Intelligent Systems 2022 (ICIVIS 2022). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0923-0_43.

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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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Ramadugu, Gangadhararamachary. "Leveraging AI for Continuous Integration and Delivery Enhancing Developer Productivity in Smart Education and Sustainable Learning." In Advances in Educational Technologies and Instructional Design. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-7723-9.ch017.

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Continuous Integration and Continuous Delivery (CI/CD) practices are crucial for enhancing developer productivity and ensuring seamless software development in smart education and sustainable learning environments. Leveraging Artificial Intelligence (AI) within CI/CD pipelines introduces automation, predictive analysis, and intelligent monitoring, reducing manual intervention and accelerating the development cycle. This chapter explores how AI-driven CI/CD enhances the efficiency of software delivery in education technology (EdTech) systems. It covers AI's role in automating testing, debugging

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Wang, Yingxu, Bernard Carlos Widrow, Lotfi A. Zadeh, et al. "Cognitive Intelligence." In Deep Learning and Neural Networks. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch084.

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The theme of IEEE ICCI*CC'16 on Cognitive Informatics (CI) and Cognitive Computing (CC) was on cognitive computers, big data cognition, and machine learning. CI and CC are a contemporary field not only for basic studies on the brain, computational intelligence theories, and denotational mathematics, but also for engineering applications in cognitive systems towards deep learning, deep thinking, and deep reasoning. This paper reports a set of position statements presented in the plenary panel (Part I) in IEEE ICCI*CC'16 at Stanford University. The summary is contributed by invited panelists who

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

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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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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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Seknazi, Eva, Elad Yoshai, Angela Kravstov, Rotem Mor Yosef, and Anna Levant. "Foundation deep learning model for accurate SEM image segmentation for CD-SEM measurements." In Metrology, Inspection, and Process Control XXXIX, edited by Matthew J. Sendelbach and Nivea G. Schuch. SPIE, 2025. https://doi.org/10.1117/12.3050983.

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Jadhav, Sanskruti Deepak, Ameya Salvi, Krishna Chaitanya Kosaraju, et al. "Containerization Approach for High-Fidelity Terramechanics Simulations." In WCX SAE World Congress Experience. SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0105.

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<div class="section abstract"><div class="htmlview paragraph">Integrated modeling of vehicle, tire and terrain is a fundamental challenge to be addressed for off-road autonomous navigation. The complexities arise due to lack of tools and techniques to predict the continuously varying terrain and environmental conditions and the resultant non-linearities. The solution to this challenge can now be found in the plethora of data driven modeling and control techniques that have gained traction in the last decade. Data driven modeling and control techniques rely on the system’s repeated

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Gupta, Saumya, Madhulika Bhatia, Meenakshi Memoria, and Preeti Manani. "Prevalence of GitOps, DevOps in Fast CI/CD Cycles." In 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON). IEEE, 2022. http://dx.doi.org/10.1109/com-it-con54601.2022.9850786.

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Nogueira, Ana Filipa, Jose C.B. Ribeiro, Mario A. Zenha-Rela, and Antoine Craske. "Improving La Redoute's CI/CD Pipeline and DevOps Processes by Applying Machine Learning Techniques." In 2018 11th International Conference on the Quality of Information and Communications Technology (QUATIC). IEEE, 2018. http://dx.doi.org/10.1109/quatic.2018.00050.

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Davaji, Benyamin, Peter A. Cook, Bahar Kor, et al. "Deep Learning for Predicting CD-SEMS of NEMS Devices." In 2022 IEEE 35th International Conference on Micro Electro Mechanical Systems Conference (MEMS). IEEE, 2022. http://dx.doi.org/10.1109/mems51670.2022.9699471.

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Liu, Bohan, He Zhang, Lanxin Yang, Liming Dong, Haifeng Shen, and Kaiwen Song. "An Experimental Evaluation of Imbalanced Learning and Time-Series Validation in the Context of CI/CD Prediction." In EASE '20: Evaluation and Assessment in Software Engineering. ACM, 2020. http://dx.doi.org/10.1145/3383219.3383222.

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Chielle, EO, and EL Kuiava. "METHOD OF HISTOPATHOLOGICAL DIAGNOSIS OF MAMMARY NODULES THROUGH DEEP LEARNING ALGORITHM." In Resumos do 54º Congresso Brasileiro de Patologia Clínica/Medicina Laboratorial. Zeppelini Editorial e Comunicação, 2022. http://dx.doi.org/10.5327/1516-3180.140s1.5491.

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Objective: Artificial intelligence systems are promising health care technologies, mainly in medical subareas such as pathology, and can be used as support methods for the histological pathogenesis of mammary nodules. This study describes the method and results of the development of an artificial intelligence software for the histopathological analysis of mammary nodules. Method: The software was developed by using two neural networks – inception and MobileNet. The database used for learning the conditions analyzed – histologically normal breast, fibroadenoma, fibrocystic changes, in situ duct

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Nabian, Mohsen, Zahra Eftekhari, and Chi Wah Wong. "CI-VAE: a Generative Deep Learning Model for Class-Specific Data Interpolation." In 2024 IEEE Conference on Artificial Intelligence (CAI). IEEE, 2024. http://dx.doi.org/10.1109/cai59869.2024.00067.

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