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Articles de revues 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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1

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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Bak, M., K. Demers, O. van Ruler, et al. "P818 Imaging-based preoperative body composition is associated with the risk of postoperative complications and postoperative endoscopic recurrence in patients with Crohn’s disease." Journal of Crohn's and Colitis 18, Supplement_1 (2024): i1521—i1522. http://dx.doi.org/10.1093/ecco-jcc/jjad212.0948.

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Abstract Background Alterations in body composition are common in Crohn’s disease (CD) patients and influence disease outcomes. However, the impact of preoperative body composition on postoperative outcomes is not completely understood. Therefore, this study aimed to investigate the association of preoperative body composition with postoperative complications and endoscopic recurrence in CD patients undergoing ileocolic (re-)resection (ICR). Methods CD patients (≥16 years) scheduled for an ICR with a computed tomography (CT) scan available (&lt;12 months prior to ICR) were identified from

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Gopinath Kathiresan. "Automated Test Case Generation with AI: A Novel Framework for Improving Software Quality and Coverage." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2880–89. https://doi.org/10.30574/wjarr.2024.23.2.2463.

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Modern software testing has become imperative as testing is being automated test case generation: it makes the test efficient, accurate and completely covered. Traditionally, scalability, adaptability, and completeness are the Achilles heels of scalability of traditional testing methods as manual and scripted. In this paper, we introduce a novel AI driven framework for automated test case generation based on deep learning and reinforcement learning using evolutionary algorithm to improve test case generation process. It provides an effective test coverage by dynamically generating and prioriti

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Mohamed Abdul Kadar. "Automated code review and vulnerability detection using graph neural networks: Enhancing DevSecOps Workflows." World Journal of Advanced Engineering Technology and Sciences 5, no. 1 (2022): 113–022. https://doi.org/10.30574/wjaets.2022.5.1.0031.

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Modern software development practices increasingly emphasize security integration throughout the development lifecycle, particularly in DevSecOps workflows. This research proposes a novel approach to automated code review and vulnerability detection using Graph Neural Networks (GNNs), which represent code as structural graphs to capture semantic relationships between code elements. We developed a comprehensive framework that converts source code into graph representations, extracts semantic features, and trains GNN models to identify security vulnerabilities and code quality issues. Our model

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Sitnikova, Oksana, Marharyta Melnyk, Olena Syrota, and Serhii Semenov. "Intelligent method for supporting decision-making on software security using hybrid models." INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, no. 1(31) (March 31, 2025): 115–26. https://doi.org/10.30837/2522-9818.2025.1.115.

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Objective. The research is aimed at developing an intelligent decision support method for software security assessment using a hybrid model based on deep learning and gradient boosting. The aim is to improve classification accuracy, interpretability and adaptability in the face of growing cyber threats. Methods. The proposed method combines deep neural networks for automated feature extraction and gradient boosting for final decision making. A classification module is built based on calculating the probabilities of software belonging to security classes. In addition, a geometric interpretation

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Researcher. "INTEGRATION OF AI AND CLOUD TECHNOLOGIES IN HEALTHCARE: A COMPREHENSIVE FRAMEWORK FOR CAREER DEVELOPMENT AND PORTFOLIO ENHANCEMENT." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 674–87. https://doi.org/10.5281/zenodo.14034269.

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This article examines the integration of artificial intelligence (AI) and cloud technologies in healthcare, focusing on the skills and strategies necessary for professionals to excel in this rapidly evolving field. Through a comprehensive analysis of current literature and industry trends, we identify key technologies driving innovation, including CI/CD automation, Kubernetes for scalable infrastructure, cloud-based document management systems, and AI-powered diagnostic tools. The article highlights the importance of practical experience in these areas and provides a framework for building a c

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Alam, Gazi Touhidul, Mohammed Majid Bakhsh, Nusrat Yasmin Nadia, and S. A. Mohaiminul Islam. "Predictive Analytics in QA Automation:." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 4, no. 2 (2025): 55–66. https://doi.org/10.60087/jklst.v4.n2.005.

