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Journal articles on the topic 'End-To-End'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

G, Shravya, and Prof Smitha G R. "CMRepo End-to-End Automation." International Journal of Innovative Research in Computer Science & Technology 7, no. 3 (2019): 79–83. http://dx.doi.org/10.21276/ijircst.2019.7.3.9.

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2

Kim, Wiback, and Hosung Nam. "End-to-end non-autoregressive fast text-to-speech." Phonetics and Speech Sciences 13, no. 4 (2021): 47–53. http://dx.doi.org/10.13064/ksss.2021.13.4.047.

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3

Abirami, N., M. Sri Nivetha, and S. Veena. "End to End Encryption using QKD Algorithm." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 936–39. http://dx.doi.org/10.31142/ijtsrd18723.

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4

Park, Mingyu, Hyeonseok Kim, and Seongkeun Park. "End-to-End autonomous driving based on Convolutional Recurrent Neural Network." Journal of Korean Institute of Intelligent Systems 29, no. 4 (2019): 297–301. http://dx.doi.org/10.5391/jkiis.2019.29.4.297.

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5

Kim, Kihun, Cheonum Park, Changki Lee, and Hyunki Kim. "Korean End-to-end Neural Coreference Resolution with BERT." Journal of KIISE 47, no. 10 (2020): 942–47. http://dx.doi.org/10.5626/jok.2020.47.10.942.

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6

Li-Hong Deng, Li-Hong Deng, Fei Deng Li-Hong Deng, Ge-Xiang Chiou Fei Deng, and Qiang Yang Ge-Xiang Chiou. "End-to-end Speaker Recognition Based on MTFC-FullRes2Net." 電腦學刊 34, no. 3 (2023): 075–91. http://dx.doi.org/10.53106/199115992023063403006.

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<p>The feature extraction ability of lightweight convolutional neural networks in speaker recognition systems is weak. And recognition accuracy is poor. Many methods use deeper, wider, and more complex network structures to improve the feature extraction ability. But it makes the parameters and inference time increase exponentially. In the paper, we introduce Res2Net in target detection task to speaker recognition task and verify its effectiveness and robustness in the speaker recognition task. And we improved and proposed FullRes2Net. It has better multi-scale feature extraction ability

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7

P L, Subramanian, Baruni M K, and Laksitha N R G. "End-to-End Secure Image Steganography using RSA Encryption." International Journal of Research Publication and Reviews 6, no. 5 (2025): 14892–98. https://doi.org/10.55248/gengpi.6.0525.1965.

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8

Rieck, Steffi, Joachim Heidelbach, and Tobias Stahl. "End-to-End- Prozessautomatisierung/End-to-end process automation." wt Werkstattstechnik online 113, no. 01-02 (2023): 42–47. http://dx.doi.org/10.37544/1436-4980-2023-01-02-46.

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Durchgängige, automatisierte Prozesse können Prozesskosten um 30 bis 40 Prozent senken und Durchlaufzeiten signifikant verkürzen. Der vorliegende Beitrag zeigt anhand eines siebenstufigen Vorgehens, wie automatisierte Prozesse umgesetzt werden. Dazu werden die Stufen (wie Aufnahme, Optimierung, Automatisierung und Monitoring) inklusive möglicher Methoden und Technologien dargestellt. Neben Robotic Process Automation (RPA) kommen hierbei Process Mining und Methoden der künstlichen Intelligenz (KI) zum Einsatz. Continuous, automated processes allow for reducing process costs by 30 to 40 percent

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9

Kim, June-Woo, and Ho-Young Jung. "End-to-end speech recognition models using limited training data*." Phonetics and Speech Sciences 12, no. 4 (2020): 63–71. http://dx.doi.org/10.13064/ksss.2020.12.4.063.

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10

Kim, June-Woo, and Ho-Young Jung. "End-to-end speech recognition models using limited training data*." Phonetics and Speech Sciences 12, no. 4 (2020): 63–71. http://dx.doi.org/10.13064/ksss.2020.12.4.063.

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11

Kim, Kihun, Cheonum Park, Changki Lee, and Hyunki Kim. "Korean End-to-End Neural Coreference Resolution Using Higher-Order Inference." KIISE Transactions on Computing Practices 26, no. 5 (2020): 250–55. http://dx.doi.org/10.5626/ktcp.2020.26.5.250.

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12

Jo, Kyeongbin, Youngjun Jung, Changki Lee, Jihee Ryu, and Joonho Lim. "Korean End-to-End Coreference Resolution with BERT for Long Document." Journal of KIISE 50, no. 1 (2023): 32–39. http://dx.doi.org/10.5626/jok.2023.50.1.32.

