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

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書目名稱Communication and Intelligent Systems
副標題Proceedings of ICCIS
編輯Harish Sharma,Vivek Shrivastava,Lipo Wang
視頻videohttp://file.papertrans.cn/231/230454/230454.mp4
概述Presents research works in the field of communication and intelligent systems.Includes original works presented at ICCIS 2023 held in Jaipur, India.Provides state of the art to research scholars and p
叢書名稱Lecture Notes in Networks and Systems
圖書封面Titlebook: Communication and Intelligent Systems; Proceedings of ICCIS Harish Sharma,Vivek Shrivastava,Lipo Wang Conference proceedings 2024 The Edito
描述This book gathers selected research papers presented at the Fifth International Conference on Communication and Intelligent Systems (ICCIS 2023), organized by Malaviya National Institute of Technology Jaipur, India, during December 16–17, 2023. This book presents a collection of state-of-the-art research work involving cutting-edge technologies for communication and intelligent systems. Over the past few years, advances in artificial intelligence and machine learning have sparked new research efforts around the globe, which explore novel ways of developing intelligent systems and smart communication technologies. The book presents single- and multi-disciplinary research on these themes to make the latest results available in a single, readily accessible source. The work is presented in three volumes.
出版日期Conference proceedings 2024
關(guān)鍵詞Intelligent Systems; Communication and Control Systems; Artificial Intelligence; Machine Learning; Patte
版次1
doihttps://doi.org/10.1007/978-981-97-2082-8
isbn_softcover978-981-97-2081-1
isbn_ebook978-981-97-2082-8Series ISSN 2367-3370 Series E-ISSN 2367-3389
issn_series 2367-3370
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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Pattern of Lung Injury in CT/HRCT Using Deep Learning Techniques,ormance metrics with lower standard deviations over the test dataset. A possible future enhancement in this area could be the introduction of an ensemble architecture for classification which has the capability to process 3D images.
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Time Domain Specifications of Step Responses of Both Underdamped and Overdamped Systems: In Correctn a fixed value of 1(like . function), the algorithm addresses the flawed settling time calculations for both underdamped and overdamped systems. To check and compare the performance of the proposed algorithm, one overdamped system and two underdamped systems: one having some steady state error and
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A Study on DL for Pulmonary Embolism Prediction Harnessing Multimodal Data,ng DL-based multimodal early fusion features from Computed Tomography of Pulmonary Angiography (CTPA) images and six Electronic Medical Record (EMR) modalities are concatenated at the input level. The performance of the six DL models using the RadFusion dataset, a manually labeled and processed data
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Performance Analysis of Machine Learning Algorithms Using Information Theoretic Class Based Multi-cheoretic dependent FSA. For our study, we considered twelve different machine learning algorithms for comparison using metrics such as accuracy, precision, recall and ROC-AUC curve. Results show a significant increase in accuracy of all the machine learning algorithms that are considered.
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The Failure Recovery Management Framelet,ers and a performance assessment matrix are used to evaluate the quality of our model. All the features of the dataset and a subset of them were used to test the work that was suggested. Reducing the number of features influences the evaluation matrix and accuracy of algorithms. Utilizing a UCI set
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發(fā)表于 2025-3-23 01:41:23 | 只看該作者
Lecture Notes in Computer Scienceormance metrics with lower standard deviations over the test dataset. A possible future enhancement in this area could be the introduction of an ensemble architecture for classification which has the capability to process 3D images.
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https://doi.org/10.1007/978-3-319-11245-9 (SVM), and Stochastic Gradient Descent (SGD) have been implemented on the thermal images for the classification of control and CVI subjects. The prediction performance of these ML models is assessed using evaluation measures such as accuracy, sensitivity, specificity, precision, F1 score, and AUC.
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