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An essential component of contemporary software development is quality assurance (QA) automation, which guarantees program dependability, effectiveness, and user pleasure. Traditional QA techniques, on the other hand, frequently have trouble finding flaws early in the software development lifecycle, which raises expenses and delays releases. By predicting possible flaws before they appear, predictive analytics which is fueled by machine learning (ML) and artificial intelligence (AI) offers a revolutionary approach to QA automation. This study examines how predictive analytics might improve sof

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Twinkle Joshi. "Architecting Agentic AI for Modern Software Testing: Capabilities, Foundations, and a Proposed Scalable Multi-Agent System for Automated Test Generation." Journal of Information Systems Engineering and Management 10, no. 52s (2025): 625–38. https://doi.org/10.52783/jisem.v10i52s.10768.

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The progression of software testing has evolved from manual processes to automated systems. However, the emergence of Agentic AI-driven testing represents the next transformative leap. These intelligent agents autonomously generate, execute, and optimize tests, redefining the quality assurance (QA) landscape. Agentic AI—defined by its capacity to independently perceive, plan, execute, and learn—has emerged as a transformative force in software testing. This article examines the impact of Agentic AI on the software testing lifecycle, highlighting its core capabilities, such as dynamic test gene

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Omolola Abimbola Akinola, Obah Tawo, Deborah Osahor, et al. "Integrating AI-driven predictive analytics with devops for real-time fraud detection in financial institutions." World Journal of Advanced Research and Reviews 19, no. 3 (2023): 1639–53. https://doi.org/10.30574/wjarr.2023.19.2.1566.

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Fraud detection remains a critical concern for financial institutions as the sophistication and frequency of fraudulent activities escalate, resulting in significant financial and reputational risks. Traditional rule-based systems are increasingly inadequate for combating dynamic and high-volume transactional fraud. This study investigates the integration of Artificial Intelligence (AI)-driven predictive analytics with DevOps methodologies to enhance real-time fraud detection capabilities in financial institutions. Employing a Systematic Literature Review (SLR) methodology, guided by the PRISM

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Jawalkar, Santosh Kumar. "Machine Learning in QA: A Vision for Predictive and Adaptive Software Testing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 05, no. 07 (2021): 1–7. https://doi.org/10.55041/ijsrem9725.

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Background & Problem Statement - Software testing is a critical phase in the software development lifecycle (SDLC), ensuring that applications function correctly, meet user requirements, and maintain high- quality standards. Traditional software testing approaches, including manual testing and rule-based automation, often face challenges in scalability, efficiency, and adaptability to dynamic software environments. Traditional testing methods are overwhelmed by complex software systems which slows down defect detection and extends both testing costs and release schedules. Machine Learning

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Song, Ahram, Yongil Kim, and Youkyung Han. "Uncertainty Analysis for Object-Based Change Detection in Very High-Resolution Satellite Images Using Deep Learning Network." Remote Sensing 12, no. 15 (2020): 2345. http://dx.doi.org/10.3390/rs12152345.

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Object-based image analysis (OBIA) is better than pixel-based image analysis for change detection (CD) in very high-resolution (VHR) remote sensing images. Although the effectiveness of deep learning approaches has recently been proved, few studies have investigated OBIA and deep learning for CD. Previously proposed methods use the object information obtained from the preprocessing and postprocessing phase of deep learning. In general, they use the dominant or most frequently used label information with respect to all the pixels inside an object without considering any quantitative criteria to

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Wang, Lukang, Min Zhang, Xu Gao, and Wenzhong Shi. "Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms." Remote Sensing 16, no. 5 (2024): 804. http://dx.doi.org/10.3390/rs16050804.

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Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting changes in the Earth’s surface, finding wide applications in urban planning, disaster management, and national security. Recently, deep learning (DL) has experienced explosive growth and, with its superior capabilities in feature learning and pattern recognition, it has introduced innovative approaches to CD. This review explores the latest techniques, applications, and challenges in DL-based CD, examining them through the lens of various learning paradigms, including fully supervised, semi-supervised, weakl

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Carreras, Joaquim. "Celiac Disease Deep Learning Image Classification Using Convolutional Neural Networks." Journal of Imaging 10, no. 8 (2024): 200. http://dx.doi.org/10.3390/jimaging10080200.

