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Titlebook: Recent Trends in Image Processing and Pattern Recognition; 5th International Co KC Santosh,Ayush Goyal,Satish K Singh Conference proceeding

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發(fā)表于 2025-3-21 20:07:50 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Recent Trends in Image Processing and Pattern Recognition
副標(biāo)題5th International Co
編輯KC Santosh,Ayush Goyal,Satish K Singh
視頻videohttp://file.papertrans.cn/824/823454/823454.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Recent Trends in Image Processing and Pattern Recognition; 5th International Co KC Santosh,Ayush Goyal,Satish K Singh Conference proceeding
描述This book constitutes the refereed proceedings of the?5th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2022, held in Kingsville, TX, USA, in collaboration with the Applied AI Research Laboratory of the University of South Dakota, during December 01-02, 2022..The 31 full papers included in this book were carefully reviewed and selected from 69 submissions. They were organized in topical sections as follows: healthcare: medical imaging and informatics; computer vision and pattern recognition; internet of things and security; and signal processing and machine learning..
出版日期Conference proceedings 2023
關(guān)鍵詞artificial intelligence; classification methods; computer security; computer vision; correlation analysi
版次1
doihttps://doi.org/10.1007/978-3-031-23599-3
isbn_softcover978-3-031-23598-6
isbn_ebook978-3-031-23599-3Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Data Characterization for Reliable AI in Medicinetly patient care. However, these algorithms are highly impacted by the characteristics of the underlying data. In this work, we discuss various data characteristics, namely ., and . that impact the design, reliability, and evolution of machine learning in medical computer vision. Further, we discuss
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Alzheimer’s Disease Detection Using Ensemble Learning and?Artificial Neural Networksr and widely used open access series of imaging studies (OASIS) dataset for Alzheimer’s disease (AD) detection. The proposed work performs an in-depth feature examination and a training-test split in a 70?:?30 ratio on the dataset and applies 8 different ML algorithms. The AD detection outcome is ob
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Significant CC400 Functional Brain Parcellations Based LeNet5 Convolutional Neural Network for?Autisare getting benefit from these advancements and one of these areas is medical imaging. Despite rapid advancements in machine learning based medical condition diagnosis systems (CADs), some ailments and disorders are hard to diagnose/classify due to the absence or the lack of consensus on biomarkers
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Analyzing Chest X-Ray to?Detect the?Evidence of?Lung Abnormality Due to?Infectious Diseasearge effect on almost every facet of life. As COVID-19 was a disease only discovered in recent history, there is comparatively little data on the disease, how it is detected, and how it is cured. Deep learning is a powerful tool that can be used to learn to classify information in ways that humans m
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A Novel Approach to?Enhance Effectiveness of?Image Segmentation Techniques on?Extremely Noisy Medicantation. In this study, I have subjected medical images from LiTS - Liver Tumour Segmentation Challenge, which are extremely noisy, to various image segmentation techniques belonging to fully automatic and semi-automatic categories. These varied techniques implement different approaches towards imag
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