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Titlebook: Computational Intelligence and Machine Learning; Proceedings of the 7 Jyotsna Kumar Mandal,Imon Mukherjee,Pankaj K. Sa Conference proceedin

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發(fā)表于 2025-3-21 20:05:14 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computational Intelligence and Machine Learning
副標(biāo)題Proceedings of the 7
編輯Jyotsna Kumar Mandal,Imon Mukherjee,Pankaj K. Sa
視頻videohttp://file.papertrans.cn/233/232430/232430.mp4
概述Presents research works in the field of computational intelligence and machine learning.Results of ICACNI 2019 held at IIIT Kalyani during 20 - 21 December 2019.Serves as a reference for researchers a
叢書名稱Advances in Intelligent Systems and Computing
圖書封面Titlebook: Computational Intelligence and Machine Learning; Proceedings of the 7 Jyotsna Kumar Mandal,Imon Mukherjee,Pankaj K. Sa Conference proceedin
描述This book focuses on both theory and applications in the broad areas of computational intelligence and machine learning. The proceedings of the Seventh International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2019) present research papers in the areas of advanced computing, networking, and informatics. It brings together contributions from scientists, professors, scholars, and students and presents essential information on the topic. It also discusses the practical challenges encountered and the solutions used to overcome them, the goal being to promote the “translation” of basic research into applied research and of applied research into practice. The works presented here also demonstrate the importance of basic scientific research in a range of fields..
出版日期Conference proceedings 2021
關(guān)鍵詞Artificial Intelligence; Natural Language Processing; Human Computer Interaction and Vision; Machine Le
版次1
doihttps://doi.org/10.1007/978-981-15-8610-1
isbn_softcover978-981-15-8609-5
isbn_ebook978-981-15-8610-1Series ISSN 2194-5357 Series E-ISSN 2194-5365
issn_series 2194-5357
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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發(fā)表于 2025-3-21 20:26:41 | 只看該作者
https://doi.org/10.1007/978-3-662-62076-2urier space of model data is independent of window size for satellite sampling and is thus concluded that large and non-overlapping windows are suited for generating a Fourier space for satellite sampling.
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發(fā)表于 2025-3-22 02:21:00 | 只看該作者
https://doi.org/10.1007/978-3-662-62076-2al relevance of various pacing protocols that can be employed in the future. The Luo–Rudy Phase I model of the mammalian ventricular cell has been used for simulations to demonstrate the effects of the rate dependence of activation in cardiac tissues, referred to as the Wenckebach periodicity.
地板
發(fā)表于 2025-3-22 06:33:33 | 只看該作者
A Deep Learning Approach for Predicting Air Pollution in Smart Citiesroblem. In this paper, a novel deep learning model has been proposed to forecast the pollution. The comparison among the existing state-of-the-art methods is also done here. Experimental results shown in this work claim that the proposed work outperforms other methods with respect to accuracy and stability.
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Minutiae Points Extraction Using Faster R-CNNaper, we propose a technique to locate and classify the minutiae points based on Faster R-CNN, which is a combination of region proposal network (RPN) and detection network. We use IIT Kanpur fingerprint database to evaluate the proposed technique and to show the performance.
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發(fā)表于 2025-3-22 22:00:54 | 只看該作者
Detection of Malaria Parasites in Thin Blood Smears Using CNN-Based Approachles are then sent to a convolutional neural network for further probing. The proposed CNN architecture is trained with a publicly available dataset along with some Giemsa-stained blood smear images collected from a hospital. The performance of malaria detection process gives a satisfactory dice score of 0.95.
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News Background Linking Using Document Similarity Techniques of topics using two methods, cosine similarity and Jensen–Shannon divergence. This paper describes the implementation and gives a brief description of both the similarity methods. It also gives a comparative study of both methods.
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