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Celiac disease (CD) is a gluten-sensitive immune-mediated enteropathy. This proof-of-concept study used a convolutional neural network (CNN) to classify hematoxylin and eosin (H&E) CD histological images, normal small intestine control, and non-specified duodenal inflammation (7294, 11,642, and 5966 images, respectively). The trained network classified CD with high performance (accuracy 99.7%, precision 99.6%, recall 99.3%, F1-score 99.5%, and specificity 99.8%). Interestingly, when the same network (already trained for the 3 class images), analyzed duodenal adenocarcinoma (3723 images), t

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Ali, Asra. "Evaluation Study for Celiac Disease Diagnosing by using Deep Learning Techniques." Wasit Journal of Computer and Mathematics Science 4, no. 2 (2025): 56–73. https://doi.org/10.31185/wjcms.373.

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Celiac disease (CD) is the autoimmune reaction that occurs as a result of ingestion of gluten, which results to mucosal injury. Proper screening is especially important here, though the existing diagnostic techniques are frequently expensive, as well as the time-consuming. This review seeks to uncover the future of deep learning techniques in changing the diagnosis of CD through imagery of biopsies. we explore Single Layer ABC-DL architectures, CNN in CD diagnosis, Transfer Learning and Multiple Instance Learning (MIL). In this review, these methods are described with regard to their current c

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Vasantrao, Chafle Pratiksha, Neha Gupta, Anoop Kumar Mishra, Girish S. Bhavekar, and Madhav Kumar Gupta. "Change detection using multispectral satellite images: a systematic review of literature." Bulletin of Electrical Engineering and Informatics 13, no. 4 (2024): 2496–507. http://dx.doi.org/10.11591/eei.v13i4.5966.

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Change detection (CD) provides information about the changes on earth’s surface over a period of time. Many algorithms have been proposed over the years for effective CD of satellite imagery. This paper presents the steps to preprocess the captured satellite images, which can be used to perform predictive analysis of earth’s surface by CD techniques. To design a highly effective system for CD, it is recommended that algorithm designers select efficient algorithms from any given application. Thus, this paper presents and investigates the review of most appropriate literature on CD, where CD tec

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Peng, Daifeng, Yongjun Zhang, and Haiyan Guan. "End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++." Remote Sensing 11, no. 11 (2019): 1382. http://dx.doi.org/10.3390/rs11111382.

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Change detection (CD) is essential to the accurate understanding of land surface changes using available Earth observation data. Due to the great advantages in deep feature representation and nonlinear problem modeling, deep learning is becoming increasingly popular to solve CD tasks in remote-sensing community. However, most existing deep learning-based CD methods are implemented by either generating difference images using deep features or learning change relations between pixel patches, which leads to error accumulation problems since many intermediate processing steps are needed to obtain

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Gudelli, Venkata Ramana. "Automating CI/CD Pipelines: A Comparative Study of Jenkins and Bitbucket." Australian Journal of Machine Learning Research & Applications 2, no. 1 (2022): 436–511. https://doi.org/10.5281/zenodo.15102615.

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As far as the author’s knowledge goes, Continuous Integration and Continuous Deployment (CI/CD) pipeline poses to be a very crucial component of modern software development which provides automated build, test, and deployment workflows. Through this research paper, the author dives deep into a comparative study of two widely adapted CI/CD tools, Jenkins and Bitbucket pipelines. These tools are used for evaluating the architecture, automation capabilities, scalability, integration options, and performance efficiency. Jenkins is an open-source automation server whereas Bitbucket pipeline i

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Lorincz, Orsolya, Levente Molnar, Zsolt Csiszovszki, et al. "65 Identification of frequently presented non-mutated tumor-specific immunogens for the development of both off-the-shelf and personalized vaccines without need for tumor biopsy." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (2021): A72. http://dx.doi.org/10.1136/jitc-2021-sitc2021.065.

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BackgroundVaccines have little chance of destroying heterogeneous tumor cells since they rarely induce polyclonal T-cell responses against the tumor. The main challenge is the accurate identification of tumor targets recognizable by T cells. Presently, 6–8% of neoepitopes selected based on the patients‘ tumor biopsies are confirmed as real T cell targets.1 2. To overcome this limitation, we developed a computational platform called Personal Antigen Selection Calculator (PASCal) that identifies frequently presented immunogenic peptide sequences built on HLA-genetics and tumor profile of thousan

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Researcher. "SECURING CI/CD PIPELINES: STRATEGIES FOR MITIGATING RISKS IN MODERN SOFTWARE DELIVERY." International Journal of Engineering and Technology Research (IJETR) 9, no. 2 (2024): 1–9. https://doi.org/10.5281/zenodo.13365012.

