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Titlebook: Evolution in Computational Intelligence; Proceedings of the 1 Vikrant Bhateja,Xin-She Yang,Ranjita Das Conference proceedings 2023 The Edit

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发表于 2025-3-21 18:55:55 | 显示全部楼层 |阅读模式
书目名称Evolution in Computational Intelligence
副标题Proceedings of the 1
编辑Vikrant Bhateja,Xin-She Yang,Ranjita Das
视频video
概述Presents research works in intelligent data engineering and analytics.Results of FICTA 2022 held at NIT Mizoram, Aizawl, Mizoram, India.Serves as a reference for researchers and practitioners in acade
丛书名称Smart Innovation, Systems and Technologies
图书封面Titlebook: Evolution in Computational Intelligence; Proceedings of the 1 Vikrant Bhateja,Xin-She Yang,Ranjita Das Conference proceedings 2023 The Edit
描述.The book presents the proceedings of the 10th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2022), held at NIT Mizoram, Aizawl, Mizoram, India during 18 – 19 June 2022. Researchers, scientists, engineers, and practitioners exchange new ideas and experiences in the domain of intelligent computing theories with prospective applications in various engineering disciplines in the book. These proceedings are divided into two volumes. It covers broad areas of information and decision sciences, with papers exploring both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. This volume is a valuable resource for postgraduate students in various engineering disciplines..
出版日期Conference proceedings 2023
关键词Computational Intelligence; Artificial Intelligence; Human Computer Interaction; Intelligent Control; In
版次1
doihttps://doi.org/10.1007/978-981-19-7513-4
isbn_softcover978-981-19-7515-8
isbn_ebook978-981-19-7513-4Series ISSN 2190-3018 Series E-ISSN 2190-3026
issn_series 2190-3018
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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,Localization and Classification of Thoracic Abnormalities from Chest Radiographs Using Deep Ensembldes two pipelines and an ensemble method. In the first pipeline, YOLOv5 and EfficientNet are used. In second pipeline, the Faster R-CNN model is used. Through the Weighted box fusion method, the fused predictions are created from pipeline results. The final detection results illustrate confidence sc
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,Brain Tumor Prediction from MRI Images Using an Ensemble Model Based on EfficientNet-B2, B4, and Re four types of MRI images, i.e., (i) Inversion Recovery with Fluid Attenuation, (ii) T1-weighted pre-contrast, (iii) T1-weighted post-contrast, and (iv) T2-weighted. Proposed deep ensembled network has demonstrated satisfactory performance and efficiency on the dataset.
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,An Approach of Hate Speech Identification on Twitter Corpus,s a need for integrated datasets and the hate speech prediction method. This paper describes the study on hate speech and offensive content identification in English language by using the various approaches based on machine learning algorithms (Support vector machine, decision tree, and so on) and N
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