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13

Thatiparthi, Jayaprakash. "Designing an End-to-End Case Management Solution: From Case Creation to Closure." International Journal of Science and Research (IJSR) 14, no. 5 (2025): 589. https://doi.org/10.21275/sr25508201524.

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14

Bae, Kyoung Yul, and Hong Woo Lee. "End to End Model and Delay Performance for V2X in 5G." Journal of Intelligence and Information Systems 22, no. 1 (2016): 107–18. http://dx.doi.org/10.13088/jiis.2016.22.1.107.

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15

Islam, Md Rakibul, Rokshana Akter Jhilik, Nazmul Alam Khan, and Usama Ali. "End-to-End Deep Learning Models for Real-Time Image Compression." International Journal of Research Publication and Reviews 6, no. 5 (2025): 18381–88. https://doi.org/10.55248/gengpi.6.0525.2022.

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16

Jeong, Yeon-Jeong, and Ki-Song Yoon. "Design & Implementation of DRM System for End-to-end Content Protection." KIPS Transactions:PartC 13C, no. 1 (2006): 35–44. http://dx.doi.org/10.3745/kipstc.2006.13c.1.035.

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17

Choi, Kyoung-Ho, and Changki Lee. "End-to-end Korean Document Summarization using Copy Mechanism and Input-feeding." Journal of KIISE 44, no. 5 (2017): 503–9. http://dx.doi.org/10.5626/jok.2017.44.5.503.

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18

Lee, Jihyun, Kyungshik Lim, Hangseok Oh, and Taekyong Nam. "An Architecture for User Level End-to-end QoS using Overlay in NGN." KIPS Transactions:PartC 12C, no. 6 (2005): 781–92. http://dx.doi.org/10.3745/kipstc.2005.12c.6.781.

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19

Reddy, S. Ramana, and C. Raja Rao C Raja Rao. "An end to end QOS Based network Extraction Scheme for 4G Systems." International Journal of Scientific Research 1, no. 7 (2012): 70–72. http://dx.doi.org/10.15373/22778179/dec2012/28.

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20

Immaneni, Jayaram. "End-to-End MLOps in Financial Services: Resilient Machine Learning with Kubernetes." International Journal of Science and Research (IJSR) 11, no. 10 (2022): 1459–67. https://doi.org/10.21275/sr221013093711.

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21

Amgothu, Sudheer. "An End-to-End CI/CD Pipeline Solution Using Jenkins and Kubernetes." International Journal of Science and Research (IJSR) 13, no. 8 (2024): 1576–78. http://dx.doi.org/10.21275/sr24826231120.

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22

HABER, I., F. M. BIENIOSEK, C. M. CELATA, et al. "End-to-end simulation: The front end." Laser and Particle Beams 20, no. 3 (2002): 431–33. http://dx.doi.org/10.1017/s0263034602203134.

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For the intense beams in heavy ion fusion accelerators, details of the beam distribution as it emerges from the source region can determine the beam behavior well downstream. This occurs because collective space-charge modes excited as the beam is born remain undamped for many focusing periods. Traditional studies of the source region in particle beam systems have emphasized the behavior of averaged beam characteristics, such as total current, rms beam size, or emittance, rather than the details of the full beam distribution function that are necessary to predict the excitation of the collecti

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23

Aleshire, Benjamin. "End-to-End Encryption." Pleiades: Literature in Context 44, no. 1 (2024): 285. http://dx.doi.org/10.1353/plc.2024.a926471.

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24

Lee, Joun Yeop, Sung Jun Cheon, Byoung Jin Choi, Nam Soo Kim, and Doo Hwa Hong. "Speech Style Modeling Method Using Mutual Information for End-to-End Speech Synthesis." Journal of Korean Institute of Communications and Information Sciences 44, no. 9 (2019): 1641–47. http://dx.doi.org/10.7840/kics.2019.44.9.1641.

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25

Abdullah, Wisam Dawood, Ali Abdullah Ali, and Layth Rafea Hazim. "Analysis of End to End Internet Traffic in Education's Networks: A New Study." Webology 19, no. 1 (2022): 3392–409. http://dx.doi.org/10.14704/web/v19i1/web19223.