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This comprehensive article explores the critical challenge of securing Continuous Integration and Continuous Deployment (CI/CD) pipelines in modern software development. It addresses the common security threats faced by organizations, including credential leaks, supply chain attacks, and unauthorized access, while offering actionable strategies to mitigate these risks. The paper delves into best practices for enhancing CI/CD security, covering crucial aspects such as secret management, encryption techniques, secure CI/CD tools, immutable infrastructure, and comprehensive security testing metho

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Saha, S., L. Kondmann, and X. X. Zhu. "DEEP NO LEARNING APPROACH FOR UNSUPERVISED CHANGE DETECTION IN HYPERSPECTRAL IMAGES." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2021 (June 17, 2021): 311–16. http://dx.doi.org/10.5194/isprs-annals-v-3-2021-311-2021.

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Abstract. Unsupervised deep transfer-learning based change detection (CD) methods require pre-trained feature extractor that can be used to extract semantic features from the target bi-temporal scene. However, it is difficult to obtain such feature extractors for hyperspectral images. Moreover, it is not trivial to reuse the models trained with the multispectral images for the hyperspectral images due to the significant difference in number of spectral bands. While hyperspectral images show large number of spectral bands, they generally show much less spatial complexity, thus reducing the requ

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Li, Zhong. "Deep Learning-Based Crohn's Disease Prediction: A Comprehensive Examination and Future Perspectives." Highlights in Science, Engineering and Technology 103 (June 26, 2024): 328–33. http://dx.doi.org/10.54097/16hgqb58.

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This study explores deep learning's (DL) role in enhancing Crohn's Disease (CD) diagnosis and treatment, aiming to overcome current diagnostic challenges and improve patient outcomes through more accurate, efficient, and personalized medical interventions. This comprehensive review scrutinized a plethora of studies focusing on the utilization of machine learning (ML) and DL methodologies for diagnosing CD. The investigation spanned various Artificial Intelligence (AI) techniques. This endeavor aimed to illustrate the transformation from traditional ML methods, which necessitate labor-intensive

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Wang, Biao, Ao He, Chunlin Wang, Xiao Xu, Hui Yang, and Yanlan Wu. "A Heterogeneity-Enhancement and Homogeneity-Restraint Network (HEHRNet) for Change Detection from Very High-Resolution Remote Sensing Imagery." Remote Sensing 15, no. 22 (2023): 5425. http://dx.doi.org/10.3390/rs15225425.

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Change detection (CD), a crucial technique for observing ground-level changes over time, is a challenging research area in the remote sensing field. Deep learning methods for CD have made significant progress in remote sensing intelligent interpretation. However, with very high-resolution (VHR) satellite imagery, technical challenges such as insufficient mining of shallow-level features, complex transmission of deep-level features, and difficulties in identifying change information features have led to severe fragmentation and low completeness issues of CD targets. To reduce costs and enhance

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Swamy, Prasadarao Velaga. "Scaling Machine Learning Model Training with CI/CD Pipelines in Cloud Environments." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 8, no. 1 (2020): 1–10. https://doi.org/10.5281/zenodo.12805504.

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As machine learning (ML) continues to advance, the need for scalable, efficient, and reliable model training has become critical. Traditional approaches to ML model training often struggle to meet these demands, prompting the integration of Continuous Integration and Continuous Deployment (CI/CD) practices with cloud environments. This survey paper explores the intersection of CI/CD pipelines and cloud-based solutions in scaling ML model training. We provide a comprehensive review of the current state of CI/CD practices tailored for ML workflows, examine the benefits and offerings of cloud env

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Swamy, Prasadarao Velaga. "Integrating Data Versioning and Management into CI/CD Pipelines for Machine Learning." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 9, no. 1 (2021): 1–7. https://doi.org/10.5281/zenodo.12805518.

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The rapid evolution and widespread adoption of machine learning (ML) applications have underscored the critical importance of data management practices that ensure reproducibility, reliability, and scalability in model development and deployment. Integrating data versioning and management into Continuous Integration and Continuous Deployment (CI/CD) pipelines for ML represents a pivotal strategy to address these challenges. This survey paper explores the significance of data versioning in CI/CD pipelines, examining key benefits such as enhanced reproducibility of experimental results, effectiv

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Shafique, Ayesha, Guo Cao, Zia Khan, Muhammad Asad, and Muhammad Aslam. "Deep Learning-Based Change Detection in Remote Sensing Images: A Review." Remote Sensing 14, no. 4 (2022): 871. http://dx.doi.org/10.3390/rs14040871.

