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Titlebook: Computer Vision and Machine Intelligence Paradigms for SDGs; Select Proceedings o R. Jagadeesh Kannan,Sabu M. Thampi,Shyh-Hau Wang Conferen

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發(fā)表于 2025-3-21 19:04:25 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computer Vision and Machine Intelligence Paradigms for SDGs
副標題Select Proceedings o
編輯R. Jagadeesh Kannan,Sabu M. Thampi,Shyh-Hau Wang
視頻videohttp://file.papertrans.cn/235/234065/234065.mp4
概述Comprises peer-reviewed papers presented during ICRTAC- CVMIP 2021.Includes contributions from academia, and industry in state-of-the-art methods in computer vision.Serves as a valuable reference reso
叢書名稱Lecture Notes in Electrical Engineering
圖書封面Titlebook: Computer Vision and Machine Intelligence Paradigms for SDGs; Select Proceedings o R. Jagadeesh Kannan,Sabu M. Thampi,Shyh-Hau Wang Conferen
描述This book constitutes refereed proceedings of the 4th International Conference on Recent Trends in Advanced Computing - Computer Vision and Machine Intelligence Paradigms for Sustainable Development Goals. This book covers novel and state-of-the-art methods in computer vision coupled with intelligent techniques including machine learning, deep learning, and soft computing techniques. The contents of this book will be useful to researchers from industry and academia. This book includes contemporary innovations, trends, and concerns in computer vision with recommended solutions to real-world problems adhering to sustainable development from researchers across industry and academia. This book serves as a valuable reference resource for academics and researchers across the globe.
出版日期Conference proceedings 2023
關鍵詞Computer Vision; Machine Intelligence; Machine Intelligence Paradigms for SDGs; ICRTAC- CVMIP 2021; ICRT
版次1
doihttps://doi.org/10.1007/978-981-19-7169-3
isbn_softcover978-981-19-7171-6
isbn_ebook978-981-19-7169-3Series ISSN 1876-1100 Series E-ISSN 1876-1119
issn_series 1876-1100
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Convergence Perceptual Model for Computing Time Series Data on Fog Environment, the edge devices and cloud storage. Computational by means of fog-node attracts the need by replacing existing cloudlets time complexity in resource management. Hence fog as things operates as a controller in cyber physical systems like controlling transmission lines of high volt system connections
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Localized Super Resolution for Foreground Images Using U-Net and MR-CNN,al Network (CNN) architecture known as U-Net for super resolution combined with Mask Region-Based CNN (MR-CNN) for foreground super resolution is analyzed. This analysis is carried out based on localized super resolution, i.e., we pass the LR Images to a pre-trained image segmentation model (MR-CNN)
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Pest Detection Using Improvised YOLO Architecture,nvolution Neural Network) and YOLO (You Only Look Once) are used to classify the features extracted by deep feature extraction. Improvised YOLO is used which has proven pest prediction of about 95%. The performance of current research is compared, and common datasets are introduced. This paper exami
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Deep Learning Based Recognition of Plant Diseases,es. To get a better performance model, the data augmentation process is used, and we got 18,392 images. In our model, we used a convolutional neural network (CNN) to classify the citrus and tomato leaf diseases. The diseases are Mancha Graxa, Citrus Canker, Tomato Early Blight, Tomato Septoria Leafs
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,Advanced Algorithmic Techniques for Topic Prediction and Recommendation—An Analysis,rending is a significant way to help users who want to grow their following by communicating their message to others. In this paper, a recommendation based on pre-trending subjects based on individual user interests, as well as prediction algorithms for identifying tweets that will shortly trend has
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