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Titlebook: Data Engineering for Smart Systems; Proceedings of SSIC Priyadarsi Nanda,Vivek Kumar Verma,Arka Prokash Ma Conference proceedings 2022 The

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樓主: 根深蒂固
51#
發(fā)表于 2025-3-30 10:33:22 | 只看該作者
Gordon J. Alderink,Blake M. Ashbyted investigators communities, intended to increase the connectivity of data network, principally in an area where formations of traditional networks are unfeasible. However, this network is capable to constitute and heal itself without having a predetermined infrastructure, but with high mobility a
52#
發(fā)表于 2025-3-30 13:24:49 | 只看該作者
53#
發(fā)表于 2025-3-30 18:52:04 | 只看該作者
54#
發(fā)表于 2025-3-30 20:56:05 | 只看該作者
Priyadarsi Nanda,Vivek Kumar Verma,Arka Prokash MaPresents recent research in the field of data engineering.Discusses the outcomes of SSIC 2021, held in Manipal University Jaipur, India.Serves as a reference guide for researchers and practitioners in
55#
發(fā)表于 2025-3-31 03:34:07 | 只看該作者
Lecture Notes in Networks and Systemshttp://image.papertrans.cn/d/image/262793.jpg
56#
發(fā)表于 2025-3-31 08:32:02 | 只看該作者
2367-3370 es as a reference guide for researchers and practitioners inThis book features original papers from the 3rd International Conference on Smart IoT Systems: Innovations and Computing (SSIC 2021), organized by Manipal University, Jaipur, India, during January 22–23, 2021. It discusses scientific works
57#
發(fā)表于 2025-3-31 12:11:59 | 只看該作者
58#
發(fā)表于 2025-3-31 14:34:15 | 只看該作者
Classification and Its Alternatives demand of business nowadays and is a stimulating task. Machine learning and deep learning are spreading its wings in this field for automatic classification of such data and documents. This paper delves into contribution of the researchers in Indian Languages for information retrieval and classification with machine learning.
59#
發(fā)表于 2025-3-31 21:33:03 | 只看該作者
Object Recognition in a Cluttered Scene,lly or partially occluded. In this paper, an object recognition system based on deep learning techniques is proposed. RetinaNet Model has been used for object detection and identification. RetinaNet model has demonstrated to work well with both small scale as well as dense objects.
60#
發(fā)表于 2025-3-31 22:48:12 | 只看該作者
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