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Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of th

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Saini, N., and A. Acharjee. "OP27 Unlocking Inflammatory Bowel Disease subtypes: a deep dive into transcriptomics and Machine Learning." Journal of Crohn's and Colitis 19, Supplement_1 (2025): i53—i55. https://doi.org/10.1093/ecco-jcc/jjae190.0027.

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Abstract Background Identifying molecular subtypes of IBD is essential to address inconsistencies in gene expression-based classifications, clinical variability, and treatment responses in Crohn's Disease (CD) and Ulcerative Colitis (UC). Building on prior efforts using different methods and datasets, this study aimed to derive and validate IBD subtypes using transcriptomics data and unsupervised machine learning. Methods This study analysed RNA-sequenced data from inflamed and non-inflamed intestinal biopsies of 2,490 adult IBD patients. K-means clustering, guided by Within Cluster Sum of Squ

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Suddala, Swathi. "Automating the Data Science Lifecycle: CI/CD for Machine Learning Deployment." International Journal for Multidisciplinary Research 4, no. 6 (2022): 1–9. https://doi.org/10.5281/zenodo.15328138.

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The incorporation of Continuous Integration (CI) and Continuous Deployment (CD) into the machine learning (ML) lifecycle is essential for facilitating the effective transition of models from the development phase to production. Unlike conventional software, ML workflows face distinct challenges such as data versioning, model drift, hyperparameter optimization, and limitations in computational resources. This paper explores optimal practices for automating the data science lifecycle through CI/CD methodologies, focusing on critical elements like automated data validatio

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Nagase, Yoshikazu, Shinya Matsuzaki, Yutaka Ueda, et al. "Association between Endometriosis and Delivery Outcomes: A Systematic Review and Meta-Analysis." Biomedicines 10, no. 2 (2022): 478. http://dx.doi.org/10.3390/biomedicines10020478.

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Endometriosis is a common benign gynecological disorder; however, delivery outcomes concerning pregnancies with endometriosis remain understudied. This study aimed to assess the effect of endometriosis on delivery outcomes, including the rate of instrumental delivery, cesarean delivery (CD), postpartum hemorrhage (PPH), and perioperative complications during CD. A systematic literature review was conducted using multiple computerized databases, and 28 studies met the inclusion criteria. Pooled analysis showed that histologically diagnosed endometriosis was associated with an increased rate of

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Myllynen, Teemu, Eunice Kamau, Sikirat Damilola Mustapha, Gideon Opeyemi Babatunde, and Anuoluwapo Collins. "Review of Advances in AI-Powered Monitoring and Diagnostics for CI/CD Pipelines." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 1 (2024): 1119–30. https://doi.org/10.54660/.ijmrge.2024.5.1.1119-1130.

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Continuous Integration and Continuous Deployment (CI/CD) pipelines are critical components of modern software development, enabling rapid delivery of reliable applications. However, ensuring the seamless operation of CI/CD pipelines remains a challenge due to the complexity of managing code changes, dependencies, and diverse testing environments. Recent advancements in artificial intelligence (AI) have introduced innovative approaches to monitoring and diagnostics within CI/CD workflows, significantly enhancing their efficiency, reliability, and resilience. This review explores the state-of-th

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Yang, Weiwei, Haifeng Song, Lei Du, Songsong Dai, and Yingying Xu. "A Change Detection Method for Remote Sensing Images Based on Coupled Dictionary and Deep Learning." Computational Intelligence and Neuroscience 2022 (January 17, 2022): 1–14. http://dx.doi.org/10.1155/2022/3404858.

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With the rapid development of remote sensing technology, change detection (CD) methods based on remote sensing images have been widely used in land resource planning, disaster monitoring, and urban expansion, among other fields. The purpose of CD is to accurately identify changes on the Earth’s surface. However, most CD methods focus on changes between the pixels of multitemporal remote sensing image pairs while ignoring the coupled relationships between them. This often leads to uncertainty about edge pixels with regard to changing objects and misclassification of small objects. To solve thes

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Tarun, Parmar. "Implementing CI/CD in Data Engineering: Streamlining Data Pipelines for Reliable and Scalable Solutions." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 13, no. 1 (2025): 1–8. https://doi.org/10.5281/zenodo.14762684.