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Network Traffic Monitoring and Analysis (NTMA) is the main element to network management, especially to correctly operate large-scale networks such as the Internet on which modern academic organizations heavily depend. Their traffic use increases significantly because students, staff members, and research labs use them to search information. It is necessary to analyze, measure, and classify this Internet traffic according to the need of different stakeholders such as Internet Service Providers and network administrators. Moreover, bandwidth congestions frequently occur, causing user dissatisfa

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26

Fei Qi, Fei Qi, and Chen-Qing Wang Fei Qi. "An End-to-End Multi-Scale Conditional Generative Adversarial Network for Image Deblurring." 電腦學刊 34, no. 3 (2023): 237–50. http://dx.doi.org/10.53106/199115992023063403017.

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<p>For image deblurring, multi-scale approaches have been widely used as deep learning methods recently. In this paper, a novel multi-scale conditional generative adversarial network (CGAN) is proposed to make full use of image features, which outperforms most state-of-the-art methods. We define a generator network and a discriminator network. First of all, we use the multi-scale residual modules proposed in this paper as main feature extraction blocks, and add skip connections to extract multi-scale image features at a finer granularity in the generator network. Secondly, we construct P

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27

Mooghala, Sridhar. "Comprehensive End - to - End Testing: Significance and Precision in Software Application Quality Assurance." International Journal of Science and Research (IJSR) 12, no. 1 (2023): 1253–57. http://dx.doi.org/10.21275/sr24127214929.

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28

STANZIALE, Rodolfo, Massimo LODI, Enrico D'ANDREA, Fulvio Sammartino, and Virgilio DI LUZIO. "Arteriovenous fistula: end-to-end or end-to side anastomosis?" Hemodialysis International 15, no. 1 (2010): 100–103. http://dx.doi.org/10.1111/j.1542-4758.2010.00503.x.

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29

Lee, Sang-Hyun, Jae-Dong kim, and Han-Seok Ko. "End-to-end based on CRNN-GLU-ATT Model for Robust Emotional Features Extraction." Journal of the Institute of Electronics and Information Engineers 57, no. 10 (2020): 45–55. http://dx.doi.org/10.5573/ieie.2020.57.10.45.

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30

Nawy, Tal. "End-to-end RNA sequencing." Nature Methods 10, no. 12 (2013): 1144–45. http://dx.doi.org/10.1038/nmeth.2750.

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31

Gras, J. J. "End-to-end defect modeling." IEEE Software 21, no. 5 (2004): 98–100. http://dx.doi.org/10.1109/ms.2004.1331312.

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32

Joel, Lucille A. "Laying Circles End to End." AJN, American Journal of Nursing 95, no. 9 (1995): 7. http://dx.doi.org/10.1097/00000446-199509000-00002.

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33

Saidi, Ali G., Nathan L. Binkert, Steven K. Reinhardt, and Trevor Mudge. "End-to-end performance forecasting." ACM SIGARCH Computer Architecture News 37, no. 3 (2009): 361–70. http://dx.doi.org/10.1145/1555815.1555800.

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34

Nazaryan, Levon, Emmanouil Panaousis, and Christos Politis. "End-to-End Security Protection." IEEE Vehicular Technology Magazine 5, no. 1 (2010): 85–90. http://dx.doi.org/10.1109/mvt.2009.935542.

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35

Jarvis, Graham. "Evolving End to End Traceability." Aerospace Testing International 2024, no. 1 (2024): 46–50. http://dx.doi.org/10.12968/s1478-2774(24)50053-9.

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36

Lu, Yung-Hsiang, Qinru Qiu, Ali R. Butt, and Kirk W. Cameron. "End-to-End Energy Management." Computer 44, no. 11 (2011): 75–77. http://dx.doi.org/10.1109/mc.2011.342.

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37

Stephenson, Peter. "End-to-End Digital Forensics." Computer Fraud & Security 2002, no. 9 (2002): 17–19. http://dx.doi.org/10.1016/s1361-3723(02)00914-4.

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38

Gilbert, Gerald, Michael Hamrick, and Yaakov S. Weinstein. "End-to-end fault tolerance." Quantum Information Processing 7, no. 6 (2008): 263–74. http://dx.doi.org/10.1007/s11128-008-0087-4.

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39

Singh, Abhayendra, Satish Narayanasamy, Daniel Marino, Todd Millstein, and Madanlal Musuvathi. "End-to-end sequential consistency." ACM SIGARCH Computer Architecture News 40, no. 3 (2012): 524–35. http://dx.doi.org/10.1145/2366231.2337220.

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40

Hamed, Abdelgaffar, and Robert M. Colomb. "End to End Development Engineering." Journal of Software Engineering and Applications 04, no. 04 (2011): 195–216. http://dx.doi.org/10.4236/jsea.2011.44023.

