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Titlebook: Proceedings of World Conference on Artificial Intelligence: Advances and Applications; WCAIAA 2024 Ashish Kumar Tripathi,Darpan Anand,Atuly

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发表于 2025-3-21 19:51:04 | 显示全部楼层 |阅读模式
书目名称Proceedings of World Conference on Artificial Intelligence: Advances and Applications
副标题WCAIAA 2024
编辑Ashish Kumar Tripathi,Darpan Anand,Atulya K. Nagar
视频video
概述Presents research works in the field of artificial intelligence.Provides original works presented at WCAIAA 2024 held in Udaipur, India.Serves as a reference for researchers and practitioners in acade
丛书名称Algorithms for Intelligent Systems
图书封面Titlebook: Proceedings of World Conference on Artificial Intelligence: Advances and Applications; WCAIAA 2024 Ashish Kumar Tripathi,Darpan Anand,Atuly
描述.This book is a collection of outstanding research papers presented at the World Conference on Artificial Intelligence: Advances and Applications (WCAIAA 2024), organized by Sir Padampat Singhania University, India, and is technically sponsored by Soft Computing Research Society during February 22–23, 2024. The topics covered are agent-based systems, evolutionary algorithms, approximate reasoning, bioinformatics and computational biology, artificial intelligence in modeling and simulation, natural language processing, brain–machine interfaces, collective intelligence, computer vision and speech understanding, data mining, swarm intelligence, machine learning, human–computer interaction, intelligent sensor, devices and applications, and intelligent database systems..
出版日期Conference proceedings 2024
关键词Artificial Intelligence; Computational Intelligence; Swarm Intelligence; Artificial Neural Network; Fuzz
版次1
doihttps://doi.org/10.1007/978-981-97-4496-1
isbn_softcover978-981-97-4498-5
isbn_ebook978-981-97-4496-1Series ISSN 2524-7565 Series E-ISSN 2524-7573
issn_series 2524-7565
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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发表于 2025-3-21 21:56:03 | 显示全部楼层
Financial Time Series Forecasting Using Hybrid Evolutionary Extreme Learning Machine,SE) and root mean squared error (RMSE). The experimental analysis proves the proposed model, FISA + ELM, produces lower MSE and RMSE values than the alternate model, CRO + ELM. Overall, we observe that FISA + ELM outperforms CRO + ELM.
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Designing an Intelligent Chatbot with Deep Learning: Leveraging FNN Algorithm for Conversational Agents to Improve the Chatbot Performance,lves epochs and batch size tuning, with model performance monitored through the TensorBoard and enhanced using callbacks, including ModelCheckpoint and EarlyStopping. The final model is saved and loaded for efficient utilization. The research extends to developing a user-friendly interface using Tkinter for seamless interaction with the chatbot.
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A Novel Approach for Energy-Efficient Clustering Using NWOT-TCHE for Trust-Based Cluster Head Election in Wireless Sensor Networks,ata packets accelerated by SN, average cluster distance, communication delay, and SN density. The result shows that this proposed approach is superior to the existing energy-efficient clustering approaches.
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Unsupervised Video Summarization Using Deep Learning Approach with Deep Feature Semantics,ken directly from the original movie, and an unsupervised .-means clustering-based algorithm is applied to summarize the video. Our video summaries are evaluated against reference approaches and state-of-the-art models on two standard datasets.
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A Novel Meta-analysis and Classification of Herbal Medicinal Plant Raw Materials for Food Consumption Prediction Using Hybrid Deep Learning Techniques Based on Augmented Reality in Computer Vision,nd in medicinal herbal plants have a significant role in food preparation. Because of their appearance, vital medicinal plants must be evaluated and examined before being consumed. Herbs may help prevent and control cardiac conditions, tumors, and mellitus. It may also help prevent blood clots and h
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