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Continuous Integration and Continuous Delivery (CI/CD) have become crucial practices in modern data engineering, streamlining the development and deployment of data pipelines. This study explores the implementation of CI/CD principles in data engineering, highlighting its benefits, methodologies, best practices, challenges, and future directions. By automating the building, testing, and deployment processes, the CI/CD ensures reliability, consistency, and efficiency in data pipeline development. The key steps in implementing CI/CD for data pipelines include version control, modular pipeline de

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Jain, Prerna, Deep K. Hathi, Hossein Honarvar, and Rahul K. Das. "Abstract 4400: Machine learning (ML) on real-world data (RWD) of front-line (1L) metastatic castration resistance prostate cancer (mCRPC) patients for dynamic prediction of time to tx discontinuation (TTD)." Cancer Research 83, no. 7_Supplement (2023): 4400. http://dx.doi.org/10.1158/1538-7445.am2023-4400.

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Abstract Background: Abiraterone (abi) and enzalutamide (enza) are two novel androgen therapies (NAT) for 1L treatment (tx) for mCRPC. As there is no head-to-head randomized controlled clinical trial (RCT) between Abi and Enza, predicting 1L comparative effectiveness of these two drugs for mCRPC, especially in patient groups under-represented in RCTs is critical. ML was performed on electronic health records (EHR) to evaluate risk factors of TTD in 1L mCRPC patients and identify predictive markers of patient subgroups with differential outcomes from enza vs. abi. Methods: Patients with curated

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Liu, Jiexi, Meng Cao, and Songcan Chen. "TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 18861–69. https://doi.org/10.1609/aaai.v39i18.34076.

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Irregularly sampled multivariate time series (ISMTS) are prevalent in reality. Due to their non-uniform intervals between successive observations and varying sampling rates among series, the channel-independent (CI) strategy, which has been demonstrated more desirable for complete multivariate time series forecasting in recent studies, has failed. This failure can be further attributed to the sampling sparsity, which provides insufficient information for effective CI learning, thereby reducing its capacity. When we resort to the channel-dependent (CD) strategy, even higher capacity cannot miti

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Li, Jinlong, Xiaochen Yuan, Jinfeng Li, Guoheng Huang, Ping Li, and Li Feng. "CD-SDN: Unsupervised Sensitivity Disparity Networks for Hyper-Spectral Image Change Detection." Remote Sensing 14, no. 19 (2022): 4806. http://dx.doi.org/10.3390/rs14194806.

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Deep neural networks (DNNs) could be affected by the regression level of learning frameworks and challenging changes caused by external factors; their deep expressiveness is greatly restricted. Inspired by the fine-tuned DNNs with sensitivity disparity to the pixels of two states, in this paper, we propose a novel change detection scheme served by sensitivity disparity networks (CD-SDN). The CD-SDN is proposed for detecting changes in bi-temporal hyper-spectral images captured by the AVIRIS sensor and HYPERION sensor over time. In the CD-SDN, two deep learning frameworks, unchanged sensitivity

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Liu, Shengcheng, Changming Zhu, Zishi Li, Zhiyuan Yang, and Wenjie Gu. "View-Driven Multi-View Clustering via Contrastive Double-Learning." Entropy 26, no. 6 (2024): 470. http://dx.doi.org/10.3390/e26060470.

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Multi-view clustering requires simultaneous attention to both consistency and the diversity of information between views. Deep learning techniques have shown impressive abilities to learn complex features when working with extensive datasets; however, existing deep multi-view clustering methods often focus only on either consistency information or diversity information, making it difficult to balance both aspects. Therefore, this paper proposes a view-driven multi-view clustering using the contrastive double-learning method (VMC-CD), aiming to generate better clustering results. This method fi

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Reddy Pothu, Avinash. "Garlic Gate: Revolutionizing Application Security with Integrated Artificial Intelligence (AI) Across SDLC, CI/CD, and Advanced Methodologies." FMDB Transactions on Sustainable Computing Systems 2, no. 3 (2024): 119–30. https://doi.org/10.69888/ftscs.2024.000259.

