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Titlebook: Soft Computing for Problem Solving; Proceedings of the S Manoj Thakur,Samar Agnihotri,Atulya K. Nagar Conference proceedings 2023 The Edito

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发表于 2025-3-21 20:08:26 | 显示全部楼层 |阅读模式
书目名称Soft Computing for Problem Solving
副标题Proceedings of the S
编辑Manoj Thakur,Samar Agnihotri,Atulya K. Nagar
视频video
概述Presents research works in the field of soft computing.Provides original works presented at SocProS 2022 held in Mandi, India.Serves as a reference for researchers and practitioners in academia and in
丛书名称Lecture Notes in Networks and Systems
图书封面Titlebook: Soft Computing for Problem Solving; Proceedings of the S Manoj Thakur,Samar Agnihotri,Atulya K. Nagar Conference proceedings 2023 The Edito
描述.This book provides an insight into the 11th International Conference on Soft Computing for Problem Solving (SocProS 2022). This international conference is a joint technical collaboration of the Soft Computing Research Society and the Indian Institute of Technology Mandi. This book presents the latest achievements and innovations in the interdisciplinary areas of Soft Computing, Machine Learning, and Data Science. It brings together the researchers, engineers, and practitioners to discuss thought-provoking developments and challenges, in order to select potential future directions. It covers original research papers in the areas including but not limited to algorithms (artificial neural network, deep learning, statistical methods, genetic algorithm, and particle swarm optimization) and applications (data mining and clustering, computer vision, medical and healthcare, finance, data envelopment analysis, business, and forecasting applications). This book is beneficial for young as well as experienced researchers dealing across complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task..
出版日期Conference proceedings 2023
关键词Evolutionary Computation; Swarm Intelligence; Deep Learning Models; Data Mining; Human-Computer Interact
版次1
doihttps://doi.org/10.1007/978-981-19-6525-8
isbn_softcover978-981-19-6524-1
isbn_ebook978-981-19-6525-8Series 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
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Lecture Notes in Networks and Systemshttp://image.papertrans.cn/s/image/870484.jpg
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https://doi.org/10.1007/978-981-19-6525-8Evolutionary Computation; Swarm Intelligence; Deep Learning Models; Data Mining; Human-Computer Interact
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978-981-19-6524-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Conference proceedings 2023nce is a joint technical collaboration of the Soft Computing Research Society and the Indian Institute of Technology Mandi. This book presents the latest achievements and innovations in the interdisciplinary areas of Soft Computing, Machine Learning, and Data Science. It brings together the research
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Medical Prescription Label Reading Using Computer Vision and Deep Learning,o two alternative architectures, CRNN alone and EAST + CRNN architecture. The cursive handwritten image is then converted to conventional text using these models. After obtaining the texts, the text is calculated using the CTC loss and the outcome is predicted.
发表于 2025-3-23 04:44:21 | 显示全部楼层
,A Study on Metric-Based and Initialization-Based Methods for Few-Shot Image Classification,mework employed by many of the discussed models to generalize to novel classification tasks after training on multiple training tasks. We also discuss other techniques, such as whole-class classification, that have produced better results than meta-learning for metric-based methods.
发表于 2025-3-23 08:32:57 | 显示全部楼层
,Encoder–Decoder (LSTM-LSTM) Network-Based Prediction Model for Trend Forecasting in Currency Market. A novel encoder–decoder network-based model is proposed for trend prediction in this work. Furthermore, a comparative analysis is drawn with existing models using the Wilcoxon test for significant differences and model performance metrics to evaluate the proposed model.
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