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41

Rekatsinas, Theodoros, Dirk Duellmann, Witold Pokorski, et al. "CASTOR end-to-end monitoring." Journal of Physics: Conference Series 219, no. 4 (2010): 042052. http://dx.doi.org/10.1088/1742-6596/219/4/042052.

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42

Jain, Manish, and Constantinos Dovrolis. "End-to-end available bandwidth." ACM SIGCOMM Computer Communication Review 32, no. 4 (2002): 295–308. http://dx.doi.org/10.1145/964725.633054.

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43

Jishnu T S and Anju Antony. "LipNet: End-to-End Lipreading." Indian Journal of Data Mining 4, no. 1 (2024): 1–4. http://dx.doi.org/10.54105/ijdm.a1632.04010524.

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Lipreading is the task of decoding text from the movement of a speaker’s mouth. This research presents the development of an advanced end-to-end lipreading system. Leveraging deep learning architectures and multimodal fusion techniques, the proposed system interprets spoken language solely from visual cues, such as lip movements. Through meticulous data collection, annotation, preprocessing, model development, and evaluation, diverse datasets encompassing various speakers, accents, languages, and environmental conditions are curated to ensure robustness and generalization. Conventional methods

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44

Jishnu, T. S. "LipNet: End-to-End Lipreading." Indian Journal of Data Mining (IJDM) 4, no. 1 (2024): 1–4. https://doi.org/10.54105/ijdm.A1632.04010524.

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<strong>Abstarct:</strong> Lipreading is the task of decoding text from the movement of a speaker’s mouth. This research presents the development of an advanced end-to-end lipreading system. Leveraging deep learning architectures and multimodal fusion techniques, the proposed system interprets spoken language solely from visual cues, such as lip movements. Through meticulous data collection, annotation, preprocessing, model development, and evaluation, diverse datasets encompassing various speakers, accents, languages, and environmental conditions are curated to ensure robustness and gen

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45

Bradner, S. "The end of end-to-end security? [Internet security." IEEE Security & Privacy Magazine 4, no. 2 (2006): 76–79. http://dx.doi.org/10.1109/msp.2006.54.

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46

Burke, TW, EB Weiser, and WJ Hoskins. "End colostomy using the end-to-end anastomosis instrument." International Journal of Gynecology & Obstetrics 25, no. 6 (1987): 487–88. http://dx.doi.org/10.1016/0020-7292(87)90089-0.

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47

Chao Wang, Chao Wang, Wei Luo Chao Wang, Jia-Rui Zhu Wei Luo, Ying-Chun Xia Jia-Rui Zhu, Jin He Ying-Chun Xia, and Li-Chuan Gu Jin He. "End-to-end Visual Grounding Based on Query Text Guidance and Multi-stage Reasoning." 電腦學刊 35, no. 1 (2024): 083–95. http://dx.doi.org/10.53106/199115992024023501006.

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<p>Visual grounding locates target objects or areas in the image based on natural language expression. Most current methods extract visual features and text embeddings independently, and then carry out complex fusion reasoning to locate target objects mentioned in the query text. However, such independently extracted visual features often contain many features that are irrelevant to the query text or misleading, thus affecting the subsequent multimodal fusion module, and deteriorating target localization. This study introduces a combined network model based on the transformer architectur

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48

Researcher. "BUILDING A ROBUST END-TO-END TESTING FRAMEWORK FOR MICROSERVICE ARCHITECTURES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 572–82. https://doi.org/10.5281/zenodo.13860001.

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This article presents a comprehensive framework for end-to-end testing of microservice architectures, addressing the challenges posed by their distributed nature and rapid evolution. Drawing from industry surveys and research studies, it highlights the widespread adoption of microservices and the associated complexities in ensuring system reliability. The proposed framework encompasses five key components: a JSON-based test definition language, a robust storage service, an intelligent orchestration service, a dynamic configuration service, and an automated notification system. Each component i

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49

Jo, Jin-Yong, and Jong-Uk Kong. "Realizing an End-to-End IP Multicast Monitoring Framework for the Support of Networked Collaboration." KIPS Transactions:PartC 16C, no. 5 (2009): 605–14. http://dx.doi.org/10.3745/kipstc.2009.16c.5.605.

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50

Fulton, Elizabeth A. "Approaches to end-to-end ecosystem models." Journal of Marine Systems 81, no. 1-2 (2010): 171–83. http://dx.doi.org/10.1016/j.jmarsys.2009.12.012.

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