Titlebook: Communication and Intelligent Systems; Proceedings of ICCIS Harish Sharma,Vivek Shrivastava,Lipo Wang Conference proceedings 2024 The Edito

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paragon

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Exploring the Potential of Deep Learning Algorithms in Medical Image Processing: A Comprehensive Anlutionizing healthcare diagnostics and treatment planning. After conducting a thorough analysis of various literature and empirical studies, we have evaluated the capabilities and drawbacks of deep learning models in managing different medical imaging modalities such as X-rays, MRIs, and CT scans.

泛滥

Convolution Neural Network (CNN)-Based Live Pig Weight Estimation in Controlled Imaging Platform,bustness. The proposed model‘s efficiency is highlighted by its convergence behavior during training and testing, showcasing its ability to accurately predict live pig weights and its potential to revolutionize the Indian meat production industry.

Herbivorous

,Latest Trends on Satellite Image Segmentation,n image segmentation using satellite imaging as well as others. There is a broad coverage of segmentation algorithms of UNET, TSVM and Random Walker. It investigates the strengths, challenges and novel aspects and compares precisions and deliberate potential research outlooks.

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Stable-Angina

2367-3370 emes to make the latest results available in a single, readily accessible source. The work is presented in three volumes.978-981-97-2078-1978-981-97-2079-8Series ISSN 2367-3370 Series E-ISSN 2367-3389

周兴旺

,Machine Learning and Healthcare: A Comprehensive Study, with legal frameworks. Despite challenges, the paper advocates for a conscientious integration of ML, emphasizing its transformative potential in healthcare and urging judicious technology amalgamation to propel advancements in patient care and clinical outcomes.

伪书

旧石器时代

,An Empirical Study on Comparison of Machine Learning Algorithms for Eye-State Classification Using lex EEG data. Logistic regression promoted interpretability, while ElasticNet classifier offered a balanced approach. The accuracy of SVM with RBF kernel was 77%, while the accuracy of logistic regression and ElasticNet classifier was found to be 57.2% and 57.8%, respectively.