Contribution Unveiling Cutting-Edge Machine Learning Techniques for Image Segmentation-Reference-Cited by-同舟云学术

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Contribution Unveiling Cutting-Edge Machine Learning Techniques for Image Segmentation

Published:2024 Issue: Volume: Page:501-511
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:en
Short-container-title:

Author:

Shaik Nazeer,Gupta Ankur,Bhati Sunita,Kumar Jaideep,Singh Jagendra,Budhiraja Ishan

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-8661-3_37

Reference11 articles.

1. Kini AS, Gopal Reddy AN, Kaur M, Satheesh S, Martinetz T, Alshazly H (2022) Ensemble deep learning and internet of things-based automated covid-19 diagnosis framework. Contrast Media Mol Imaging 10. Article ID 7377502. https://doi.org/10.1155/2022/7377502

2. Mall S (2023) Heart diagnosis using deep neural network. In: Accepted in 3rd international conference on computational intelligence and knowledge economy ICCIKE 2023, Amity University, Dubai

3. Aditi Sharan (2017) Term Co-occurrence and context window based combined approach for query expansion with the semantic notion of terms. Int J Web Sci (IJWS), Inderscience 3(1)

4. Yadav CS, Yadav A, Pattanayak HS, Kumar R, Khan AA, Haq MA, Alhussen A, Alharby S (2022) Malware analysis in IoT and android systems with defensive mechanism. Electronics 11:2354. https://doi.org/10.3390/electronics11152354

5. Goswami A, Sharma D, Mathuku H, Gangadharan SMP, Yadav CS (2022) Change detection in remote sensing image data comparing algebraic and machine learning methods. Electronics 1505208

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1. Optimizing Drug Discovery for Breast Cancer in a Laboratory Environment Using Machine Learning;2024 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET);2024-03-21

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