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Application security has become a top priority in SDLCs and CI/CD pipelines as cyber threats grow increasingly sophisticated. Meet Garlic Gate, a state-of-the-art, machine learning-powered framework that integrates effortlessly with SDLC and CI/CD processes to protect your applications. Its advanced architecture is designed to adapt to changing vulnerabilities, offering real-time threat detection, risk assessment, and dynamic learning to prevent or mitigate security issues. Using AI-driven algorithms, Garlic Gate overcomes the challenges of traditional application security methods, such as ine

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Shally, Garg. "NLP-Based Automated Release Notes from CI/CD Commit Messages." Journal of Advances in Developmental Research 12, no. 2 (2021): 1–12. https://doi.org/10.5281/zenodo.15049882.

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Natural Language Processing (NLP) and AI/ML inCI/CD pipelines is changing the automation of release note generation by improving efficiency, accuracy, and contextual relevance. Notable advancements include transformer-based models for enhanced summarization, adaptive learning techniques for tailored documentation, and Explainable AI (XAI) for more transparency. These advancements improve the coherence, interpretability, and user focus of AI-generated release notes, hence streamlining software development processes. Recent developments highlight real-time learning, accurate fine-tuning, and imp

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Levartovsky, Asaf, Yiftach Barash, Shomron Ben-Horin, et al. "Machine learning for prediction of intra-abdominal abscesses in patients with Crohn’s disease visiting the emergency department." Therapeutic Advances in Gastroenterology 14 (January 2021): 175628482110531. http://dx.doi.org/10.1177/17562848211053114.

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Background: Intra-abdominal abscess (IA) is an important clinical complication of Crohn’s disease (CD). A high index of clinical suspicion is needed as imaging is not routinely used during hospital admission. This study aimed to identify clinical predictors of an IA among hospitalized patients with CD using machine learning. Methods: We created an electronic data repository of all patients with CD who visited the emergency department of our tertiary medical center between 2012 and 2018. We searched for the presence of an IA on abdominal imaging within 7 days from visit. Machine learning models

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Chandrasekhara Mokkapati, Shalu Jain, and Pandi Kirupa Gopalakrishna Pandian. "Implementing CI/CD in Retail Enterprises: Leadership Insights for Managing Multi-Billion Dollar Projects." Innovative Research Thoughts 9, no. 1 (2023): 391–405. http://dx.doi.org/10.36676/irt.v9.i1.1458.

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In the fast-paced world of retail enterprises, the adoption of Continuous Integration and Continuous Deployment (CI/CD) has become a cornerstone for driving agility, innovation, and competitive advantage. This paper explores the critical leadership insights necessary for successfully managing CI/CD implementations in multi-billion-dollar retail projects. Retail enterprises face unique challenges, including complex legacy systems, diverse technology stacks, and the need for seamless integration across global operations. These complexities require a strategic approach to CI/CD that balances tech

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Researcher. "ENHANCING DEVELOPER PRODUCTIVITY THROUGH AUTOMATED CI/CD PIPELINES: A COMPREHENSIVE ANALYSIS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 882–91. https://doi.org/10.5281/zenodo.13929524.

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This comprehensive article explores the transformative impact of Continuous Integration and Continuous Deployment (CI/CD) pipelines on modern software development practices. It delves into the core components of CI/CD pipelines, including automated testing, code analysis, and deployment strategies, examining how these elements contribute to enhanced developer productivity and software quality. Through an analysis of case studies from the tech, finance, and e-commerce sectors, the article demonstrates the tangible benefits of CI/CD implementation, such as significant reductions in lead times, i

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Verstockt, S., N. Verplaetse, D. Raimondi, et al. "DOP31 Serum protein markers for early and differential IBD diagnosis validated by machine learning approaches." Journal of Crohn's and Colitis 14, Supplement_1 (2020): S070. http://dx.doi.org/10.1093/ecco-jcc/jjz203.070.

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Abstract Background The inflammatory bowel diseases (IBD), Crohn’s disease (CD) and ulcerative colitis (UC) are chronic inflammatory conditions with a polygenic and multifactorial pathogenesis. Intensified treatment early in the disease course of IBD results in better outcomes. This is, however, challenged by the diagnostic delay faced in IBD, and especially in CD. Therefore, markers supporting early and differential diagnosis are needed. In this study, we aimed to discriminate IBD patients from non-IBD controls, and CD from UC patients, using serum protein profiles combined with an IBD polyge

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